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Image segmentation has been a well-addressed problem in pattern recognition for the last few decades. As a sub-problem of image segmentation, the background separation in biomedical images generated by magnetic resonance imaging (MRI) has also been of interest in the applied mathematics literature. Level set evolution of active contours idea can successfully be applied to MRI images to extract the region of interest (ROI) as a crucial preprocessing step for medical image analysis. In this study, we use the classical level set solution to create binary masks of various brain MRI images in which black color implies background and white color implies the ROI. We further used the MRI image and mask image pairs to train a deep neural network (DNN) architecture called U-Net, which has been proven to be a successful model for biomedical image segmentation. Our experiments have shown that a properly trained U-Net can achieve a matching performance of the level set method. Hence we were able to train a U-Net by using automatically generated input and label data successfully. The trained network can detect ROI in MRI images faster than the level-set method and can be used as a preprocessing tool for more enhanced medical image analysis studies.

Several GWAS reported Myocyte Enhancer Factor 2 C (MEF2C) gene associations with white matter microstructure and psychiatric disorders, and MEF2C involvement in pathways related to neuronal development suggests a common biological factor underlying these phenotypes. We aim to refine the MEF2C effects in the brain relying on an integrated analysis of white matter and psychiatric phenotypes in an extensively characterized sample. This study included 870 Brazilian adults (47% from an attention-deficit/hyperactivity disorder outpatient clinic) assessed through standardized psychiatric interviews, 139 of which underwent a magnetic resonance imaging scan. We evaluated variants in the MEF2C region using two approaches: 1) a gene-wide analysis, which uses the sum of polymorphism effects, and 2) SNP analyses, restricted to the independent variants within the gene. The outcomes included psychiatric phenotypes and fractional anisotropy for brain images. Results: The gene-wide analyses pointed to a nominal association between MEF2C and the Temporal Portion of the Superior Longitudinal Fasciculus (SLFTEMP). The SNP analysis identified four independent variants significantly associated with SLFTEMP and one (rs4218438) with Substance Use Disorder. Our findings showing specific associations of MEF2C variants with temporal-frontal circuitry components may help to elucidate how the MEF2C gene underlies a broad range of psychiatric phenotypes since these regions are relevant to executive and cognitive functions.

PURPOSE: Ischemic injury in the kidney is a common pathophysiological event associated with both acute kidney injury and chronic kidney disease; however, regional ischemia-reperfusion as seen in thromboembolic renal disease is often undetectable and thus subclinical. Here, we assessed the metabolic alterations following subclinical focal ischemia-reperfusion injury with hyperpolarized [1- 13 C]pyruvate MRI in a porcine model. METHODS: Five pigs were subjected to 60 min of focal kidney ischemia. After 90 min of reperfusion, a multiparametric proton MRI protocol was performed on a clinical 3T scanner system. Metabolism was evaluated using 13 C MRI following infusion of hyperpolarized [1- 13 C]pyruvate. Ratios of pyruvate to its detectable metabolites (lactate, bicarbonate, and alanine) were used to quantify metabolism. RESULTS: The focal ischemia-reperfusion injury resulted in injured areas with a mean size of 0.971 cm 3 (±1.019). Compared with the contralateral kidney, the injured areas demonstrated restricted diffusion (1269 ± 83.59 × 10 -6 mm 2 /s vs. 1530 ± 52.73 × 10 -6 mm 2 /s; p = 0.006) and decreased perfusion (158.8 ± 29.4 mL/100 mL/min vs. 274 ± 63.1 mL/100 mL/min; p = 0.014). In the metabolic assessment, the injured areas displayed increased lactate/pyruvate ratios compared with the entire ipsilateral and the contralateral kidney (0.35 ± 0.13 vs. 0.27 ± 0.1 vs. 0.25 ± 0.1; p = 0.0086). Alanine/pyruvate ratio was unaltered, and we were unable to quantify bicarbonate due to low signal. CONCLUSION: MRI with hyperpolarized [1- 13 C]pyruvate in a clinical setup is capable of detecting the acute, subtle, focal metabolic changes following ischemia. This may prove to be a valuable future addition to the renal MRI suite.

Funding Information: The authors acknowledge the support of FCT-Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020, UIDP/50025/2020 and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures, Nanomodelling and Nanofabrication-i3N and the PTNMR Network. Publisher Copyright: © 2023 Informa UK Limited, trading as Taylor & Francis Group. NMR spectroscopy was used to characterise the velocity profile in nematic solutions of poly(γ-benzyl-L-glutamate) (PBLG) in m-cresol during the application of a simple shear-flow at constant shear-rate values. In this study, the rheo-NMR technique was used that allows an insight over the fluid dynamics during the application of a shear flow in the presence of an external magnetic field, in a perpendicular direction to the shear gradient. Using this Magnetic Resonance Imaging (MRI) based technique, the velocity profile developed inside the system is accessed. publishersversion inpress

Background Optical scanning technologies are increasingly being utilised to supplement treatment workflows in radiation oncology, such as surface-guided radiotherapy or 3D printing custom bolus. One limitation of optical scanning devices is the absence of internal anatomical information of the patient being scanned. As a result, conventional radiation therapy treatment planning using this imaging modality is not feasible. Deep learning is useful for automating various manual tasks in radiation oncology, most notably, organ segmentation and treatment planning. Deep learning models have also been used to transform MRI datasets into synthetic CT datasets, facilitating the development of MRI-only radiation therapy planning. Aims To train a pix2pix generative adversarial network to transform 3D optical scan data into estimated MRI datasets for a given patient to provide additional anatomical data for a select few radiation therapy treatment sites. The proposed network may provide useful anatomical information for treatment planning of surface mould brachytherapy, total body irradiation, and total skin electron therapy, for example, without delivering any imaging dose. Methods A 2D pix2pix GAN was trained on 15,000 axial MRI slices of healthy adult brains paired with corresponding external mask slices. The model was validated on a further 5000 previously unseen external mask slices. The predictions were compared with the “ground-truth” MRI slices using the multi-scale structural similarity index (MSSI) metric. A certified neuro-radiologist was subsequently consulted to provide an independent review of the model’s performance in terms of anatomical accuracy and consistency. The network was then applied to a 3D photogrammetry scan of a test subject to demonstrate the feasibility of this novel technique. Results The trained pix2pix network predicted MRI slices with a mean MSSI of 0.831 ± 0.057 for the 5000 validation images indicating that it is possible to estimate a significant proportion of a patient’s gross cranial anatomy from a patient’s exterior contour. When independently reviewed by a certified neuro-radiologist, the model’s performance was described as “quite amazing, but there are limitations in the regions where there is wide variation within the normal population.” When the trained network was applied to a 3D model of a human subject acquired using optical photogrammetry, the network could estimate the corresponding MRI volume for that subject with good qualitative accuracy. However, a ground-truth MRI baseline was not available for quantitative comparison. Conclusions A deep learning model was developed, to transform 3D optical scan data of a patient into an estimated MRI volume, potentially increasing the usefulness of optical scanning in radiation therapy planning. This work has demonstrated that much of the human cranial anatomy can be predicted from the external shape of the head and may provide an additional source of valuable imaging data. Further research is required to investigate the feasibility of this approach for use in a clinical setting and further improve the model’s accuracy.

