Publications by authors named "Shreyas S Vasanawala"

114 Publications

Quantification of the Hemodynamic Changes of Cirrhosis with Free-Breathing Self-Navigated MRI.

J Magn Reson Imaging 2021 Feb 16. Epub 2021 Feb 16.

Department of Radiology, University of California San Diego, La Jolla, California, USA.

Background: Non-invasive assessment of the hemodynamic changes of cirrhosis might help guide management of patients with liver disease but are currently limited.

Purpose: To determine whether free-breathing 4D flow MRI can be used to quantify the hemodynamic effects of cirrhosis and introduce hydraulic circuit indexes of severity.

Study Type: Retrospective.

Population: Forty-seven patients including 26 with cirrhosis.

Field Strength/sequence: 3 T/free-breathing 4D flow MRI with soft gating and golden-angle view ordering.

Assessment: Measurements of the supra-celiac abdominal aorta, supra-renal abdominal aorta (SRA), celiac trunk (CeT), superior mesenteric artery (SMA), splenic artery (SpA), common hepatic artery (CHA), portal vein (PV), and supra-renal inferior vena cava (IVC) were made by two radiologists. Measures of hepatic vascular resistance (hepatic arterial relative resistance [HARR]; portal resistive index [PRI]) were proposed and calculated.

Statistical Analysis: Bland-Altman, Pearson's correlation, Tukey's multiple comparison, and Cohen's kappa. P < 0.05 was considered significant.

Results: Forty-four of 47 studies yielded adequate image quality for flow quantification (94%). Arterial structures showed high inter-reader concordance (range; ρ = 0.948-0.987) and the IVC (ρ = 0.972), with moderate concordance in the PV (ρ = 0.866). Conservation of mass analysis showed concordance between large vessels (SRA vs. IVC; ρ = 0.806), small vessels (celiac vs. CHA + SpA; ρ = 0.939), and across capillary beds (CeT + SMA vs. PV; ρ = 0.862). Splanchnic flow was increased in patients with portosystemic shunting (PSS) relative to control patients and patients with cirrhosis without PSS (P < 0.05, difference range 0.11-0.68 liter/m). HARR was elevated and PRI was decreased in patients with PSS (3.55 and 1.49, respectively) compared to both the control (2.11/3.18) and non-PSS (2.11/2.35) cohorts.

Data Conclusion: 4D flow MRI with self-navigation was technically feasible, showing promise in quantifying the hemodynamic effects of cirrhosis. Proposed quantitative metrics of hepatic vascular resistance correlated with PSS.

Level Of Evidence: 3 TECHNICAL EFFICACY STAGE: 2.
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http://dx.doi.org/10.1002/jmri.27488DOI Listing
February 2021

Free-breathing mapping of hepatic iron overload in children using 3D multi-echo UTE cones MRI.

Magn Reson Med 2021 May 11;85(5):2608-2621. Epub 2021 Jan 11.

Departments of Radiology and Electrical Engineering, Stanford University, Magnetic Resonance Systems Research Lab (MRSRL), Stanford, California, USA.

Purpose: To enable motion-robust, ungated, free-breathing mapping of hepatic iron overload in children with 3D multi-echo UTE cones MRI.

Methods: A golden-ratio re-ordered 3D multi-echo UTE cones acquisition was developed with chemical-shift encoding (CSE). Multi-echo complex-valued source images were reconstructed via gridding and coil combination, followed by confounder-corrected (=1/ ) mapping. A phantom containing 15 different concentrations of gadolinium solution (0-300 mM) was imaged at 3T. 3D multi-echo UTE cones with an initial TE of 0.036 ms and Cartesian CSE-MRI (IDEAL-IQ) sequences were performed. With institutional review board approval, 85 subjects (81 pediatric patients with iron overload + 4 healthy volunteers) were imaged at 3T using 3D multi-echo UTE cones with free breathing (FB cones), IDEAL-IQ with breath holding (BH Cartesian), and free breathing (FB Cartesian). Overall image quality of maps was scored by 2 blinded experts and compared by a Wilcoxon rank-sum test. For each pediatric subject, the paired maps were assessed to determine if a corresponding artifact-free 15 mm region-of-interest (ROI) could be identified at a mid-liver level on both images. Agreement between resulting quantification from FB cones and BH/FB Cartesian was assessed with Bland-Altman and linear correlation analyses.

Results: ROI-based regression analysis showed a linear relationship between gadolinium concentration and in IDEAL-IQ (y = 8.83x - 52.10, R = 0.995) as well as in cones (y = 9.19x - 64.16, R = 0.992). ROI-based Bland-Altman analysis showed that the mean difference (MD) was 0.15% and the SD was 5.78%. However, IDEAL-IQ measurements beyond 200 mM substantially deviated from a linear relationship for IDEAL-IQ (y = 5.85x + 127.61, R = 0.827), as opposed to cones (y = 10.87x - 166.96, R = 0.984). In vivo, FB cones had similar image quality with BH and FB Cartesian in 15 and 42 cases, respectively. FB cones had better image quality scores than BH and FB Cartesian in 3 and 21 cases, respectively, where BH/FB Cartesian exhibited severe ghosting artifacts. ROI-based Bland-Altman analyses were 2.23% (MD) and 6.59% (SD) between FB cones and BH Cartesian and were 0.21% (MD) and 7.02% (SD) between FB cones and FB Cartesian, suggesting a good agreement between FB cones and BH (FB) Cartesian . Strong linear relationships were observed between BH Cartesian and FB cones (y = 1.00x + 1.07, R = 0.996) and FB Cartesian and FB cones (y = 0.98x + 1.68, R = 0.999).

Conclusion: Golden-ratio re-ordered 3D multi-echo UTE Cones MRI enabled motion-robust, ungated, and free-breathing mapping of hepatic iron overload, with comparable measurements and image quality to BH Cartesian, and better image quality than FB Cartesian.
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http://dx.doi.org/10.1002/mrm.28610DOI Listing
May 2021

Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging.

Proc IEEE Int Symp Biomed Imaging 2020 Apr 22;2020:1056-1059. Epub 2020 May 22.

Department of Radiology.

