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Background and purpose: Degenerative cervical myelopathy (DCM) is the most common cause of spinal cord (SC) dysfunction. In routine clinical practice, SC changes are well depicted using conventional MRI, especially T2-weighted imaging. However, this modality usually fails to provide satisfactory clinico-radiological correlations. In this context we assessed the potential value of quantitative changes measured with a T1 MP2RAGE sequence. Materials and methods: 18 patients diagnosed with chronic onset of DCM and 17 healthy controls (HC) were enrolled in the study. Clinical presentation was assessed using the modified Japanese Orthopaedic Association (mJOA) scale. Sagittal cervical SC T2-w 3D SPACE imaging and T1 MP2RAGE mapping were performed at baseline and 3-months postoperatively. Data were processed using Matlab and the SC Toolbox. Results: mJOA score increased from 13.3 ± 2.1 preoperatively to 14.4 ± 1.9 at follow-up (p = 0.027). Site of maximum compression (Cmax) was at C3–C4 cervical levels in 4 patients, C4-C5 in 8 patients, C5-C6 in 5 patients and C6-C7 in 1 patient. SC compression was multi-level in 7 patients and single-level in 11 patients. T2-w hyperintensity was present in 15 patients. Mean SC T1 values in the whole SC in the DCM group at baseline showed significant difference as compared to mean SC T1 values in HC group (962.2 ± 62 vs. 924.9 ± 34 ms, respectively (p < 0.0001)) but no differences could be observed between baseline and 3-month follow-up (962.4 ± 59 ms (p = 0.86)). Z-scores at baseline were − 0.05 ± 1 for mild, 1.2 ± 1.9 for moderate and 2.5 ± 1.2 for severe. Mean baseline and 3-month follow-up SC T1 values were weakly but significantly correlated to preoperative (R2 = 0.33 (p = 0.013) and postoperative mJOA (R2 = 0.29 (p = 0.024). Baseline T1 value at C2 level was significantly correlated with mJOA at 3-month follow-up (p = 0.048). Conclusions: T1-MP2RAGE mapping in patients with DCM demonstrated both focal and diffuse cervical SC alteration. It could thus be a biomarker for patients with DCM managed surgically. [ABSTRACT FROM AUTHOR]

Purpose: To compare the performance of a learned magnetization‐prepared gradient echo (L‐MPGRE) sequence against a commonly used sequence for 3D T2 and T1ρ mapping of the knee joint, the magnetization‐prepared angle‐modulated partitioned k‐space spoiled gradient echo snapshots (MAPSS), on bi‐exponential (BE), stretched‐exponential (SE), and mono‐exponential (ME) relaxation models. Methods: We used a combined differentiable and non‐differentiable optimization to learn pulse sequence structure and its parameters for 3D T2 and T1ρ mapping of the knee joint using ME, SE, and BE models. The learned pulse sequence framework was used to improve quantitative accuracy and SNR and to reduce filtering effects. We compare the measured multi‐compartment values between the two sequences (n = 8), and their repeatability (n = 4) in healthy volunteers (n = 12 total). Results: The voxel‐wise median absolute percentage difference (MAPD) between the T2 and T1ρ maps obtained with each sequence was 18.6% and 19.9%, respectively. The T2 and T1ρ repeatability tests showed a MAPD of 18.5% and 19.1% for MAPSS, and 16.8% and 15.5% for L‐MPGRE. Bland–Altman region of interest (ROI)‐wise analysis shows that bias is small, close to −1.5%, and the coefficient of variation is around 5.5% when comparing ROIs from both sequences. Conclusion: The L‐MPGRE sequences can be used as a replacement for MAPSS for T2 and T1ρ mapping in the knee cartilage with advantages, achieving similar accuracy and 15% better repeatability in only half of its scan time. [ABSTRACT FROM AUTHOR]

Purpose: To achieve automatic hyperparameter estimation for the model‐based recovery of quantitative MR maps from undersampled data, we propose a Bayesian formulation that incorporates the signal model and sparse priors among multiple image contrasts. Theory: We introduce a novel approximate message passing framework "AMP‐PE" that enables the automatic and simultaneous recovery of hyperparameters and quantitative maps. Methods: We employed the variable‐flip‐angle method to acquire multi‐echo measurements using gradient echo sequence. We explored undersampling schemes to incorporate complementary sampling patterns across different flip angles and echo times. We further compared AMP‐PE with conventional compressed sensing approaches such as the l1$$ {l}_1 $$‐norm minimization, PICS and other model‐based approaches such as GraSP, MOBA. Results: Compared to conventional compressed sensing approaches such as the l1$$ {l}_1 $$‐norm minimization and PICS, AMP‐PE achieved superior reconstruction performance with lower errors in T2∗$$ {\mathrm{T}}_2^{\ast } $$ mapping and comparable performance in T1$$ {\mathrm{T}}_1 $$ and proton density mappings. When compared to other model‐based approaches including GraSP and MOBA, AMP‐PE exhibited greater robustness and outperformed GraSP in reconstruction error. AMP‐PE offers faster speed than MOBA. AMP‐PE performed better than MOBA at higher sampling rates and worse than MOBA at a lower sampling rate. Notably, AMP‐PE eliminates the need for hyperparameter tuning, which is a requisite for all the other approaches. Conclusion: AMP‐PE offers the benefits of model‐based recovery with the additional key advantage of automatic hyperparameter estimation. It works adeptly in situations where ground‐truth is difficult to obtain and in clinical environments where it is desirable to automatically adapt hyperparameters to individual protocol, scanner and patient. [ABSTRACT FROM AUTHOR]

Developmental dyslexia (DD) is one of the most common learning disorders, affecting millions of children and adults worldwide. To date, scientific research has attempted to explain DD primarily based on pathophysiological alterations in the cerebral cortex. In contrast, several decades ago, pioneering research on five post-mortem human brains suggested that a core characteristic of DD might be morphological alterations in a specific subdivision of the visual thalamus—the magnocellular lateral geniculate nucleus (M-LGN). However, due to considerable technical challenges in investigating LGN subdivisions non-invasively in humans, this finding was never confirmed in vivo , and its relevance for DD pathology remained highly controversial. Here, we leveraged recent advances in high resolution MRI at high field strength (7 T) to investigate the M-LGN in DD in vivo. Using a case-control design, we acquired data from a large sample of young adults with DD (n = 26; age 28 ± 7 years; 13 females) and matched control participants (n = 28; age 27 ± 6 years; 15 females). Each participant completed a comprehensive diagnostic behavioural test battery and participated in two MRI sessions, including three functional MRI experiments and one structural MRI acquisition. We measured blood oxygen level-dependent responses and longitudinal relaxation rates to compare both groups on LGN subdivision function and myelination. Based on previous research, we hypothesized that the M-LGN is altered in DD and that these alterations are associated with a key DD diagnostic score, i.e. rapid letter and number naming. The results showed aberrant responses of the M-LGN in DD compared to controls, which was reflected in a different functional lateralization of this subdivision between groups. These alterations were associated with rapid letter and number naming performance, specifically in male DD. We also found lateralization differences in the longitudinal relaxation rates of the M-LGN in DD relative to controls. Conversely, the other main subdivision of the LGN, the parvocellular LGN (P-LGN), showed comparable blood oxygen level-dependent responses and longitudinal relaxation rates between groups. The present study is the first to unequivocally show that M-LGN alterations are a hallmark of DD, affecting both the function and microstructure of this subdivision. It further provides a first functional interpretation of M-LGN alterations and a basis for a better understanding of sex-specific differences in DD with implications for prospective diagnostic and treatment strategies. [ABSTRACT FROM AUTHOR]

Background: There is evidence that iron metabolism may play a role in the underlying pathophysiological mechanism of migraine. Studies using R 2 ∗ (=1/ T 2 ∗) relaxometry, a common MRI-based iron mapping technique, have reported increased R 2 ∗ values in various brain structures of migraineurs, indicating iron accumulation compared to healthy controls. Purpose: To investigate whether there are short-term changes in R 2 ∗ during a migraine attack. Population: 26-year-old male patient diagnosed with episodic migraine with aura according to ICHD-3 criteria. Sequence: 3 T, 64-channel head coil, for quantification of R 2 ∗ relaxation a multi-echo gradient echo (GRE) sequence with TE = 4.92, 9.84, 14.7, 19.6, 24.6 and 29.51 ms, TR = 35 ms, flip angle = 15°, and 0.9 × 0.9 × 0.9 mm3 isotropic resolution was used. Assessment: Quantitative MRI, including R 2 ∗ relaxometry and diffusion tensor imaging (DTI), was acquired from a migraine patient on 21 consecutive days, including migraine-free days and days with a migraine attack. Statistical test: Statistical analysis was performed using R, the Shapiro–Wilk test, the t -test and Mann Whitney U test, analysis of variance (ANOVA) or Kruskal–Wallis test, depending on the distribution of the data. p -value <0.05 was considered significant. Results: Significant difference in R 2 ∗ was found between the left and right hemispheres during a migraine attack. An increase in R 2 ∗ was observed in the left hemisphere, whereas in the right hemisphere R 2 ∗ was found to decrease. In the left cerebral white matter, R 2 ∗ increased by 1.8% (p = 0.021), in the right cerebral white matter, R 2 ∗ anisotropy decreased by 17% (p = 0.011) during a migraine attack. Data conclusion: Our study showed a decrease and increase in iron content during the migraine cycle. Furthermore, during a migraine attack, white matter iron content increased, accompanied by a decrease in anisotropic tissue components, suggesting additional changes in vascular components. [ABSTRACT FROM AUTHOR]

