Accelerated free-breathing liver fat and R 2 * quantification using multi-echo stack-of-radial MRI with motion-resolved multidimensional regularized reconstruction: Initial retrospective evaluation.

PURPOSE: To improve image quality, mitigate quantification biases and variations for free-breathing liver proton density fat fraction (PDFF) and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ quantification accelerated by radial k-space undersampling. METHODS: A free-breathing multi-echo stack-of-radial MRI method was developed with compressed sensing with multidimensional regularization. It was validated in motion phantoms with reference acquisitions without motion and in 11 subjects (6 patients with nonalcoholic fatty liver disease) with reference breath-hold Cartesian acquisitions. Images, PDFF, and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ maps were reconstructed using different radial view k-space sampling factors and reconstruction settings. Results were compared with reference-standard results using Bland-Altman analysis. Using linear mixed-effects model fitting (p < 0.05 considered significant), mean and SD were evaluated for biases and variations of PDFF and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , respectively, and coefficient of variation on the first echo image was evaluated as a surrogate for image quality. RESULTS: Using the empirically determined optimal sampling factor of 0.25 in the accelerated in vivo protocols, mean differences and limits of agreement for the proposed method were [-0.5; -33.6, 32.7] s-1 for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-1.0%; -5.8%, 3.8%] for PDFF, close to those of a previous self-gating method using fully sampled radial views: [-0.1; -27.1, 27.0] s-1 for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-0.4%; -4.5%, 3.7%] for PDFF. The proposed method had significantly lower coefficient of variation than other methods (p < 0.001). Effective acquisition time of 64 s or 59 s was achieved, compared with 171 s or 153 s for two baseline protocols with different radial views corresponding to sampling factor of 1.0. CONCLUSION: This proposed method may allow accelerated free-breathing liver PDFF and R 2 * $$ {\mathrm{R}}_2^{\ast } $$ mapping with reduced biases and variations.

Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI.

PURPOSE: Accurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset. METHODS: The proposed automatic pipeline adopted Shifted Window (Swin) Transformers and employed a coarse-to-fine segmentation strategy: (1) initial 3D needle feature segmentation with 3D Swin UNEt TRansfomer (UNETR); (2) generation of a 2D reformatted image containing the needle feature; (3) fine 2D needle feature segmentation with 2D Swin Transformer and calculation of 3D needle tip position and axis orientation. Pre-training and data augmentation were performed to improve network training. The pipeline was evaluated via cross-validation with 49 in vivo intra-procedural 3D MR images from preclinical pig experiments. The needle tip and axis localization errors were compared with human intra-reader variation using the Wilcoxon signed rank test, with p < 0.05 considered significant. RESULTS: The average end-to-end computational time for the pipeline was 6 s per 3D volume. The median Dice scores of the 3D Swin UNETR and 2D Swin Transformer in the pipeline were 0.80 and 0.93, respectively. The median 3D needle tip and axis localization errors were 1.48 mm (1.09 pixels) and 0.98°, respectively. Needle tip localization errors were significantly smaller than human intra-reader variation (median 1.70 mm; p < 0.01). CONCLUSION: The proposed automatic pipeline achieved rapid pixel-level 3D needle localization on intra-procedural 3D MRI without requiring a large 3D training dataset and has the potential to assist MRI-guided percutaneous interventions.

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.

OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs. MATERIALS AND METHODS: 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC). RESULTS: ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis. DISCUSSION: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

High-resolution prostate diffusion MRI using eddy current-nulled convex optimized diffusion encoding and random matrix theory-based denoising.