Accelerating data acquisition in magnetic resonance imaging (MRI) has been of perennial interest due to its prohibitively slow data acquisition process. Recent trends in accelerating MRI employ data-centric deep learning frameworks due to its fast inference time and 'one-parameter-fit-all' principle unlike in traditional model-based acceleration techniques. Unrolled deep learning framework that combines the deep priors and model knowledge are robust compared to naive deep learning based framework. In this paper, we propose a novel multi-scale unrolled deep learning framework which learns deep image priors through multi-scale CNN and is combined with unrolled framework to enforce data-consistency and model knowledge. Essentially, this framework combines the best of both learning paradigms:model-based and data-centric learning paradigms. Proposed method is verified using several experiments on numerous data sets.
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http://dx.doi.org/10.1109/isbi45749.2020.9098684DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717063PMC
April 2020

DIAGNOSTIC IMAGE QUALITY ASSESSMENT AND CLASSIFICATION IN MEDICAL IMAGING: OPPORTUNITIES AND CHALLENGES.

Proc IEEE Int Symp Biomed Imaging 2020 Apr 22;2020:337-340. Epub 2020 May 22.

Department of Radiology, Stanford University.

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.
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http://dx.doi.org/10.1109/isbi45749.2020.9098735DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710391PMC
April 2020

Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks.

IEEE Signal Process Mag 2020 Jan 17;37(1):111-127. Epub 2020 Jan 17.

Stanford University.

Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover high-resolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve overall patient experience. However, CS has several shortcomings which limit its clinical translation such as: 1) artifacts arising from inaccurate sparse modelling assumptions, 2) extensive parameter tuning required for each clinical application, and 3) clinically infeasible reconstruction times. Recently, CS has been extended to incorporate deep neural networks as a way of learning complex image priors from historical exam data. Commonly referred to as unrolled neural networks, these techniques have proven to be a compelling and practical approach to address the challenges of sparse CS. In this tutorial, we will review the classical compressed sensing formulation and outline steps needed to transform this formulation into a deep learning-based reconstruction framework. Supplementary open source code in Python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying unrolled neural networks in the clinical setting.
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http://dx.doi.org/10.1109/MSP.2019.2950433DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664163PMC
January 2020

Wasserstein GANs for MR Imaging: From Paired to Unpaired Training.

IEEE Trans Med Imaging 2021 Jan 29;40(1):105-115. Epub 2020 Dec 29.

Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network - a cascade of convolutional and data consistency layers. The discriminator is also a multilayer CNN that plays the role of a critic scoring the quality of reconstructed images based on the Wasserstein distance. Our experiments with knee MRI datasets demonstrate that the proposed unpaired training enables diagnostic-quality reconstruction when high-quality image labels are not available for the input types of interest, or when the amount of labels is small. In addition, our adversarial training scheme can achieve better image quality (as rated by expert radiologists) compared with the paired training schemes with pixel-wise loss.
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http://dx.doi.org/10.1109/TMI.2020.3022968DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797774PMC
January 2021

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.

J Magn Reson Imaging 2020 Aug 24. Epub 2020 Aug 24.

Department of Radiology, Stanford University, Stanford, California, USA.

Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
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http://dx.doi.org/10.1002/jmri.27331DOI Listing
August 2020

Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.

Magn Reson Med 2021 01 22;85(1):152-167. Epub 2020 Jul 22.

Department of Radiology, Stanford University, Stanford, CA, USA.

Purpose: To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data.

Methods: We propose DL-ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE-related field-of-view (FOV) limitations in previously proposed deep learning-based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art parallel imaging and compressed sensing method known as -ESPIRiT. The reconstruction accuracy of both methods is evaluated on retrospectively undersampled datasets (R = 12) with respect to standard image quality metrics as well as automatic deep learning-based segmentations of left ventricular volumes. Feasibility of DL-ESPIRiT is demonstrated on two prospectively undersampled datasets acquired in a single heartbeat per slice.

Results: The (2+1)D DL-ESPIRiT method produces higher fidelity image reconstructions when compared to -ESPIRiT reconstructions with respect to standard image quality metrics (P < .001). As a result of improved image quality, segmentations made from (2+1)D DL-ESPIRiT images are also more accurate than segmentations from -ESPIRiT images.

Conclusions: DL-ESPIRiT synergistically combines a robust parallel imaging model and deep learning-based priors to produce high-fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof-of-concept is shown, further experiments are necessary to determine the efficacy of DL-ESPIRiT in prospectively undersampled data.
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http://dx.doi.org/10.1002/mrm.28420DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722220PMC
January 2021

Rosette Trajectories Enable Ungated, Motion-Robust, Simultaneous Cardiac and Liver T * Iron Assessment.

J Magn Reson Imaging 2020 12 26;52(6):1688-1698. Epub 2020 May 26.

Department of Radiology, Stanford University, Palo Alto, California, USA.

Background: Quantitative T * MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T * estimates.

Purpose: To develop and evaluate a motion-robust, simultaneous cardiac and liver T * imaging approach using non-Cartesian, rosette sampling and a model-based reconstruction as compared to clinical-standard Cartesian MRI.

Study Type: Prospective.

Phantom/population: Six ferumoxytol-containing phantoms (26-288 μg/mL). Eight healthy subjects and 18 patients referred for clinically indicated iron overload assessment.

Field Strength/sequence: 1.5T, 2D Cartesian and rosette gradient echo (GRE) ASSESSMENT: GRE T * values were validated in ferumoxytol phantoms. In healthy subjects, test-retest and spatial coefficient of variation (CoV) analysis was performed during three breathing conditions. Cartesian and rosette T * were compared using correlation and Bland-Altman analysis. Images were rated by three experienced radiologists on a 5-point scale.

Statistical Tests: Linear regression, analysis of variance (ANOVA), and paired Student's t-testing were used to compare reproducibility and variability metrics in Cartesian and rosette scans. The Wilcoxon rank test was used to assess reader score comparisons and reader reliability was measured using intraclass correlation analysis.

Results: Rosette R2* (1/T *) was linearly correlated with ferumoxytol concentration (r = 1.00) and not significantly different than Cartesian values (P = 0.16). During breath-holding, ungated rosette liver and heart T * had lower spatial CoV (liver: 18.4 ± 9.3% Cartesian, 8.8% ± 3.4% rosette, P = 0.02, heart: 37.7% ± 14.3% Cartesian, 13.4% ± 1.7% rosette, P = 0.001) and higher-quality scores (liver: 3.3 [3.0-3.6] Cartesian, 4.7 [4.1-4.9] rosette, P = 0.005, heart: 3.0 [2.3-3] Cartesian, 4.5 [3.8-5.0] rosette, P = 0.005) compared to Cartesian values. During free-breathing and failed breath-holding, Cartesian images had very poor to average image quality with significant artifacts, whereas rosette remained very good, with minimal artifacts (P = 0.001).

