Faculty Bibliography

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.

Purpose To develop a three-dimensional breath-hold (BH) magnetic resonance (MR) cholangiopancreatographic protocol with sampling perfection with application-optimized contrast using different flip-angle evolutions (SPACE) acquisition and sparsity-based iterative reconstruction (SPARSE) of prospectively sampled 5% k-space data and to compare the results with conventional respiratory-triggered (RT) acquisition. Materials and Methods This HIPAA-compliant prospective study was institutional review board approved. Twenty-nine patients underwent conventional RT SPACE and BH-accelerated SPACE acquisition with 5% k-space sampling at 3 T. Spatial resolution and other parameters were matched when possible. BH SPACE images were reconstructed by enforcing joint multicoil sparsity in the wavelet domain (SPARSE-SPACE). Two board-certified radiologists independently evaluated BH SPARSE-SPACE and RT SPACE images for image quality parameters in the pancreatic duct and common bile duct by using a five-point scale. The Wilcoxon signed-rank test was used to compare BH SPARSE-SPACE and RT SPACE images. Results Acquisition time for BH SPARSE-SPACE was 20 seconds, which was significantly (P < .001) shorter than that for RT SPACE (mean +/- standard deviation, 338.8 sec +/- 69.1). Overall image quality scores were higher for BH SPARSE-SPACE than for RT SPACE images for both readers for the proximal, middle, and distal pancreatic duct, but the difference was not statistically significant (P > .05). For reader 1, distal common bile duct scores were significantly higher with BH SPARSE-SPACE acquisition (P = .036). More patients had acceptable or better overall image quality (scores >/= 3) with BH SPARSE-SPACE than with RT SPACE acquisition, respectively, for the proximal (23 of 29 [79%] vs 22 of 29 [76%]), middle (22 of 29 [76%] vs 18 of 29 [62%]), and distal (20 of 29 [69%] vs 13 of 29 [45%]) pancreatic duct and the proximal (25 of 28 [89%] vs 22 of 28 [79%]) and distal (25 of 28 [89%] vs 24 of 28 [86%]) common bile duct. Conclusion BH SPARSE-SPACE showed similar or superior image quality for the pancreatic and common duct compared with that of RT SPACE despite 17-fold shorter acquisition time. (c) RSNA, 2016.

Heart disease is a worldwide public health problem; assessment of cardiac function is an important part of the diagnosis and management of heart disease. Magnetic resonance imaging (MRI) of the heart can provide clinically useful information on cardiac function, although it is still not routinely used in clinical practice, in part because of limited imaging speed. New accelerated methods for performing cardiovascular MRI (CMR) have the potential to provide both increased imaging speed and robustness to CMR, as well as access to increased functional information. In this review, we will briefly discuss the main methods currently employed to accelerate CMR methods, such as parallel imaging, k-t undersampling and compressed sensing, as well as new approaches that extend the idea of compressed sensing and exploit sparsity to provide richer information of potential use in clinical practice.

PURPOSE: To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing. METHODS: Radial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting undersampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients. RESULTS: XD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts. CONCLUSION: XD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value. Magn Reson Med, 2015. (c) 2015 Wiley Periodicals, Inc.

BACKGROUND: To evaluate the influence of temporal sparsity regularization and radial undersampling on compressed sensing reconstruction of dynamic contrast-enhanced (DCE) MRI, using the iterative Golden-angle RAdial Sparse Parallel (iGRASP) MRI technique in the setting of breast cancer evaluation. METHODS: DCE-MRI examinations of the breast (n = 7) were conducted using iGRASP at 3 Tesla. Images were reconstructed with five different radial undersampling schemes corresponding to temporal resolutions between 2 and 13.4 s/frame and with four different weights for temporal sparsity regularization (lambda = 0.1, 0.5, 2, and 6 times of noise level). Image similarity to time-averaged reference images was assessed by two breast radiologists and using quantitative metrics. Temporal similarity was measured in terms of wash-in slope and contrast kinetic model parameters. RESULTS: iGRASP images reconstructed with lambda = 2 and 5.1 s/frame had significantly (P < 0.05) higher similarity to time-averaged reference images than the images with other reconstruction parameters (mutual information (MI) >5%), in agreement with the assessment of two breast radiologists. Higher undersampling (temporal resolution < 5.1 s/frame) required stronger temporal sparsity regularization (lambda >/= 2) to remove streaking aliasing artifacts (MI > 23% between lambda = 2 and 0.5). The difference between the kinetic-model transfer rates of benign and malignant groups decreased as temporal resolution decreased (82% between 2 and 13.4 s/frame). CONCLUSION: This study demonstrates objective spatial and temporal similarity measures can be used to assess the influence of sparsity constraint and undersampling in compressed sensing DCE-MRI and also shows that the iGRASP method provides the flexibility of optimizing these reconstruction parameters in the postprocessing stage using the same acquired data. J. Magn. Reson. Imaging 2015.

Background: Conventional cine MRI provides data on the variation of cardiac dimensions across the cardiac cycle; cardiac function analysis primarily focuses on the difference between end-diastolic (ED) and endsystolic (ES) dimensions of the left and right ventricles (LV and RV). With cardiorespiratory-synchronized (CRS) CMR, there is an additional effective dimension of information available, related to the effect of the respiratory cycle phase on cardiac dimensions. However, there are currently no established ways to analyze this potentially useful additional physiological data. We have developed a set of tools for the functional analysis of CRS CMR, particularly for the study of the respiratory effects on LV-RV interaction, and derived some initial normative values for the results. Methods: We have developed a set of interactive CMR function analysis programs. Images from CRS CMR are organized in a two-dimensional matrix, sorted by cardiac and respiratory cycle phases. The user can interactively position an analysis line across the ventricles in a representative image; this line can then be automatically tracked across the other cardiac and respiratory phases. The intensity profile along the line is then used to automatically track the corresponding positions of the edges of the LV and RV free walls and the interventricular septum (IVS). A variety of absolute and normalized variables can be derived from these varying positions, including ED and ES dimensions, and displayed as functional images over the cardiac and respiratory cycle dimensions. CRS CMR was performed with a sparsity-based method (XD-GRASP), using continuous acquisition of radial k-space samples with golden-angle increments and retrospective cardiac and respiratory phase sorting in reconstruction. An initial set of CRS CMR data from 9 normal subjects (age 28.33 +/- 5.85) was analyzed, as well as from 3 patients (age 40 +/- 9.66, one with HCM). Results: On visual inspection of the images, it is apparent that there is a clear shift in the relative position of the IVS over the respiratory cycle, to the left in inspiration and to the right in expiration, reflecting the LV-RV interaction; this is much more prominent near ED than ES. For the normal subjects, in midlevel short-axis views, the respiratory-related absolute shift in IVS position was 1.07-3.23 mm at ED and 0.69-2.14 mm at ES; corresponding values normalized to ED dimension were 2.65-7.08 pp and 1.99-5.18 pp. The ED-ES difference for the normalized shift ranges was -1.9-4.35 pp (median 1.35, first quartile 0.68). For the HCM patient, the difference between the shift ranges was 0.79 pp. Linear regression when plotting NCD against NEDD (reflecting the Frank-Starling relationship and giving an estimate of contractility) was 0.68 +/- 0.11 in the normal subjects. Conclusions: Novel physiologic data on LV-RV interaction can be derived from CRS CMR; this seems to show consistent ranges in normal subjects, and may provide useful information on disease-related changes in cardiac function. (Figure Presented)