Background: Multiple sclerosis (MS) is a demyelination disease. Myelin water is a biomarker of myelin and thus myelin water imaging is a vital tool to provide insight into the demyelination process. Purpose: This study aimed to characterize the multiple compartments including myelin water fraction (MWF), gray matter (GM) cellular water, white matter (WM) cellular water, and cerebrospinal fluid (CSF) using multiple inversion recovery (mIR) magnetic resonance fingerprinting (MRF) on a clinical MS cohort. Methods: The Phantom experiment was conducted with tubes containing different WM and GM concentrations extracted from pig brains. For the in‐vivo experiment, 23 healthy control (HC) volunteers and 18 MS patients were recruited for this study. The experiments were performed using a clinical 3T MRI. A multi‐slice, fast imaging with a steady‐state precession (FISP) based mIR MRF protocol was used to obtain the MWF measurements, with 6 min of scan time for each volunteer. The quantification was based on the iterative non‐negative least squares (NNLS) with reweighting. The brain compartments quantified were myelin water, WM cellular water, GM cellular water, and CSF. A radiologist with 6 years of experience labeled the MS lesions on FLAIR, MPRAGE, and MWF. Statistical analysis was performed by applying unpaired and paired student's t‐tests to compare the MWF results in different groups and in normal‐appearing white matter (NAWM) and MS lesions. Results: The phantom result demonstrated the ability to detect MWF with various myelin concentrations. The maps derived from mIR MRF, including MWF, WM cellular water, GM cellular water, and CSF were consistent with the anatomical structures observed in FLAIR and MPRAGE. The MWF values in the NAWM of MS patients were significantly different from those in HC, with values of 0.32 ± 0.025 and 0.25 ± 0.036, respectively. Additionally, the MWF values in WM lesions were significantly smaller than in NAWM at 0.034 ± 0.036. Conclusion: The mIR‐MRF technique, using multi‐compartment analysis, can simultaneously generate maps of MWF, WM cellular water, GM cellular water, and CSF with sufficient brain coverage and in a reasonably short scan time. The MWF map might provide insights into the demyelination associated with MS. [ABSTRACT FROM AUTHOR]

Background: Irregular cardiac motion can render conventional segmented cine MRI nondiagnostic. Clustering has been proposed for cardiac motion binning and may be optimized for complex arrhythmias. Purpose: To develop an adaptive cluster optimization method for irregular cardiac motion, and to generate the corresponding time‐resolved cine images. Study Type: Prospective. Subjects: Thirteen with atrial fibrillation, four with premature ventricular contractions, and one patient in sinus rhythm. Field Strength/Sequence: Free‐running balanced steady state free precession (bSSFP) with sorted golden‐step, reference real‐time sequence. Assessment: Each subject underwent both the sorted golden‐step bSSFP and the reference Cartesian real‐time imaging. Golden‐step bSSFP images were reconstructed using the dynamic regularized adaptive cluster optimization (DRACO) method and k‐means clustering. Image quality (4‐point Likert scale), signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), edge sharpness, and ventricular function were assessed. Statistical Tests: Paired t‐tests, Friedman test, regression analysis, Fleiss' Kappa, Bland–Altman analysis. Significance level P < 0.05. Results: The DRACO method had the highest percent of images with scores ≥3 (96% for diastolic frame, 93% for systolic frame, and 93% for multiphase cine) and the percentages were significantly higher compared with both the k‐means and real‐time methods. Image quality scores, SNR, and CNR were significantly different between DRACO vs. k‐means and between DRACO vs. real‐time. Cardiac function analysis showed no significant differences between DRACO vs. the reference real‐time. Conclusion: DRACO with time‐resolved reconstruction generated high quality images and has early promise for quantitative cine cardiac MRI in patients with complex arrhythmias including atrial fibrillation. Level of Evidence: 1. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Background: Recent multicenter, multivendor MRI‐based R2* vs. liver iron concentration (LIC) calibrations (i.e., MCMV calibrations) may facilitate broad clinical dissemination of R2*‐based LIC quantification. However, these calibrations are based on a centralized offline R2* reconstruction, and their applicability with vendor‐provided R2* maps is unclear. Purpose: To determine R2* ranges of agreement between the centralized and three MRI vendors' R2* reconstructions. Study Type: Prospective. Subjects: Two hundred and seven subjects (mean age 37.6 ± 19.6 years; 117 male) with known or suspected iron overload from four academic medical centers. Field Strength/Sequence: Standardized multiecho spoiled gradient echo sequence at 1.5 T and 3.0 T for R2* mapping and a multiple spin‐echo sequence at 1.5 T for LIC quantification. MRI vendors: GE Healthcare, Philips Healthcare, and Siemens Healthineers. Assessment: R2* maps were generated using both the centralized and vendor reconstructions, and ranges of agreement were determined. R2*‐LIC linear calibrations were determined for each site, field strength, and reconstruction and compared with the MCMV calibrations. Statistical Tests: Bland–Altman analysis to determine ranges of agreement. Linear regression, analysis of covariance F tests, and Tukey's multiple comparison testing to assess reproducibility of calibrations across sites and vendors. A P value <0.05 was considered significant. Results: The upper limits of R2* ranges of agreement were approximately 500, 375, and 330 s−1 for GE, Philips, and Siemens reconstructions, respectively, at 1.5 T and approximately 700 and 800 s−1 for GE and Philips, respectively, at 3.0 T. Within the R2* ranges of agreement, vendor R2*‐LIC calibrations demonstrated high reproducibility (no significant differences between slopes or intercepts; P ≥ 0.06) and agreed with the MCMV calibrations (overlapping 95% confidence intervals). Data Conclusion: Based on the determined upper limits, R2* measurements obtained from vendor‐provided R2* maps may be reliably and practically used to quantify LIC less than approximately 8–13 mg/g using the MCMV calibrations and similar acquisition parameters as this study. Evidence Level: 1 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Purpose: To introduce quantitative rapid gradient‐echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength. Methods: QRAGE leverages a newly developed multi‐echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of TI and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model‐based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block‐Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high‐precision imaging. Results: In vivo brain measurements show the promising performance of QRAGE, with test–retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T1, and < 0.7 ms for T2*. QRAGE achieves whole‐brain coverage at a 1‐mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE. Conclusion: QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Fingerprinting (MRF) can be accelerated with simultaneous multislice (SMS) imaging for joint T1 and T2 quantification. However, the high inter‐slice and in‐plane acceleration in SMS‐MRF causes severe aliasing artifacts, limiting the multiband (MB) factors to typically 2 or 3. Deep learning has demonstrated superior performance compared to the conventional dictionary matching approach for single‐slice MRF, but its effectiveness in SMS‐MRF remains unexplored. In this paper, we introduced a new deep learning approach with decoupled spatiotemporal feature learning for SMS‐MRF to achieve high MB factors for accurate and volumetric T1 and T2 quantification in neuroimaging. The proposed method leverages information from both spatial and temporal domains to mitigate the significant aliasing in SMS‐MRF. Neural networks, trained using either acquired SMS‐MRF data or simulated data generated from single‐slice MRF acquisitions, were evaluated. The performance was further compared with both dictionary matching and a deep learning approach based on residual channel attention U‐Net. Experimental results demonstrated that the proposed method, trained with acquired SMS‐MRF data, achieves the best performance in brain T1 and T2 quantification, outperforming dictionary matching and residual channel attention U‐Net. With a MB factor of 4, rapid T1 and T2 mapping was achieved with 1.5 s per slice for quantitative brain imaging. [ABSTRACT FROM AUTHOR]

Quantitative MRI and MRS have become important tools for the assessment and management of patients with neuromuscular disorders (NMDs). Despite significant progress, there is a need for new objective measures with improved specificity to the underlying pathophysiological alteration. This would enhance our ability to characterize disease evolution and improve therapeutic development. In this study, qMRI methods that are commonly used in clinical studies involving NMDs, like water T2 (T2H2O) and T1 and fat‐fraction (FF) mapping, were employed to evaluate disease activity and progression in the skeletal muscle of golden retriever muscular dystrophy (GRMD) dogs. Additionally, extracellular volume (ECV) fraction and single‐voxel bicomponent water T2 relaxometry were included as potential markers of specific histopathological changes within the tissue. Apart from FF, which was not significantly different between GRMD and control dogs and showed no trend with age, T2H2O, T1, ECV, and the relative fraction of the long‐T2 component, A2, were significantly elevated in GRMD dogs across all age ranges. Moreover, longitudinal assessment starting at 2 months of age revealed significant decreases in T2H2O, T1, ECV, A2, and the T2 of the shorter‐T2 component, T21, in both control and GRMD dogs during their first year of life. Notably, insights from ECV and bicomponent water T2 indicate that (I) the elevated T2H2O and T1 values observed in dystrophic muscle are primarily driven by an expansion of the extracellular space, likely driven by the edematous component of inflammatory responses to tissue injury and (II) the significant decrease of T2H2O and T1 with age in control and GRMD dogs reflects primarily the progressive increase in fiber diameter and protein content during tissue development. Our study underscores the potential of multicomponent water T2 relaxometry and ECV to provide valuable insights into muscle pathology in NMDs. [ABSTRACT FROM AUTHOR]