Data Conclusion: Rosette k-sampling with a model-based reconstruction offers a clinically useful motion-robust T * mapping approach for iron quantification. J. MAGN. RESON. IMAGING 2020;52:1688-1698.
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http://dx.doi.org/10.1002/jmri.27196DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699670PMC
December 2020

Extreme MRI: Large-scale volumetric dynamic imaging from continuous non-gated acquisitions.

Magn Reson Med 2020 10 9;84(4):1763-1780. Epub 2020 Apr 9.

Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.

Purpose: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging.

Theory And Methods: The problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three-dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (a) A compressed representation using multiscale low-rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (b) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory-resolved reconstruction for pulmonary imaging.

Results: The proposed technique shows transient dynamics that are not seen in gating-based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features.

Conclusions: We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.
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http://dx.doi.org/10.1002/mrm.28235DOI Listing
October 2020

Invited Commentary: Reducing Sedation and Anesthesia in Pediatric Patients at MRI.

Radiographics 2020 Mar-Apr;40(2):503-504. Epub 2020 Feb 7.

Department of Radiology, Stanford University, Stanford, California.

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http://dx.doi.org/10.1148/rg.2020190211DOI Listing
June 2020

4D flow vs. 2D cardiac MRI for the evaluation of pulmonary regurgitation and ventricular volume in repaired tetralogy of Fallot: a retrospective case control study.

Int J Cardiovasc Imaging 2020 Apr 1;36(4):657-669. Epub 2020 Jan 1.

Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.

Lengthy exams and breath-holding limit the use of pediatric cardiac MRI (CMR). 3D time-resolved flow MRI (4DF) is a free-breathing, single-sequence exam that obtains magnitude (anatomic) and phase contrast (PC) data. We compare the accuracy of gadobenate dimeglumine-enhanced 4DF on a 1.5 T magnet to 2D CMR in children with repaired tetralogy of Fallot (rTOF) to measure pulmonary net flow (PNF) as a reflection of pulmonary regurgitation, forward flow (FF) and ventricular volumetry. Thirty-four consecutive cases were included. 2D PCs were obtained at the valve level. Using 4DF, we measured PNF at the valve and at the main and branch pulmonary arteries. PNF measured at the valve by 4DF demonstrated the strongest correlation (r = 0.87, p < 0.001) and lowest mean difference (3.5 ± 9.4 mL/beat) to aortic net flow (ANF). Semilunar FF and stroke volume of the respective ventricle demonstrated moderate-strong correlation by 4DF (r = 0.66-0.81, p < 0.001) and strong correlation by 2D (r = 0.81-0.84, p < 0.001) with similar correlations and mean differences between techniques (p > 0.05). Ventricular volumes correlated strongly between 2D and 4DF (r = 0.75-0.96, p < 0.001), though 4DF overestimated right ventricle volumes by 11.8-19.2 mL/beat. Inter-rater reliability was excellent for 2D and 4DF volumetry (ICC = 0.91-0.99). Ejection fraction moderately correlated (r = 0.60-0.75, p < 0.001) with better reliability by 4DF (ICC: 0.80-0.85) than 2D (ICC: 0.69-0.89). 4DF exams were shorter than 2D (9 vs. 71 min, p < 0.001). 4DF provides highly reproducible and accurate measurements of flow with slight overestimation of RV volumes compared to 2D in pediatric rTOF. 4DF offers important advantages in this population with long-term monitoring needs.
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http://dx.doi.org/10.1007/s10554-019-01751-1DOI Listing
April 2020

Reversal of epigenetic aging and immunosenescent trends in humans.

Aging Cell 2019 12 8;18(6):e13028. Epub 2019 Sep 8.

Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

Epigenetic "clocks" can now surpass chronological age in accuracy for estimating biological age. Here, we use four such age estimators to show that epigenetic aging can be reversed in humans. Using a protocol intended to regenerate the thymus, we observed protective immunological changes, improved risk indices for many age-related diseases, and a mean epigenetic age approximately 1.5 years less than baseline after 1 year of treatment (-2.5-year change compared to no treatment at the end of the study). The rate of epigenetic aging reversal relative to chronological age accelerated from -1.6 year/year from 0-9 month to -6.5 year/year from 9-12 month. The GrimAge predictor of human morbidity and mortality showed a 2-year decrease in epigenetic vs. chronological age that persisted six months after discontinuing treatment. This is to our knowledge the first report of an increase, based on an epigenetic age estimator, in predicted human lifespan by means of a currently accessible aging intervention.
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http://dx.doi.org/10.1111/acel.13028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6826138PMC
December 2019

Data-driven self-calibration and reconstruction for non-cartesian wave-encoded single-shot fast spin echo using deep learning.

J Magn Reson Imaging 2020 03 19;51(3):841-853. Epub 2019 Jul 19.

Department of Radiology, Stanford University, Stanford, California, USA.

Background: Current self-calibration and reconstruction methods for wave-encoded single-shot fast spin echo imaging (SSFSE) requires long computational time, especially when high accuracy is needed.

Purpose: To develop and investigate the clinical feasibility of data-driven self-calibration and reconstruction of wave-encoded SSFSE imaging for computation time reduction and quality improvement.

Study Type: Prospective controlled clinical trial.

Subjects: With Institutional Review Board approval, the proposed method was assessed on 29 consecutive adult patients (18 males, 11 females, range, 24-77 years).

Field Strength/sequence: A wave-encoded variable-density SSFSE sequence was developed for clinical 3.0T abdominal scans to enable 3.5× acceleration with full-Fourier acquisitions. Data-driven calibration of wave-encoding point-spread function (PSF) was developed using a trained deep neural network. Data-driven reconstruction was developed with another set of neural networks based on the calibrated wave-encoding PSF. Training of the calibration and reconstruction networks was performed on 15,783 2D wave-encoded SSFSE abdominal images.

Assessment: Image quality of the proposed data-driven approach was compared independently and blindly with a conventional approach using iterative self-calibration and reconstruction with parallel imaging and compressed sensing by three radiologists on a scale from -2 to 2 for noise, contrast, sharpness, artifacts, and confidence. Computation time of these two approaches was also compared.

