Faculty Bibliography

PURPOSE: The inhomogeneity of flip angle distribution is a major challenge impeding the application of high-field MRI. We report a method combining spatially selective excitation using generalized spatial encoding magnetic fields (SAGS) with radiofrequency (RF) shimming to achieve homogeneous excitation. This method can be an alternative approach to address the challenge of B1+ inhomogeneity using nonlinear gradients. METHODS: We proposed a two-step algorithm that jointly optimizes the combination of nonlinear spatial encoding magnetic fields and the combination of multiple RF transmitter coils and then optimizes the locations, RF amplitudes, and phases of the spokes. RESULTS: Our results show that jointly designed SAGS and RF shimming can provide a more homogeneous flip angle distribution than using SAGS or RF shimming alone. Compared with RF shimming alone, our approach can reduce the relative standard deviation of flip angle by 56% and 52% using phantom and human head data, respectively. CONCLUSION: The jointly designed SAGS and RF shimming method can be used to achieve homogeneous flip angle distributions when fully parallel RF transmission is not available. Magn Reson Med, 2016. (c) 2016 International Society for Magnetic Resonance in Medicine.

OBJECTIVE: To determine whether patient-specific 3D printed renal tumor models change pre-operative planning decisions made by urological surgeons in preparation for complex renal mass surgical procedures. MATERIALS AND METHODS: From our ongoing IRB approved study on renal neoplasms, ten renal mass cases were retrospectively selected based on Nephrometry Score greater than 5 (range 6-10). A 3D post-contrast fat-suppressed gradient-echo T1-weighted sequence was used to generate 3D printed models. The cases were evaluated by three experienced urologic oncology surgeons in a randomized fashion using (1) imaging data on PACS alone and (2) 3D printed model in addition to the imaging data. A questionnaire regarding surgical approach and planning was administered. The presumed pre-operative approaches with and without the model were compared. Any change between the presumed approaches and the actual surgical intervention was recorded. RESULTS: There was a change in planned approach with the 3D printed model for all ten cases with the largest impact seen regarding decisions on transperitoneal or retroperitoneal approach and clamping, with changes seen in 30%-50% of cases. Mean parenchymal volume loss for the operated kidney was 21.4%. Volume losses >20% were associated with increased ischemia times and surgeons tended to report a different approach with the use of the 3D model compared to that with imaging alone in these cases. The 3D printed models helped increase confidence regarding the chosen operative procedure in all cases. CONCLUSIONS: Pre-operative physical 3D models created from MRI data may influence surgical planning for complex kidney cancer.

PURPOSE: Free-breathing whole-heart coronary MR angiography (MRA) commonly uses navigators to gate respiratory motion, resulting in lengthy and unpredictable acquisition times. Conversely, self-navigation has 100% scan efficiency, but requires motion correction over a broad range of respiratory displacements, which may introduce image artifacts. We propose replacing navigators and self-navigation with a respiratory motion-resolved reconstruction approach. METHODS: Using a respiratory signal extracted directly from the imaging data, individual signal-readouts are binned according to their respiratory states. The resultant series of undersampled images are reconstructed using an extradimensional golden-angle radial sparse parallel imaging (XD-GRASP) algorithm, which exploits sparsity along the respiratory dimension. Whole-heart coronary MRA was performed in 11 volunteers and four patients with the proposed methodology. Image quality was compared with that obtained with one-dimensional respiratory self-navigation. RESULTS: Respiratory-resolved reconstruction effectively suppressed respiratory motion artifacts. The quality score for XD-GRASP reconstructions was greater than or equal to self-navigation in 80/88 coronary segments, reaching diagnostic quality in 61/88 segments versus 41/88. Coronary sharpness and length were always superior for the respiratory-resolved datasets, reaching statistical significance (P < 0.05) in most cases. CONCLUSION: XD-GRASP represents an attractive alternative for handling respiratory motion in free-breathing whole heart MRI and provides an effective alternative to self-navigation. Magn Reson Med, 2016. (c) 2016 Wiley Periodicals, Inc.

The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities. LEVEL OF EVIDENCE: 5 J. Magn. Reson. Imaging 2016.

While current state of the art MR-PET scanners enable simultaneous MR and PET measurements, the acquired data sets are still usually reconstructed separately. We propose a new multi-modality reconstruction framework using second order Total Generalized Variation (TGV) as a dedicated multi-channel regularization functional that jointly reconstructs images from both modalities. In this way, information about the underlying anatomy is shared during the image reconstruction process while unique differences are preserved. Results from numerical simulations and in-vivo experiments using a range of accelerated MR acquisitions and different MR image contrasts demonstrate improved PET image quality, resolution, and quantitative accuracy.

INTRODUCTION: Quantitative T2 mapping may provide an objective biomarker for occult nervous tissue pathology in relapsing-remitting multiple sclerosis (RRMS). We applied a novel echo modulation curve (EMC) algorithm to identify T2 changes in normal-appearing brain regions of subjects with RRMS (N = 27) compared to age-matched controls (N = 38). METHODS: The EMC algorithm uses Bloch simulations to model T2 decay curves in multi-spin-echo MRI sequences, independent of scanner, and scan-settings. T2 values were extracted from normal-appearing white and gray matter brain regions using both expert manual regions-of-interest and user-independent FreeSurfer segmentation. RESULTS: Compared to conventional exponential T2 modeling, EMC fitting provided more accurate estimations of T2 with less variance across scans, MRI systems, and healthy individuals. Thalamic T2 was increased 8.5% in RRMS subjects (p < 0.001) and could be used to discriminate RRMS from healthy controls well (AUC = 0.913). Manual segmentation detected both statistically significant increases (corpus callosum & temporal stem) and decreases (posterior limb internal capsule) in T2 associated with RRMS diagnosis (all p < 0.05). In healthy controls, we also observed statistically significant T2 differences for different white and gray matter structures. CONCLUSIONS: The EMC algorithm precisely characterizes T2 values, and is able to detect subtle T2 changes in normal-appearing brain regions of RRMS patients. These presumably capture both axon and myelin changes from inflammation and neurodegeneration. Further, T2 variations between different brain regions of healthy controls may correlate with distinct nervous tissue environments that differ from one another at a mesoscopic length-scale.