Quantitative magnetic resonance imaging (qMRI) involves mapping microstructure in standardized units sensitive to histological properties and supplements conventional MRI, which relies on contrast weighted images where intensities have no biophysical meaning. While measuring tissue properties such as myelin, iron or water content is desired in a disease context, qMRI changes may typically reflect mixed influences from aging or pre-clinical degeneration. We used a fast multi-parameter mapping (MPM) protocol for clinical routine at 3T to reconstruct whole-brain quantitative maps of magnetization transfer saturation (MT), proton density (PD), longitudinal (R1), and transverse relaxation rate (R2*) with 1.6 mm isotropic resolution. We report reference MPM values from a healthy population with age and gender distributions typical of multiple sclerosis in whole brain white matter (WM), T2-weighted WM hyperintensities, cortical grey matter and deep grey matter regions and present post-processing optimizations including integration of lesions and normalization of PD maps against cerebrospinal fluid (CSF) for standardized research in multiple sclerosis (MS) and potentially also in related disorders. PD maps were affected by WM abnormalities in MS using WM calibration. The results acknowledge the impact of non-linear age effects on MPM and suggest using CSF calibration for future clinical application in MS. [ABSTRACT FROM AUTHOR]

Background: The Gas6/TAM (Tyro3, Axl, and Mer) receptor system has been implicated in demyelination and delayed remyelination in experimental animal models, but data in humans are scarce. We aimed to investigate the role of Gas6/TAM in neurodegenerative processes in multiple sclerosis (MS). Methods: From a prospective 5-year follow-up study, soluble Gas6/TAM biomarkers were analyzed in cerebrospinal fluid (CSF) by enzyme-linked immunosorbent assay (ELISA) at baseline in patients with relapsing–remitting MS (RRMS) (n = 40), progressive MS (PMS) (n = 20), and healthy controls (HC) (n = 25). Brain volumes, including myelin content (MyC) and white matter (WM) were measured by synthetic magnetic resonance imaging at baseline, 12 months, and 60-month follow-up. Associations with brain volume changes were investigated in multivariable linear regression models. Gas6/TAM concentrations were also determined at 12 months follow-up in RRMS to assess treatment response. Results: Baseline concentrations of Tyro3, Axl, and Gas6 were significantly higher in PMS vs. RRMS and HC. Mer was higher in PMS vs. HC. Tyro3 and Gas6 were associated with reduced WM (β = 25.5, 95% confidence interval [CI] [6.11–44.96, p = 0.012; β = 11.4, 95% CI [0.42–22.4], p = 0.042, respectively) and MyC (β = 7.95, 95%CI [1.84–14.07], p = 0.012; β = 4.4, 95%CI [1.04–7.75], p = 0.012 respectively) at 60 months. Patients with evidence of remyelination at last follow-up had lower baseline soluble Tyro3 (p = 0.033) and Gas6 (p = 0.014). Except Mer, Gas6/TAM concentrations did not change with treatment in RRMS. Discussion: Our data indicate a potential role for the Gas6/TAM receptor system in neurodegenerative processes influencing demyelination and ineffective remyelination. [ABSTRACT FROM AUTHOR]

In a 0.3 T permanent-magnet magnetic resonance imaging (MRI) system, quantifying myelin content is challenging owing to long imaging times and low signal-to-noise ratio. macromolecular proton fraction (MPF) offers a quantitative assessment of myelin in the nervous system. We aimed to demonstrate the practical feasibility of MPF mapping in the brain using a 0.3 T MRI. Both 0.3 T and 3.0 T MRI systems were used. The MPF-mapping protocol used a standard 3D fast spoiled gradient-echo sequence based on the single-point reference method. Proton density, T1, and magnetization transfer-weighted images were obtained from a protein phantom at 0.3 T and 3.0 T to calculate MPF maps. MPF was measured in all phantom sections to assess its relationship to protein concentration. We acquired MPF maps for 16 and 8 healthy individuals at 0.3 T and 3.0 T, respectively, measuring MPF in nine brain tissues. Differences in MPF between 0.3 T and 3.0 T, and between 0.3 T and previously reported MPF at 0.5 T, were investigated. Pearson's correlation coefficient between protein concentration and MPF at 0.3 T and 3.0 T was 0.92 and 0.90, respectively. The 0.3 T MPF of brain tissue strongly correlated with 3.0 T MPF and literature values measured at 0.5 T. The absolute mean differences in MPF between 0.3 T and 0.5 T were 0.42% and 1.70% in white and gray matter, respectively. Single-point MPF mapping using 0.3 T permanent-magnet MRI can effectively assess myelin content in neural tissue. [ABSTRACT FROM AUTHOR]

Purpose: Echo modulation curve (EMC) modeling enables accurate quantification of T2 relaxation times in multi‐echo spin‐echo (MESE) imaging. The standard EMC‐T2 mapping framework, however, requires sufficient echoes and cumbersome pixel‐wise dictionary‐matching steps. This work proposes a deep learning version of EMC‐T2 mapping, called DeepEMC‐T2mapping, to efficiently estimate accurate T2 maps from fewer echoes. Methods: DeepEMC‐T2 mapping was developed using a modified U‐Net to estimate both T2 and proton density (PD) maps directly from MESE images. The network implements several new features to improve the accuracy of T2/PD estimation. A total of 67 MESE datasets acquired in axial orientation were used for network training and evaluation. An additional 57 datasets acquired in coronal orientation with different scan parameters were used to evaluate the generalizability of the framework. The performance of DeepEMC‐T2 mapping was evaluated in seven experiments. Results: Compared to the reference, DeepEMC‐T2 mapping achieved T2 estimation errors from 1% to 11% and PD estimation errors from 0.4% to 1.5% with ten/seven/five/three echoes, which are more accurate than standard EMC‐T2 mapping. By incorporating datasets acquired with different scan parameters and orientations for joint training, DeepEMC‐T2 exhibits robust generalizability across varying imaging protocols. Increasing the echo spacing and including longer echoes improve the accuracy of parameter estimation. The new features proposed in DeepEMC‐T2 mapping all enabled more accurate T2 estimation. Conclusions: DeepEMC‐T2 mapping enables simplified, efficient, and accurate T2 quantification directly from MESE images without dictionary matching. Accurate T2 estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times. [ABSTRACT FROM AUTHOR]

Background: The factors contributing to osteoarthritis progression after anterior cruciate ligament (ACL) injury and reconstruction (ACLR) are not fully understood. Quantitative magnetic resonance imaging (MRI) offers a noninvasive way to evaluate cartilage biochemical composition using T1ρ mapping, thereby detecting early cartilage degeneration. The specific impact of preoperative quantitative MRI on long-term outcomes after ACLR remains underreported. Purpose/Hypothesis: The purpose of this study was to investigate the effects of various factors, including preoperative cartilage T1ρ values, on the 10-year outcomes after ACLR. It was hypothesized that individual differences in baseline cartilage degeneration would influence osteoarthritis progression at 10 years postoperatively. Study Design: Case series; Level of evidence, 4. Methods: This case series involved 13 patients with primary ACL injury who underwent anatomical double-bundle ACLR using hamstring tendon autograft between April 2012 and August 2013. All patients underwent preoperative quantitative MRI using a 3.0-T scanner with an 8-channel knee coil. Cartilage was divided into 18 compartments for compositional analysis, and average T1ρ values were calculated for each compartment. At the 10-year follow-up, 9 of the patients were available for patient-reported outcome (PRO) measures and radiographs. Results: The 9 patients (6 female, 3 male) had a mean age of 26.4 years and a mean body mass index of 23.3 at surgery. All patients exhibited favorable PRO scores at the 10-year follow-up, but 5 patients showed osteoarthritis progression according to Kellgren-Lawrence (KL) grade. Although preoperative T1ρ values did not significantly correlate with PRO scores, patients with KL grade progression had significantly higher preoperative T1ρ values in the posterolateral tibial cartilage than those without progression (P =.04). Conclusion: Ten years after anatomical double-bundle ACLR, most patients reported favorable outcomes. Preoperative T1ρ values were not directly correlated with PROs, although the presence of a patient subset with progressive KL grades suggests that preoperative posterolateral tibial cartilage condition may influence long-term osteoarthritis progression. [ABSTRACT FROM AUTHOR]

Objective: The image quality of synthetized FLAIR (fluid attenuated inversion recovery) images is generally inferior to its conventional counterpart, especially regarding the lesion contrast mismatch. This work aimed to improve the lesion appearance through a hybrid methodology. Materials and methods: We combined a full brain 5-min MR-STAT acquisition followed by FLAIR synthetization step with an ultra-under sampled conventional FLAIR sequence and performed the retrospective and prospective analysis of the proposed method on the patient datasets and a healthy volunteer. Results: All performance metrics of the proposed hybrid FLAIR images on patient datasets were significantly higher than those of the physics-based FLAIR images (p < 0.005), and comparable to those of conventional FLAIR images. The small difference between prospective and retrospective analysis on a healthy volunteer demonstrated the validity of the retrospective analysis of the hybrid method as presented for the patient datasets. Discussion: The proposed hybrid FLAIR achieved an improved lesion appearance in the clinical cases with neurological diseases compared to the physics-based FLAIR images, Future prospective work on patient data will address the validation of the method from a diagnostic perspective by radiological inspection of the new images over a larger patient cohort. [ABSTRACT FROM AUTHOR]