Statistical Tests: Wilcoxon signed-rank tests were used to compare image quality and two-tailed t-tests were used to compare computation time with P values of under 0.05 considered statistically significant.

Results: An average 2.1-fold speedup in computation was achieved using the proposed method. The proposed data-driven self-calibration and reconstruction approach significantly reduced the perceived noise level (mean scores 0.82, P < 0.0001).

Data Conclusion: The proposed data-driven calibration and reconstruction achieved twice faster computation with reduced perceived noise, providing a fast and robust self-calibration and reconstruction for clinical abdominal SSFSE imaging.

Level Of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:841-853.
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http://dx.doi.org/10.1002/jmri.26871DOI Listing
March 2020

Simultaneous PET/MRI in the Evaluation of Breast and Prostate Cancer Using Combined Na[F] F and [F]FDG: a Focus on Skeletal Lesions.

Mol Imaging Biol 2020 04;22(2):397-406

Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA, 94305, USA.

Purpose: The purpose of this study is to prospectively evaluate the performance of sodium F]fluoride (Na[F]F)/2-deoxy-2-[F]fluoro-D-glucose ([F]FDG) simultaneous time-of-flight enabled positron emission tomography (PET)/magnetic resonance imaging (MRI) for the detection of skeletal metastases in selected patients with advanced breast and prostate cancers.

Procedure: The institutional review board approved this HIPAA-compliant protocol. Written informed consent was obtained from each patient. A total of 74 patients (23 women and 51 men with breast and prostate cancer, respectively) referred for standard-of-care whole-body bone scintigraphy (WBBS) were enrolled in this prospective study. All patients underwent a [Tc]methyldiphosphonate ([Tc]MDP) WBBS followed by Na[F]F/[F]FDG PET/MRI. Lesions detected by each imaging modality were tabulated and a lesion-based and patient-based analysis was conducted.

Results: On a patient-based analysis, [Tc]MDP WBBS identified skeletal lesions in 37 patients and PET/MRI in 45 patients. On a lesion-based analysis, WBBS identified a total of 81 skeletal lesions, whereas PET/MRI identified 140 lesions. Additionally, PET/MRI showed extra-skeletal lesions in 19 patients, including lymph nodes (16), prostate (4) lung (3), and liver (2) lesions.

Conclusions: The ability of Na[F]F/[F]FDG PET/MRI to identify more skeletal lesions than Tc-MDP WBBS and to additionally identify extra-skeletal disease may be beneficial for patient care and represent an alternative to the single modalities performed separately. Na[F]F/[F]FDG PET/MRI is a promising approach for evaluation of skeletal and extra-skeletal lesions in a selected population of breast and prostate cancer patients.
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http://dx.doi.org/10.1007/s11307-019-01392-9DOI Listing
April 2020

How Often is the Dynamic Contrast Enhanced Score Needed in PI-RADS Version 2?

Curr Probl Diagn Radiol 2020 May - Jun;49(3):173-176. Epub 2019 May 10.

Department of Radiology, Stanford University, Stanford, CA.. Electronic address:

Background: Prostate imaging reporting and data system version 2 (PI-RADS v2) relegates dynamic contrast enhanced (DCE) imaging to a minor role. We sought to determine how often DCE is used in PI-RADS v2 scoring.

Materials And Methods: We retrospectively reviewed data from 388 patients who underwent prostate magnetic resonance imaging and subsequent biopsy from January 2016 through December 2017. In accordance with PI-RADS v2, DCE was deemed necessary if a peripheral-zone lesion had a diffusion-weighted imaging score of 3, or if a transition-zone lesion had a T2 score of 3 and diffusion-weighted imaging experienced technical failure. Receiver operating characteristic curve analysis assessed the accuracy of prostate-specific antigen density (PSAD) at different threshold values for differentiating lesions that would be equivocal with noncontrast technique. Accuracy of PSAD was compared to DCE using McNemar's test.

Results: Sixty-nine lesions in 62 patients (16%) required DCE for PI-RADS scoring. Biopsy of 10 (14%) of these lesions showed clinically significant cancer (Gleason score ≥7). In the subgroup of patients with equivocal lesions, those with clinically significant cancer had significantly higher PSADs than those with clinically insignificant lesions (means of 0.18 and 0.13 ng/mL/mL, respectively; P= 0.038). In this subgroup, there was no statistical difference in accuracy in determining clinically significant cancer between a PSAD threshold value of 0.13 and DCE (P= 0.25).

Conclusions: Only 16% of our patients needed DCE to generate the PI-RADS version 2 score, raising the possibility of limiting the initial screening prostate MRI to a noncontrast exam. PSAD may also be used to further decrease the need for or to replace DCE altogether.
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http://dx.doi.org/10.1067/j.cpradiol.2019.05.008DOI Listing
February 2021

Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.

Magn Reson Med 2019 10 22;82(4):1398-1411. Epub 2019 May 22.

Department of Radiology, Stanford University, Stanford, California.

Purpose: To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique.

Methods: A residual convolutional neural network to correct off-resonance artifacts (Off-ResNet) was trained with a prospective study of pediatric MRA exams. Each exam acquired a short readout scan (1.18 ms ± 0.38) and a long readout scan (3.35 ms ± 0.74) at 3 T. Short readout scans, with longer scan times but negligible off-resonance blurring, were used as reference images and augmented with additional off-resonance for supervised training examples. Long readout scans, with greater off-resonance artifacts but shorter scan time, were corrected by autofocus and Off-ResNet and compared with short readout scans by normalized RMS error, structural similarity index, and peak SNR. Scans were also compared by scoring on 8 anatomical features by two radiologists, using analysis of variance with post hoc Tukey's test and two one-sided t-tests. Reader agreement was determined with intraclass correlation.

Results: The total scan time for long readout scans was on average 59.3% shorter than short readout scans. Images from Off-ResNet had superior normalized RMS error, structural similarity index, and peak SNR compared with uncorrected images across ±1 kHz off-resonance (P < .01). The proposed method had superior normalized RMS error over -677 Hz to +1 kHz and superior structural similarity index and peak SNR over ±1 kHz compared with autofocus (P < .01). Radiologic scoring demonstrated that long readout scans corrected with Off-ResNet were noninferior to short readout scans (P < .05).

