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In this study, we examine the effects of a recently developed, iron-based coupling medium (IBCM) on guidance magnetic resonance (MR) scans during transcranial, magnetic-resonance-guided, focused ultrasound surgery (tMRgFUS) procedures. More specifically, this study tests the hypotheses that the use of the IBCM will (a) not adversely affect image quality, (b) remove aliasing from small field-of-view scans, and (c) decouple image quality from the motion state of the coupling fluid.An IBCM, whose chemical synthesis and characterization are reported elsewhere, was used as a coupling medium during tMRgFUS procedures on gel phantoms. Guidance magnetization-prepared rapid-gradient-echo (MP-RAGE), TSE, and GRE scans were acquired with fields of view of 28 and 18 cm. Experiments were repeated with the IBCM in several distinct flow states. GRE scans were used to estimate temperature time courses as a gel target was insonated. IBCM performance was measured by computing (i) the root mean square difference (RMSD) of TSE and GRE pixel values acquired using water and the IBCM, relative to the use of water; (ii) through-time temperature uncertainty for GRE scans; and (iii) Bland-Altman analysis of the temperature time courses. Finally, guidance TSE and GRE scans of a human volunteer were acquired during a separate sham tMRgFUS procedure. As a control, all experiments were repeated using a water coupling medium.Use of the IBCM reduced RMSD in TSE scans by a factor of 4 or more for all fields of view and nonstationary motion states, but did not reduce RMSD estimates in MP-RAGE scans. With the coupling media in a stationary state, the IBCM altered estimates of temperature uncertainty relative to the use of water by less than 0.2°C. However, with a high flow state, the IBCM reduced temperature uncertainties by the statistically significant amounts (at the 0.01 level) of 0.5°C (28 cm field of view) and 5°C (18 cm field of view). Bland-Altman analyses found a 0.1°C ± 0.5°C difference between temperature estimates acquired using water and the IBCM as coupling media. Finally, scans of a human volunteer using the IBCM indicate more conspicuous grey/white matter contrast, a reduction in aliasing, and a less than 0.2°C change in temperature uncertainty.The use of an IBCM during tMRgFUS procedures does not adversely affect image quality for TSE and GRE scans, can decouple image quality from the motion state of the coupling fluid, and can remove aliasing from scans where the field of view is set to be much smaller than the spatial extent of the coupling fluid.

Background: Peripheral artery disease (PAD) results in exercise-induced ischemia in leg muscles. 31 Phosphorus (P) magnetic resonance spectroscopy demonstrates prolonged phosphocreatine recovery time constant after exercise in PAD but has low signal to noise, low spatial resolution, and requires multinuclear hardware. Chemical exchange saturation transfer (CEST) is a quantitative magnetic resonance imaging method for imaging substrate (CEST asymmetry [CEST asym ]) concentration by muscle group. We hypothesized that kinetics measured by CEST could distinguish between patients with PAD and controls. Methods: Patients with PAD and age-matched normal subjects were imaged at 3T with a transmit-receive coil around the calf. Four CEST mages were acquired over 24-second intervals. The subjects then performed plantar flexion exercise on a magnetic resonance imaging-compatible ergometer until calf exhaustion. Twenty-five CEST images were obtained at end exercise. Regions of interest were drawn around individual muscle groups, and (CEST asym ) decay times were fitted by exponential curve to CEST values. In 10 patients and 11 controls, 31 P spectra were obtained 20 minutes later after repeat exercise. Five patients and 5 controls returned at a mean of 1±1 days later for repeat CEST studies. Results: Thirty-five patients with PAD (31 male, age 66±8 years) and 29 controls (11 male, age 63±8 years) were imaged with CEST. The CEST asym decay times for the whole calf (341±332 versus 153±72 seconds; P asym decay and phosphocreatine recovery time constant was good. Conclusions: CEST is a magnetic resonance imaging method that can distinguish energetics in patients with PAD from age-matched normal subjects on a per muscle group basis. CEST agrees reasonably well with the gold standard 31 P magnetic resonance spectroscopy. Moreover, CEST has higher spatial resolution, creates an image, and does not require multinuclear hardware and thus may be more suitable for clinical studies in PAD.