Correctly diagnosing and classifying seizures and epilepsies is paramount to ensure the delivery of optimal care to patients with epilepsy. Focal seizures, defined as those that originate within networks limited to one hemisphere, are primarily subdivided into focal aware, focal impaired awareness, and focal to bilateral tonic–clonic seizures. Focal epilepsies account for most epilepsy cases both in children and adults. In children, focal epilepsies are typically subdivided in three groups: self-limited focal epilepsy syndromes (e.g., self-limited epilepsy with centrotemporal spikes), focal epilepsy of unknown cause but which do not meet criteria for a self-limited focal epilepsy syndrome, and focal epilepsy of known cause (e.g., structural lesions—developmental or acquired). In adults, focal epilepsies are often acquired and may be caused by a structural lesion such as stroke, infection and traumatic brain injury, or brain tumors, vascular malformations, metabolic disorders, autoimmune, and/or genetic causes. In addition to seizure semiology, neuroimaging, neurophysiology, and neuropathology constitute the cornerstones of a diagnostic evaluation. Patients with focal epilepsy who become drug-resistant should promptly undergo assessment in an epilepsy center. After excluding pseudo-resistance, these patients should be considered for presurgical evaluation as a means to identify the location and extent of the epileptogenic zone and assess their candidacy for a surgical procedure. The goal of this seminar in epileptology is to summarize clinically relevant information concerning focal epilepsies. This contributes to the ILAE's mission to ensure that worldwide healthcare professionals, patients, and caregivers continue to have access to high-quality educational resources concerning epilepsy.

PURPOSE: Hip osteoarthritis (HOA) is known to have a multifactorial pathogenesis. Recent studies suggest that spinopelvic alignment may represent an important additional pathogenic abnormality resulting in HOA. This study aims to assess the correlation between spinopelvic parameters (pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS) and lumbar lordosis (LL)) obtained in the supine position on MRI and HOA, lateral center edge (LCE) angle, and patient reported back pain. METHODS: Asymptomatic participants from the whole-body MRI cohort (FF4) from the cross-sectional case-control "Cooperative Health Research in the Region of Augsburg" study (KORA) were included. Whole-body MRI was performed in a standardized fashion in each case, on which hip osteoarthritis (HOA), anatomical spinopelvic parameters and lateral center edge angle were measured. Presence of back pain was assessed using a standardized questionnaire. Correlations were estimated by logistic regression models providing odds ratio. RESULTS: Among 340 subjects (mean age 56.3±9.3 years; 56.5% male), HOA was present in 89.1% (male: 87.0%, female: 91.7%, p=0.17). The LCE angle was 30.0° ± 5.5 (men: 29.8° ± 5.9; women: 30.1° ± 5.1; p=0.696). Mean PI was 54.0° ± 11.3°, PT was 13.7° ± 5.9°, SS was 40.3° ± 8.8° (significantly smaller in women p

(1) Background: Radial RARE-EPI MRI facilitates simultaneous T2 and T2* mapping (2in1-RARE-EPI). With modest undersampling (R = 2), the speed gain of 2in1-RARE-EPI relative to Multi-Spin-Echo and Multi-Gradient-Recalled-Echo references is limited. Further reduction in scan time is crucial for clinical studies investigating T2 and T2* as imaging biomarkers. We demonstrate the feasibility of further acceleration, utilizing compressed sensing (CS) reconstruction of highly undersampled 2in1-RARE-EPI. (2) Methods: Two-fold radially-undersampled 2in1-RARE-EPI data from phantoms, healthy volunteers (n = 3), and multiple sclerosis patients (n = 4) were used as references, and undersampled (Rextra = 1–12, effective undersampling Reff = 2–24). For each echo time, images were reconstructed using CS-reconstruction. For T2 (RARE module) and T2* mapping (EPI module), a linear least-square fit was applied to the images. T2 and T2* from CS-reconstruction of undersampled data were benchmarked against values from CS-reconstruction of the reference data. (3) Results: We demonstrate accelerated simultaneous T2 and T2* mapping using undersampled 2in1-RARE-EPI with CS-reconstruction is feasible. For Rextra = 6 (TA = 01:39 min), the overall MAPE was ≤8% (T2*) and ≤4% (T2); for Rextra = 12 (TA = 01:06 min), the overall MAPE was

Purpose To relate [18F]fluoride uptake on PET with abnormalities on magnetic resonance imaging (MRI) and conventional radiography (CR) in ankylosing spondylitis (AS) patients. Methods Ten clinically active AS patients (female 6/10, age 38 ± 11 years) were included, and both spine and SI-joints were examined. PET scans were dichotomously scored for enhanced [18F]fluoride uptake, MRI scans were scored for fatty lesions, erosions, ankylosis, and bone marrow edema (BME), and CR was scored for erosions, syndesmophytes, and ankylosis. The overlap of lesions across all modalities was evaluated through univariate and multivariate analyses using a generalized mixed model. Results In the spine, 69 lesions with enhanced [18F]fluoride uptake, 257 MRI lesions, and 88 CR lesions were observed. PET lesions were mostly located in costovertebral and facet joints, outside the field of view (FOV) of the MRI and CR. However, PET lesions inside the FOV of MRI and CR partially showed no abnormality on MRI and CR. In lesions with abnormalities on multiple modalities, both univariate and multivariate analysis showed that PET activity had the strongest association with BME on MRI and ankylosis on CR. In the SI joints, 15 lesions (75%) with PET uptake were found, with 87% showing abnormalities on MRI and CR. Conclusion [18F]fluoride PET lesions are often found outside the scope of MRI and CR, and even in the same location show only partial overlap with abnormalities on MRI (especially BME) and CR (especially ankylosis). This suggests that [18F]fluoride PET partially visualizes aspects of AS separate from MRI and CR, providing novel information. Clinical trial registration NL43223.029.13 registered at 02-05-2013. https://www.toetsingonline.nl/to/ccmo_search.nsf/fABRpop?readform&unids=C1257BA2002CC066C1257B4E0049A65A

Traumatic brain injury (TBI) is associated with persistent impairments in multiple domains, including cognitive and neuropsychiatric function. Previous literature has suggested that the risk of such impairments may differ as a function of the initial severity of injury, with moderate-severe TBI (msTBI) associated with more severe cognitive dysfunction and mild TBI (mTBI) associated with a higher risk of developing an anxiety disorder. Despite this, relatively few pre-clinical studies have investigated the time course of behavioral change after different severities of injury. The current study compared the temporal profile of functional deficits incorporating locomotion, cognition, and anxiety up to 12 months post-injury after an mTBI, repeated mild TBI (rmTBI), and single msTBI in an experimental model of diffuse TBI. Injury appeared to alter the effect of aging on locomotor activity, with both msTBI and rmTBI rats showing a decrease in locomotion at 12 months relative to their earlier performance on the task, an effect not observed in shams or after a single mTBI. Further, mTBI seemed to be associated with decreased anxiety over time, as measured by increased time spent in the open arm of the elevated plus maze from 3 to 12 months post-injury. No significant findings were observed on spatial memory or volumetric magnetic resonance imaging. Future studies will need to use a more comprehensive behavioral battery, capable of capturing subtle alterations in function, and longer time points, following rats into old age, in order to more fully assess the evolution of persistent behavioral deficits in key domains after different severities of TBI, as well as their accompanying neuroimaging changes. Given the prevalence and significance of such deficits post-TBI for a person's quality of life, as well as the elevated risk of neurodegenerative disease post-injury, such investigations may play a critical role in identifying optimal windows of therapeutic intervention post-injury. Refereed/Peer-reviewed

Objectives Approximately 50% of comatose patients after cardiac arrest never regain consciousness. Cerebral ischaemia may lead to cytotoxic and/or vasogenic oedema, which can be detected by diffusion tensor imaging (DTI). Here, we evaluate the potential value of free water corrected mean diffusivity (MD) and fractional anisotropy (FA) based on DTI, for the prediction of neurological recovery of comatose patients after cardiac arrest. Methods A total of 50 patients after cardiac arrest were included in this prospective cohort study in two Dutch hospitals. DTI was obtained 2–4 days after cardiac arrest. Outcome was assessed at 6 months, dichotomised as poor (cerebral performance category 3–5; n = 20) or good (n = 30) neurological outcome. We calculated the whole brain mean MD and FA and compared between patients with good and poor outcomes. In addition, we compared a preliminary prediction model based on clinical parameters with or without the addition of MD and FA. Results We found significant differences between patients with good and poor outcome of mean MD (good: 726 [702–740] × 10-6 mm2/s vs. poor: 663 [575–736] × 10-6 mm2/s; p = 0.01) and mean FA (0.30 ± 0.03 vs. 0.28 ± 0.03; p = 0.03). An exploratory prediction model combining clinical parameters, MD and FA increased the sensitivity for reliable prediction of poor outcome from 60 to 85%, compared to the model containing clinical parameters only, but confidence intervals are overlapping. Conclusions Free water-corrected MD and FA discriminate between patients with good and poor outcomes after cardiac arrest and hold the potential to add to multimodal outcome prediction. Key Points • Whole brain mean MD and FA differ between patients with good and poor outcome after cardiac arrest. • Free water-corrected MD can better discriminate between patients with good and poor outcome than uncorrected MD. • A combination of free water-corrected MD (sensitive to grey matter abnormalities) and FA (sensitive to white matter abnormalities) holds potential to add to the prediction of outcome.