Objectives: Signal drift has been put forward as one of the fundamental confounding factors in diffusion MRI (dMRI) of the brain. This study characterizes signal drift in dMRI of the brain, evaluates correction methods, and exemplifies its impact on parameter estimation for three intravoxel incoherent motion (IVIM) protocols. Materials and methods: dMRI of the brain was acquired in ten healthy subjects using protocols designed to enable retrospective characterization and correction of signal drift. All scans were acquired twice for repeatability analysis. Three temporal polynomial correction methods were evaluated: (1) global, (2) voxelwise, and (3) spatiotemporal. Effects of acquisition order were simulated using estimated drift fields. Results: Signal drift was around 2% per 5 min in the brain as a whole, but reached above 5% per 5 min in the frontal regions. Only correction methods taking spatially varying signal drift into account could achieve effective corrections. Altered acquisition order introduced both systematic changes and differences in repeatability in the presence of signal drift. Discussion: Signal drift in dMRI of the brain was found to be spatially varying, calling for correction methods taking this into account. Without proper corrections, choice of protocol can affect dMRI parameter estimates and their repeatability. [ABSTRACT FROM AUTHOR]

Low‐field strength scanners present an opportunity for more inclusive imaging exams and bring several challenges including lower signal‐to‐noise ratio (SNR) and longer scan times. Magnetic resonance fingerprinting (MRF) is a rapid quantitative multiparametric method that can enable multiple quantitative maps simultaneously. To demonstrate the feasibility of an MRF sequence for knee cartilage evaluation in a 0.55T system we performed repeatability and accuracy experiments with agar‐gel phantoms. Additionally, five healthy volunteers (age 32 ± 4 years old, 2 females) were scanned at 3T and 0.55T. The MRI acquisition protocols include a stack‐of‐stars T1ρ‐enabled MRF sequence, a VIBE sequence with variable flip angles (VFA) for T1 mapping, and fat‐suppressed turbo flash (TFL) sequences for T2 and T1ρ mappings. Double‐Echo steady‐state (DESS) sequence was also used for cartilage segmentation. Acquisitions were performed at two different field strengths, 0.55T and 3T, with the same sequences but protocols were slightly different to accommodate differences in signal‐to‐noise ratio and relaxation times. Cartilage segmentation was done using five compartments. T1, T2, and T1ρ values were measured in the knee cartilage using both MRF and conventional relaxometry sequences. The MRF sequence demonstrated excellent repeatability in a test–retest experiment with model agar‐gel phantoms, as demonstrated with correlation and Bland–Altman plots. Underestimation of T1 values was observed on both field strengths, with the average global difference between reference values and the MRF being 151 ms at 0.55T and 337 ms at 3T. At 0.55T, MRF measurements presented significant biases but strong correlations with the reference measurements. Although a larger error was present in T1 measurements, MRF measurements trended similarly to the conventional measurements for human subjects and model agar‐gel phantoms. [ABSTRACT FROM AUTHOR]

This study aimed to optimize the sampling of spin‐lock times (TSLs) in quantitative T1ρ mapping for improved reproducibility. Two new TSL sampling schemes were proposed: (i) reproducibility‐guided random sampling (RRS) and (ii) reproducibility‐guided optimal sampling (ROS). They were compared to the existing linear sampling (LS) and precision‐guided sampling (PS) schemes for T1ρ reproducibility through numerical simulations, phantom experiments, and volunteer studies. Each study evaluated the four sampling schemes with three commonly used T1ρ preparations based on composite and balanced spin‐locking. Additionally, the phantom and volunteer studies investigated the impact of B0 and B1 field inhomogeneities on T1ρ reproducibility, respectively. The reproducibility was assessed using the coefficient of variation (CoV) by repeating the T1ρ measurements eight times for phantom experiments and five times for volunteer studies. Numerical simulations resulted in lower mean CoVs for the proposed RRS (1.74%) and ROS (0.68%) compared to LS (2.93%) and PS (3.68%). In the phantom study, the mean CoVs were also lower for RRS (2.7%) and ROS (2.6%) compared to LS (4.1%) and PS (3.1%). Furthermore, the mean CoVs of the proposed RRS and ROS were statistically lower (P < 0.001) compared to existing LS and PS schemes at a B1 offset of 20%. In the volunteer study, consistently lower mean CoVs were observed in bilateral thigh muscles for RRS (9.3%) and ROS (9.2%) compared to LS (10.9%) and PS (10.2%), and the difference was more prominent at B0 offsets higher than 50 Hz. The proposed sampling schemes improve the reproducibility of quantitative T1ρ mapping by optimizing the selection of TSLs. This improvement is especially beneficial for longitudinal studies that track and monitor disease progression and treatment response. [ABSTRACT FROM AUTHOR]

Quantitative maps of rotating frame relaxation (RFR) time constants are sensitive and useful magnetic resonance imaging tools with which to evaluate tissue integrity in vivo. However, to date, only moderate image resolutions of 1.6 x 1.6 x 3.6 mm3 have been used for whole‐brain coverage RFR mapping in humans at 3 T. For more precise morphometrical examinations, higher spatial resolutions are desirable. Towards achieving the long‐term goal of increasing the spatial resolution of RFR mapping without increasing scan times, we explore the use of the recently introduced Transform domain NOise Reduction with DIstribution Corrected principal component analysis (T‐NORDIC) algorithm for thermal noise reduction. RFR acquisitions at 3 T were obtained from eight healthy participants (seven males and one female) aged 52 ± 20 years, including adiabatic T1ρ, T2ρ, and nonadiabatic Relaxation Along a Fictitious Field (RAFF) in the rotating frame of rank n = 4 (RAFF4) with both 1.6 x 1.6 x 3.6 mm3 and 1.25 x 1.25 x 2 mm3 image resolutions. We compared RFR values and their confidence intervals (CIs) obtained from fitting the denoised versus nondenoised images, at both voxel and regional levels separately for each resolution and RFR metric. The comparison of metrics obtained from denoised versus nondenoised images was performed with a two‐sample paired t‐test and statistical significance was set at p less than 0.05 after Bonferroni correction for multiple comparisons. The use of T‐NORDIC on the RFR images prior to the fitting procedure decreases the uncertainty of parameter estimation (lower CIs) at both spatial resolutions. The effect was particularly prominent at high‐spatial resolution for RAFF4. Moreover, T‐NORDIC did not degrade map quality, and it had minimal impact on the RFR values. Denoising RFR images with T‐NORDIC improves parameter estimation while preserving the image quality and accuracy of all RFR maps, ultimately enabling high‐resolution RFR mapping in scan times that are suitable for clinical settings. [ABSTRACT FROM AUTHOR]

Spectroscopic imaging, rooted in Dixon's two‐echo spin sequence to distinguish water and fat, has evolved significantly in acquisition and processing. Yet precise fat quantification remains a persistent challenge in ongoing research. With adequate phase characterization and correction, the fat composition models will impact measurements of fatty tissue. However, the effect of the used fat model in low‐fat regions such as healthy muscle is unknown. In this study, we investigate the effect of assumed fat composition, in terms of chain length and double bond count, on fat fraction quantification in healthy muscle, while addressing phase and relaxometry confounders. For this purpose, we acquired bilateral thigh datasets from 38 healthy volunteers. Fat fractions were estimated using the IDEAL algorithm employing three different fat models fitted with and without the initial phase constrained. After data processing and model fitting, we used a convolutional neural net to automatically segment all thigh muscles and subcutaneous fat to evaluate the fitted parameters. The fat composition was compared with those reported in the literature. Overall, all the observed estimated fat composition values fall within the range of previously reported fatty acid composition based on gas chromatography measurements. All methods and models revealed different estimates of the muscle fat fractions in various evaluated muscle groups. Lateral differences changed from 0.5% to 5.3% in the hamstring muscle groups depending on the chosen method. The lowest observed left–right differences in each muscle group were all for the fat model estimating the number of double bonds with the initial phase unconstrained. With this model, the left–right differences were 0.64% ± 0.31%, 0.50% ± 0.27%, and 0.50% ± 0.40% for the quadriceps, hamstrings, and adductors muscle groups, respectively. Our findings suggest that a fat model estimating double bond numbers while allowing separate phases for each chemical species, given some assumptions, yields the best fat fraction estimate for our dataset. [ABSTRACT FROM AUTHOR]

Background: The fundamental question of normal brain myelination in human is still poorly understood. Methods: Age-dependent global, regional, and interhemispheric sex-related differences in brain myelination of 42 (19 men, 23 women) healthy adults (19–67 years) were explored using the MRI method of fast macromolecular fraction (MPF) mapping. Results: Higher brain myelination in males compared to females was found in global white matter (WM), most WM tracts, juxtacortical WM regions, and putamen. The largest differences between men and women, exceeding 4%, were observed bilaterally in the frontal juxtacortical WM; angular, inferior occipital, and cuneus WM; external capsule; and inferior and superior fronto-orbital fasciculi. The majority of hemispheric differences in MPF were common to men and women. Sex-specific interhemispheric differences were found in juxtacortical WM; men more often had left-sided asymmetry, while women had right-sided asymmetry. Most regions of deep gray matter (GM), juxtacortical WM, and WM tracts (except for projection pathways) showed a later peak age of myelination in women compared to men, with a difference of 3.5 years on average. Body mass index (BMI) was associated with higher MPF and later peak age of myelination independent of age and sex. Conclusions: MPF mapping showed high sensitivity to assess sex-related differences in normal brain myelination, providing the basis for using this method in clinics. [ABSTRACT FROM AUTHOR]