Purpose: To provide a robust whole-brain quantitative magnetization transfer (MT) imaging method that is not limited by long acquisition times.Methods: Two variants of a spiral 2D interleaved multi-slice spoiled gradient echo (SPGR) sequence are used for rapid quantitative MT imaging of the brain at 3 T. A dual flip angle, steady-state prepared, double-contrast method is used for combined B1 and-T1 mapping in combination with a single-contrast MT-prepared acquisition over a range of different saturation flip angles (50 deg to 850 deg) and offset frequencies (1 kHz and 10 kHz). Five sets (containing minimum 6 to maximum 18 scans) with different MT-weightings were acquired. In addition, main magnetic field inhomogeneities (ΔB0) were measured from two Cartesian low-resolution 2D SPGR scans with different echo times. Quantitative MT model parameters were derived from all sets using a two-pool continuous-wave model analysis, yielding the pool-size ratio, F, their exchange rate, kf, and their transverse relaxation time, T2r.Results: Whole-brain quantitative MT imaging was feasible for all sets with total acquisition times ranging from 7:15 min down to 3:15 min. For accurate modeling, B1-correction was essential for all investigated sets, whereas ΔB0-correction showed limited bias for the observed maximum off-resonances at 3 T.Conclusion: The combination of rapid B1-T1 mapping and MT-weighted imaging using a 2D multi-slice spiral SPGR research sequence offers excellent prospects for rapid whole-brain quantitative MT imaging in the clinical setting.

PURPOSE In this study, we compared hyperpolarized 3He and 129Xe images from patients with cystic fibrosis using two commonly applied magnetic resonance sequences, standard gradient echo (GRE) and balanced steady-state free precession (TrueFISP) to quantify regional similarities and differences in signal distribution and defect analysis. MATERIALS AND METHODS Ten patients (7M/3F) with cystic fibrosis underwent hyperpolarized gas MR imaging with both 3He and 129Xe. Six had MRI with both GRE, and TrueFISP sequences and four patients had only GRE sequence but not TrueFISP. Ventilation defect percentages (VDPs) were calculated as lung voxels with

Purpose To develop a real-time dynamic vocal tract imaging method using an accelerated spiral GRE sequence and low rank plus sparse reconstruction. Methods Spiral k-space sampling has high data acquisition efficiency and thus is suited for real-time dynamic imaging; further acceleration can be achieved by undersampling k-space and using a model-based reconstruction. Low rank plus sparse reconstruction is a promising method with fast computation and increased robustness to global signal changes and bulk motion, as the images are decomposed into low rank and sparse terms representing different dynamic components. However, the combination with spiral scanning has not been well studied. In this study an accelerated spiral GRE sequence was developed with an optimized low rank plus sparse reconstruction and compared with L1-SPIRiT and XD-GRASP methods. The off-resonance was also corrected using a Chebyshev approximation method to reduce blurring on a frame-by-frame basis. Results The low rank plus sparse reconstruction method is sensitive to the weights of the low rank and sparse terms. The optimized reconstruction showed advantages over other methods with reduced aliasing and improved SNR. With the proposed method, spatial resolution of 1.3*1.3 mm2 with 150 mm field-of-view (FOV) and temporal resolution of 30 frames-per-second (fps) was achieved with good image quality. Blurring was reduced using the Chebyshev approximation method. Conclusion This work studies low rank plus sparse reconstruction using the spiral trajectory and demonstrates a new method for dynamic vocal tract imaging which can benefit studies of speech disorders.

PURPOSE To develop and evaluate a high spatial resolution (1.25 × 1.25 mm2 ) spiral first-pass myocardial perfusion imaging technique with whole-heart coverage at 3T, to better assess transmural differences in perfusion between the endocardium and epicardium, to quantify the myocardial ischemic burden, and to improve the detection of obstructive coronary artery disease. METHODS Whole-heart high-resolution spiral perfusion pulse sequences and corresponding motion-compensated reconstruction techniques for both interleaved single-slice (SS) and simultaneous multi-slice (SMS) acquisition with or without outer-volume suppression (OVS) were developed. The proposed techniques were evaluated in 34 healthy volunteers and 8 patients (55 data sets). SS and SMS images were reconstructed using motion-compensated L1-SPIRiT and SMS-Slice-L1-SPIRiT, respectively. Images were blindly graded by 2 experienced cardiologists on a 5-point scale (5, excellent; 1, poor). RESULTS High-quality perfusion imaging was achieved for both SS and SMS acquisitions with or without OVS. The SS technique without OVS had the highest scores (4.5 [4, 5]), which were greater than scores for SS with OVS (3.5 [3.25, 3.75], P < .05), MB = 2 without OVS (3.75 [3.25, 4], P < .05), and MB = 2 with OVS (3.75 [2.75, 4], P < .05), but significantly higher than those for MB = 3 without OVS (4 [4, 4], P = .95). SMS image quality was improved using SMS-Slice-L1-SPIRiT as compared to SMS-L1-SPIRiT (P < .05 for both reviewers). CONCLUSION We demonstrated the successful implementation of whole-heart spiral perfusion imaging with high resolution at 3T. Good image quality was achieved, and the SS without OVS showed the best image quality. Evaluation in patients with expected ischemic heart disease is warranted.

