Faculty Bibliography

BACKGROUND:The value of dynamic contrast-enhanced (DCE) sequences in prostate MRI compared with noncontrast MRI is controversial. PURPOSE/OBJECTIVE:To evaluate the population net benefit of risk stratification using DCE-MRI for detection of high-grade prostate cancer (HGPCA), with or without high spatiotemporal resolution DCE imaging. STUDY TYPE/METHODS:Decision curve analysis. POPULATION/METHODS:Previously published patient studies on MRI for HGPCA detection, one using DCE with golden-angle radial sparse parallel (GRASP) images and the other using standard DCE-MRI. FIELD STRENGTH/SEQUENCE/UNASSIGNED:GRASP or standard DCE-MRI at 3 T. ASSESSMENT/RESULTS:Each study reported the proportion of lesions with HGPCA in each Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) category (1-5), before and after reclassification of peripheral zone lesions from PI-RADS 3-4 based on contrast-enhanced images. This additional risk stratifying information was translated to population net benefit, when biopsy was hypothetically performed for: all lesions, no lesions, PI-RADS ≥3 (using NC-MRI), and PI-RADS ≥4 on DCE. STATISTICAL TESTS/UNASSIGNED:Decision curve analysis was performed for both GRASP and standard DCE-MRI data, translating the avoidance of unnecessary biopsies and detection of HGPCA to population net benefit. We standardized net benefit values for HGPCA prevalence and graphically summarized the comparative net benefit of biopsy strategies. RESULTS:For a clinically relevant range of risk thresholds for HGPCA (>11%), GRASP DCE-MRI with biopsy of PI-RADS ≥4 lesions provided the highest net benefit, while biopsy of PI-RADS ≥3 lesions provided highest net benefit at low personal risk thresholds (2-11%). In the same range of risk thresholds using standard DCE-MRI, the optimal strategy was biopsy for all lesions (0-15% risk threshold) or PI-RADS ≥3 on NC-MRI (16-33% risk threshold). DATA CONCLUSION/UNASSIGNED:GRASP DCE-MRI may potentially enable biopsy of PI-RADS ≥4 lesions, providing relatively preserved detection of HGPCA and avoidance of unnecessary biopsies compared with biopsy of all PI-RADS ≥3 lesions. J. Magn. Reson. Imaging 2019.

PURPOSE/OBJECTIVE:We present a novel, geometrically adjustable, receive coil array whose diameter can be tailored to the subject in order to maximize sensitivity for a range of body sizes. THEORY AND METHODS/UNASSIGNED:A key mechanical feature of the size-adaptable receive array is its trellis structure that was motivated by similar structures found in gardening and fencing. Our implementation is a cylindrical trellis that features encircling, diagonally interleaved slats, which are linked together at intersecting points. The ensemble allows expansion or contraction to be controlled with the angle between the slats. This mechanical frame provides a base for radiofrequency coils wherein approximately constant overlap, and therefore coupling between adjacent elements, is maintained when the trellis is expanded or contracted. We demonstrate 2 trellis coil concepts for imaging lower extremity at 3T: a single-row 8-channel array built on a trellis support structure and a multirow 24-channel array in which the coil elements themselves form the trellis structure. RESULTS:We show that the adjustable trellis array can accommodate a range of subject sizes with robust signal-to-noise ratio, loading, and coupling. CONCLUSION/CONCLUSIONS:The trellis coil concept enables an array of surface coils to expand and contract with negligible effect on tuning, matching, and decoupling. This allows an encircling array to conform closely to anatomy of various sizes, which provides significant gains in signal-to-noise ratio.