OBJECTIVE: To develop and evaluate a technique combining eddy current-nulled convex optimized diffusion encoding (ENCODE) with random matrix theory (RMT)-based denoising to accelerate and improve the apparent signal-to-noise ratio (aSNR) and apparent diffusion coefficient (ADC) mapping in high-resolution prostate diffusion-weighted MRI (DWI). MATERIALS AND METHODS: Eleven subjects with clinical suspicion of prostate cancer were scanned at 3T with high-resolution (HR) (in-plane: 1.0 × 1.0 mm2) ENCODE and standard-resolution (1.6 × 2.2 mm2) bipolar DWI sequences (both had 7 repetitions for averaging, acquisition time [TA] of 5 min 50 s). HR-ENCODE was retrospectively analyzed using three repetitions (accelerated effective TA of 2 min 30 s). The RMT-based denoising pipeline utilized complex DWI signals and Marchenko-Pastur distribution-based principal component analysis to remove additive Gaussian noise in images from multiple coils, b-values, diffusion encoding directions, and repetitions. HR-ENCODE with RMT-based denoising (HR-ENCODE-RMT) was compared with HR-ENCODE in terms of aSNR in prostate peripheral zone (PZ) and transition zone (TZ). Precision and accuracy of ADC were evaluated by the coefficient of variation (CoV) between repeated measurements and mean difference (MD) compared to the bipolar ADC reference, respectively. Differences were compared using two-sided Wilcoxon signed-rank tests (P < 0.05 considered significant). RESULTS: HR-ENCODE-RMT yielded 62% and 56% higher median aSNR than HR-ENCODE (b = 800 s/mm2) in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT achieved 63% and 70% lower ADC-CoV than HR-ENCODE in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT ADC and bipolar ADC had low MD of 22.7 × 10-6 mm2/s in PZ and low MD of 90.5 × 10-6 mm2/s in TZ. CONCLUSIONS: HR-ENCODE-RMT can shorten the acquisition time and improve the aSNR of high-resolution prostate DWI and achieve accurate and precise ADC measurements in the prostate.

ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function.

BACKGROUND: Despite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion. METHODS: A Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Calinski-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function. RESULTS: A total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p < 0.001), and the Calinski-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p < 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p < 0.05), whereas the SNR and CNR values were not significantly different (p = 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible. CONCLUSION: ECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.

High-Resolution 3D MRI With Deep Generative Networks via Novel Slice-Profile Transformation Super-Resolution.

High-resolution magnetic resonance imaging (MRI) sequences, such as 3D turbo or fast spin-echo (TSE/FSE) imaging, are clinically desirable but suffer from long scanning time-related blurring when reformatted into preferred orientations. Instead, multi-slice two-dimensional (2D) TSE imaging is commonly used because of its high in-plane resolution but is limited clinically by poor through-plane resolution due to elongated voxels and the inability to generate multi-planar reformations due to staircase artifacts. Therefore, multiple 2D TSE scans are acquired in various orthogonal imaging planes, increasing the overall MRI scan time. In this study, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic super-resolution. Compared to SMORE SR method and the networks trained by conventional downsampling, our SPTSR framework demonstrated the best overall image quality from 50 testing cases, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.

Using Free-Breathing MRI to Quantify Pancreatic Fat and Investigate Spatial Heterogeneity in Children.