Background Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis. Methods Convolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain. Results LV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87 ± 0.04, a Hausdorff distance of 2.7 ± 1.0 pixels, and a mean surface distance of 0.41 ± 0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38 ± 0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p Conclusions Deep learning enables fully-automatic global and segmental circumferential strain analysis of DENSE CMR providing excellent agreement with conventional user-assisted methods. Deep learning-based automatic strain analysis may facilitate greater clinical use of DENSE for the quantification of global and segmental strain in patients with cardiac disease.

Background Hyperpolarized gas with helium (HHe-3) MR (magnetic resonance) is a noninvasive imaging method which maps and quantifies regions of ventilation heterogeneity (VH) in the lung. VH is an important feature of asthma, but little is known as to how VH informs patient phenotypes. Purpose To determine if VH indicators quantified by HHe-3 MR imaging (MRI) predict phenotypic characteristics and map to regions of inflammation in children with problematic wheeze or asthma. Methods Sixty children with poorly-controlled wheeze or asthma underwent HHe-3 MRI, including 22 with bronchoalveolar lavage (BAL). The HHe-3 signal intensity defined four ventilation compartments. The non-ventilated and hypoventilated compartments divided by the total lung volume defined a VH index (VHI %). Results Children with VHI % in the upper quartile had significantly greater airflow limitation, bronchodilator responsiveness, blood eosinophils, expired nitric oxide (FeNO), and BAL eosinophilic or neutrophilic granulocyte patterns compared to children with VHI % in the lower quartile. Lavage return from hypoventilated bronchial segments had greater eosinophil % than from ventilated segments. Conclusion In children with asthma, greater VHI % as measured by HHe-3 MRI identifies a severe phenotype with higher type 2 inflammatory markers, and maps to regions of lung eosinophilia. Listed on ClinicalTrials. gov (NCT02577497).

Background: The distal superficial femoral artery (SFA) is most commonly affected in peripheral artery disease (PAD). The effects of the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor alirocumab added to statin therapy on SFA atherosclerosis, downstream flow, and walking performance are unknown. Methods: Thirty-five patients with PAD on maximally tolerated statin therapy were recruited. Patients were randomized to alirocumab 150 mg subcutaneously ( n = 18) or matching placebo ( n = 17) therapy every 2 weeks for 1 year. The primary outcome was change in SFA plaque volume by black blood magnetic resonance imaging (MRI). Secondary outcomes were changes in calf muscle perfusion by cuff/occlusion hyperemia arterial spin labeling MRI, 6-minute walk distance (6MWD), low-density lipoprotein (LDL) cholesterol, and other biomarkers. Results: Age (mean ± SD) was 64 ± 8 years, 20 (57%) patients were women, 17 (49%) were Black individuals, LDL was 107 ± 36 mg/dL, and the ankle–brachial index 0.71 ± 0.20. The LDL fell more with alirocumab than placebo (mean [95% CI]) (−49.8 [−66.1 to −33.6] vs −7.7 [−19.7 to 4.3] mg/dL; p < 0.0001). Changes in SFA plaque volume and calf perfusion showed no difference between groups when adjusted for baseline (+0.25 [−0.29 to 0.79] vs −0.04 [−0.47 to 0.38] cm3; p = 0.37 and 0.22 [−8.67 to 9.11] vs 3.81 [−1.45 to 9.08] mL/min/100 g; p = 0.46, respectively), nor did 6MWD. Conclusion: In this exploratory study, the addition of alirocumab therapy to statins did not alter SFA plaque volume, calf perfusion or 6MWD despite significant LDL lowering. Larger studies with longer follow up that include plaque characterization may improve understanding of the effects of intensive LDL-lowering therapy in PAD (ClinicalTrials.gov Identifier: NCT02959047).

Purpose Magnetization transfer ratio (MTR) histograms are used widely for the assessment of diffuse pathological changes in the brain. For broad clinical application, MTR scans should not only be fast, but confounding factors should also be minimized for high reproducibility. To this end, a 1-minute whole-brain spiral MTR method with intrinsic B1 -field correction is introduced. Methods A spiral multislice spoiled gradient-echo sequence with adaptable magnetization-transfer saturation pulses (angle β) is proposed. After a low-resolution single-shot spiral readout and a dummy preparation period, high-resolution images are acquired using an interleaved spiral readout. For whole-brain MTR imaging, 50 interleaved slices with three different magnetization-transfer contrasts (β = 0°, 350°, and 550°) together with an intrinsic B1 -field map are recorded in 58.5 seconds on a clinical 3T system. From the three contrasts, two sets of MTR images are derived and used for subsequent B1 correction, assuming a linear dependency on β. For validation, a binary spin bath model is used. Results For the proposed B1 -correction scheme, numerical simulations indicate for brain tissue a decrease of about a factor of 10 for the B1 -related bias on MTR. As a result, following B1 correction, MTR differences in gray and white matter become markedly accentuated, and the reproducibility of MTR histograms from scan-rescan experiments is improved. Furthermore, B1 -corrected MTR histograms show a lower variability for age-matched normal-appearing brain tissue. Conclusion From its speed and offering intrinsic B1 correction, the proposed method shows excellent prospects for clinical studies that explore magnetization-transfer effects based on MTR histogram analysis.