Monitoring disease evolution in Multiple sclerosis (MS) subjects may aid in decision making for personalizing treatment and disease evolution prediction. We investigate the use of disability progression, using clinical features, the expanded disability status scale (EDSS), and their relationship with texture features and Amplitude Modulation-Frequency Modulation (AM-FM) features extracted from MRI MS detectable lesions for the prognosis of future disability on magnetic resonance imaging (MRI). MS detectable brain lesions from N=38 symptomatic untreated subjects diagnosed with clinically isolated syndrome (CIS), were manually segmented, by an experienced MS neurologist, on transverse T2-weighted (T2W) images obtained from serial brain MRI scans at the baseline (Time0M) and the repeat (Time16-12M) examinations. The subjects were separated into two different groups based on their EDSS: (G1: 1≤EDSS2Y ≤ 3.5 (N=26) and G2: 3.5 < EDSS2Y ≤ 8.5 (N=12) and were monitored over ten years' time (Time10). After intensity normalization and image registration, texture and AM-FM features were extracted from all MS lesions at Time0M and Time6-12M. The extracted features were used to develop models that correlated with the disease progression in Time10Y. We found statistically significant differences for features extracted from the two different groups (G1 vs G2 at Time10Y) and these might be used to predict the development and or the severity of the MS disease. The best model for classifying G1 vs G2 subjects at Time10Y included information taken from the MS lesion images, texture features and AM-FM features extracted from those MS lesion images (with a correct classification score of %CC=94). The proposed methodology may contribute to additional factors for predicting the development and assessing the severity of the MS disease. However, a larger scale study is needed to establish the application in clinical practice and for computing additional features that may provide information for better and earlier differentiation between normal tissue and MS lesions.

To compare magnetic resonance imaging (MRI)-detected structural abnormalities in patients with symptomatic midfoot osteoarthritis (OA), patients with persistent midfoot pain, and asymptomatic controls, and to explore the association between MRI features, pain, and foot-related disability.One hundred seven adults consisting of 50 patients with symptomatic and radiographically confirmed midfoot OA, 22 adults with persistent midfoot pain but absence of radiographic OA, and 35 asymptomatic adults underwent 3T MRI of the midfoot and clinical assessment. MRIs were read for the presence and severity of abnormalities (bone marrow lesions [BMLs], subchondral cysts, osteophytes, joint space narrowing [JSN], effusion-synovitis, tenosynovitis, and enthesopathy) using the Foot Osteoarthritis MRI Score. Pain and foot-related disability were assessed with the Manchester Foot Pain and Disability Index.The severity sum score of BMLs in the midfoot was greater in patients with midfoot pain and no signs of OA on radiography compared to controls (P = 0.007), with a pattern of involvement in the cuneiform-metatarsal joints similar to that in patients with midfoot OA. In univariable models, BMLs (ρ = 0.307), JSN (ρ = 0.423), and subchondral cysts (ρ = 0.302) were positively associated with pain (P 0.01). In multivariable models, MRI abnormalities were not associated with pain and disability when adjusted for covariates.In individuals with persistent midfoot pain but no signs of OA on radiography, MRI findings suggested an underrecognized prevalence of OA, particularly in the second and third cuneiform-metatarsal joints, where BML patterns were consistent with previously recognized sites of elevated mechanical loading. Joint abnormalities were not strongly associated with pain or foot-related disability.

A compact lumped-element balun is proposed for 128 MHz frequency 3 Tesla (T) magnetic resonance imaging (MRI) applications. The miniaturization is achieved by the inductor-capacitor (L-C) embraced structure, which places vertically-interdigital-capacitor (VIC) inside spiral inductor for higher integration. The L-C surrounded structure only takes up one element’s area without increasing the number of substrate layers. The balun is built on a 10-layer thickness low temperature co-fired ceramic (LTCC) substrate and has the smallest reported size of only 0.007×0.008 × 0.0009 λg. Moreover, the proposed balun also has a 2nd-order harmonic suppression of 32 dB. Furthermore, comparisons and discussions are also implemented.

Tuberculosis (TB) remains a major health burden worldwide. Ten million people were diagnosed with TB in 2020. With 1.5 million deaths in 2020, TB is the 13thcause of mortality and the second most common cause of infectious deaths, preceded only by COVID-19. The vast majority of cases are reported from low- and middle-income countries. MRI of the spine is a reliable tool for the diagnosis of TB spondylitis. Findings such as involvement of the endplates and vertebral body, thoracic spine, para-spinal collection and psoas abscesses and relative sparing of the intervertebral disc are highly suggestive of spinal TB rather than other diagnoses. Combined with Gene Xpert tests, MRI can render very high diagnostic accuracy. This is essential for early detection of tuberculous disease of the spine and timely initiation of medical treatmen. We present a series of 35 patients with typical MRI findings of spinal tuberculosis.

Introduction: Utilization of cardiac magnetic resonance imaging (CMRI) has been increasing year by year for the most cardiovascular diseases. In this paper, we documented a real-life experience of our center as a high-volume CMRI performing center. Methods: We have retrospectively analyzed the 100 patients who have undergone CMRI at our center during the last 1 year. All the preliminary diagnoses, specialty or subspecialty of referring physicians, patient characteristics and CMRI findings were analyzed. Results: In 87 of 100 scans, a gadolinium-based contrast agent was used and in none of these procedures neither complications nor adverse events related to the contrast agent has occurred. Among these 100 consecutive CMRIs were referred to by a clinical cardiologist, invasive cardiologists, heart failure specialist, cardiovascular imaging specialists, electrophysiologists, and other specialists. On referral from a clinical cardiologist, the CMRI findings were high consistency. In these patients, the biggest number of preliminary diagnoses belongs to hypertrophic cardiomyopathy. The most common MRI finding was reduced left ventricular ejection fraction. In 25 patients we observed extracardiac findings. Conclusion: CMRI is increasingly occurring in cardiovascular imaging and diagnosis of various cardiovascular diseases. CMRI not only produces high-resolution morphological images but also provides quantitative information on the severity of reGürgitant or stenotic lesions in valvular diseases or cardiac shunts with the velocity and flow measurements.

Objective To investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with laboratory and pulmonary findings. Methods Structural MRI and T2 mapping of bilateral thigh muscles from patients with IIM and healthy volunteers were segmented using dedicated software based on a pre-trained convolutional neural network. Incremental and federated learning were implemented for continuous adaptation and improvement. Muscle T2 values derived from DL segmentation were compared between patients and healthy controls, and T2 values of patients were further analyzed with serum muscle enzymes, and interstitial lung disease (ILD) which was diagnosed and graded based on chest HRCT. Results Overall, 64 patients (27 patients with dermatomyositis, 29 with polymyositis, and 8 with antisynthetase syndrome (ASS)) and 10 healthy controls were included. By using DL-based muscle segmentation, T2 values generated from T2 maps accurately differentiated patients from those of controls (p < 0.001) with a cutoff value of 36.4 ms (sensitivity 96.9%, and specificity 100%). In patients with IIM, muscle T2 values positively correlated with all the serum muscle enzymes (all p < 0.05). ILD score of patients with ASS was markedly higher than that of those without ASS (p = 0.011), while dissociation between the severity of muscular involvement and ILD was observed (p = 0.080). Conclusion Automatic DL could be used to segment thigh muscles and help quantitatively assess muscular inflammation of IIM through T2 mapping. Key Points • Muscle T2 mapping automatically segmented by deep learning can differentiate IIM from healthy controls. • T2 value, an indicator of active muscle inflammation, positively correlates with serum muscle enzymes. • T2 mapping can detect muscle disease in patients with normal muscle enzyme levels.