BACKGROUND: MRI acquisition for pediatric pancreatic fat quantification is limited by breath-holds (BH). Full segmentation (FS) or small region of interest (ROI) analysis methods may not account for pancreatic fat spatial heterogeneity, which may limit accuracy. PURPOSE: To improve MRI acquisition and analysis for quantifying pancreatic proton-density fat fraction (pPDFF) in children by investigating free-breathing (FB)-MRI, characterizing pPDFF spatial heterogeneity, and relating pPDFF to clinical markers. STUDY TYPE: Prospective. POPULATION: A total of 34 children, including healthy (N = 16, 8 female) and overweight (N = 18, 5 female) subjects. FIELD STRENGTH AND SEQUENCES: 3 T; multiecho gradient-echo three-dimensional (3D) stack-of-stars FB-MRI, multiecho gradient-echo 3D Cartesian BH-MRI. ASSESSMENT: A radiologist measured FS- and ROI-based pPDFF on FB-MRI and BH-MRI PDFF maps, with anatomical images as references. Regional pPDFF in the pancreatic head, body, and tail were measured on FB-MRI. FS-pPDFF, ROI-pPDFF, and regional pPDFF were compared, and related to clinical markers, including hemoglobin A1c. STATISTICAL TESTS: T-test, Bland-Altman analysis, Lin's concordance correlation coefficient (CCC), one-way analysis of variance, and Spearman's rank correlation coefficient were used. P < 0.05 was considered significant. RESULTS: FS-pPDFF and ROI-pPDFF from FB-MRI and BH-MRI had mean difference = 0.4%; CCC was 0.95 for FS-pPDFF and 0.62 for ROI-pPDFF. FS-pPDFF was higher than ROI-pPDFF (10.4% ± 6.4% vs. 4.2% ± 2.8%). Tail-pPDFF (11.6% ± 8.1%) was higher than body-pPDFF (8.9% ± 6.3%) and head-pPDFF (8.7% ± 5.2%). Head-pPDFF and body-pPDFF positively correlated with hemoglobin A1c. DATA CONCLUSION: FB-MRI pPDFF is comparable to BH-MRI. Spatial heterogeneity affects pPDFF quantification. Regional measurements of pPDFF in the head and body were correlated with hemoglobin A1c, a marker of insulin sensitivity. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network.

PURPOSE: Stack-of-radial MRI allows free-breathing abdominal scans, however, it requires relatively long acquisition time. Undersampling reduces scan time but can cause streaking artifacts and degrade image quality. This study developed deep learning networks with adversarial loss and evaluated the performance of reducing streaking artifacts and preserving perceptual image sharpness. METHODS: A 3D generative adversarial network (GAN) was developed for reducing streaking artifacts in stack-of-radial abdominal scans. Training and validation datasets were self-gated to 5 respiratory states to reduce motion artifacts and to effectively augment the data. The network used a combination of three loss functions to constrain the anatomy and preserve image quality: adversarial loss, mean-squared-error loss and structural similarity index loss. The performance of the network was investigated for 3-5 times undersampled data from 2 institutions. The performance of the GAN for 5 times accelerated images was compared with a 3D U-Net and evaluated using quantitative NMSE, SSIM and region of interest (ROI) measurements as well as qualitative scores of radiologists. RESULTS: The 3D GAN showed similar NMSE (0.0657 vs. 0.0559, p = 0.5217) and significantly higher SSIM (0.841 vs. 0.798, p < 0.0001) compared to U-Net. ROI analysis showed GAN removed streaks in both the background air and the tissue and was not significantly different from the reference mean and variations. Radiologists' scores showed GAN had a significant improvement of 1.6 point (p = 0.004) on a 4-point scale in streaking score while no significant difference in sharpness score compared to the input. CONCLUSION: 3D GAN removes streaking artifacts and preserves perceptual image details.

Uncertainty-aware physics-driven deep learning network for free-breathing liver fat and R2 * quantification using self-gated stack-of-radial MRI.

PURPOSE: To develop a deep learning-based method for rapid liver proton-density fat fraction (PDFF) and R2 * quantification with built-in uncertainty estimation using self-gated free-breathing stack-of-radial MRI. METHODS: This work developed an uncertainty-aware physics-driven deep learning network (UP-Net) to (1) suppress radial streaking artifacts because of undersampling after self-gating, (2) calculate accurate quantitative maps, and (3) provide pixel-wise uncertainty maps. UP-Net incorporated a phase augmentation strategy, generative adversarial network architecture, and an MRI physics loss term based on a fat-water and R2 * signal model. UP-Net was trained and tested using free-breathing multi-echo stack-of-radial MRI data from 105 subjects. UP-Net uncertainty scores were calibrated in a validation dataset and used to predict quantification errors for liver PDFF and R2 * in a testing dataset. RESULTS: Compared with images reconstructed using compressed sensing (CS), UP-Net achieved structural similarity index >0.87 and normalized root mean squared error <0.18. Compared with reference quantitative maps generated using CS and graph-cut (GC) algorithms, UP-Net achieved low mean differences (MD) for liver PDFF (-0.36%) and R2 * (-0.37 s-1 ). Compared with breath-holding Cartesian MRI results, UP-Net achieved low MD for liver PDFF (0.53%) and R2 * (6.75 s-1 ). UP-Net uncertainty scores predicted absolute liver PDFF and R2 * errors with low MD of 0.27% and 0.12 s-1 compared to CS + GC results. The computational time for UP-Net was 79 ms/slice, whereas CS + GC required 3.2 min/slice. CONCLUSION: UP-Net rapidly calculates accurate liver PDFF and R2 * maps from self-gated free-breathing stack-of-radial MRI. The pixel-wise uncertainty maps from UP-Net predict quantification errors in the liver.