Purpose Intraoperative T2 -weighted (T2-w) imaging unreliably captures image contrast specific to thermal ablation after transcranial MR-guided focused ultrasound surgery, impeding dynamic imaging feedback. Using a porcine thalamotomy model, we test the unproven hypothesis that intraoperative DWI can improve dynamic feedback by detecting lesioning within 30 minutes of transcranial MR-guided focused ultrasound surgery. Methods Twenty-five thermal lesions were formed in six porcine models using a clinical transcranial MR-guided focused ultrasound surgery system. A novel diffusion-weighted pulse sequence monitored the formation of T2-w and diffusion-weighted lesion contrast after ablation. Using postoperative T2-w contrast to indicate lesioning, apparent intraoperative image contrasts and diffusion coefficients at each lesion site were computed as a function of time after ablation, observed peak temperature, and observed thermal dose. Lesion sizes segmented from imaging and thermometry were compared. Image reviewers estimated the time to emergence of lesion contrast. Intraoperative image contrasts were analyzed using receiver operator curves. Results On average, the apparent diffusion coefficient at lesioned sites decreased within 5 minutes after ablation relative to control sites. In-plane lesion areas on intraoperative DWI varied from postoperative T2-w MRI and MR thermometry by 9.6 ± 9.7 mm2 and - 4.0 ± 7.1 mm2 , respectively. The 0.25, 0.5, and 0.75 quantiles of the earliest times of observed T2-w and diffusion-weighted lesion contrast were 10.7, 21.0, and 27.8 minutes and 3.7, 8.6, and 11.8 minutes, respectively. The T2-w and diffusion-weighted contrasts and apparent diffusion coefficient values produced areas under the receiver operator curve of 0.66, 0.80, and 0.74, respectively. Conclusion Intraoperative DWI can detect MR-guided focused ultrasound surgery lesion formation in the brain within several minutes after treatment.

To develop a new approach to 2D turbo spin -echo (TSE) imaging using annular spiral rings with a retraced in/out trajectory, dubbed "SPRING-RIO TSE", for fast TA long spiral trajectory was split into annular segmentations that were then incorporated into a 2D TSE acquisition module to fully exploit the sampling efficiency of spiral rings. A retraced in/out trajectory strategy coupled with spiral-ring TSE was introduced to increase SNR, mitigate TSimulation and phantom studies demonstrated the performance of the proposed SPRING-RIO TSE pulses sequence, as well as that of trajectory correction and off-resonance correction. Volunteer data showed that the proposed method achieves high-quality 2D T2D T

Transcranial focused ultrasound with the InSightec Exablate system uses thermal ablation for the treatment of movement and mood disorders and blood brain barrier disruption for tumor therapy. The system uses computed tomography (CT) images to calculate phase corrections that account for aberrations caused by the human skull. This work investigates whether magnetic resonance (MR) images can be used as an alternative to CT images to calculate phase corrections. Phase corrections were calculated using the gold standard hydrophone method and the standard of care InSightec ray tracing method. MR binary image mask, MR-simulated-CT (MRsimCT), and CT images of three ex vivo human skulls were supplied as inputs to the InSightec ray tracing method. The degassed ex vivo human skulls were sonicated with a 670 kHz hemispherical phased array transducer (InSightec Exablate 4000). 3D raster scans of the beam profiles were acquired using a hydrophone mounted on a 3-axis positioner system. Focal spots were evaluated using six metrics: pressure at the target, peak pressure, intensity at the target, peak intensity, positioning error, and focal spot volume. Targets at the geometric focus and 5 mm lateral to the geometric focus were investigated. There was no statistical difference between any of the metrics at either target using either MRsimCT or CT for phase aberration correction. As opposed to the MRsimCT, the use of CT images for aberration correction requires registration to the treatment day MR images; CT misregistration within a range of ± 2 degrees of rotation error along three dimensions was shown to reduce focal spot intensity by up to 9.4%. MRsimCT images used for phase aberration correction for the skull produce similar results as CT-based correction, while avoiding both CT to MR registration errors and unnecessary patient exposure to ionizing radiation.