PURPOSE/OBJECTIVE:To investigate how high-permittivity materials (HPMs) can improve SNR when placed between MR detectors and the imaged body. METHODS:We used a simulation framework based on dyadic Green's functions to calculate the electromagnetic field inside a uniform dielectric sphere at 7 Tesla, with and without a surrounding layer of HPM. SNR-optimizing (ideal) current patterns were expressed as the sum of signal-optimizing (signal-only) current patterns and dark mode current patterns that minimize sample noise while contributing nothing to signal. We investigated how HPM affects the shape and amplitude of these current patterns, sample noise, and array SNR. RESULTS:Ideal and signal-only current patterns were identical for a central voxel. HPMs introduced a phase shift into these patterns, compensating for signal propagation delay in the HPMs. For an intermediate location within the sphere, dark mode current patterns were present and illustrated the mechanisms by which HPMs can reduce sample noise. High-amplitude signal-only current patterns were observed for HPM configurations that shield the electromagnetic field from the sample. For coil arrays, these configurations corresponded to poor SNR in deep regions but resulted in large SNR gains near the surface due to enhanced fields in the vicinity of the HPM. For very high relative permittivity values, HPM thicknesses corresponding to even multiples of λ/4 resulted in coil SNR gains throughout the sample. CONCLUSION/CONCLUSIONS:HPMs affect both signal sensitivity and sample noise. Lower amplitude signal-only optimal currents corresponded to higher array SNR performance and could guide the design of coils integrated with HPM.

BACKGROUND:Patient-specific 3D models are being used increasingly in medicine for many applications including surgical planning, procedure rehearsal, trainee education, and patient education. To date, experiences on the use of 3D models to facilitate patient understanding of their disease and surgical plan are limited. The purpose of this study was to investigate in the context of renal and prostate cancer the impact of using 3D printed and augmented reality models for patient education. METHODS:Patients with MRI-visible prostate cancer undergoing either robotic assisted radical prostatectomy or focal ablative therapy or patients with renal masses undergoing partial nephrectomy were prospectively enrolled in this IRB approved study (n = 200). Patients underwent routine clinical imaging protocols and were randomized to receive pre-operative planning with imaging alone or imaging plus a patient-specific 3D model which was either 3D printed, visualized in AR, or viewed in 3D on a 2D computer monitor. 3D uro-oncologic models were created from the medical imaging data. A 5-point Likert scale survey was administered to patients prior to the surgical procedure to determine understanding of the cancer and treatment plan. If randomized to receive a pre-operative 3D model, the survey was completed twice, before and after viewing the 3D model. In addition, the cohort that received 3D models completed additional questions to compare usefulness of the different forms of visualization of the 3D models. Survey responses for each of the 3D model groups were compared using the Mann-Whitney and Wilcoxan rank-sum tests. RESULTS:All 200 patients completed the survey after reviewing their cases with their surgeons using imaging only. 127 patients completed the 5-point Likert scale survey regarding understanding of disease and surgical procedure twice, once with imaging and again after reviewing imaging plus a 3D model. Patients had a greater understanding using 3D printed models versus imaging for all measures including comprehension of disease, cancer size, cancer location, treatment plan, and the comfort level regarding the treatment plan (range 4.60-4.78/5 vs. 4.06-4.49/5, p < 0.05). CONCLUSIONS:All types of patient-specific 3D models were reported to be valuable for patient education. Out of the three advanced imaging methods, the 3D printed models helped patients to have the greatest understanding of their anatomy, disease, tumor characteristics, and surgical procedure.