Conclusion: The proposed method can correct off-resonance artifacts from rapid long-readout 3D cones scans to a noninferior image quality compared with diagnostically standard short readout scans.
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http://dx.doi.org/10.1002/mrm.27825DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6626585PMC
October 2019

Targeted rapid knee MRI exam using T shuffling.

J Magn Reson Imaging 2019 06 13;49(7):e195-e204. Epub 2019 Jan 13.

Department of Radiology, Stanford University, Stanford, California, USA.

Background: MRI is commonly used to evaluate pediatric musculoskeletal pathologies, but same-day/near-term scheduling and short exams remain challenges.

Purpose: To investigate the feasibility of a targeted rapid pediatric knee MRI exam, with the goal of reducing cost and enabling same-day MRI access.

Study Type: A cost effectiveness study done prospectively.

Subjects: Forty-seven pediatric patients.

Field Strength/sequence: 3T. The 10-minute protocol was based on T Shuffling, a four-dimensional acquisition and reconstruction of images with variable T contrast, and a T 2D fast spin-echo (FSE) sequence. A distributed, compressed sensing-based reconstruction was implemented on a four-node high-performance compute cluster and integrated into the clinical workflow.

Assessment: In an Institutional Review Board-approved study with informed consent/assent, we implemented a targeted pediatric knee MRI exam for assessing pediatric knee pain. Pediatric patients were subselected for the exam based on insurance plan and clinical indication. Over a 2-year period, 47 subjects were recruited for the study and 49 MRIs were ordered. Date and time information was recorded for MRI referral, registration, and completion. Image quality was assessed from 0 (nondiagnostic) to 5 (outstanding) by two readers, and consensus was subsequently reached.

Statistical Tests: A Wilcoxon rank-sum test assessed the null hypothesis that the targeted exam times compared with conventional knee exam times were unchanged.

Results: Of the 49 cases, 20 were completed on the same day as exam referral. Median time from registration to exam completion was 18.7 minutes. Median reconstruction time for T Shuffling was reduced from 18.9 minutes to 95 seconds using the distributed implementation. Technical fees charged for the targeted exam were one-third that of the routine clinical knee exam. No subject had to return for additional imaging.

Data Conclusion: The targeted knee MRI exam is feasible and reduces the imaging time, cost, and barrier to same-day MRI access for pediatric patients.

Level Of Evidence: 2 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.
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http://dx.doi.org/10.1002/jmri.26600DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6551292PMC
June 2019

Evaluation of the routine use of pelvic MRI in women presenting with symptomatic uterine fibroids: When is pelvic MRI useful?

J Magn Reson Imaging 2019 06 5;49(7):e271-e281. Epub 2019 Jan 5.

Department of Radiology, Stanford University, Stanford, California, USA.

Background: Pelvic ultrasound (US) diagnosis of uterine fibroids may overlook coexisting gynecological conditions that contribute to women's symptoms.

Purpose: To determine the added value of pelvic MRI for women diagnosed with symptomatic fibroids by US, and to identify clinical factors associated with additional MRI findings.

Study Type: Retrospective observational study.

Population: In all, 367 consecutive women with fibroids diagnosed by US and referred to our multidisciplinary fibroid center between 2013-2017.

Field Strength/sequence: All patients had both pelvic US and MRI prior to their consultations. MRIs were performed at 1.5 T or 3 T and included multiplanar T -weighted sequences, and precontrast and postcontrast T -weighted imaging.

Assessment: Demographics, symptoms, uterine fibroid symptom severity scores, and health-related quality of life scores, as well as imaging findings were evaluated.

Statistical Tests: Patients were separated into two subgroups according to whether MRI provided additional findings to the initial US. Univariate and multivariate regression analyses were performed.

Results: Pelvic MRI provided additional information in 162 patients (44%; 95% confidence interval [CI] 39-49%). The most common significant findings were adenomyosis (22%), endometriosis (17%), and partially endocavitary fibroids (15%). Women with pelvic pain, health-related quality of life scores less than 30 out of 100, or multiple fibroids visualized on US had greater odds of additional MRI findings (odds ratio [OR] 1.68, 2.26, 1.63; P = 0.02, 0.004, 0.03, respectively), while nulliparous women had reduced odds (OR 0.55, P = 0.01). Patients with additional MRI findings were treated less often with uterine fibroid embolization (14% vs. 36%, P < 0.001) or MR-guided focused US (1% vs. 5%, P = 0.04), and more often with medical management (17% vs. 8%, P = 0.01).

Data Conclusion: Pelvic MRI revealed additional findings in more than 40% of women presenting with symptoms initially ascribed to fibroids by US. Further evaluation using MRI is particularly useful for parous women with pelvic pain, poor quality of life scores, and/or multiple fibroids.

Level Of Evidence: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019.
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http://dx.doi.org/10.1002/jmri.26620DOI Listing
June 2019

View-Sharing Artifact Reduction With Retrospective Compressed Sensing Reconstruction in the Context of Contrast-Enhanced Liver MRI for Hepatocellular Carcinoma (HCC) Screening.

J Magn Reson Imaging 2019 04 2;49(4):984-993. Epub 2018 Nov 2.

Stanford University, School of Medicine, Department of Radiology, Stanford, California, USA.

Background: View-sharing (VS) increases spatiotemporal resolution in dynamic contrast-enhanced (DCE) MRI by sharing high-frequency k-space data across temporal phases. This temporal sharing results in respiratory motion within any phase to propagate artifacts across all shared phases. Compressed sensing (CS) eliminates the need for VS by recovering missing k-space data from pseudorandom undersampling, reducing temporal blurring while maintaining spatial resolution.

Purpose: To evaluate a CS reconstruction algorithm on undersampled DCE-MRI data for image quality and hepatocellular carcinoma (HCC) detection.

Study Type: Retrospective.

Subjects: Fifty consecutive patients undergoing MRI for HCC screening (29 males, 21 females, 52-72 years).

Field Strength/sequence: 3.0T MRI. Multiphase 3D-SPGR T -weighted sequence undersampled in arterial phases with a complementary Poisson disc sampling pattern reconstructed with VS and CS algorithms.