This report gives comprehensive review of the nano-emulsion project between UVA and Virginia Tech which has been generously supported by the Focused Ultrasound Foundation. The purpose of this project is to develop an acoustic coupling bath that is effectively invisible to MRI scans and also remains acoustically compatible with clinical transcranial FUS procedures. This remains important to the clinical translation of FUS because the commonly used acoustic coupling bath of degassed water persistently degrades the quality of MR guidance imaging. Our project proposes an acoustic coupling bath doped with specially designed iron oxide nanoparticles that 1) have a high ability to decay MRI signals, (as quantified by the r2 relaxivity), 2) have diameters less than 100 nm, and 3) are coated with a hydrophilic coating. We hypothesize that these three innovations will produce a lightly doped water bath with identical mechanical, acoustic, thermal, biocompatibility, and electromagnetic properties as water, but with virtually no observable appearance or effect on guidance MR imaging. Finally, the low particle concentrations, small particle sizes, and specific coating will prevent the particles from stabilizing gas bubbles and seeding cavitation nuclei in the transducer’s pre-focal field.We are pleased to report nearly total success on this project, with an affirmation of all hypotheses stated above.

Purpose The aim of this study was to develop a method able to quantify levator veli palatini (LVP) muscle shortening and contraction velocities using dynamic magnetic resonance imaging (MRI) throughout speech samples and relate these measurements to velopharyngeal portal dimensions. Method Six healthy adults (3 men and 3 women, M = 24.5 years) produced syllables representing 4 different manners of production during real-time dynamic MRI scans. We acquired an oblique-coronal slice of the velopharyngeal mechanism, which captured the length of the LVP, and manually segmented each frame. LVP shortening and muscle velocities were calculated from the acquired images. Results Using our method, we found that subjects demonstrated greater LVP shortening and higher maximum contraction velocities during fricative and plosive syllable production than during nasal or vowel syllable production. LVP shortening and maximum contraction velocity positively correlated with velopharyngeal port depth. Conclusions In vivo LVP function differs between manners of production, as expected, and an individual's velopharyngeal portal dimensions influence LVP function. These measures, contextualized with the force–length and force–velocity muscle relationships, provide new insight into LVP function. Future studies could use this method to investigate LVP function in healthy speakers and individuals with velopharyngeal dysfunction and how function relates to velopharyngeal anatomy.

Purpose Automatic segmentation of organs-at-risk (OARs) is a key step in radiation treatment planning to reduce human efforts and bias. Deep convolutional neural networks (DCNN) have shown great success in many medical image segmentation applications but there are still challenges in dealing with large 3D images for optimal results. The purpose of this study is to develop a novel DCNN method for thoracic OARs segmentation using cropped 3D images. Methods To segment the five organs (left and right lungs, heart, esophagus and spinal cord) from the thoracic CT scans, preprocessing to unify the voxel spacing and intensity was first performed, a 3D U-Net was then trained on resampled thoracic images to localize each organ, then the original images were cropped to only contain one organ and served as the input to each individual organ segmentation network. The segmentation maps were then merged to get the final results. The network structures were optimized for each step, as well as the training and testing strategies. A novel testing augmentation with multiple iterations of image cropping was used. The networks were trained on 36 thoracic CT scans with expert annotations provided by the organizers of the 2017 AAPM Thoracic Auto-segmentation Challenge and tested on the challenge testing dataset as well as a private dataset. Results The proposed method earned second place in the live phase of the challenge and first place in the subsequent ongoing phase using a newly developed testing augmentation approach. It showed superior-than-human performance on average in terms of Dice scores (spinal cord: 0.893 ± 0.044, right lung: 0.972 ± 0.021, left lung: 0.979 ± 0.008, heart: 0.925 ± 0.015, esophagus: 0.726 ± 0.094), mean surface distance (spinal cord: 0.662 ± 0.248 mm, right lung: 0.933 ± 0.574 mm, left lung: 0.586 ± 0.285 mm, heart: 2.297 ± 0.492 mm, esophagus: 2.341 ± 2.380 mm) and 95% Hausdorff distance (spinal cord: 1.893 ± 0.627 mm, right lung: 3.958 ± 2.845 mm, left lung: 2.103 ± 0.938 mm, heart: 6.570 ± 1.501 mm, esophagus: 8.714 ± 10.588 mm). It also achieved good performance in the private dataset and reduced the editing time to 7.5 min per patient following automatic segmentation. Conclusions The proposed DCNN method demonstrated good performance in automatic OAR segmentation from thoracic CT scans. It has the potential for eventual clinical adoption of deep learning in radiation treatment planning due to improved accuracy and reduced cost for OAR segmentation.