Purpose: To develop a fast denoising framework for high‐dimensional MRI data based on a self‐supervised learning scheme, which does not require ground truth clean image. Theory and Methods: Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non‐linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep‐learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self‐supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC‐MRF) dataset and in vivo DWI image dataset to show the generalizability. Results: The proposed method drastically improved denoising performance in the presence of mild‐to‐severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images. Conclusion: The proposed MD‐S2S (Multidimensional‐Self2Self) denoising technique could be further applied to various multi‐dimensional MRI data and improve the quantification accuracy of tissue parameter maps. [ABSTRACT FROM AUTHOR]

Purpose: To assess the potential for accelerating continuous‐wave (CW) T1ρ dispersion measurement with compressed sensing approach via studying the effect that the data reduction has on the ability to detect differences between intact and degenerated articular cartilage with different spin‐lock amplitudes and to assess quantitative bias due to acceleration. Methods: Osteochondral plugs (n = 27, 4 mm diameter) from femur (n = 14) and tibia (n = 13) regions from human cadaver knee joints were obtained from commercial biobank (Science Care, USA) under Ethical permission 134/2015. MRI of specimens was performed at 9.4T with magnetization prepared radial balanced SSFP (bSSFP) readout sequence, and the CWT1ρ relaxation time maps were computed from the measured data. The relaxation time maps were evaluated in the cartilage zones for different acceleration factors. For reference, Osteoarthritis Research Society International (OARSI) grading and biomechanical measurements were performed and correlated with the MRI findings. Results: Four‐fold acceleration of CWT1ρ dispersion measurement by compressed sensing approach was feasible without meaningful loss in the sensitivity to osteoarthritic (OA) changes within the articular cartilage. Differences were significant between intact and OA groups in the superficial and transitional zones, and CWT1ρ correlated moderately with the reference measurements (0.3 < r < 0.7). Conclusion: CWT1ρ was able to differentiate between intact and OA cartilage even with four‐fold acceleration. This indicates that acceleration of CWT1ρ dispersion measurement by compressed sensing approach is feasible with negligible loss in the sensitivity to osteoarthritic changes in articular cartilage. [ABSTRACT FROM AUTHOR]

Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility‐weighted images were obtained using a 3D multigradient‐recalled‐echo sequence. For brain data, 3D anatomical T1‐ and T2‐weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann–Whitney U‐test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD. [ABSTRACT FROM AUTHOR]

The choroid plexus (CP) is a vital brain structure essential for cerebrospinal fluid (CSF) production. Moreover, alterations in the CP’s structure and function are implicated in molecular conditions and neuropathologies including multiple sclerosis, Alzheimer’s disease, and stroke. Our goal is to provide the first characterization of the association between variation in the CP microstructure and macrostructure/volume using advanced magnetic resonance imaging (MRI) methodology, and blood-based biomarkers of Alzheimer's disease (Aß42/40 ratio; pTau181), neuroinflammation and neuronal injury (GFAP; NfL). We hypothesized that plasma biomarkers of brain pathology are associated with disordered CP structure. Moreover, since cerebral microstructural changes can precede macrostructural changes, we also conjecture that these differences would be evident in the CP microstructural integrity. Our cross-sectional study was conducted on a cohort of 108 well-characterized individuals, spanning 22-94 years of age, after excluding participants with cognitive impairments and non-exploitable MR imaging data. Established automated segmentation methods were used to identify the CP volume/macrostructure using structural MR images, while the microstructural integrity of the CP was assessed using our advanced quantitative high-resolution MR imaging of longitudinal and transverse relaxation times (T1 and T2). After adjusting for relevant covariates, positive associations were observed between pTau181, NfL and GFAP and all MRI metrics. These associations reached significance (p<0.05) except for CP volume vs. pTau181 (p=0.14), CP volume vs. NfL (p=0.35), and T2 vs. NFL (p=0.07). Further, negative associations between Aß42/40 and all MRI metrics were observed but reached significance only for Aß42/40 vs. T2 (p=0.04). These novel findings demonstrate that reduced CP macrostructural and microstructural integrity is positively associated with blood-based biomarkers of AD pathology, neurodegeneration/neuroinflammation and neurodegeneration. Degradation of the CP structure may co-occur with AD pathology and neuroinflammation ahead of clinically detectable cognitive impairment, making the CP a potential structure of interest for early disease detection or treatment monitoring. [ABSTRACT FROM AUTHOR]

Purpose: Few studies have assessed trochanteric vascularity despite its implications for bone healing and surgical approaches. This study aimed to assess the regional arterial contributions of the medial femoral circumflex artery (MFCA) versus the lateral femoral circumflex artery (LFCA) to trochanteric vascularity. Methods: Ten adult human cadaveric pelvises to mid‐femur specimens were obtained. One hip was randomly assigned experimental (either MFCA or LFCA MRI‐contrast infusion) and contralateral as control (MFCA and LFCA magnetic resonance imaging [MRI]‐contrast infusion). Vascular dissection was performed for MFCA and LFCA cannulation. Pre‐ and post‐contrast 3T MRI was completed, and intra‐osseous contributions were quantified by region: greater trochanter (GT), intertrochanteric (IT), lesser trochanter (LT) and subtrochanteric (ST). A polyurethane compound mixed with barium sulfate was injected into the LFCA cannula, and into the MFCA cannula for the contralateral hip. Computed tomography (CT) imaging was completed to assess terminal branch locations. Results: MFCA provided the majority of arterial contributions to the full trochanteric region (68.5% MFCA, 31.5% LFCA; standard deviation [SD]: 10.7%, p < 0.001). Over 70% of arterial contributions to ST, LT and IT regions are derived from MFCA. GT contributions were more balanced (52.5% MFCA, 47.5% LFCA; SD: 33.7%; p = 0.853). Significant differences were found between MFCA and LFCA contributions in all regions except for the GT. CT revealed multiple consistent MFCA and LFCA trochanteric terminal branches. Conclusions: MFCA provided the dominant trochanteric arterial supply, which highlights MFCA's importance to overall hip vascularity. LFCA's trochanteric contribution was smaller but still provided trochanteric contributions, especially the GT region. Knowledge of trochanteric arterial contributions can be beneficial for optimizing surgical approaches and fixation to protect terminal branches during trochanteric fracture, nonunion treatment and trochanteric osteotomies. Level of Evidence: Not applicable. [ABSTRACT FROM AUTHOR]

We proposed an end-to-end deep learning convolutional neural network (DCNN) for region-of-interest based multi-parameter quantification (RMQ-Net) to accelerate quantitative ultrashort echo time (UTE) MRI of the knee joint with automatic multi-tissue segmentation and relaxometry mapping. The study involved UTE-based T1 (UTE-T1) and Adiabatic T1ρ (UTE-AdiabT1ρ) mapping of the knee joint of 65 human subjects, including 20 normal controls, 29 with doubtful-minimal osteoarthritis (OA), and 16 with moderate-severe OA. Comparison studies were performed on UTE-T1 and UTE-AdiabT1ρ measurements using 100%, 43%, 26%, and 18% UTE MRI data as the inputs and the effects on the prediction quality of the RMQ-Net. The RMQ-net was modified and retrained accordingly with different combinations of inputs. Both ROI-based and voxel-based Pearson correlation analyses were performed. High Pearson correlation coefficients were achieved between the RMQ-Net predicted UTE-T1 and UTE-AdiabT1ρ results and the ground truth for segmented cartilage with acceleration factors ranging from 2.3 to 5.7. With an acceleration factor of 5.7, the Pearson r-value achieved 0.908 (ROI-based) and 0.945 (voxel-based) for UTE-T1, and 0.733 (ROI-based) and 0.895 (voxel-based) for UTE-AdiabT1ρ, correspondingly. The results demonstrated that RMQ-net can significantly accelerate quantitative UTE imaging with automated segmentation of articular cartilage in the knee joint. [ABSTRACT FROM AUTHOR]