Assessment: VS and CS reconstructions evaluated by blinded assessments of image quality and anatomic delineation on Likert scales (1-4 and 1-5, respectively), and HCC detection by OPTN/UNOS criteria including a diagnostic confidence score (1-5). Blinded side-by-side reconstruction comparisons for lesion depiction and overall series preference (-3-3).

Statistical Analysis: Two-tailed Wilcoxon signed rank tests for paired nonparametric analyses with Bonferroni-Holm multiple-comparison corrections. McNemar's test for differences in lesion detection frequency and transplantation eligibility.

Results: CS compared with VS demonstrated significantly improved contrast (mean 3.6 vs. 2.9, P < 0.0001) and less motion artifact (mean 3.6 vs. 3.2, P = 0.006). CS compared with VS demonstrated significantly improved delineations of liver margin (mean 4.5 vs. 3.8, P = 0.0002), portal veins (mean 4.5 vs. 3.7, P < 0.0001), and hepatic veins (mean 4.6 vs. 3.5, P < 0.0001), but significantly decreased delineation of hepatic arteries (mean 3.2 vs. 3.7, P = 0.004). No significant differences were seen in the other assessments.

Data Conclusion: Applying a CS reconstruction to data acquired for a VS reconstruction significantly reduces motion artifacts in a clinical DCE protocol for HCC screening.

Level Of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:984-993.
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http://dx.doi.org/10.1002/jmri.26276DOI Listing
April 2019

Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization.

Magn Reson Med 2019 02 22;81(2):1181-1190. Epub 2018 Oct 22.

Department of Electrical Engineering, Stanford University, Stanford, California.

Purpose: The goal of this work is to propose a motion robust reconstruction method for diffusion-weighted MRI that resolves shot-to-shot phase mismatches without using phase estimation.

Methods: Assuming that shot-to-shot phase variations are slowly varying, spatial-shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low-rank constraint on the spatial-shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method.

Results: The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase-estimation-based methods to achieve even higher image quality.

Conclusion: We introduced the shot-locally low-rank method, a reconstruction technique for multishot diffusion-weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.
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http://dx.doi.org/10.1002/mrm.27488DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289606PMC
February 2019

Conical ultrashort echo time (UTE) MRI in the evaluation of pediatric acute appendicitis.

Abdom Radiol (NY) 2019 01;44(1):22-30

Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA.

Purpose: Magnetic resonance imaging (MRI) sequences with conical k-space trajectories are able to decrease motion artifacts while achieving ultrashort echo times (UTE). We assessed the performance of free-breathing conical UTE MRI in the evaluation of the pediatric pelvis for suspected appendicitis.

Methods: Our retrospective review of 84 pediatric patients who underwent MRI for suspected appendicitis compared three contrast-enhanced sequences: free-breathing conical UTE, breath-hold three-dimensional (3D) spoiled gradient echo (BH-SPGR), and free-breathing high-resolution 3D SPGR (FB-SPGR). Two radiologists performed blinded and independent evaluations of each sequence for image quality (four point scale), anatomic delineation (four point scale), and diagnostic confidence (five point scale). Subsequently, the three sequences were directly compared for overall image quality (- 3 to + 3 scale). Scores were compared using Kruskal-Wallis and Wilcoxon signed-rank tests.

Results: UTE demonstrated significantly better perceived signal-to-noise ratio (SNR) and fewer artifacts than BH-SPGR and FB-SPGR (means of 3.6 and 3.4, 3.4 and 3.2, 3.1 and 2.7, respectively; p < 0.0006). BH-SPGR and FB-SPGR demonstrated significantly better contrast than UTE (means of 3.6, 3.4, and 3.2, respectively; p < 0.03). In the remaining categories, UTE performed significantly better than FB-SPGR (p < 0.00001), while there was no statistical difference between UTE and BH-SPGR. Direct paired comparisons of overall image quality demonstrated the readers significantly preferred UTE over both BH-SPGR (mean + 0.5, p < 0.00001) and FB-SPGR (mean + 1.2, p < 0.00001).

Conclusions: In the evaluation of suspected appendicitis, free-breathing conical UTE MRI performed better in the assessed metrics than FB-SPGR. When compared to BH-SPGR, UTE demonstrated superior perceived SNR and fewer artifacts.
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http://dx.doi.org/10.1007/s00261-018-1705-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296767PMC
January 2019

Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.

IEEE Trans Med Imaging 2019 01 23;38(1):167-179. Epub 2018 Jul 23.

Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise l/l cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold. The LSGAN learns the texture details, while the l/l cost suppresses high-frequency noise. A discriminator network, which is a multilayer convolutional neural network (CNN), plays the role of a perceptual cost that is then jointly trained based on high-quality MR images to score the quality of retrieved images. In the operational phase, an initial aliased estimate (e.g., simply obtained by zero-filling) is propagated into the trained generator to output the desired reconstruction. This demands a very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. Images rated by expert radiologists corroborate that GANCS retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary-learning-based CS schemes as well as with deep-learning-based schemes using pixel-wise training. In addition, it offers reconstruction times of under a few milliseconds, which are two orders of magnitude faster than the current state-of-the-art CS-MRI schemes.
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http://dx.doi.org/10.1109/TMI.2018.2858752DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542360PMC
January 2019

Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks.

Radiology 2018 11 24;289(2):366-373. Epub 2018 Jul 24.

From the Departments of Electrical Engineering (F.C., J.M.P.) and Radiology (J.Y.C., J.S., S.T.C., S.S.V.), Stanford University, Stanford, Calif 94305-9510; Global MR Applications and Workflow, GE Healthcare, Menlo Park, Calif (V.T.); GE Global Research Center, Herzliya, Israel (I.M.); Department of Electrical Engineering and Computer Sciences, University of California-Berkeley, Berkeley, Calif (J.I.T.); Department of Radiology, VA Palo Alto Healthcare System, Palo Alto, Calif (S.T.C.); and GE Global Research Center, Niskayuna, NY (C.J.H.).

Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness. © RSNA, 2018 Online supplemental material is available for this article.
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http://dx.doi.org/10.1148/radiol.2018180445DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6209075PMC
November 2018

High-resolution 3D volumetric contrast-enhanced MR angiography with a blood pool agent (ferumoxytol) for diagnostic evaluation of pediatric brain arteriovenous malformations.

J Neurosurg Pediatr 2018 09 8;22(3):251-260. Epub 2018 Jun 8.

2Department of Radiology, Lucile Packard Children's Hospital, Palo Alto.

