Faculty Bibliography

Non-Cartesian magnetic resonance imaging (MRI) sequences have shown great promise for abdominal examination during free breathing, but break down in the presence of bulk patient motion (i.e. voluntary or involuntary patient movement resulting in translation, rotation or elastic deformations of the body). This work describes a data-consistency-driven image stabilization technique that detects and excludes bulk movements during data acquisition. Bulk motion is identified from changes in the signal intensity distribution across different elements of a multi-channel receive coil array. A short free induction decay signal is acquired after excitation and used as a measure to determine alterations in the load distribution. The technique has been implemented on a clinical MR scanner and evaluated in the abdomen. Six volunteers were scanned and two radiologists scored the reconstructions. To show the applicability to other body areas, additional neck and knee images were acquired. Data corrupted by bulk motion were successfully detected and excluded from image reconstruction. An overall increase in image sharpness and reduction of streaking and shine-through artifacts were seen in the volunteer study, as well as in the neck and knee scans. The proposed technique enables automatic real-time detection and exclusion of bulk motion during MR examinations without user interaction. It may help to improve the reliability of pediatric MRI examinations without the use of sedation.

OBJECTIVE: To examine the prognostic capabilities of intravoxel incoherent motion (IVIM) metrics and their ability to predict response to neoadjuvant treatment (NAT). Additionally, to observe changes in IVIM metrics between pre- and post-treatment MRI. METHODS: This IRB-approved, HIPAA-compliant retrospective study observed 31 breast cancer patients (32 lesions). Patients underwent standard bilateral breast MRI along with diffusion-weighted imaging before and after NAT. Six patients underwent an additional IVIM-MRI scan 12-14 weeks after initial scan and 2 cycles of treatment. In addition to apparent diffusion coefficients (ADC) from monoexponential decay, IVIM mean values (tissue diffusivity Dt, perfusion fraction fp, and pseudodiffusivity Dp) and histogram metrics were derived using a biexponential model. An additional filter identified voxels of highly vascular tumor tissue (VTT), excluding necrotic or normal tissue. Clinical data include histology of biopsy and clinical response to treatment through RECIST assessment. Comparisons of treatment response were made using Wilcoxon rank-sum tests. RESULTS: Average, kurtosis, and skewness of pseudodiffusion Dp significantly differentiated RECIST responders from nonresponders. ADC and Dt values generally increased ( approximately 70%) and VTT% values generally decreased ( approximately 20%) post-treatment. CONCLUSION: Dp metrics showed prognostic capabilities; slow and heterogeneous pseudodiffusion offer poor prognosis. Baseline ADC/Dt parameters were not significant predictors of response. This work suggests that IVIM mean values and heterogeneity metrics may have prognostic value in the setting of breast cancer NAT.

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.

OBJECTIVES: The aim of this study was to assess the applicability of Dixon radial volumetric encoding (Dixon-RAVE) for comprehensive dynamic contrast-enhanced 3D magnetic resonance imaging (MRI) of the breast using a combination of radial sampling, model-based fat/water separation, compressed sensing, and parallel imaging. MATERIALS AND METHODS: In this Health Insurance Portability and Accountability Act-compliant prospective study, 24 consecutive patients underwent bilateral breast MRI, including both conventional fat-suppressed and non-fat-suppressed precontrast T1-weighted volumetric interpolated breath-hold examination (VIBE). Afterward, 1 continuous Dixon-RAVE scan was performed with the proposed approach while the contrast agent was injected. This scan was immediately followed by the acquisition of 4 conventional fat-saturated VIBE scans. From the comprehensive Dixon-RAVE data set, different image contrasts were reconstructed that are comparable to the separate conventional VIBE scans.Two radiologists independently rated image quality, conspicuity of fibroglandular tissue from fat (FG), and degree of fat suppression (FS) on a 5-point Likert-type scale for the following 3 comparisons: precontrast fat-suppressed (pre-FS), precontrast non-fat-suppressed (pre-NFS), and dynamic fat-suppressed (dyn-FS) images. RESULTS: When scores were averaged over readers, Dixon-RAVE achieved significantly higher (P < 0.001) degree of fat suppression compared with VIBE, for both pre-FS (4.25 vs 3.67) and dyn-FS (4.10 vs 3.46) images. Although Dixon-RAVE had lower image quality score compared with VIBE for the pre-FS (3.56 vs 3.67, P = 0.490), the pre-NFS (3.54 vs 3.88, P = 0.009), and the dyn-FS images (3.06 vs 3.67, P < 0.001), acceptable or better diagnostic quality was achieved (score >/= 3). The FG score for Dixon-RAVE in comparison to VIBE was significantly higher for the pre-FS image (4.23 vs 3.85, P = 0.044), lower for the pre-NFS image (3.98 vs 4.25, P = 0.054), and higher for the dynamic fat-suppressed image (3.90 vs 3.85, P = 0.845). CONCLUSIONS: Dixon-RAVE can serve as a one-stop-shop approach for comprehensive T1-weighted breast MRI with diagnostic image quality, high spatiotemporal resolution, reduced overall scan time, and improved fat suppression compared with conventional imaging.

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.

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.

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.