Pregnancies complicated by gestational diabetes and fetal growth restriction: an analysis of maternal and fetal body composition using magnetic resonance imaging.

INTRODUCTION: Maternal body composition may influence fetal body composition. OBJECTIVE: The objective of this pilot study was to investigate the relationship between maternal and fetal body composition. METHODS: Three pregnant women cohorts were studied: healthy, gestational diabetes (GDM), and fetal growth restriction (FGR). Maternal body composition (visceral adipose tissue volume (VAT), subcutaneous adipose tissue volume (SAT), pancreatic and hepatic proton-density fat fraction (PDFF) and fetal body composition (abdominal SAT and hepatic PDFF) were measured using MRI between 30 to 36 weeks gestation. RESULTS: Compared to healthy and FGR fetuses, GDM fetuses had greater hepatic PDFF (5.2 [4.2, 5.5]% vs. 3.2 [3, 3.3]% vs. 1.9 [1.4, 3.7]%, p = 0.004). Fetal hepatic PDFF was associated with maternal SAT (r = 0.47, p = 0.02), VAT (r = 0.62, p = 0.002), and pancreatic PDFF (r = 0.54, p = 0.008). When controlling for maternal SAT, GDM increased fetal hepatic PDFF by 0.9 ([0.51, 1.3], p = 0.001). CONCLUSION: In this study, maternal SAT, VAT, and GDM status were positively associated with fetal hepatic PDFF.

Free-breathing radial magnetic resonance elastography of the liver in children at 3 T: a pilot study.

BACKGROUND: Magnetic resonance (MR) elastography of the liver measures hepatic stiffness, which correlates with the histopathological staging of liver fibrosis. Conventional Cartesian gradient-echo (GRE) MR elastography requires breath-holding, which is challenging for children. Non-Cartesian radial free-breathing MR elastography is a potential solution to this problem. OBJECTIVE: To investigate radial free-breathing MR elastography for measuring hepatic stiffness in children. MATERIALS AND METHODS: In this prospective pilot study, 14 healthy children and 9 children with liver disease were scanned at 3 T using 2-D Cartesian GRE breath-hold MR elastography (22 s/slice) and 2-D radial GRE free-breathing MR elastography (163 s/slice). Each sequence was acquired twice. Agreement in the stiffness measurements was evaluated using Lin's concordance correlation coefficient (CCC) and within-subject mean difference. The repeatability was assessed using the within-subject coefficient of variation and intraclass correlation coefficient (ICC). RESULTS: Fourteen healthy children and seven children with liver disease completed the study. Median (±interquartile range) normalized measurable liver areas were 62.6% (±26.4%) and 44.1% (±39.6%) for scan 1, and 60.3% (±21.8%) and 43.9% (±44.2%) for scan 2, for Cartesian and radial techniques, respectively. Hepatic stiffness from the Cartesian and radial techniques had close agreement with CCC of 0.89 and 0.94, and mean difference of 0.03 kPa and -0.01 kPa, for scans 1 and 2. Cartesian and radial techniques achieved similar repeatability with within-subject coefficient of variation=1.9% and 3.4%, and ICC=0.93 and 0.92, respectively. CONCLUSION: In this pilot study, radial free-breathing MR elastography was repeatable and in agreement with Cartesian breath-hold MR elastography in children.