OBJECTIVE Patients with brain arteriovenous malformations (AVMs) often require repeat imaging with MRI or MR angiography (MRA), CT angiography (CTA), and digital subtraction angiography (DSA). The ideal imaging modality provides excellent vascular visualization without incurring added risks, such as radiation exposure. The purpose of this study is to evaluate the performance of ferumoxytol-enhanced MRA using a high-resolution 3D volumetric sequence (fe-SPGR) for visualizing and grading pediatric brain AVMs in comparison with CTA and DSA, which is the current imaging gold standard. METHODS In this retrospective cohort study, 21 patients with AVMs evaluated by fe-SPGR, CTA, and DSA between April 2014 and August 2017 were included. Two experienced raters graded AVMs using Spetzler-Martin criteria on all imaging studies. Lesion conspicuity (LC) and diagnostic confidence (DC) were assessed using a 5-point Likert scale, and interrater agreement was determined. The Kruskal-Wallis test was performed to assess the raters' grades and scores of LC and DC, with subsequent post hoc pairwise comparisons to assess for statistically significant differences between pairs of groups at p < 0.05. RESULTS Assigned Spetzler-Martin grades for AVMs on DSA, fe-SPGR, and CTA were not significantly different (p = 0.991). LC and DC scores were higher with fe-SPGR than with CTA (p < 0.05). A significant difference in LC scores was found between CTA and fe-SPGR (p < 0.001) and CTA and DSA (p < 0.001) but not between fe-SPGR and DSA (p = 0.146). A significant difference in DC scores was found among DSA, fe-SPGR, and CTA (p < 0.001) and between all pairs of the groups (p < 0.05). Interrater agreement was good to very good for all image groups (κ = 0.77-1.0, p < 0.001). CONCLUSIONS Fe-SPGR performed robustly in the diagnostic evaluation of brain AVMs, with improved visual depiction of AVMs compared with CTA and comparable Spetzler-Martin grading relative to CTA and DSA.
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http://dx.doi.org/10.3171/2018.3.PEDS17723DOI Listing
September 2018

4D flow MRI quantification of mitral and tricuspid regurgitation: Reproducibility and consistency relative to conventional MRI.

J Magn Reson Imaging 2018 10 11;48(4):1147-1158. Epub 2018 Apr 11.

Department of Radiology, UC San Diego, La Jolla, California, USA.

Background: In patients with mitral or tricuspid valve regurgitation, evaluation of regurgitant severity is essential for determining the need for surgery. While transthoracic echocardiography is widely accessible, it has limited reproducibility for grading inlet valve regurgitation. Multiplanar cardiac MRI is the quantitative standard but requires specialized local expertise, and is thus not widely available. Volumetric 4D flow MRI has potential for quantitatively grading the severity of inlet valve regurgitation in adult patients.

Purpose: To evaluate the accuracy and reproducibility of volumetric 4D flow MRI for quantification of inlet valvular regurgitation compared to conventional multiplanar MRI, which may simplify and improve accessibility of cardiac MRI.

Study Type: This retrospective, HIPAA-compliant imaging-based comparison study was conducted at a single institution.

Subjects: Twenty-one patients who underwent concurrent multiplanar and 4D flow cardiac MRI between April 2015 and January 2017.

Field Strength/sequences: 3T; steady-state free-precession (SSFP), 2D phase contrast (2D-PC), and postcontrast 4D flow.

Assessment: We evaluated the intertechnique (4D flow vs. 2D-PC), intermethod (direct vs. indirect measurement), interobserver and intraobserver reproducibility of measurements of regurgitant flow volume (RFV), fraction (RF), and volume (RVol).

Statistical Tests: Statistical analysis included Pearson correlation, Bland-Altman statistics, and intraclass correlation coefficients.

Results: There was high concordance between 4D flow and multiplanar MRI, whether using direct or indirect methods of quantifying regurgitation (r = 0.813-0.985). Direct interrogation of the regurgitant jet with 4D flow showed high intraobserver consistency (r = 0.976-0.999) and interobserver consistency (r = 0.861-0.992), and correlated well with traditional indirect measurements obtained as the difference between stroke volume and forward outlet valve flow.

Data Conclusion: 4D flow MRI provides highly reproducible measurements of mitral and tricuspid regurgitant volume, and may be used in place of conventional multiplanar MRI.

Level Of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1147-1158.
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http://dx.doi.org/10.1002/jmri.26040DOI Listing
October 2018

The impact of computed high b-value images on the diagnostic accuracy of DWI for prostate cancer: A receiver operating characteristics analysis.

Sci Rep 2018 02 21;8(1):3409. Epub 2018 Feb 21.

Stanford University School of Medicine, Department of Radiology, Stanford, CA94305, USA.

To evaluate the performance of computed high b value diffusion-weighted images (DWI) in prostate cancer detection. 97 consecutive patients who had undergone multiparametric MRI of the prostate followed by biopsy were reviewed. Five radiologists independently scored 138 lesions on native high b-value images (b = 1200 s/mm), apparent diffusion coefficient (ADC) maps, and computed high b-value images (contrast equivalent to b = 2000 s/mm) to compare their diagnostic accuracy. Receiver operating characteristic (ROC) analysis and McNemar's test were performed to assess the relative performance of computed high b value DWI, native high b-value DWI and ADC maps. No significant difference existed in the area under the curve (AUC) for ROCs comparing B1200 (b = 1200 s/mm) to computed B2000 (c-B2000) in 5 readers. In 4 of 5 readers c-B2000 had significantly increased sensitivity and/or decreased specificity compared to B1200 (McNemar's p < 0.05), at selected thresholds of interpretation. ADC maps were less accurate than B1200 or c-B2000 for 2 of 5 readers (P < 0.05). This study detected no consistent improvement in overall diagnostic accuracy using c-B2000, compared with B1200 images. Readers detected more cancer with c-B2000 images (increased sensitivity) but also more false positive findings (decreased specificity).
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http://dx.doi.org/10.1038/s41598-018-21523-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5821845PMC
February 2018

Pelvic Blood Flow Predicts Fibroid Volume and Embolic Required for Uterine Fibroid Embolization: A Pilot Study With 4D Flow MR Angiography.

AJR Am J Roentgenol 2018 Jan 1;210(1):189-200. Epub 2017 Nov 1.

