Faculty Bibliography

PURPOSE: To demonstrate dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of the prostate with both high spatial and temporal resolution via a combination of golden-angle radial k-space sampling, compressed sensing, and parallel-imaging reconstruction (GRASP), and to compare image quality and lesion depiction between GRASP and conventional DCE in prostate cancer patients. MATERIALS AND METHODS: Twenty prostate cancer patients underwent two 3T prostate MRI examinations on separate dates, one using standard DCE (spatial resolution 3.0 x 1.9 x 1.9 mm, temporal resolution 5.5 sec) and the other using GRASP (spatial resolution 3.0 x 1.1 x 1.1 mm, temporal resolution 2.3 sec). Two radiologists assessed measures of image quality and dominant lesion size. The experienced reader recorded differences in contrast arrival times between the dominant lesion and benign prostate. RESULTS: Compared with standard DCE, GRASP demonstrated significantly better clarity of the capsule, peripheral/transition zone boundary, urethra, and periprostatic vessels; image sharpness; and lesion conspicuity for both readers (P < 0.001-0.020). GRASP showed improved interreader correlation for lesion size (GRASP: r = 0.691-0.824, standard: r = 0.495-0.542). In 8/20 cases, only GRASP showed earlier contrast arrival in tumor than benign; in no case did only standard DCE show earlier contrast arrival in tumor. CONCLUSION: High spatiotemporal resolution prostate DCE is possible with GRASP, which has the potential to improve image quality and lesion depiction as compared with standard DCE.J. Magn. Reson. Imaging 2014. (c) 2014 Wiley Periodicals, Inc.

PURPOSE: To apply the low-rank plus sparse (L+S) matrix decomposition model to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest. THEORY AND METHODS: The L+S model is natural to represent dynamic MRI data. Incoherence between k-t space (acquisition) and the singular vectors of L and the sparse domain of S is required to reconstruct undersampled data. Incoherence between L and S is required for robust separation of background and dynamic components. Multicoil L+S reconstruction is formulated using a convex optimization approach, where the nuclear norm is used to enforce low rank in L and the l1 norm is used to enforce sparsity in S. Feasibility of the L+S reconstruction was tested in several dynamic MRI experiments with true acceleration, including cardiac perfusion, cardiac cine, time-resolved angiography, and abdominal and breast perfusion using Cartesian and radial sampling. RESULTS: The L+S model increased compressibility of dynamic MRI data and thus enabled high-acceleration factors. The inherent background separation improved background suppression performance compared to conventional data subtraction, which is sensitive to motion. CONCLUSION: The high acceleration and background separation enabled by L+S promises to enhance spatial and temporal resolution and to enable background suppression without the need of subtraction or modeling. Magn Reson Med, 2014. (c) 2014 Wiley Periodicals, Inc.

Radial spin-echo diffusion imaging allows motion-robust imaging of tissues with very low T2 values like articular cartilage with high spatial resolution and signal-to-noise ratio (SNR). However, in vivo measurements are challenging, due to the significantly slower data acquisition speed of spin-echo sequences and the less efficient k-space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled diffusion-tensor imaging (DTI). A model-based reconstruction implicitly exploits redundancies in the diffusion-weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a total variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (three and two volunteers, respectively). Evaluation of the new approach was conducted by comparing the results with reconstructions performed with gridding, combined parallel imaging and compressed sensing and a recently proposed model-based approach. The experiments demonstrated improvements in terms of reduction of noise and streaking artifacts in the quantitative parameter maps, as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin-echo diffusion-tensor imaging without degrading parameter quantification and/or SNR

PURPOSE: Quantitative T2 -relaxation-based contrast has the potential to provide valuable clinical information. Practical T2 -mapping, however, is impaired either by prohibitively long acquisition times or by contamination of fast multiecho protocols by stimulated and indirect echoes. This work presents a novel postprocessing approach aiming to overcome the common penalties associated with multiecho protocols, and enabling rapid and accurate mapping of T2 relaxation values. METHODS: Bloch simulations are used to estimate the actual echo-modulation curve (EMC) in a multi-spin-echo experiment. Simulations are repeated for a range of T2 values and transmit field scales, yielding a database of simulated EMCs, which is then used to identify the T2 value whose EMC most closely matches the experimentally measured data at each voxel. RESULTS: T2 maps of both phantom and in vivo scans were successfully reconstructed, closely matching maps produced from single spin-echo data. Results were consistent over the physiological range of T2 values and across different experimental settings. CONCLUSION: The proposed technique allows accurate T2 mapping in clinically feasible scan times, free of user- and scanner-dependent variations, while providing a comprehensive framework that can be extended to model other parameters (e.g., T1 , B1 + , B0 , diffusion) and support arbitrary acquisition schemes. Magn Reson Med, 2014. (c) 2014 Wiley Periodicals, Inc.

The task of imaging is to gather spatiotemporal information which can be organized into a coherent map. Tomographic imaging in particular involves the use of multiple projections, or other interactions of a probe (light, sound, etc.) with a body, in order to determine cross-sectional information. Though the probes and the corresponding imaging modalities may vary, and though the methodology of particular imaging approaches is in constant ferment, the conceptual underpinnings of tomographic imaging have in many ways remained fixed for many decades. Recent advances in applied mathematics, however, have begun to roil this intellectual landscape. The advent of compressed sensing, anticipated in various algorithms dating back many years but unleashed in full theoretical force in the last decade, has changed the way imagers have begun to think about data acquisition and image reconstruction. The power of incoherent sampling and sparsity-enforcing reconstruction has been demonstrated in various contexts and, when combined with other modern fast imaging techniques, has enabled unprecedented increases in imaging efficiency. Perhaps more importantly, however, such approaches have spurred a shift in perspective, prompting us to focus less on nominal data sufficiency than on information content. Beginning with examples from MRI, then proceeding through selected other modalities such as CT and PET, as well as multimodality combinations, this paper explores the potential of newly evolving acquisition and reconstruction paradigms to change the way we do imaging in the lab and in the clinic.

We investigated how high permittivity materials affect the S-parameters and transmit

Compressed sensing is a powerful rapid imaging approach for Magnetic Resonance Imaging

Purpose: Despite the extensive opportunities offered by current state-of-the-art PET-MR systems [1], their use is still far from routine clinical practice. While it is feasible to acquire PET data from a single bed position in about 5 minutes, collecting the clinically relevant variety of traditional MR contrasts requires substantially more time. This bottleneck formed by the traditional MR paradigm leads to a relatively inefficient use of the PET component and is particularly prohibitive for multiplebed-position PET protocols. This work proposes a one-step procedure that merges the MR fingerprinting (MRF) framework [2] with the PET acquisition, and employs a dedicated multi-modality reconstruction exploiting joint information among multiple contrast weightings to enable a 6 minute comprehensive PET-MR exam, which can provide the majority of clinical MR contrasts alongside quantitative parametric maps of the relaxation parameters (T1, T2) together with improved PET images. Theory & Methods: Although MRF is inherently robust against incoherent undersampling artifacts, there is a limit beyond which the final image quality will suffer. Instead of relaying purely on incoherence between undersampling artifacts and simulated signal evolutions (standard MRF reconstruction), we propose an extension of a recently proposed nonlinear joint multimodality reconstruction [3] to simultaneously reconstruct the series of MRF images and the PET image by enforcing joint sparsity, thereby reducing residual undersampling artifacts in MR while at the same time improving PET reconstruction quality. The joint MRF-PET reconstruction is performed by minimizing the …