Interstitial Optical Monitoring of Focal Laser Ablation.

Focal laser ablation is a minimally invasive method of treating cancerous lesions in organs such as prostate, liver and brain. Oncologic control is achieved by inducing hyperthermia throughout the target while minimizing damage to surrounding tissue. Consequently, successful clinical outcomes are contingent upon achieving desired ablation volumes. Magnetic resonance thermometry is frequently used to monitor the formation of the induced thermal damage zone and inform the decision to terminate energy delivery. However, due to the associated cost and complexity there is growing interest in the development of alternative approaches. Here we investigate the utility of real-time interstitial interrogation of laser-tissue interaction as an inexpensive alternative monitoring modality that provides direct assessment of tissue coagulation without the need for organ specific calibration. The optical contrast mechanism was determined using a Monte Carlo model. Subsequently, four interstitial probe designs were manufactured and assessed in a tissue mimicking phantom under simultaneous magnetic resonance imaging. Finally, the optimal probe design was evaluated in ex vivo bovine muscle. It was found to be capable of providing sufficient feedback to achieve pre-defined ablation radii in the range 4-7 mm with a mean absolute error of 0.3 mm. This approach provides an inexpensive monitoring modality that may facilitate widespread adoption of focal laser ablation.

Free-breathing 3D stack-of-radial MRI quantification of liver fat and R2* in adults with fatty liver disease.

PURPOSE: To investigate the agreement, intra-session repeatability, and inter-reader agreement of liver proton-density fat fraction (PDFF) and R2* quantification using free-breathing 3D stack-of-radial MRI, with and without self-gated motion compensation, compared to reference breath-hold techniques in subjects with fatty liver disease (FLD). METHODS: In this institutional review board-approved prospective study, thirty-eight adults with FLD and/or iron overload (24 male, 58 ± 12 years) were imaged at 3T using free-breathing stack-of-radial MRI, breath-hold 3D Cartesian MRI, and breath-hold single-voxel MR spectroscopy (SVS). Each sequence was acquired twice in random order. To assess agreement compared to reference breath-hold techniques, the dependency of liver PDFF and/or R2* quantification on the sequence, radial sampling factor, and radial self-gating temporal resolution was assessed by calculating the Bayesian mean difference (MDB) of the posteriors. Intra-session repeatability and inter-reader agreement (two independent readers) were assessed by the coefficient of repeatability (CR) and intraclass correlation coefficient (ICC), respectively. RESULTS: Thirty-five participants (21 male, 57 ± 12 years) were included for analysis. Both free-breathing radial MRI techniques (with and without self-gating) achieved ICC ≥ 0.92 for quantifying PDFF and R2*, and quantified PDFF with MDB < 1.2% compared to breath-hold techniques. Free-breathing radial MRI required self-gating to accurately quantify R2* (MDB < 10s-1 with self-gating; MDB < 50s-1 without self-gating). The radial sampling factor affected PDFF and R2* quantification while the radial self-gating temporal resolution only affected R2* quantification. Repeated self-gated free-breathing radial MRI scans achieved CR < 3% and CR < 27 s-1 for PDFF and R2*, respectively. CONCLUSION: A free-breathing stack-of-radial MRI technique with self-gating demonstrated agreement, repeatability, and inter-reader agreement compared to reference breath-hold techniques for quantification of liver PDFF and R2* in adults with FLD.

Accelerated k-space shift calibration for free-breathing stack-of-radial MRI quantification of liver fat and R2∗.