Background: It is challenging to diagnose suspected Duchenne muscular dystrophy (DMD) patients in the very early stage of the disease. More evidence is needed to demonstrate the potential of quantitative MRI (qMRI) in precisely identifying patients before substantial physical decline occurs. Purpose: To assess the early diagnostic performance of multi‐parametric qMRI for DMD patients, and the ability to identify DMD patients with mild functional decline. Study Type: Prospective. Subjects: One hundred and forty DMD subjects (9.0 ± 2.2 years old), 24 male healthy controls (HCs) (9.2 ± 2.5 years old). Field Strength/Sequence: 3.0 T/3‐point Dixon, T1‐mapping, and T2‐mapping. Assessment: qMRI measurements (fat fraction [FF], T1, and T2) of 11 thigh muscles (rectus femoris [RF], vastus lateralis [VL], vastus intermedius, vastus medialis, gracilis, sartorius, adductor longus, adductor magnus [AM], semitendinosus, semimembranosus, biceps femoris long head [BFLH]) on the right side were conducted. NorthStar ambulatory assessment (NSAA) score used to evaluate the function of DMD patients and divided them into three subgroups: mild (76–100 score), moderate (51–75 score), and severe (0–50 score) functional decline. Statistical Tests: Independent t‐test, ANOVA analysis, and receiver operating characteristic (ROC) curves. A P‐value <0.05 was considered statistically significant. Results: Compared with HCs, FF and T2 were significantly higher in the group of all DMD patients, while T1 was significantly lower. The combination of T1 and T2 in RF, VL, AM, and BFLH achieved excellent area under curve (AUCs) (0.967–0.992) in differentiating five DMD patients without abnormal fat infiltration from HCs. Overall, T2 reached higher AUCs than FF and T1 in distinguishing DMD with mild functional decline from HCs, whereas FF achieved higher AUCs than T1 and T2 in distinguishing three DMD subgroups with functional decline. Data Conclusion: Multi‐parametric qMRI demonstrate effective diagnostic capabilities for DMD patients in the early stage of the disease, and can identify patients with mild physical decline. Level of Evidence: 2 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Achilles tendon injury is a common sports injury, and an in‐depth understanding of its healing process is essential for improving rehabilitation strategies. As a non‐invasive imaging technology with excellent anatomical and functional information extraction abilities, magnetic resonance imaging (MRI) has been widely used in the evaluation and monitoring of Achilles tendon injury. MRI scans at different stages of Achilles tendon healing can provide information about the structure of the Achilles tendon tissue, blood supply, composition, and metabolism. The change pattern on dynamic MRI evaluation is closely related to the specific stage of Achilles tendon healing and tissue characteristics. For example, the signal strength of dynamic enhanced MRI sequences can reflect blood supply to the Achilles tendon, whereas some quantitative MRI techniques can provide information on the recovery of water and collagen contents in the Achilles tendon. This article discusses the pathophysiological changes after Achilles tendon injury and summarizes the clinical and research status of the MRI techniques used for monitoring Achilles tendon healing. The feasibility of various MRI techniques for monitoring Achilles tendon healing and their correlation with histology, biochemistry, and biomechanics are reviewed, along with the challenges, limitations, and potential opportunities for their application. Level of Evidence: 1 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Purpose: To develop neural network (NN)‐based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér–Rao bound. Theory and Methods: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications. Results: In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér–Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as nonlinear least‐squares fitting, while state‐of‐the‐art NNs show larger deviations. Conclusion: The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy. [ABSTRACT FROM AUTHOR]

Neurodegenerative disorders, including Multiple Sclerosis (MS), are heterogenous disorders which affect the myelin sheath of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) provides a non-invasive method for studying, diagnosing, and monitoring disease progression. As an emerging research area, many studies have attempted to connect MR metrics to underlying pathophysiological presentations of heterogenous neurodegeneration. Most commonly, small animal models are used, including Experimental Autoimmune Encephalomyelitis (EAE), Theiler's Murine Encephalomyelitis (TMEV), and toxin models including cuprizone (CPZ), lysolecithin, and ethidium bromide (EtBr). A contrast and comparison of these models is presented, with focus on the cuprizone model, followed by a review of literature studying neurodegeneration using MRI and the cuprizone model. Conventional MRI methods including T1 Weighted (T1W) and T2 Weighted (T2W) Imaging are mentioned. Quantitative MRI methods which are sensitive to diffusion, magnetization transfer, susceptibility, relaxation, and chemical composition are discussed in relation to studying the CPZ model. Overall, additional studies are needed to improve both the sensitivity and specificity of MRI metrics for underlying pathophysiology of neurodegeneration and the relationships in attempts to clear the clinico-radiological paradox. We therefore propose a multiparametric approach for the investigation of MR metrics for underlying pathophysiology. [ABSTRACT FROM AUTHOR]

Identification and diagnosis of periprosthetic joint infection (PJI) are challenging, requiring a multi-disciplinary approach involving clinical evaluation, laboratory tests, and imaging studies. MRI is advantageous to alternative imaging techniques due to superior soft tissue contrast and absence of ionizing radiation. However, the presence of metallic implants can cause signal loss and artifacts. Metal artifact suppression (MARS) MRI techniques have been developed that mitigate metal artifacts and improve periprosthetic soft tissue visualization. This paper provides a review of the various MARS MRI techniques, their clinical applicability and accuracy in PJI diagnosis and evaluation, and current challenges and future perspectives. [ABSTRACT FROM AUTHOR]

Background: Meningioma subtype is crucial in treatment planning and prognosis delineation, for grade 1 meningiomas. T2 relaxometry could provide detailed microscopic information but is often limited by long scanning times. Purpose: To investigate the potential of T2 maps derived from multiple overlapping‐echo detachment imaging (MOLED) for predicting meningioma subtypes and Ki‐67 index, and to compare the diagnostic efficiency of two different region‐of‐interest (ROI) placements (whole‐tumor and contrast‐enhanced, respectively). Study Type: Prospective. Phantom/Subjects: A phantom containing 11 tubes of MnCl2 at different concentrations, eight healthy volunteers, and 75 patients with grade 1 meningioma. Field Strength/Sequence: 3 T scanner. MOLED, T2‐weighted spin‐echo sequence, T2‐dark‐fluid sequence, and postcontrast T1‐weighted gradient echo sequence. Assessment: Two ROIs were delineated: the whole‐tumor area (ROI1) and contrast‐enhanced area (ROI2). Histogram parameters were extracted from T2 maps. Meningioma subtypes and Ki‐67 index were reviewed by a neuropathologist according to the 2021 classification criteria. Statistical Tests: Linear regression, Bland–Altman analysis, Pearson's correlation analysis, independent t test, Mann–Whitney U test, Kruskal–Wallis test with Bonferroni correction, and multivariate logistic regression analysis with the P‐value significance level of 0.05. Results: The MOLED T2 sequence demonstrated excellent accuracy for phantoms and volunteers (Meandiff = −1.29%, SDdiff = 1.25% and Meandiff = 0.36%, SDdiff = 2.70%, respectively), and good repeatability for volunteers (average coefficient of variance = 1.13%; intraclass correlation coefficient = 0.877). For both ROI1 and ROI2, T2 variance had the highest area under the curves (area under the ROC curve = 0.768 and 0.761, respectively) for meningioma subtyping. There was no significant difference between the two ROIs (P = 0.875). Significant correlations were observed between T2 parameters and Ki‐67 index (r = 0.237–0.374). Data Conclusion: MOLED T2 maps can effectively differentiate between meningothelial, fibrous, and transitional meningiomas. Moreover, T2 histogram parameters were significantly correlated with the Ki‐67 index. Level of Evidence: 1 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Thyroid eye disease (TED) is a complex autoimmune disorder that impairs various orbital structures, leading to cosmetic damage and vision loss. Magnetic resonance imaging (MRI) is a fundamental diagnostic tool utilized in clinical settings of TED, for its accurate demonstration of orbital lesions and indication of disease conditions. The application of quantitative MRI has brought a new prospect to the management and research of TED, offering more detailed information on morphological and functional changes in the orbit. Therefore, many researchers concentrated on the implementation of different quantitative MRI techniques on TED for the exploration of clinical practices. Despite the abundance of studies utilizing quantitative MRI in TED, there remain considerable barriers and disputes on the best exploitation of this tool. This could possibly be attributed to the complexity of TED and the fast development of MRI techniques. It is necessary that clinical and radiological aspects of quantitative MRI in TED be better integrated into comprehensive insights. Hence, this review traces back 30 years of publications regarding quantitative MRI utilized in TED and elucidates this promising application in the facets of imaging techniques and clinical practices. We believe that a deeper understanding of the application of quantitative MRI in TED will enhance the efficacy of the multidisciplinary management of TED. Level of Evidence: 2 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Accurate prediction of renal mass subtypes, along with the WHO/ISUP grade and pathological T (pT) stage of clear cell renal cell carcinoma (ccRCC), is crucial for optimal decision making. Our study aimed to investigate the feasibility and reproducibility of motion-robust radial T2 mapping in differentiating lipid-poor angiomyolipoma (MFAML) from RCC and characterizing the WHO/ISUP grade and pT stage of ccRCC. Finally, 92 patients undergoing renal radial T2 mapping and ZOOMit DWI were recruited. The T2 values and apparent diffusion coefficient (ADC) were analyzed. Correlation coefficients were calculated between ADC and T2 values. Notably, ccRCC exhibited higher T2 and ADC values than MFAML (p < 0.05). T2 values were lower in the higher WHO/ISUP grade and pT stage of ccRCC (all p < 0.05). ADC showed no significant difference for pT stage (p = 0.056). T2 values revealed a higher area under the curve (AUC) in evaluating the WHO/ISUP grade compared to ADC (0.936 vs. 0.817, p = 0.027). T2 values moderately positively correlated with ADC (r = 0.675, p < 0.001). In conclusion, quantitative motion-robust radial T2 mapping is feasible for characterizing solid renal masses and could provide additional value for multiparametric imaging in predicting WHO/ISUP grade and pT stage of ccRCC. [ABSTRACT FROM AUTHOR]