Objectives: The precise location of multiple sclerosis (MS) cortical lesions can be very challenging at 3 T, yet distinguishing them from subcortical lesions is essential for the diagnosis and prognosis of the disease. Compressed sensing-accelerated fluid and white matter suppression imaging (CS-FLAWS) is a new magnetic resonance imaging sequence derived from magnetization-prepared 2 rapid acquisition gradient echo with promising features for the detection and classification of MS lesions. The objective of this study was to compare the diagnostic performances of CS-FLAWS(evaluated imaging) and phase sensitive inversion recovery (PSIR; reference imaging) for classification of cortical lesions (primary objective) and infratentorial lesions (secondary objective) in MS, in combination with 3-dimensional (3D) double inversion recovery (DIR)., Materials and Methods: Prospective 3 T scans (MS first diagnosis or follow-up) acquired between March and August 2021 were retrospectively analyzed. All underwent 3D CS-FLAWS, axial 2D PSIR, and 3D DIR. Double-blinded reading sessions exclusively in axial plane and final consensual reading were performed to assess the number of cortical and infratentorial lesions. Wilcoxon test was used to compare the 2 imaging datasets (FLAWS + DIR and PSIR + DIR), and intraobserver and interobserver agreement was assessed using the intraclass correlation coefficient., Results: Forty-two patients were analyzed (38 with relapsing-remitting MS, 29 women, 42.7 +/- 12.6 years old). Compressed sensing-accelerated FLAWS allowed the identification of 263 cortical lesions versus 251 with PSIR (P = 0.74) and 123 infratentorial lesions versus 109 with PSIR (P = 0.63), corresponding to a nonsignificant difference between the 2 sequences. Compressed sensing-accelerated FLAWS exhibited fewer false-negative findings than PSIR either for cortical lesions (1 vs 13; P < 0.01) or infratentorial lesions (1 vs 15; P < 0.01). No false-positive findings were found with any of the 2 sequences. Diagnostic confidence was high for each contrast., Conclusion: Three-dimensional CS-FLAWS is as accurate as 2D PSIR imaging for classification of cortical and infratentorial MS lesions, with fewer false-negative findings, opening the way to a reliable full brain MS exploration in a clinically acceptable duration (5 minutes 15 seconds).

The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.

Objectives: The purpose the study was to evaluate the role of diffusion weighted magnetic resonance imaging (DW-MRI) in diagnosis of benign and malignant breast lesions, to calculate a cut-off apparent diffusion coefficient (ADC) value and to explore use of relative ADC (r ADC) for improving sensitivity and specificity of MRI in diagnosis of breast cancer. Methods: This retrospective study based on a cohort of patients who underwent dynamic contrast enhanced (DCE)-MRI having suspicious breast mass by ultrasonography and mammography to whom DWI sequence was added to the routine diagnostic MRI. ADC and r ADC (lesion/normal breast tissue) values of breast masses were calculated. The threshold ADC values used to differentiate benign and malignant lesions were determined using receiver operating characteristic analysis, sensitivity, specificity, positive predictive value and negative predictive value were calculated. Results: Malignant masses had significantly lower ADC (mean: 1.03 ± 0.36 × 10-3 mm2/s) and r ADC (mean: 0.66 ± 0.22 × 10-3 mm2/s) values than those of benign masses with ADC (mean: 1.50 ± 0.56 × 10−3 mm2/s) and r ADC (mean: 0.97 ± 0.31 × 10-3 mm2/s) values, respectively (p = 0.001 for both). The best cut-off value for the lesion ADC was 1.09 × 10-3 mm2/s with a sensitivity of 72.73%, and specificity of 79.17%. The best cut-off value for r ADC was 0.83 with sensitivity of 78.79% and specificity of 70.83%. Conclusions: DWI has high diagnostic value with high sensitivity and specificity differentiating benign and malignant breast lesions. ADC and r ADC values can improve the diagnostic accuracy of differentiating benign and malignant breast lesions.

Different types of medical imaging are increasingly being used to explain specific aspects of injuries to patients during consultations. However, there are no validated questionnaires available yet that specifically measure patients’ satisfaction with the use of such images. The objective of this study was to develop and evaluate a patient-centred measure of satisfaction with the use of medical imaging modalities in clinical treatment. A preliminary 22-item version of the Questionnaire for Patient Satisfaction with Imaging (QPSI) was developed based on extant literature and interviews with trauma patients. Final item selection and psychometric evaluations were conducted amongst a sample of 106 hospital trauma patients who were shown medical images of their injuries. The psychometric analyses resulted in a final 13-item questionnaire comprising two subscales that measure the importance of seeing the images (9 items) and the clearness of the image (4 items). Both subscales showed adequate internal consistency (α = 0.84 and 0.75). The subscales were weakly intercorrelated (ρ = 0.34) and were both significantly and independently associated with patients’ global ratings of satisfaction with the use of imaging. The final two-dimensional QPSI is an innovative, reliable and valid questionnaire for measuring patients’ satisfaction with imaging-based information during clinical consultations.

Background and purpose A wide range of neuroradiological findings has been reported in patients with coronavirus disease 2019 (COVID-19), ranging from subcortical white matter changes to infarcts, haemorrhages and focal contrast media enhancement. These have been descriptively but inconsistently reported and correlations with clinical findings and biomarkers have been difficult to extract from the literature. The purpose of this study was to quantify the extents of neuroradiological findings in a cohort of patients with COVID-19 and neurological symptoms, and to investigate correlations with clinical findings, duration of intensive care and biomarkers in blood. Material and methods Patients with positive SARS-CoV-2 and at least one new-onset neurological symptom were included from April until July 2020. Nineteen patients were examined regarding clinical symptoms, biomarkers in blood and MRI of the brain. In order to quantify the MRI findings, a semi-quantitative neuroradiological severity scale was constructed a priori, and applied to the MR images by two specialists in neuroradiology. Results and conclusions The score from the severity scale correlated significantly with blood biomarkers of CNS injury (glial fibrillary acidic protein, total-tau, ubiquitin carboxyl-terminal hydrolase L1) and inflammation (C-reactive protein), Glasgow Coma Scale score, and the number of days spent in intensive care. The underlying radiological assessments had inter-rater agreements of 90.5%/86% (for assessments with 2/3 alternatives). Total intraclass correlation was 0.80. Previously reported neuroradiological findings in COVID-19 have been diverse and heterogenous. In this study, the extent of findings in MRI examination of the brain, quantified using a structured report, shows correlation with relevant biomarkers.