PURPOSE: To develop an accelerated k-space shift calibration method for free-breathing 3D stack-of-radial MRI quantification of liver proton-density fat fraction (PDFF) and R2∗ . METHODS: Accelerated k-space shift calibration was developed to partially skip acquisition of k-space shift data in the through-plane direction then interpolate in processing, as well as to reduce the in-plane averages. A multi-echo stack-of-radial sequence with the baseline calibration was evaluated on a phantom versus vendor-provided reference-standard PDFF and R2∗ values at 1.5T, and in 13 healthy subjects and 5 clinical subjects at 3T with respect to reference-standard breath-hold Cartesian acquisitions. PDFF and R2∗ maps were calculated with different calibration acceleration factors offline and compared to reference-standard values using Bland-Altman analysis. Bias and uncertainty were evaluated using normal distribution and Bayesian probability of difference (P < .05 considered significant). RESULTS: Bland-Altman plots of phantom and in vivo data showed that substantial acceleration was highly feasible in both through-plane and in-plane directions. Compared to the baseline calibration without acceleration, Bayesian analysis revealed no significant differences on biases and uncertainties of PDFF and R2∗ measurements with all acceleration methods in this study, except the method with through-plane acceleration equaling slices and averages equaling 20 for PDFF and R2∗ (both P < .001) for the phantom. A six-fold reduction in equivalent calibration acquisition time (time saving ≥25 s and ≥80.7%) was achieved using recommended acceleration factors for the in vivo protocols in this study. CONCLUSION: This proposed method may allow accelerated calibration for free-breathing stack-of-radial MRI PDFF and R2∗ mapping.

3D Neural Networks for Visceral and Subcutaneous Adipose Tissue Segmentation using Volumetric Multi-Contrast MRI.

Individuals with obesity have larger amounts of visceral (VAT) and subcutaneous adipose tissue (SAT) in their body, increasing the risk for cardiometabolic diseases. The reference standard to quantify SAT and VAT uses manual annotations of magnetic resonance images (MRI), which requires expert knowledge and is time-consuming. Although there have been studies investigating deep learning-based methods for automated SAT and VAT segmentation, the performance for VAT remains suboptimal (Dice scores of 0.43 to 0.89). Previous work had key limitations of not fully considering the multi-contrast information from MRI and the 3D anatomical context, which are critical for addressing the complex spatially varying structure of VAT. An additional challenge is the imbalance between the number and distribution of pixels representing SAT/VAT. This work proposes a network based on 3D U-Net that utilizes the full field-of-view volumetric T1-weighted, water, and fat images from dual-echo Dixon MRI as the multi-channel input to automatically segment SAT and VAT in adults with overweight/obesity. In addition, this work extends the 3D U-Net to a new Attention-based Competitive Dense 3D U-Net (ACD 3D U-Net) trained with a class frequency-balancing Dice loss (FBDL). In an initial testing dataset, the proposed 3D U-Net and ACD 3D U-Net with FBDL achieved 3D Dice scores of (mean ± standard deviation) 0.99 ±0.01 and 0.99±0.01 for SAT, and 0.95±0.04 and 0.96 ±0.04 for VAT, respectively, compared to manual annotations. The proposed 3D networks had rapid inference time (<60 ms/slice) and can enable automated segmentation of SAT and VAT.Clinical relevance- This work developed 3D neural networks to automatically, accurately, and rapidly segment visceral and subcutaneous adipose tissue on MRI, which can help to characterize the risk for cardiometabolic diseases such as diabetes, elevated glucose levels, and hypertension.

Responsive Nanoparticles to Enable a Focused Ultrasound-Stimulated Magnetic Resonance Imaging Spotlight.