PURPOSE: Images acquired with spiral k-space trajectories can suffer from off-resonance image blur. Previous work has shown that averaging 2 images acquired with a retraced, in/out (RIO) trajectory self-corrects image blur so long as off-resonant spins accrue less than 1 half-cycle of relative phase over the readout. Practical scenarios frequently exceed this threshold. Here, we derive and characterize a more-robust off-resonance image blur correction method for RIO acquisitions. METHODS: Phantom and human volunteer data were acquired using a RIO trajectory with readout durations ranging from 4 to 60 ms. The resulting images were deblurred using 3 candidate methods: conventional linear correction of the component images; semiautomatic deblurring of the component images using an established minimized phase objective function; and semiautomatic deblurring of the average of the component images using a maximized energy objective function, derived below. Deblurring errors were estimated relative to images acquired with 4 ms readouts. RESULTS: All 3 methods converged to similar solutions in cases where less than 2 and 4 cycles of phase accrued over the readout in in vivo and phantom images, respectively (

PURPOSE To use deep learning for suppression of the artifact-generating T1 -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time. METHODS A U-Net was trained to suppress the artifact-generating T1 -relaxation echo using complementary phase-cycled data as the ground truth. A data-augmentation method was developed that generates synthetic DENSE images with arbitrary displacement-encoding frequencies to suppress the T1 -relaxation echo modulated for a range of frequencies. The resulting U-Net (DAS-Net) was compared with k-space zero-filling as an alternative method. Non-phase-cycled DENSE images acquired in shorter breath-holds were processed by DAS-Net and compared with DENSE images acquired with phase cycling for the quantification of myocardial strain. RESULTS The DAS-Net method effectively suppressed the T1 -relaxation echo and its artifacts, and achieved root Mean Square(RMS) error = 5.5 ± 0.8 and structural similarity index = 0.85 ± 0.02 for DENSE images acquired with a displacement encoding frequency of 0.10 cycles/mm. The DAS-Net method outperformed zero-filling (root Mean Square error = 5.8 ± 1.5 vs 13.5 ± 1.5, DAS-Net vs zero-filling, P < .01; and structural similarity index = 0.83 ± 0.04 vs 0.66 ± 0.03, DAS-Net vs zero-filling, P < .01). Strain data for non-phase-cycled DENSE images with DAS-Net showed close agreement with strain from phase-cycled DENSE. CONCLUSION The DAS-Net method provides an effective alternative approach for suppression of the artifact-generating T1 -relaxation echo in DENSE MRI, enabling a 42% reduction in scan time compared to DENSE with phase-cycling.

Delayed enhancement (DE) MRI is an important tool in diagnosis of cardiovascular disease as it can reveal different characteristics of the myocardium scars including infarction and no-reflow. Automatic segmentation of different regions has the advantage of improved accuracy and reduced inter-observer variability in quantifying key imaging biomarkers such as percentage of scars. In recent years deep learning has led to drastic performance improvement in automatic segmentation tasks using the UNet architecture. Cardiac MRI segmentation is a challenging task due to the high variability in imaging contrast, orientation and signal-to-noise ratio; specifically, for short-axis views, as they are double-oblique slices, due to the different orientations of the heart and operator choice, the overall appearance of the image can vary significantly, which poses a challenge to the neural networks as they cannot learn a consistent global anatomy. In this paper we developed a rotation-based augmentation to address this issue in both training and testing steps by eliminating the variance in orientations and demonstrated its effectiveness. 2D dilated UNet was used as the backbone network structure. On the test dataset, Dice scores of 0.933 for LV blood pool, 0.824 for myocardium, 0.578 for infarction and 0.697 for no re-flow regions were archived using the proposed framework.

The choroid plexus is the primary organ that secretes the cerebrospinal fluid. Its structure and function may be associated with the brain drainage pathway and the clearance of amyloid-beta in Alzheimer's Disease. However, choroid plexus segmentation methods have rarely been studied. Therefore, the purpose of this work is to fill the gap using a deep convolutional network. MR images of 10 healthy subjects ( $75.5\pm 8.0$ years) were retrospectively selected from the Alzheimer's Disease Neuroimaging Initiative database (ADNI). The benchmark of choroid plexus segmentation was provided by the FreeSurfer package and manual correction. A 3D U-Net was developed and optimized in the patch extraction, augmentation, and loss function. In leave-one-out cross-validations, the optimized U-Net provided superior performance compared to the FreeSurfer results (Dice score $0.732\pm 0.046$ vs $0.581\pm 0.093$ , Jaccard coefficient $0.579\pm 0.057$ vs $0.416\pm 0.091$ , 95% Hausdorff distance $1.871\pm 0.549$ vs $7.257\pm 5.038$ , and sensitivity $0.761 \pm 0.078$ vs $0.539\pm 0.117$ ).