BACKGROUND:Computed tomography (CT) and spirometry are the current standard methods for assessing lung anatomy and pulmonary ventilation, respectively. However, CT provides limited ventilation information and spirometry only provides global measures of lung ventilation. Thus, a method that can enable simultaneous examination of lung anatomy and ventilation is of clinical interest. PURPOSE/OBJECTIVE:To develop and test a 4D respiratory-resolved sparse lung MRI (XD-UTE: eXtra-Dimensional Ultrashort TE imaging) approach for simultaneous evaluation of lung anatomy and pulmonary ventilation. STUDY TYPE/METHODS:Prospective. POPULATION/METHODS:In all, 23 subjects (11 volunteers and 12 patients, mean age = 63.6 ± 8.4). FIELD STRENGTH/SEQUENCE/UNASSIGNED:3T MR; a prototype 3D golden-angle radial UTE sequence, a Cartesian breath-hold volumetric-interpolated examination (BH-VIBE) sequence. ASSESSMENT/RESULTS:All subjects were scanned using the 3D golden-angle radial UTE sequence during normal breathing. Ten subjects underwent an additional scan during alternating normal and deep breathing. Respiratory-motion-resolved sparse reconstruction was performed for all the acquired data to generate dynamic normal-breathing or deep-breathing image series. For comparison, BH-VIBE was performed in 12 subjects. Lung images were visually scored by three experienced chest radiologists and were analyzed by two observers who segmented the left and right lung to derive ventilation parameters in comparison with spirometry. STATISTICAL TESTS/UNASSIGNED:Nonparametric paired two-tailed Wilcoxon signed-rank test; intraclass correlation coefficient, Pearson correlation coefficient. RESULTS:XD-UTE achieved significantly improved image quality compared both with Cartesian BH-VIBE and radial reconstruction without motion compensation (P < 0.05). The global ventilation parameters (a sum of the left and right lung measures) were in good correlation with spirometry in the same subjects (correlation coefficient = 0.724). There were excellent correlations between the results obtained by two observers (intraclass correlation coefficient ranged from 0.8855-0.9995). DATA CONCLUSION/UNASSIGNED:Simultaneous evaluation of lung anatomy and ventilation using XD-UTE is demonstrated, which have shown good potential for improved diagnosis and management of patients with heterogeneous lung diseases. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

PURPOSE/OBJECTIVE:Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning. METHODS:Reconstructions were trained from undersampled data using data sets with varying SNR, sampling pattern, image contrast, and synthetic data generated from a public image database. The performance of the trained reconstructions was evaluated on 10 in vivo patient knee MRI acquisitions from 2 different pulse sequences that were not used during training. Transfer learning was evaluated by fine-tuning baseline trainings from synthetic data with a small subset of in vivo MR training data. RESULTS:Deviations in SNR between training and testing led to substantial decreases in reconstruction image quality, whereas image contrast was less relevant. Trainings from heterogeneous training data generalized well toward the test data with a range of acquisition parameters. Trainings from synthetic, non-MR image data showed residual aliasing artifacts, which could be removed by transfer learning-inspired fine-tuning. CONCLUSION/CONCLUSIONS:This study presents insights into the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing. It also provides an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases.

The dynamics of large spin-1/2 ensembles are commonly described by the Bloch equation, which is characterized by the magnetization's non-linear response to the driving magnetic field. Consequently, most magnetic field variations result in non-intuitive spin dynamics, which are sensitive to small calibration errors. Although simplistic field variations result in robust spin dynamics, they do not explore the richness of the system's phase space. Here, we identify adiabaticity conditions that span a large experiment design space with tractable dynamics. All dynamics are trapped in a one-dimensional subspace, namely in the magnetization's absolute value, which is in a transient state, while its direction adiabatically follows the steady state. In this hybrid state, the polar angle is the effective drive of the spin dynamics. As an example, we optimize this drive for robust and efficient quantification of spin relaxation times and utilize it for magnetic resonance imaging of the human brain.

We develop and evaluate a neural network-based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications

Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deep learning techniques from computer vision and image processing have been successfully applied to non-linear image reconstruction in the spirit of compressed sensing for both low dose computed tomography and accelerated MRI. The additional integration of multi-coil information to recover missing k-space lines in the MRI reconstruction process, is still studied less frequently, even though it is the de-facto standard for currently used accelerated MR acquisitions. This manuscript provides an overview of the recent machine learning approaches that have been proposed specifically for improving parallel imaging. A general background introduction to parallel MRI is given that is structured around the classical view of image space and k-space based methods. Both linear and non-linear methods are covered, followed by a discussion of recent efforts to further improve parallel imaging using machine learning, and specifically using artificial neural networks. Image-domain based techniques that introduce improved regularizers are covered as well as k-space based methods, where the focus is on better interpolation strategies using neural networks. Issues and open problems are discussed as well as recent efforts for producing open datasets and benchmarks for the community.

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background