Magnetic resonance imaging (MRI)-guided high-intensity focused ultrasound (HIFU) has been applied as a therapeutic tool in the clinic, and enhanced MRI contrast for depiction of target tissues will improve the precision and applicability of HIFU therapy. This work presents a "spotlight MRI" contrast enhancement technique, which combines four essential components: periodic HIFU stimulation, strong modulation of T1 caused by HIFU, rapid MRI signal collection, and spotlight MRI spectral signal processing. The T1 modulation is enabled by a HIFU-responsive nanomaterial based on mesoporous silica nanoparticles with Pluronic polymers (Poloxamers) and MRI contrast agents attached. With periodic HIFU stimulation in a precisely defined region containing the nanomaterial, strong periodic MRI T1-weighted signal changes are generated. Rapid MRI signal collection of the periodic signal changes is realized by a rapid dynamic 3D MRI technique, and spotlight MRI spectral signal processing creates modulation enhancement maps (MEM) that suppress background signal and spotlight the spatial location with nanomaterials experiencing HIFU stimulation. In particular, a framework is presented to analyze the trade-offs between different parameter choices for the signal processing method. The optimal parameter choices under a specific experimental setting achieved MRI contrast enhancement of more than 2 orders of magnitude at the HIFU focal point, compared to controls.

Right Ventricular Function and T1-Mapping in Boys With Duchenne Muscular Dystrophy.

BACKGROUND: Clinical management of boys with Duchenne muscular dystrophy (DMD) relies on in-depth understanding of cardiac involvement, but right ventricular (RV) structural and functional remodeling remains understudied. PURPOSE: To evaluate several analysis methods and identify the most reliable one to measure RV pre- and postcontrast T1 (RV-T1) and to characterize myocardial remodeling in the RV of boys with DMD. STUDY TYPE: Prospective. POPULATION: Boys with DMD (N = 27) and age-/sex-matched healthy controls (N = 17) from two sites. FIELD STRENGTH/SEQUENCE: 3.0 T using balanced steady state free precession, motion-corrected phase sensitive inversion recovery and modified Look-Locker inversion recovery sequences. ASSESSMENT: Biventricular mass (Mi), end-diastolic volume (EDVi) and ejection fraction (EF) assessment, tricuspid annular excursion (TAE), late gadolinium enhancement (LGE), pre- and postcontrast myocardial T1 maps. The RV-T1 reliability was assessed by three observers in four different RV regions of interest (ROI) using intraclass correlation (ICC). STATISTICAL TESTS: The Wilcoxon rank sum test was used to compare RV-T1 differences between DMD boys with negative LGE(-) or positive LGE(+) and healthy controls. Additionally, correlation of precontrast RV-T1 with functional measures was performed. A P-value <0.05 was considered statistically significant. RESULTS: A 1-pixel thick RV circumferential ROI proved most reliable (ICC > 0.91) for assessing RV-T1. Precontrast RV-T1 was significantly higher in boys with DMD compared to controls. Both LGE(-) and LGE(+) boys had significantly elevated precontrast RV-T1 compared to controls (1543 [1489-1597] msec and 1550 [1402-1699] msec vs. 1436 [1399-1473] msec, respectively). Compared to healthy controls, boys with DMD had preserved RVEF (51.8 [9.9]% vs. 54.2 [7.2]%, P = 0.31) and significantly reduced RVMi (29.8 [9.7] g vs. 48.0 [15.7] g), RVEDVi (69.8 [29.7] mL/m2 vs. 89.1 [21.9] mL/m2 ), and TAE (22.0 [3.2] cm vs. 26.0 [4.7] cm). Significant correlations were found between precontrast RV-T1 and RVEF (ß = -0.48%/msec) and between LV-T1 and LVEF (ß = -0.51%/msec). DATA CONCLUSION: Precontrast RV-T1 is elevated in boys with DMD compared to healthy controls and is negatively correlated with RVEF. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Deep Learning-Based Parameter Mapping with Uncertainty Estimation for Fat Quantification using Accelerated Free-Breathing Radial MRI.

Deep learning has been applied to remove artifacts from undersampled MRI and to replace time-consuming signal fitting in quantitative MRI, but these have usually been treated as separate tasks, which does not fully exploit the shared information. This work proposes a new two-stage framework that completes these two tasks in a concerted approach and also estimates the pixel-wise uncertainty levels. Results from accelerated free-breathing radial MRI for liver fat quantification demonstrate that the proposed framework can achieve high image quality from undersampled radial data, high accuracy for liver fat quantification, and detect uncertainty caused by noisy input data. The proposed framework achieved 3-fold acceleration to <1 min scan time and reduced the computational time for signal fitting to <100 ms/slice in free-breathing liver fat quantification.