Background and objectives: In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes. Methods: We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue. Results and conclusions: Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions. [ABSTRACT FROM AUTHOR]

Purpose: To develop a highly accelerated multi‐echo spin‐echo method, TEMPURA, for reducing the acquisition time and/or increasing spatial resolution for kidney T2 mapping. Methods: TEMPURA merges several adjacent echoes into one k‐space by either combining independent echoes or sharing one echo between k‐spaces. The combined k‐space is reconstructed based on compressed sensing theory. Reduced flip angles are used for the refocusing pulses, and the extended phase graph algorithm is used to correct the effects of indirect echoes. Two sequences were developed: a fast breath‐hold sequence; and a high‐resolution sequence. The performance was evaluated prospectively on a phantom, 16 healthy subjects, and two patients with different types of renal tumors. Results: The fast TEMPURA method reduced the acquisition time from 3–5 min to one breath‐hold (18 s). Phantom measurements showed that fast TEMPURA had a mean absolute percentage error (MAPE) of 8.2%, which was comparable to a standardized respiratory‐triggered sequence (7.4%), but much lower than a sequence accelerated by purely k‐t undersampling (21.8%). High‐resolution TEMPURA reduced the in‐plane voxel size from 3 × 3 to 1 × 1 mm2, resulting in improved visualization of the detailed anatomical structure. In vivo T2 measurements demonstrated good agreement (fast: MAPE = 1.3%–2.5%; high‐resolution: MAPE = 2.8%–3.3%) and high correlation coefficients (fast: R = 0.85–0.98; high‐resolution: 0.82–0.96) with the standardized method, outperforming k‐t undersampling alone (MAPE = 3.3–4.5%, R = 0.57–0.59). Conclusion: TEMPURA provides fast and high‐resolution renal T2 measurements. It has the potential to improve clinical throughput and delineate intratumoral heterogeneity and tissue habitats at unprecedented spatial resolution. [ABSTRACT FROM AUTHOR]

Purpose: T1 mapping is a widely used quantitative MRI technique, but its tissue‐specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well‐established inversion‐recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. Methods: The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open‐source platforms. Intersubmission and intrasubmission comparisons were performed. Results: Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. Conclusion: The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo. [ABSTRACT FROM AUTHOR]

Purpose: MR‐STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large‐scale nonlinear optimization problem. Managing reconstruction times is one of the main challenges of MR‐STAT. In this work we leverage GPU hardware to reduce MR‐STAT reconstruction times. A highly optimized, GPU‐compatible Bloch simulation toolbox is developed as part of this work that can be utilized for other quantitative MRI techniques as well. Methods: The Julia programming language was used to develop a flexible yet highly performant and GPU‐compatible Bloch simulation toolbox called BlochSimulators.jl. The runtime performance of the toolbox is benchmarked against other Bloch simulation toolboxes. Furthermore, a (partially matrix‐free) modification of a previously presented (matrix‐free) MR‐STAT reconstruction algorithm is proposed and implemented using the Julia language on GPU hardware. The proposed algorithm is combined with BlochSimulators.jl and the resulting MR‐STAT reconstruction times on GPU hardware are compared to previously presented MR‐STAT reconstruction times. Results: The BlochSimulators.jl package demonstrates superior runtime performance on both CPU and GPU hardware when compared to other existing Bloch simulation toolboxes. The GPU‐accelerated partially matrix‐free MR‐STAT reconstruction algorithm, which relies on BlochSimulators.jl, allows for reconstructions of 68 seconds per two‐dimensional (2D slice). Conclusion: By combining the proposed Bloch simulation toolbox and the partially matrix‐free reconstruction algorithm, 2D MR‐STAT reconstructions can be performed in the order of one minute on a modern GPU card. The Bloch simulation toolbox can be utilized for other quantitative MRI techniques as well, for example for online dictionary generation for MR Fingerprinting. [ABSTRACT FROM AUTHOR]

Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation. [ABSTRACT FROM AUTHOR]

Objective: In chronic low back pain (CLBP), the relationship between spinal pathologies and paraspinal muscles fat infiltration remains unclear. This study aims to evaluate the relationship between MRI findings and paraspinal muscles morphology and fat infiltration in CLBP patients by quantitative MRI. Methods: All the CLBP patients were enrolled from July 2021 to December 2022 in four medical institutions. The cross-sectional area (CSA) and proton density fat fraction (PDFF) of the multifidus (MF) and erector spinae (ES) muscles at the central level of the L4/5 and L5/S1 intervertebral discs were measured. MRI findings included degenerative lumbar spondylolisthesis (DLS), intervertebral disc degeneration (IVDD), facet arthrosis, disc bulge or herniation, and disease duration. The relationship between MRI findings and the paraspinal muscles PDFF and CSA in CLBP patients was analyzed. Results: A total of 493 CLBP patients were included in the study (198 females, 295 males), with an average age of 45.68 ± 12.91 years. Our research indicates that the number of MRI findings are correlated with the paraspinal muscles PDFF at the L4/5 level, but is not significant. Moreover, the grading of IVDD is the primary factor influencing the paraspinal muscles PDFF at the L4-S1 level (BES at L4/5=1.845, P < 0.05); DLS was a significant factor affecting the PDFF of MF at the L4/5 level (B = 4.774, P < 0.05). After including age, gender, and Body Mass Index (BMI) as control variables in the multivariable regression analysis, age has a significant positive impact on the paraspinal muscles PDFF at the L4-S1 level, with the largest AUC for ES PDFF at the L4/5 level (AUC = 0.646, cut-off value = 47.5), while males have lower PDFF compared to females. BMI has a positive impact on the ES PDFF only at the L4/5 level (AUC = 0.559, cut-off value = 24.535). Conclusion: The degree of paraspinal muscles fat infiltration in CLBP patients is related to the cumulative or synergistic effects of multiple factors, especially at the L4/L5 level. Although age and BMI are important factors affecting the degree of paraspinal muscles PDFF in CLBP patients, their diagnostic efficacy is moderate. [ABSTRACT FROM AUTHOR]

Objective: In chronic low back pain (CLBP), the relationship between spinal pathologies and paraspinal muscles fat infiltration remains unclear. This study aims to evaluate the relationship between MRI findings and paraspinal muscles morphology and fat infiltration in CLBP patients by quantitative MRI. Methods: All the CLBP patients were enrolled from July 2021 to December 2022 in four medical institutions. The cross-sectional area (CSA) and proton density fat fraction (PDFF) of the multifidus (MF) and erector spinae (ES) muscles at the central level of the L4/5 and L5/S1 intervertebral discs were measured. MRI findings included degenerative lumbar spondylolisthesis (DLS), intervertebral disc degeneration (IVDD), facet arthrosis, disc bulge or herniation, and disease duration. The relationship between MRI findings and the paraspinal muscles PDFF and CSA in CLBP patients was analyzed. Results: A total of 493 CLBP patients were included in the study (198 females, 295 males), with an average age of 45.68 ± 12.91 years. Our research indicates that the number of MRI findings are correlated with the paraspinal muscles PDFF at the L4/5 level, but is not significant. Moreover, the grading of IVDD is the primary factor influencing the paraspinal muscles PDFF at the L4-S1 level (BES at L4/5=1.845, P < 0.05); DLS was a significant factor affecting the PDFF of MF at the L4/5 level (B = 4.774, P < 0.05). After including age, gender, and Body Mass Index (BMI) as control variables in the multivariable regression analysis, age has a significant positive impact on the paraspinal muscles PDFF at the L4-S1 level, with the largest AUC for ES PDFF at the L4/5 level (AUC = 0.646, cut-off value = 47.5), while males have lower PDFF compared to females. BMI has a positive impact on the ES PDFF only at the L4/5 level (AUC = 0.559, cut-off value = 24.535). Conclusion: The degree of paraspinal muscles fat infiltration in CLBP patients is related to the cumulative or synergistic effects of multiple factors, especially at the L4/L5 level. Although age and BMI are important factors affecting the degree of paraspinal muscles PDFF in CLBP patients, their diagnostic efficacy is moderate. [ABSTRACT FROM AUTHOR]

Diffusion Kurtosis Imaging (DKI)-derived metrics are recognized as indicators of maturation in neonates with low-grade germinal matrix and intraventricular hemorrhage (GMH-IVH). However, it is not yet known if these factors are associated with neurodevelopmental outcomes. The objective of this study was to acquire DKI-derived metrics in neonates with low-grade GMH-IVH, and to demonstrate their association with later neurodevelopmental outcomes. In this prospective study, neonates with low-grade GMH-IVH and control neonates were recruited, and DKI were performed between January 2020 and March 2021. These neonates underwent the Bayley Scales of Infant Development test at 18 months of age. Mean kurtosis (MK), radial kurtosis (RK) and gray matter values were measured. Spearman correlation analyses were conducted for the measured values and neurodevelopmental outcome scores. Forty controls (18 males, average gestational age (GA) 30 weeks ± 1.3, corrected GA at MRI scan 38 weeks ± 1) and thirty neonates with low-grade GMH-IVH (13 males, average GA 30 weeks ± 1.5, corrected GA at MRI scan 38 weeks ± 1). Neonates with low-grade GMH-IVH exhibited lower MK and RK values in the PLIC and the thalamus (P < 0.05). The MK value in the thalamus was associated with Mental Development Index (MDI) (r = 0.810, 95% CI 0.695–0.13; P < 0.001) and Psychomotor Development Index (PDI) (r = 0.852, 95% CI 0.722–0.912; P < 0.001) scores. RK value in the caudate nucleus significantly and positively correlated with MDI (r = 0.496, 95% CI 0.657–0.933; P < 0.001) and PDI (r = 0.545, 95% CI 0.712–0.942; P < 0.001) scores. The area under the curve (AUC) were used to assess diagnostic performance of MK and RK in thalamus (AUC = 0.866, 0.787) and caudate nucleus (AUC = 0.833, 0.671) for predicting neurodevelopmental outcomes. As quantitative neuroimaging markers, MK in thalamus and RK in caudate nucleus may help predict neurodevelopmental outcomes in neonates with low-grade GMH-IVH. [ABSTRACT FROM AUTHOR]