Accurate segmentation of different sub-regions of gliomas including peritumoral edema, necrotic core, enhancing and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape, segmentation of the sub-regions is very challenging. Recent development using deep learning models has proved its effectiveness in the past several brain segmentation challenges as well as other semantic and medical image segmentation problems. Most models in brain tumor segmentation use a 2D/3D patch to predict the class label for the center voxel and variant patch sizes and scales are used to improve the model performance. However, it has low computation efficiency and also has limited receptive field. U-Net is a widely used network structure for end-to-end segmentation and can be used on the entire image or extracted patches to provide classification labels over the entire input voxels so that it is more efficient and expect to yield better performance with larger input size. In this paper we developed a deep-learning-based segmentation method using an ensemble of 3D U-Nets with different hyper-parameters. Furthermore, we estimated the uncertainty of the segmentation from the probabilistic outputs of each network and studied the correlation between the uncertainty and the performances. Preliminary results showed effectiveness of the segmentation model. Finally, we developed a linear model for survival prediction using extracted imaging and non-imaging features, which, despite the simplicity, can effectively reduce overfitting and regression errors.

Purpose To develop and evaluate a simultaneous multislice (SMS) spiral perfusion pulse sequence with whole-heart coverage. Methods An orthogonal set of phase cycling angles following a Hadamard pattern was incorporated into a golden-angle (GA) variable density spiral perfusion sequence to perform SMS imaging at different multiband (MB) factors. Images were reconstructed using an SMS extension of L1-SPIRiT that we have termed SMS-L1-SPIRiT. The proposed sequence was evaluated in 40 subjects (10 each for MB factors of 1, 2, 3, and 4). Images were blindly graded by 2 cardiologists on a 5-point scale (5, excellent). To quantitatively evaluate the reconstruction performance against images acquired without SMS, the MB =1 data were used to retrospectively simulate data acquired at MB factors of 2 to 4. Results Analysis of the SMS point-spread function for the desired slice showed that the proposed sampling strategy significantly canceled the main-lobe energy of the other slices and has low side-lobe energy resulting in an incoherent temporal aliasing pattern when rotated by the GA. Retrospective experiments demonstrated the SMS-L1-SPIRiT method removed aliasing from the interfering slices and showed excellent agreement with the ground-truth MB =1 images. Clinical evaluation demonstrated high-quality perfusion images with average image-quality scores of 4.3 ± 0.5 (MB =2), 4.2 ± 0.5 (MB =3), and 4.4 ± 0.4 (MB =4) with no significant quality difference in image quality between MB factors (P = 0.38). Conclusion SMS spiral perfusion at MB factors 2, 3, and 4 produces high-quality perfusion images with whole-heart coverage in a clinical setting with high sampling efficiency.

This paper describes an adaptive approach to regularizing model-based reconstructions in magnetic resonance imaging to account for local structure or image content. In conjunction with common models like wavelet and total variation sparsity, this content-aware regularization avoids oversmoothing or compromising image features while suppressing noise and incoherent aliasing from accelerated imaging. To evaluate this regularization approach, the experiments reconstruct images from single- and multi-channel, Cartesian and non-Cartesian, brain and cardiac data. These reconstructions combine common analysis-form regularizers and autocalibrating parallel imaging (when applicable). In most cases, the results show widespread improvement in structural similarity and peak-signal-to-error ratio relative to the fully sampled images. These results suggest that this content-aware regularization can preserve local image structures such as edges while providing denoising power superior to sparsity-promoting or sparsity-reweighted regularization.

Xie, T, Crump, KB, Ni, R, Meyer, CH, Hart, JM, Blemker, SS, and Feng, X. Quantitative relationships between individual lower-limb muscle volumes and jump and sprint performances of basketball players. J Strength Cond Res 34(3): 623-631, 2020-Lower body skeletal muscles play an essential role in athletic performance; however, because of the difficulty in obtaining detailed information of each individual muscle, the quantitative relationships between individual muscle volumes and performance are not well studied. The aim of this study was to accurately measure individual muscle volumes and identify the muscles with strong correlations with jump and sprint performance metrics for basketball players. Ten male varsity basketball players and 8 club players were scanned using magnetic resonance imaging (MRI) and instructed to perform various jump and sprint tests. The volumes of all lower-limb muscles were calculated from MRI and normalized by body surface area to reduce the effect of the body size differences. In analysis, feature selection was first used to identify the most relevant muscles, followed by correlation analysis to quantify the relationships between the selected muscles and each performance metric. Vastus medialis and semimembranosus were found to be the most relevant muscles for jump while adductor longus and vastus medialis were selected for sprint. Strong correlations (r = 0.664-0.909) between the selected muscles and associated performance tests were found for varsity players, and moderate correlations (r = -0.203 to 0.635) were found for club players. One possible application is that for well-trained varsity players, a targeted training scheme focusing on the selected muscles may be an effective method to further improve jump and sprint performances.