Lobar hematomas represent around half of all supratentorial hemorrhages and have high mortality and morbidity. Their management depends on the underlying cause. Apart from local causes such as vascular malformation, which are rare and can usually be easily excluded thanks to imaging, the vast majority of lobar hematomas equally frequently result from either hypertensive arteriolopathy (HA) or cerebral amyloid angiopathy (CAA). Distinguishing between CAA and HA is important for prognostication (risk of recurrence nearly sevenfold higher in the former), for decision-making regarding, e.g., antithrombotic therapies (for other indications) and for clinical trials of new therapies. Currently, a non-invasive diagnosis of probable CAA can be made using the MR-based modified Boston criteria, which have excellent specificity but moderate sensitivity against histopathological reference, leading to the clinically largely irrelevant diagnosis of "possible CAA". Furthermore, the Boston criteria cannot be applied when both lobar and deep MRI hemorrhagic markers are present, a not uncommon situation. Here we propose to test whether new CT and MR-based imaging biomarkers, namely finger-like projections of the hematoma and adjacent subarachnoid hemorrhage on acute-stage CT or MRI, and remote punctate diffusion-weighted imaging ischemic lesions on acute or subacute-stage MRI, have the potential to improve the performance of the Boston criteria. Furthermore, we also propose to test whether clinical-radiological biomarkers may also allow a positive diagnosis of HA to be made in lobar hematomas, which, if feasible, would not only further reduce the prevalence of "possible CAA" but also permit a diagnosis of HA and/or CAA to be made in the presence of mixed deep and lobar MRI hemorrhagic markers.

ObjectiveTo investigate monoaminergic network abnormalities in patients with multiple sclerosis (MS) according to their fatigue and depressive status through a positron emission tomography (PET)-based constrained independent component analysis (ICA) on resting state (RS) functional MRI (fMRI).MethodsIn this prospective study, 213 patients with MS (mean age=40.6±12.5 years; 94/119 men/women; 153 relapsing-remitting; 60 progressive) and 62 healthy controls (HCs, mean age=39.0±10.4 years; 30/32 men/women) underwent neurological, fatigue, depression and RS fMRI assessment. Patterns of dopamine, norepinephrine-related and serotonin-related RS functional connectivity (FC) were derived by ICA, constrained to PET atlases for dopamine, norepinephrine and serotonin transporters, obtained in HCs’ brain.ResultsCompared with HCs, patients with MS showed abnormalities in all three explored monoaminergic networks, mostly with decreased RS FC within PET-guided monoaminergic networks in frontal regions and subcortical areas including the cerebellum and thalamus, and increased RS FC in temporo-parieto-occipital cortical areas, including bilateral precunei.MS-related fatigue was associated with decreased RS FC within the PET-guided dopamine network in the left thalamus and left cerebellum, and with increased RS FC within the PET-guided serotonin network in the left middle occipital gyrus. MS-related depression was associated with more distributed abnormalities involving the three explored monoaminergic networks, resulting in overall reduced RS FC in the frontal lobe, limbic areas and the precuneus.ConclusionsPatients with MS present diffuse dysregulation in the monoaminergic networks. Specific alterations in these networks were associated with fatigue and depression, providing a pathological marker for these bothersome symptoms and putative targets for their treatment.

The tumor microenvironment, especially the vasculature, undergoes dynamic remodeling during tumor growth and progression. The aim of this study was to investigate changes in the structure and function of tumor microenvironment (TME), with a special focus on vasculature, during the growth of the Lewis Lung Carcinoma tumor (LLC). We have used several MRI techniques and ultrasound imaging of live animals to assess how heterogenous TME features change in time. Lewis lung carcinoma bearing C57BL/6 mice were examined for three weeks. During this time, assessment of tumor vasculature was performed with Time of Flight (TOF) angiography, Dynamic Contrast Enhanced (DCE) MRI and Power Doppler Ultrasound. Additionally, diffusion and perfusion were analyzed using Diffusion Weighted MRI (DWI). Consecutive measurements of the same animals revealed an approximately twofold decrease in the density of LLC vessels in time. Heterogeneity of vasculature was best uncovered by changes in histogram based DCE analysis and revealed deterioration of tumor vessels during its progression. The tumor vasculature became less dense and with slower blood flow, with larger and more permeable vessels. As a rule, tumor tissue perfusion and diffusion parameters decreased in time, but locally increase was observed. Time- and spatial heterogeneity of tumor microenvironment, including vasculature, was revealed by 3D imaging, demonstrating that local changes are often contradictory to parameters averaged over the whole tumor volume.

The advent of immunotherapy for patients with hepatocellular carcinoma (HCC) has changed the treatment landscape and conferred a survival benefit on patients with advanced HCC, who typically have a very poor prognosis. The most pronounced improvements in response, as documented by standardized response criteria based on CT or MRI, have been achieved when immunotherapy is combined with other systemic or locoregional therapies. Immune checkpoint inhibitor treatments result in unique patterns on CT and MRI that challenge the application of conventional response criteria such as RECIST, modified RECIST, and European Association for the Study of the Liver criteria. Thus, newer criteria have been developed to gauge therapy response or disease progression for patients receiving immunotherapy, including immune-related RECIST (iRECIST) and immune-modified RECIST (imRECIST), though these remain unvalidated. In this review, we describe the current landscape of immunotherapeutic agents used for HCC, summarize the results of published studies, review the pathobiologic mechanisms that provide a rationale for the use of these agents, and report on the status of response assessment for immunotherapy either alone or in combination with other treatment options. Finally, consensus statements are provided to inform radiologists about essential considerations in the era of a rapidly changing treatment paradigm for patients with HCC.

Increased diagnoses of silent prostate cancer (PCa) have led to overtreatment and consequent functional side effects. Focal therapy (FT) applies energy to a prostatic index lesion treating only the clinically significant PCa focus. We analysed the potential predictive factors of FT failure. We collected data from patients who underwent robot-assisted radical prostatectomy (RARP) in two high-volume hospitals from January 2017 to January 2020. The inclusion criteria were: one MRI-detected lesion with a Gleason Score (GS) of ≤7, ≤cT2a, PSA of ≤10 ng/mL, and GS 6 on a random biopsy with ≤2 positive foci out of 12. Potential oncological safety of FT was defined as the respect of clinicopathological inclusion criteria on histology specimens, no extracapsular extension, and no biochemical, local, or metastatic recurrence within 12 months. To predict FT failure, we performed uni- and multivariate logistic regression. Sixty-seven patients were enrolled. The MRI index lesion median size was 11 mm; target lesions were ISUP grade 1 in 27 patients and ISUP grade 2 in 40. Potential FT failure occurred in 32 patients, and only the PSA value resulted as a predictive parameter (p < 0.05). The main issue for FT is patient selection, mainly because of multifocal csPCa foci. Nevertheless, FT could represent a therapeutic alternative for highly selected low-risk PCa patients.

Objectives: To compare alveolar bone height and width measurements from zero-echo-time MRI (ZTE-MRI) and cone beam CT (CBCT), in human specimens. Material and methods: Twenty posterior edentulous sites in human cadaver specimens were imaged with CBCT and ZTE-MRI. Bone height and width at 1, 3, 5, 7 and 9 mm from the top of the alveolar ridge was measured by two trained observers in cross-sections of a site where an implant was to be planned. Twenty percent of the sample was measured in duplicate to assess method error and intra-observer reproducibility (ICC). The differences between CBCT and ZTE-MRI measurements were compared (t-test). Results: Inter- and intra-observer reproducibility was >0.90. The method error (average between observers) for bone height was 0.45 mm and 0.39 mm, and for bone width (average) was 0.52 mm and 0.80 mm (CBCT and ZTE-MRI, respectively). The majority of the bone measurement differences were statistically insignificant, except bone width measurements at 5 mm (p ≤.05 for both observers). Mean measurement differences were not larger than the method error. Conclusion: ZTE-MRI is not significantly different from CBCT when comparing measurements of alveolar bone height and width.