Background and purpose: Because Becker muscular dystrophy (BMD) is a heterogeneous disease and only few studies have evaluated adult patients, it is currently still unclear which outcome measures should be used in future clinical trials. Methods: Muscle magnetic resonance imaging, patient‐reported outcome measures and a wide range of clinical outcome measures, including motor function, muscle strength and timed‐function tests, were evaluated in 21 adults with BMD at baseline and at 9 and 18 months of follow‐up. Results: Proton density fat fraction increased significantly in 10/17 thigh muscles after 9 months, and in all thigh and lower leg muscles after 18 months. The 32‐item Motor Function Measurement (MFM‐32) scale (−1.3%, p = 0.017), North Star Ambulatory Assessment (−1.3 points, p = 0.010) and patient‐reported activity limitations scale (−0.3 logits, p = 0.018) deteriorated significantly after 9 months. The 6‐min walk distance (−28.7 m, p = 0.042), 10‐m walking test (−0.1 m/s, p = 0.032), time to climb four stairs test (−0.03 m/s, p = 0.028) and Biodex peak torque measurements of quadriceps (−4.6 N m, p = 0.014) and hamstrings (−5.0 N m, p = 0.019) additionally deteriorated significantly after 18 months. At this timepoint, domain 1 of the MFM‐32 was the only clinical outcome measure with a large sensitivity to change (standardized response mean 1.15). Discussion: It is concluded that proton density fat fraction imaging of entire thigh muscles is a sensitive outcome measure to track progressive muscle fat replacement in patients with BMD, already after 9 months of follow‐up. Finally, significant changes are reported in a wide range of clinical and patient‐reported outcome measures, of which the MFM‐32 appeared to be the most sensitive to change in adults with BMD. [ABSTRACT FROM AUTHOR]

Purpose: Quantitative multi‐parameter mapping (MPM) provides maps of physical quantities representing physiologically meaningful tissue characteristics, which allows to investigate microstructure‐function relationships reflecting normal or pathologic processes in the brain. However, the achievable spatial resolution and stability of MPM for basic research or clinical applications is severely constrained by SNR limits of the MR acquisition process, resulting in relatively long acquisition times. To increase SNR, we denoise MPM acquisitions using principal component analysis along tensors exploiting the Marchenko‐Pastur law (tMPPCA). Methods: tMPPCA denoising was applied to three sets of MPM raw data before the quantification of maps of proton density, magnetization transfer saturation, R1, and R2*. The regional SNR gain for high‐resolution MPM was investigated as well as reproducibility gains for clinically optimized protocols with moderate and high acceleration factors at different image resolutions. Results: Substantial noise reduction in raw data was achieved, resulting in reduced noise for quantitative mapping up to sixfold without introducing bias of mean values (below 1%). Scan–rescan fluctuations were reduced up to threefold. Denoising allowed to decrease the voxel volume fourfold at the same scan time or reduce the scan time twofold at same voxel volume without loss of sensitivity. Conclusions: tMPPCA denoising can (a) improve of fine spatial and temporal patterns, (b) considerably reduce scan time for clinical applications, or (c) increase resolution to potentially push cutting‐edge MPM protocols from the upper to the lower limit of the mesoscopic scale. [ABSTRACT FROM AUTHOR]

Purpose: This paper proposes a novel self‐supervised learning framework that uses model reinforcement, REference‐free LAtent map eXtraction with MOdel REinforcement (RELAX‐MORE), for accelerated quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll an iterative model‐based qMRI reconstruction into a deep learning framework, enabling accelerated MR parameter maps that are highly accurate and robust. Methods: Unlike conventional deep learning methods which require large amounts of training data, RELAX‐MORE is a subject‐specific method that can be trained on single‐subject data through self‐supervised learning, making it accessible and practically applicable to many qMRI studies. Using quantitative T1$$ {\mathrm{T}}_1 $$ mapping as an example, the proposed method was applied to the brain, knee and phantom data. Results: The proposed method generates high‐quality MR parameter maps that correct for image artifacts, removes noise, and recovers image features in regions of imperfect image conditions. Compared with other state‐of‐the‐art conventional and deep learning methods, RELAX‐MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping. Conclusion: This work demonstrates the feasibility of a new self‐supervised learning method for rapid MR parameter mapping, that is readily adaptable to the clinical translation of qMRI. [ABSTRACT FROM AUTHOR]

The MR transverse relaxation rate, R 2 * , has been widely used to detect iron and myelin content in tissue. However, it is also sensitive to macroscopic B0 inhomogeneities. One approach to correct for the B0 effect is to fit gradient-echo signals with the three-parameter model, a sinc function-weighted monoexponential decay. However, such three-parameter models are subject to increased noise sensitivity. To address this issue, this study presents a two-stage fitting procedure based on the three-parameter model to mitigate the B0 effect and reduce the noise sensitivity of R 2 * measurement in the mouse brain at 7T. MRI scans were performed on eight healthy mice. The gradient-echo signals were fitted with the two-stage fitting procedure to generate R 2 c o r r _ t * . The signals were also fitted with the monoexponential and three-parameter models to generate R 2 n o c o r r * and R 2 c o r r * , respectively. Regions of interest (ROIs), including the corpus callosum, internal capsule, somatosensory cortex, caudo-putamen, thalamus, and lateral ventricle, were selected to evaluate the within-ROI mean and standard deviation (SD) of the R 2 * measurements. The results showed that the Akaike information criterion of the monoexponential model was significantly reduced by using the three-parameter model in the selected ROIs (p = 0.0039–0.0078). However, the within-ROI SD of R 2 c o r r * using the three-parameter model was significantly higher than that of the R 2 n o c o r r * in the internal capsule, caudo-putamen, and thalamus regions (p = 0.0039), a consequence partially due to the increased noise sensitivity of the three-parameter model. With the two-stage fitting procedure, the within-ROI SD of R 2 c o r r * was significantly reduced by 7.7–30.2% in all ROIs, except for the somatosensory cortex region with a fast in-plane variation of the B0 gradient field (p = 0.0039–0.0078). These results support the utilization of the two-stage fitting procedure to mitigate the B0 effect and reduce noise sensitivity for R 2 * measurement in the mouse brain. [ABSTRACT FROM AUTHOR]

Background: Parsonage‐Turner syndrome (PTS) is characterized by severe, acute upper extremity pain and subsequent paresis and most commonly involves the long thoracic nerve (LTN). While MR neurography (MRN) can detect LTN hourglass‐like constrictions (HGCs), quantitative muscle MRI (qMRI) can quantify serratus anterior muscle (SAM) neurogenic changes. Purpose/Hypothesis: 1) To characterize qMRI findings in LTN‐involved PTS. 2) To investigate associations between qMRI and clinical assessments of HGCs/electromyography (EMG). Study Type: Prospective. Population: 30 PTS subjects (25 M/5 F, mean/range age = 39/15–67 years) with LTN involvement who underwent bilateral chest wall qMRI and unilateral brachial plexus MRN. Field Strength/Sequences: 3.0 Tesla/multiecho spin‐echo T2‐mapping, diffusion‐weighted echo‐planar‐imaging, multiecho gradient echo. Assessment: qMRI was performed to obtain T2, muscle diameter fat fraction (FF), and cross‐sectional area of the SAM. Clinical reports of MRN and EMG were obtained; from MRN, the number of HGCs; from EMG, SAM measurements of motor unit recruitment levels, fibrillations, and positive sharp waves. qMRI/MRN were performed within 90 days of EMG. EMG was performed on average 185 days from symptom onset (all ≥2 weeks from symptom onset) and 5 days preceding MRI. Statistical Tests: Paired t‐tests were used to compare qMRI measures in the affected SAM versus the contralateral, unaffected side (P < 0.05 deemed statistically significant). Kendall's tau was used to determine associations between qMRI against HGCs and EMG. Results: Relative to the unaffected SAM, the affected SAM had increased T2 (50.42 ± 6.62 vs. 39.09 ± 4.23 msec) and FF (8.45 ± 9.69 vs. 4.03% ± 1.97%), and decreased muscle diameter (74.26 ± 21.54 vs. 88.73 ± 17.61 μm) and cross‐sectional area (9.21 ± 3.75 vs. 16.77 ± 6.40 mm2). There were weak to negligible associations (tau = −0.229 to <0.001, P = 0.054–1.00) between individual qMRI biomarkers and clinical assessments of HGCs and EMG. Data Conclusion: qMRI changes in the SAM were observed in subjects with PTS involving the LTN. Level of Evidence: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]