PURPOSE: To develop a real-time dynamic MRI method for comprehensive evaluation of velum movement during speech. METHODS: Dynamic MRI has been used to study velopharyngeal insufficiency (VPI) by imaging the movement of the velum during speech, because it can provide good anatomic details with no exposed radiation. To be able to comprehensively evaluate dynamic velum movement, a real-time spiral non-balanced SSFP sequence was developed with simultaneous dual-planar coverage and improved spatial and temporal resolution using a combination of parallel imaging and spatial and temporal compressed sensing to achieve 6x acceleration. New off-resonance correction and post-processing methods were also developed to reduce blurring and slice crosstalk. RESULTS: The method demonstrated good image quality for visualizing dynamic velum movement with reduced blurring and improved image homogeneity. Spatial resolution of 1.2*1.2 mm(2) with 150 mm field-of-view (FOV) and temporal resolution of 20 frames-per-second with simultaneous dual-planar coverage was achieved. CONCLUSIONS: This work describes a new technique for studying speech disorders using dual-planar accelerated spiral dynamic MRI.

Purpose To develop a rapid pulse sequence for volumetric MR thermometry. Methods Simulations were carried out to assess temperature deviation, focal spot distortion/blurring, and focal spot shift across a range of readout durations and maximum temperatures for Cartesian, spiral-out, and retraced spiral-in/out (RIO) trajectories. The RIO trajectory was applied for stack-of-spirals 3D imaging on a real-time imaging platform and preliminary evaluation was carried out compared to a standard 2D sequence in vivo using a swine brain model, comparing maximum and mean temperatures measured between the two methods, as well as the temporal standard deviation measured by the two methods. Results In simulations, low-bandwidth Cartesian trajectories showed substantial shift of the focal spot, whereas both spiral trajectories showed no shift while maintaining focal spot geometry. In vivo, the 3D sequence achieved real-time 4D monitoring of thermometry, with an update time of 2.9-3.3 s. Conclusion Spiral imaging, and RIO imaging in particular, is an effective way to speed up volumetric MR thermometry. Magn Reson Med 79:3122-3127, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Purpose To develop and evaluate a protocol for hyperpolarized helium-3 (HHe) ventilation magnetic resonance imaging (MRI) of the lungs of non-sedated infants and children. Materials and methods HHe ventilation MRI was performed on seven children ≤ 4 years old. Contiguous 2D–spiral helium-3 images were acquired sequentially with a scan time of ≤ 0.2 s/slice. Results Motion-artifact–free, high signal-to-noise ratio (SNR) images of lung ventilation were obtained. Gas was homogeneously distributed in healthy individuals; focal ventilation defects were found in patients with respiratory diseases. Conclusion HHe ventilation MRI can aid assessment of pediatric lung disease even at a young age.

Background Volumetric thermometry with fine spatiotemporal resolution is desirable to monitor MR-guided focused ultrasound (MRgFUS) procedures in the brain, but requires some form of accelerated imaging. Accelerated MR temperature imaging methods have been developed that undersample k-space and leverage signal correlations over time to suppress the resulting undersampling artifacts. However, in transcranial MRgFUS treatments, the water bath surrounding the skull creates signal variations that do not follow those correlations, leading to temperature errors in the brain due to signal aliasing. Methods To eliminate temperature errors due to the water bath, a spatially-segmented iterative reconstruction method was developed. The method fits a k-space hybrid signal model to reconstruct temperature changes in the brain, and a conventional MR signal model in the water bath. It was evaluated using single-channel 2DFT Cartesian, golden angle radial, and spiral data from gel phantom heating, and in vivo 8-channel 2DFT data from a FUS thalamotomy. Water bath signal intensity in phantom heating images was scaled between 0-100% to investigate its effect on temperature error. Temperature reconstructions of retrospectively undersampled data were performed using the spatially-segmented method, and compared to conventional whole-image k-space hybrid (phantom) and SENSE (in vivo) reconstructions. Results At 100% water bath signal intensity, 3 ×-undersampled spatially-segmented temperature reconstruction error was nearly 5-fold lower than the whole-image k-space hybrid method. Temperature root-mean square error in the hot spot was reduced on average by 27 × (2DFT), 5 × (radial), and 12 × (spiral) using the proposed method. It reduced in vivo error 2 × in the brain for all acceleration factors, and between 2 × and 3 × in the temperature hot spot for 2-4 × undersampling compared to SENSE. Conclusions Separate reconstruction of brain and water bath signals enables accelerated MR temperature imaging during MRgFUS procedures with low errors due to undersampling using Cartesian and non-Cartesian trajectories. The spatially-segmented method benefits from multiple coils, and reconstructs temperature with lower error compared to measurements from SENSE-reconstructed images. The acceleration can be applied to increase volumetric coverage and spatiotemporal resolution.