In the COVID-19 pandemic era, safety concerns have been raised regarding the risk of severe infection following administration of ocrelizumab (OCR), a B-cell-depleting therapy. We enrolled all relapsing remitting multiple sclerosis (RRMS) patients who received maintenance doses of OCR from January 2020 to June 2021. Data were extracted in December 2021. Standard interval dosing (SID) was defined as a regular maintenance interval of OCR infusion every 6 months, whereas extended interval dosing (EID) was defined as an OCR infusion delay of at least 4 weeks. Three infusions were considered in defining SID vs. EID (infusions A, B, and C). Infusion A was the last infusion before January 2020. The primary study outcome was a comparison of disease activity during the A-C interval, which was defined as either clinical (new relapses) or radiological (new lesions on T1-gadolinium or T2-weighted magnetic resonance imaging (MRI) sequences). Second, we aimed to assess confirmed disability progression (CDP). A total cohort of 278 patients (174 on SID and 104 on EID) was enrolled. Patients who received OCR on EID had a longer disease duration and a higher rate of vaccination against severe acute respiratory syndrome-coronavirus 2 (p

Background and ObjectivesThe timing of neurodegeneration in multiple sclerosis (MS) remains unclear. It is critical to understand the dynamics of neuroaxonal loss if we hope to prevent or forestall permanent disability in MS. We therefore used a deeply phenotyped longitudinal cohort to assess and compare rates of neurodegeneration in retina and brain throughout the MS disease course.MethodsWe analyzed 597 patients with MS who underwent longitudinal optical coherence tomography imaging annually for 4.5 ± 2.4 years and 432 patients who underwent longitudinal MRI scans for 10 ± 3.4 years, quantifying macular ganglion cell-inner plexiform layer (GCIPL) volume and cortical gray matter (CGM) volume. The association between the slope of decline in the anatomical structure and the age of entry in the cohort (categorized by the MRI cohort's age quartiles) was assessed by hierarchical linear models.ResultsThe rate of CGM volume loss declined with increasing age of study entry (1.3% per year atrophy for the age of entry in the cohort younger than 35 years; 1.1% for older than 35 years and younger than 41; 0.97% for older than 41 years and younger than 49; 0.9% for older than 49 years) while the rate of GCIPL thinning was highest in patients in the youngest quartile, fell by more than 50% in the following age quartile, and then stabilized (0.7% per year thinning for the age of entry in the cohort younger than 35 years; 0.29% for age older than 35 and younger than 41 years; 0.34% for older than 41 and younger than 49 years; 0.33% for age older than 49 years).DiscussionAn age-dependent reduction in retinal and cortical volume loss rates during relapsing-remitting MS suggests deceleration in neurodegeneration in the earlier period of disease and further indicates that the period of greatest adaptive immune–mediated inflammatory activity is also the period with the greatest neuroaxonal loss.

Brain tumour is a severe disease that may lead to death if unrecognized and untreated. Brain tumor identification and segmentation is a complex and task in medical image processing. For radiologists, diagnosing and classifying tumor from various images is a challenging process. When the data size is large, deep learning methods outperform conventional learning algorithms. Convolutional Neural Networks are found to be one of the popular deep learning architectures. We propose a deep network with an Efficient Regularized CNN with Dimensionality Reduction Module (ERCNN-DRM), which works with less training data and produces more precise classification with minimal processing time and regularisation. The images are pre-processed, segmented and then the dimension reduced features are extracted using the proposed algorithms and then the proposed regularized classification takes place. The experiment is conducted on TCIA dataset which contains a total of 696 MRI, 224 of which are benign and 472 of which are malignant. The proposed scheme produces accuracy rate of 96.7% and reduces the complexity by working on dimensional reduced data. Performance measures such as accuracy, recall, precision, F-measures are analysed and the system is found to be significant than other state-of-the art.

Purpose Mild traumatic brain injury (TBI) is one of the most common causes of morbidity worldwide. Patients at risk of unfavourable outcome may benefit from additional attention and help but identification of these patients necessitates the development of diagnostic methods to assess indices of brain injury at an early stage. The aim of this overview is to highlight studies that reflect the growing scientific attention to the early diagnosis and prognostication of mild TBI. Recent findings The value of serum biomarkers for the diagnosis of TBI severity has been acknowledged in recent studies. The diagnostic and prognostic utility of several biomarkers of brain injury, such as glial fibrillary acidic protein, and of inflammation, such as interleukin (IL)-6 and IL-10, holds promise for application in daily clinical practice in a point-of-care platform. Besides head CT imaging, early advanced MRI brain imaging has been reported as a tool for assessment of injury severity and prognostication. The introduction of direct oral anticoagulants (DOACs) has raised new challenges for the treatment of intracranial traumatic haemorrhage at the Emergency Department. Promising findings of new diagnostic markers of brain injury severity highlight the potential prognostic value of serum biomarkers and early MRI imaging. The accurate assessment of patients at risk of incomplete recovery after mTBI will enhance more timely and individualized treatment.

We analysed a dataset comprising 118 subjects who were scanned three times (at baseline, 1-year follow-up, and 7-year follow-up) using structural magnetic resonance imaging (MRI) over the course of 7 years. We aimed to examine whether it is possible to identify individual subjects based on a restricted number of neuroanatomical features measured 7 years previously. We used FreeSurfer to compute 15 standard brain measures (total intracranial volume [ICV], total cortical thickness [CT], total cortical surface area [CA], cortical grey matter [CoGM], cerebral white matter [CeWM], cerebellar cortex [CBGM], cerebellar white matter [CBWM], subcortical volumes [thalamus, putamen, pallidum, caudatus, hippocampus, amygdala and accumbens] and brain stem volume). We used linear discriminant analysis (LDA), random forest machine learning (RF) and a newly developed rule-based identification approach (RBIA) for the identification process. Using RBIA, different sets of neuroanatomical features (ranging from 2 to 14) obtained at baseline were combined by if–then rules and compared to the same set of neuroanatomical features derived from the 7-year follow-up measurement. We achieved excellent identification results with LDA, while the identification results for RF were very good but not perfect. The RBIA produced the best results, achieving perfect participant identification for some four-feature sets. The identification results improved substantially when using larger feature sets, with 14 neuroanatomical features providing perfect identification. Thus, this study shows again that the human brain is highly individual in terms of neuroanatomical features. These results are discussed in the context of the current literature on brain plasticity and the scientific attempts to develop brain-fingerprinting techniques., European Journal of Neuroscience, 56 (5), ISSN:0953-816X, ISSN:1460-9568

Motivation: Within the field of bio-implants one topic deals with the in-vitro production of 3D tissues from cell cultures. A major challenge in the cultivation of biopreparations is the imitation of the vascular system with its surrounding tissue. Additiv manufacturing of such vascularisation could be supported with an adequate template from high-resolution 3D radiographs. Goals: Evaluation of highresolution 3D-imaging methods and 3D-analysis approaches to extract the vascularisation from tissue samples. Methods: To provide such template, vascular structure of small tissues samples (partially treated with contrast medium) were recorded and compared using high-resolution CT and MRI imaging modalities. Optimal measurement parameters were selected for the acquisition of very small vessels. Interactive and semi-automated segmentation of the vessel system were investigated and compared. Results: MRI scans yield a higher contrast than CT scans, but are much slower. Duration of interactive segmentation ranges between 1 to 12 hours. Runtime of the (semi) automatic method was between 5 and 20 minutes, not counting manual adjustment of the parameters. Correlation between manual and automatic segmentation yield Hausdorff distances of 0.024 (CT) and 0.74 (MRI) and Dice coefficients of 0.7 (CT) and 0.39 (MRI). Conclusion: Both imaging methods are appropriate for high-resolution vessel detection and segmentation, nevertheless, MRI with no contrast agent seems preferable if the imaging time can be reduced.

Since the inline positron emission tomography (PET)/magnetic resonance imaging (MRI) system appeared in clinical, more than a decade has passed. In this article, we have reviewed recently-published articles about PET/MRI. There have been articles about staging in rectal and breast cancers by PET/MRI using fluorodeoxyglucose (FDG) with higher diagnostic performance in oncology. Assessing possible metastatic bone lesions is considered a proper target by FDG PET/MRI. Other than FDG, PET/MRI with prostate specific membrane antigen (PSMA)-targeted tracers or fibroblast activation protein inhibitor have been reported. Especially, PSMA PET/MRI has been reported to be a promising tool for determining appropriate sites in biopsy. Independent of tracers, the clinical application of artificial intelligence (AI) for images obtained by PET/MRI is one of the current topics in this field, suggesting clinical usefulness for differentiating breast lesions or grading prostate cancer. In addition, AI has been reported to be helpful for noise reduction for reconstructing images, which would be promising for reducing radiation exposure. Furthermore, PET/MRI has a clinical role in neuroscience, including localization of the epileptogenic zone. PET/MRI with new PET tracers could be useful for differentiation among neurological disorders. Clinical applications of integrated PET/MRI in various fields are expected to be reported in the future.