Reproducibility of Left Ventricular CINE DENSE Strain in Pediatric Subjects with Duchenne Muscular Dystrophy.

Cardiomyopathy is the leading cause of mortality in boys with Duchenne muscular dystrophy (DMD). Left ventricular (LV) peak mid-wall circumferential strain (Ecc) is a sensitive early biomarker for evaluating both the subtle and variable onset and the progression of cardiomyopathy in pediatric subjects with DMD. Cine Displacement Encoding with Stimulated Echoes (DENSE) has proven sensitive to changes in Ecc, but its reproducibility has not been reported in a pediatric cohort or a DMD cohort. The objective was to quantify the intra-observer repeatability, and intra-exam and inter-observer reproducibility of global and regional Ecc derived from cine DENSE in DMD patients (N = 10) and age-and sex-matched controls (N = 10). Global and regional Ecc measures were considered reproducible in the intra-exam, intra-observer, and inter-observer comparisons. Intra-observer repeatability was highest, followed by intra-exam reproducibility and then inter-observer reproducibility. The smallest detectable change in Ecc was 0.01 for the intra-observer comparison, which is below the previously reported yearly decrease of 0.013 ± 0.015 in Ecc in DMD patients.

Free-Breathing Volumetric Liver R2* and Proton Density Fat Fraction Quantification in Pediatric Patients Using Stack-of-Radial MRI With Self-Gating Motion Compensation.

BACKGROUND: Stack-of-radial multiecho gradient-echo MRI is promising for free-breathing liver R2* quantification and may benefit children. PURPOSE: To validate stack-of-radial MRI with self-gating motion compensation in phantoms, and to evaluate it in children. STUDY TYPE: Prospective. PHANTOMS: Four vials with different R2* driven by a motion stage. SUBJECTS: Sixteen pediatric patients with suspected nonalcoholic fatty liver disease or steatohepatitis (five females, 13 ± 4 years, body mass index 29.2 ± 8.6 kg/m2 ). FIELD STRENGTH/SEQUENCES: Stack-of-radial, and 2D and 3D Cartesian multiecho gradient-echo sequences at 3T. ASSESSMENT: Ungated and gated stack-of-radial proton density fat fraction (PDFF) and R2* maps were reconstructed without and with self-gating motion compensation. Stack-of-radial R2* measurements of phantoms without and with motion were validated against reference 2D Cartesian results of phantoms without motion. In subjects, free-breathing stack-of-radial and reference breath-hold 3D Cartesian were acquired. Subject inclusion for statistical analysis and region of interest placement were determined independently by two observers. STATISTICAL TESTS: Phantom results were fitted with a weighted linear model. Demographic differences between excluded and included subjects were tested by multivariate analysis of variance. PDFF and R2* measurements were compared using Bland-Altman analysis. Interobserver agreement was assessed by the intraclass correlation coefficient (ICC). RESULTS: Ungated stack-of-radial R2* inside moving phantom vials showed a significant positive bias of 64.3 s-1 (P < 0.00001), unlike gated results (P > 0.31). Subject inclusion decisions for statistical analysis from two observers were consistent. No significant differences were found between four excluded and 12 included subjects (P = 0.14). Compared to breath-hold Cartesian, ungated and gated free-breathing stack-of-radial exhibited mean R2* differences of 18.5 s-1 and 3.6 s-1 . Mean PDFF differences were 1.1% and 1.0% for ungated and gated measurements, respectively. Interobserver agreement was excellent (ICC for PDFF = 0.99, ICC for R2* = 0.90; P < 0.0003). DATA CONCLUSION: Stack-of-radial MRI with self-gating motion compensation seems to allow free-breathing liver R2* and PDFF quantification in children. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.