1 Department of Radiology, University of California, San Diego, 200 W Arbor Dr, MC 0834, San Diego, CA 92103-0834.

Objective: We report here an initial experience using 4D flow MRI in pelvic imaging-specifically, in imaging uterine fibroids. We hypothesized that blood flow might correlate with fibroid volume and that quantifying blood flow might help to predict the amount of embolic required to achieve stasis at subsequent uterine fibroid embolization (UFE).

Materials And Methods: Thirty-three patients with uterine fibroids and seven control subjects underwent pelvic MRI with 4D flow imaging. Of the patients with fibroids, 10 underwent 4D flow imaging before UFE and seven after UFE; in the remaining 16 patients with fibroids, UFE had yet to be performed. Four-dimensional flow measurements were performed using Arterys CV Flow. The flow fraction of the internal iliac artery was expressed as the ratio of internal iliac artery flow to external iliac artery flow and was compared between groups. The flow ratios between the internal iliac arteries on each side were calculated. Fibroid volume versus internal iliac flow fraction, embolic volume versus internal iliac flow fraction, and embolic volume ratio between sides versus the ratio of internal iliac artery flows between sides were compared.

Results: The mean internal iliac flow fraction was significantly higher in the 26 patients who underwent imaging before UFE (mean ± standard error, 0.78 ± 0.06) than in the seven patients who underwent imaging after UFE (0.48 ± 0.07, p < 0.01) and in the seven control patients without fibroids (0.48 ± 0.08, p < 0.0001). The internal iliac flow fraction correlated well with fibroid volumes before UFE (r = 0.7754, p < 0.0001) and did not correlate with fibroid volumes after UFE (r = -0.3051, p = 0.51). The ratio of embolic required to achieve stasis between sides showed a modest correlation with the ratio of internal iliac flow (r = 0.6776, p = 0.03).

Conclusion: Internal iliac flow measured by 4D flow MRI correlates with fibroid volume and is predictive of the ratio of embolic required to achieve stasis on each side at subsequent UFE and may be useful for preprocedural evaluation of patients with uterine fibroids.
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http://dx.doi.org/10.2214/AJR.17.18127DOI Listing
January 2018

Body diffusion-weighted imaging using magnetization prepared single-shot fast spin echo and extended parallel imaging signal averaging.

Magn Reson Med 2018 06 17;79(6):3032-3044. Epub 2017 Oct 17.

Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, California, USA.

Purpose: This work demonstrates a magnetization prepared diffusion-weighted single-shot fast spin echo (SS-FSE) pulse sequence for the application of body imaging to improve robustness to geometric distortion. This work also proposes a scan averaging technique that is superior to magnitude averaging and is not subject to artifacts due to object phase.

Theory And Methods: This single-shot sequence is robust against violation of the Carr-Purcell-Meiboom-Gill (CPMG) condition. This is achieved by dephasing the signal after diffusion weighting and tipping the MG component of the signal onto the longitudinal axis while the non-MG component is spoiled. The MG signal component is then excited and captured using a traditional SS-FSE sequence, although the echo needs to be recalled prior to each echo. Extended Parallel Imaging (ExtPI) averaging is used where coil sensitivities from the multiple acquisitions are concatenated into one large parallel imaging (PI) problem. The size of the PI problem is reduced by SVD-based coil compression which also provides background noise suppression. This sequence and reconstruction are evaluated in simulation, phantom scans, and in vivo abdominal clinical cases.

Results: Simulations show that the sequence generates a stable signal throughout the echo train which leads to good image quality. This sequence is inherently low-SNR, but much of the SNR can be regained through scan averaging and the proposed ExtPI reconstruction. In vivo results show that the proposed method is able to provide diffusion encoded images while mitigating geometric distortion artifacts compared to EPI.

Conclusion: This work presents a diffusion-prepared SS-FSE sequence that is robust against the violation of the CPMG condition while providing diffusion contrast in clinical cases. Magn Reson Med 79:3032-3044, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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http://dx.doi.org/10.1002/mrm.26971DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6312718PMC
June 2018

Self-Calibrating Wave-Encoded Variable-Density Single-Shot Fast Spin Echo Imaging.

J Magn Reson Imaging 2018 04 14;47(4):954-966. Epub 2017 Sep 14.

Department of Radiology, Stanford University, Stanford, California, USA.

Background: It is highly desirable in clinical abdominal MR scans to accelerate single-shot fast spin echo (SSFSE) imaging and reduce blurring due to T decay and partial-Fourier acquisition.

Purpose: To develop and investigate the clinical feasibility of wave-encoded variable-density SSFSE imaging for improved image quality and scan time reduction.

Study Type: Prospective controlled clinical trial.

Subjects: With Institutional Review Board approval and informed consent, the proposed method was assessed on 20 consecutive adult patients (10 male, 10 female, range, 24-84 years).

Field Strength/sequence: A wave-encoded variable-density SSFSE sequence was developed for clinical 3.0T abdominal scans to enable high acceleration (3.5×) with full-Fourier acquisitions by: 1) introducing wave encoding with self-refocusing gradient waveforms to improve acquisition efficiency; 2) developing self-calibrated estimation of wave-encoding point-spread function and coil sensitivity to improve motion robustness; and 3) incorporating a parallel imaging and compressed sensing reconstruction to reconstruct highly accelerated datasets.

Assessment: Image quality was compared pairwise with standard Cartesian acquisition independently and blindly by two radiologists on a scale from -2 to 2 for noise, contrast, confidence, sharpness, and artifacts. The average ratio of scan time between these two approaches was also compared.

Statistical Tests: A Wilcoxon signed-rank tests with a P value under 0.05 considered statistically significant.

Results: Wave-encoded variable-density SSFSE significantly reduced the perceived noise level and improved the sharpness of the abdominal wall and the kidneys compared with standard acquisition (mean scores 0.8, 1.2, and 0.8, respectively, P < 0.003). No significant difference was observed in relation to other features (P = 0.11). An average of 21% decrease in scan time was achieved using the proposed method.

Data Conclusion: Wave-encoded variable-density sampling SSFSE achieves improved image quality with clinically relevant echo time and reduced scan time, thus providing a fast and robust approach for clinical SSFSE imaging.

Level Of Evidence: 1 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2018;47:954-966.
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http://dx.doi.org/10.1002/jmri.25853DOI Listing
April 2018