Chloé Chamard,1,2 Jerome J Maller,3,4 Nicolas Menjot,5 Eloi Debourdeau,1 Virginie Nael,6 Karen Ritchie,2,7 Isabelle Carriere,2,&ast; Vincent Daien1,2,8,&ast; 1Department of Ophthalmology, Gui de Chauliac Hospital, Montpellier, F-34000, France; 2Institute for Neurosciences of Montpellier INM, University Montpellier, INSERM, Montpellier, F-34091, France; 3General Electric Healthcare, Melbourne, VIC, Australia; 4Monash Alfred Psychiatry Research Centre, Melbourne, VIC, Australia; 5Department of Neuroradiology, Gui de Chauliac Hospital, Montpellier, F-34000, France; 6Bordeaux Population Health Research Center, UMR 1219, University Bordeaux, INSERM, Bordeaux, F-33000, France; 7Department of Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; 8The Save Sight Institute, Sydney Medical School, the University of Sydney, Sydney, NSW, Australia&ast;These authors contributed equally to this workCorrespondence: Chloé Chamard, Department of Ophthalmology, Gui de Chauliac Hospital, 80 avenue Augustin Fliche, Montpellier, F-34000, France, Tel +33 6 33 55 65 06, Email chloe.chamard@gmail.comPurpose: Visual impairment is a major cause of disability and impairment of cognitive function in older people. Brain structural changes associated with visual function impairment are not well understood. The objective of this study was to assess the association between visual function and cortical thickness in older adults.Methods: Participants were selected from the French population-based ESPRIT cohort of 2259 community-dwelling adults ≥ 65 years old enrolled between 1999 and 2001. We considered visual function and brain MRI images at the 12-year follow-up in participants who were right-handed and free of dementia and/or stroke, randomly selected from the whole cohort. High-resolution structural T1-weighted brain scans acquired with a 3-Tesla scanner. Regional reconstruction and segmentation involved using the FreeSurfer image-analysis suite.Results: A total of 215 participants were included (mean [SD] age 81.8 [3.7] years; 53.0% women): 30 (14.0%) had central vision loss and 185 (86.0%) normal central vision. Vision loss was associated with thinner cortical thickness in the right insula (within the lateral sulcus of the brain) as compared with the control group (mean thickness 2.38 [0.04] vs 2.50 [0.03] mm, 4.8% thinning, pcorrected= 0.04) after adjustment for age, sex, lifetime depression and cardiovascular disease.Conclusion: The present study describes a significant thinning of the right insular cortex in older adults with vision loss. The insula subserves a wide variety of functions in humans ranging from sensory and affective processing to high-level cognitive processing. Reduced insula thickness associated with vision loss may increase cognitive burden in the ageing brain.Keywords: visual function, vision, cortical thickness, brain, morphometry, MRI

Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency. To reduce supervision requirements, the recent deep image prior framework instead conjoins untrained MRI priors with the imaging operator during inference. Yet, canonical convolutional architectures are suboptimal in capturing long-range relationships, and priors based on randomly initialized networks may yield suboptimal performance. To address these limitations, here we introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adversarial TransformERs (SLATER). SLATER embodies a deep adversarial network with cross-attention transformers to map noise and latent variables onto coil-combined MR images. During pre-training, this unconditional network learns a high-quality MRI prior in an unsupervised generative modeling task. During inference, a zero-shot reconstruction is then performed by incorporating the imaging operator and optimizing the prior to maximize consistency to undersampled data. Comprehensive experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against state-of-the-art unsupervised methods.

Down's syndrome results from trisomy of chromosome 21, a genetic change which also confers a probable 100% risk for the development of Alzheimer's disease neuropathology (amyloid plaque and neurofibrillary tangle formation) in later life. We aimed to assess the effectiveness of diffusion-weighted imaging and connectomic modelling for predicting brain amyloid plaque burden, baseline cognition and longitudinal cognitive change using support vector regression. Ninety-five participants with Down's syndrome successfully completed a full Pittsburgh Compound B (PiB) PET-MR protocol and memory assessment at two timepoints. Our findings indicate that graph theory metrics of node degree and strength based on the structural connectome are effective predictors of global amyloid deposition. We also show that connection density of the structural network at baseline is a promising predictor of current cognitive performance. Directionality of effects were mainly significant reductions in the white matter connectivity in relation to both PiB+ status and greater rate of cognitive decline. Taken together, these results demonstrate the integral role of the white matter during neuropathological progression and the utility of machine learning methodology for non-invasively evaluating Alzheimer's disease prognosis.

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder resulting from the absent expression of maternally imprinted, paternally expressed genes located in the chromosomal region 15q11-13. This absence of expression is usually either due to a paternal deletion or maternal uniparental disomy (mUPD). Both genetic subtypes share the same core clinical symptoms, such as developmental delays, intellectual disability, hyperphagia, behavioural and social difficulties, and a heightened risk for anxiety and depression. However, only people with PWS due to mUPD seem to be at particularly high risk for developing psychosis. Research on the causes of this differential risk is very scarce. This PhD study aims to propose potential mechanisms of psychosis in PWS and design and develop a study to test them. I hypothesised that the GABA/glutamate equilibrium is disrupted in PWS due to the absence of paternal expression. In addition, the overexpression of maternal genes on chromosome 15 would cause cholinergic dysfunction in people with mUPD and further disrupt GABAergic, glutamatergic, dopaminergic and cholinergic functions, increasing the probability of psychotic symptoms. In line with the predictive processing model of psychosis, the neurotransmitter disruptions proposed would cause a reduction in the use of priors to formulate predictions, detect and generate prediction errors, and update the prediction system. A case-control study comprising clinical, cognitive, neuroimaging, and remote behavioural testing was designed to investigate the hypotheses. The study was organised into four modules. The EEG module comprised the Global-Local paradigm, a sensory gating paradigm, and resting state. The MRS module measured metabolites in the anterior cingulate cortex, the auditory and visual cortex. The cognitive module contained measures of IQ, processing speed and working memory. The behavioural module was added during the COVID-19 pandemic to enable online data collection and focused on measuring the use of priors in perception. All modules were assessed for use in a PWS population and were feasible, with adjustments. Nineteen participants took part in at least part of the study, demonstrating its feasibility in a PWS population. Because of time constraints, COVID-19, and resources available, not all participants completed all measures. For the same reasons, only the EEG Global-Local data was analysed. The EEG paradigms were collected on 19 participants (11 with deletion, 4 with mUPD), and the global-local paradigm was analysed. Preliminary analysis revealed that people with mUPD have smaller P300 in response to an oddball paradigm than deletion and control participants. Participants with deletion have smaller P300 responses than controls showing impaired attention switching and a flaw in the predicting coding system of people with PWS, particularly in those with mUPD. Contrary to what was expected, the mismatch negativity response to the oddball paradigm was not different between mUPD and controls but was significantly earlier in the deletion group. This could indicate a chronic fight or flight state having the potential to disrupt neurotransmission, interoception and perception further, causing additional disruption of the predictive processing system. A planned analysis of the remaining measures is presented in this thesis and will be applied in the future when sufficient data is analysed.

Accurate localization of the site(s) of active disease is key to informing decision-making in the management of refractory pituitary adenomas when autonomous hormone secretion and/or continued tumor growth challenge conventional therapeutic approaches. In this context, the use of non-standard MR sequences, alternative post-acquisition image processing, or molecular (functional) imaging may provide valuable additional information to inform patient management.

One of 5 speakers presenting in "Mature QIN Tools" session at Annual QIN Meeting, 2023.

Delineating visual field maps and iso-eccentricities from fMRI data is an important but time-consuming task for many neuroimaging studies on the human visual cortex because the traditional methods of doing so using retinotopic mapping experiments require substantial expertise as well as scanner, computer, and human time. Automated methods based on gray-matter anatomy or a combination of anatomy and functional mapping can reduce these requirements but are less accurate than experts. Convolutional Neural Networks (CNNs) are powerful tools for automated medical image segmentation. We hypothesize that CNNs can define visual area boundaries with high accuracy. We trained U-Net CNNs with ResNet18 backbones to predict either V1, V2, and V3 boundaries or 5 regions of iso-eccentricity using human-labeled maps. Separate CNNs were trained to predict these regions using different combinations of the following input data: (1) anatomical data from a T1-weighted image only, (2) anatomical data from T1-weighted and T2*-weighted images, (3) white-matter tract endpoints from diffusion-weighted imaging, (4) functional data from retinotopic mapping. All CNNs using functional data had cross-validated accuracy that was statistically indistinguishable from the inter-rater reliability of the training dataset (dice coefficient of 92%) while the CNNs lacking functional data had lower but similar accuracies (~75%). Existing models that do not use CNNs had accuracies lower than any of the CNNs. These results demonstrate that with current methods and data quality, CNNs can replace the time and effort of human experts in manually defining early retinotopic maps, but cannot yet replace the acquisition of functional data.

Background Gastrectomy with extended (D2) lymphadenectomy is considered standard of care for gastric cancer to provide the best possible outcomes and pathologic staging. However, D2 gastrectomy is a technically demanding operation and reported to be associated with increased complications and mortality. Application of sentinel lymph node (SLN) concept in gastric cancer has the potential to reduce patient morbidity; however, SLN techniques are not established for gastrectomy, in part due to lack of practical tracers. An effective and convenient tracer with enhanced SLN accumulation is critically needed. Methods Mannose-labelled magnetic tracer ‘FerroTrace’ and fluorescent dye indocyanine green (ICG) were injected laparoscopically into the stomach submucosa of 8 healthy swine under general anaesthesia. Intraoperative fluorescence imaging was used to highlight draining lymphatic pathways containing ICG, while preoperative T2-weighted MRI and ex vivo magnetometer probe measurements were used to identify nodes containing FerroTrace. Lymphadenectomy was performed either robotically (n = 2) or via laparotomy (n = 6). Results Mixing ICG and FerroTrace ensured concurrence of fluorescent and magnetic signals in SLNs. An initial trial with robotic dissection removed all magnetic LNs (n = 4). In the subsequent laparotomy study that targeted all ICG-LNs based on intraoperative fluorescence imaging, dissection removed an average of 4.7 ± 1.2 fluorescent, and 2.0 ± 1.3 magnetic LNs per animal. Both MRI and magnetometer detected 100% of SLNs (n = 7). FerroTrace demonstrated high specificity to SLNs, which contained 76 ± 30% of total lymphotropic iron, and 88 ± 20 % of the overall magnetometer signal. Conclusions Through utilisation of this dual tracer approach, SLNs were identified via preoperative MRI, visualised intraoperatively with fluorescence imaging, and confirmed with a magnetometer. This combination pairs the sensitivity of ICG with SLN-specific FerroTrace and can be used for reliable SLN detection in gastric cancer, with potential applications in neoadjuvant therapy. Graphical abstract

Abstract Magnetic resonance imaging (MRI) is widely regarded as the primary modality for the morphological assessment of cartilage and all other joint tissues involved in osteoarthritis. 2D fast spin echo fat-suppressed intermediate-weighted (FSE FS IW) sequences with a TE between 30 and 40ms have stood the test of time and are considered the cornerstone of MRI protocols for clinical practice and trials. These sequences offer a good balance between sensitivity and specificity and provide appropriate contrast and signal within the cartilage as well as between cartilage, articular fluid, and subchondral bone. Additionally, FS IW sequences enable the evaluation of menisci, ligaments, synovitis/effusion, and bone marrow edema-like signal changes. This review article provides a rationale for the use of FSE FS IW sequences in the morphological assessment of cartilage and osteoarthritis, along with a brief overview of other clinically available sequences for this indication. Additionally, the article highlights ongoing research efforts aimed at improving FSE FS IW sequences through 3D acquisitions with enhanced resolution, shortened examination times, and exploring the potential benefits of different magnetic field strengths. While most of the literature on cartilage imaging focuses on the knee, the concepts presented here are applicable to all joints. Key points 1. MRI is currently considered the modality of reference for a “whole-joint” morphological assessment of osteoarthritis. 2. Fat-suppressed intermediate-weighted sequences remain the keystone of MRI protocols for the assessment of cartilage morphology, as well as other structures involved in osteoarthritis. 3. Trends for further development in the field of cartilage and joint imaging include 3D FSE imaging, faster acquisition including AI-based acceleration, and synthetic imaging providing multi-contrast sequences.

PurposeTo investigate the role of non-echo planar diffusion weighted imaging (DWI) using \"periodically rotated overlapping parallel lines with enhanced reconstruction\" (PROPELLER) sequence for the diagnosis of cholesteatoma compared to surgical and histopathological results in an attempt to determine the factors causing false negative and false positive diagnoses.MethodsPatients who had PROPELLER DWI before ear surgery were retrospectively reviewed. The presence of a lesion with diffusion restriction on PROPELLER DWI was accepted as positive for cholesteatoma, and the results were compared to the intraoperative and histopathological findings.ResultsA total of 112 ears in 109 patients were reviewed. On PROPELLER DWI, a lesion with diffusion restriction was found in 101 (90.2%) ears, while in 11 (9.8%) of the patients, no diffusion restriction was found. Surgery and histopathological analysis revealed a cholesteatoma in 100 (89.3%) ears, while in 12 (10.7%) ears, no cholesteatoma was found surgically. There were 96 (85.7%) true positives, 7 (6.2%) true negatives, 5 (4.5%) false positives and 4 (3.6%) false negatives. The accuracy, sensitivity, specificity, positive predictive and negative predictive values of non-echo planar DWI were calculated to be 91.96%, 96%, 58.33%, 95.05%, and 63.64%, respectively.ConclusionNon-echo planar DWI using PROPELLER sequence has high accuracy, sensitivity and positive predictive value and can be used for the detection of cholesteatoma. The external auditory canal, postoperative ears and small lesions should be evaluated with caution to avoid false results.

Myelin repair is an unrealized therapeutic goal in the treatment of multiple sclerosis (MS). Uncertainty remains about the optimal techniques for assessing therapeutic efficacy and imaging biomarkers are required to measure and corroborate myelin restoration. We analyzed myelin water fraction imaging from ReBUILD, a double-blind, randomized placebo-controlled (delayed treatment) remyelination trial, that showed a significant reduction in VEP latency in patients with MS. We focused on brain regions rich in myelin. Fifty MS subjects in two arms underwent 3T MRI at baseline and months 3 and 5. Half of the cohort was randomly assigned to receive treatment from baseline through 3 mo, whereas the other half received treatment from 3 mo to 5 mo post-baseline. We computed myelin water fraction changes occurring in normal-appearing white matter of corpus callosum, optic radiations, and corticospinal tracts. An increase in myelin water fraction was documented in the normal-appearing white matter of the corpus callosum, in correspondence with the administration of the remyelinating treatment clemastine. This study provides direct, biologically validated imaging-based evidence of medically induced myelin repair. Moreover, our work strongly suggests that significant myelin repair occurs outside of lesions. We therefore propose myelin water fraction within the normal-appearing white matter of the corpus callosum as a biomarker for clinical trials looking at remyelination.