Faculty Bibliography

First-pass cardiac perfusion MRI is a natural candidate for compressed sensing acceleration since its representation in the combined temporal Fourier and spatial domain is sparse and the required incoherence can be effectively accomplished by k-t random undersampling. However, the required number of samples in practice (three to five times the number of sparse coefficients) limits the acceleration for compressed sensing alone. Parallel imaging may also be used to accelerate cardiac perfusion MRI, with acceleration factors ultimately limited by noise amplification. In this work, compressed sensing and parallel imaging are combined by merging the k-t SPARSE technique with sensitivity encoding (SENSE) reconstruction to substantially increase the acceleration rate for perfusion imaging. We also present a new theoretical framework for understanding the combination of k-t SPARSE with SENSE based on distributed compressed sensing theory. This framework, which identifies parallel imaging as a distributed multisensor implementation of compressed sensing, enables an estimate of feasible acceleration for the combined approach. We demonstrate feasibility of 8-fold acceleration in vivo with whole-heart coverage and high spatial and temporal resolution using standard coil arrays. The method is relatively insensitive to respiratory motion artifacts and presents similar temporal fidelity and image quality when compared to Generalized autocalibrating partially parallel acquisitions (GRAPPA) with 2-fold acceleration

The quality of an RF detector coil design is commonly judged on how it compares with other coil configurations. The aim of this article is to develop a tool for evaluating the absolute performance of RF coil arrays. An algorithm to calculate the ultimate intrinsic signal-to-noise ratio (SNR) was implemented for a spherical geometry. The same imaging tasks modeled in the calculations were reproduced experimentally using a 32-element head array. Coil performance maps were then generated based on the ratio of experimentally measured SNR to the ultimate intrinsic SNR, for different acceleration factors associated with different degrees of parallel imaging. The relative performance in all cases was highest near the center of the samples (where the absolute SNR was lowest). The highest performance was found in the unaccelerated case and a maximum of 85% was observed with a phantom whose electrical properties are consistent with values in the human brain. The performance remained almost constant for 2-fold acceleration, but deteriorated at higher acceleration factors, suggesting that larger arrays are needed for effective highly-accelerated parallel imaging. The method proposed here can serve as a tool for the evaluation of coil designs, as well as a tool to guide the development of original designs which may begin to approach the optimal performance.

Imaging of cardiac perfusion with MR is a challenging area of research especially due to the motion of the heart and limited time of data acquisition. Compressed sensing is a popular signal estimation method recently adopted by researchers in MRI which can improve the spatial and/or temporal resolution of the acquired images by reducing the number of necessary samples for image reconstruction. This paper focuses on performance of temporal regularization with total variation and wavelets in compressed sensing. The impact of the choice of regularization parameters on the image quality and the temporal variation of intensity in region of interests (ROIs) are discussed. It is found that selecting the regularization parameter so as to optimize the quality of the reconstructed image sequence as a whole, leads to erroneous reconstruction of certain regions due to over regularization.

Introduction: Robust implementation of first-pass cardiac perfusion MRI for clinical use can be particularly challenging due to competing constraints of spatial and temporal resolution, and spatial coverage [1]. k-t SENSE [2] can be used to achieve high accelerations, but dynamic training data are required which reduces the effective acceleration rate. An alternative acceleration technique is compressed sensing (CS) [3], where spatial and temporal correlations result in sparsity of image series content, which may in turn be exploited to achieve high levels of undersampling without losing image information. We have recently presented the combination of compressed sensing and parallel imaging (JOCS: JOint-CS [4]) to increase the acceleration rate of CS alone. In this work, we demonstrate first-pass cardiac perfusion MRI with whole-heart coverage and high spatial and temporal resolution using the JOCS technique. Purpose: Evaluate the feasibility of highly-accelerated first-pass cardiac perfusion MRI with whole-heart coverage per heartbeat using JOCS. Methods: First-pass cardiac perfusion MRI with 0.1 mmol/kg of Gd-DTPA (Magnevist) was performed in two healthy volunteers and one patient with coronary artery disease. A modified multislice TurboFLASH sequence was employed on a whole-body 3 T scanner (Siemens;Tim-Trio) using the 12-element body matrix coil array. The relevant imaging parameters include: FOV = 320 mm x 320 mm, image-resolution = 1.7 mm x 1.7 mm, slice-thickness = 8 mm, TE/TR = 1.3 ms/2.5 ms, repetitions = 40. Acceleration was accomplished using ky-t random undersampling to produce the required incoherence. Breath-hold measurements with acceleration factor of R = 8 (allowing 10 (Figure presented) (Figure presented) acquired slices per heartbeat, temporal-resolution = 60 ms/slice) were performed. In the patient, delayed-enhancement images were obtained using a phase-sensitive inversion recovery (PSIR) [6] pulse sequence, 15 minutes after the administration of the contrast agent. Image reconstruction was performed using the JOCS algorithm [5]. A Fourier transform along the time dimension and finite differences along the spatial dimensions were used as sparsifying transforms. Results: Fig. 1 shows the reconstructed images (10 slices) for the peak blood and peak myocardial wall enhancement phases for one volunteer study. The reconstructed images covered most of the heart with adequate blood and myocardial wall enhancement and good image quality. Fig. 2 shows perfusion images at peak myocardial wall enhancement in three short-axis views (mid-to-apical) with perfusion defects for the patient study. The corresponding PSIR delayed-enhancement images show myocardial scarring regions that correlate well with the perfusion defect regions. Conclusion: JOCS enables first-pass cardiac perfusion MRI studies with whole-heart coverage and high spatial (<2 mm) and temporal (60 ms/slice) resolution. Future work will explore 3D imaging and the use of larger numbers of coils

Introduction: Imaging of the carotid artery with black-blood MRI can be used to identify plaques that are vulnerable for rupture [1, 2]. 3D imaging is particularly interesting to overcome the SNR and volumetric coverage limitations of 2D multi-slice techniques. However, 3D scans are more susceptible to motion artifacts, particularly swallowing-related artifacts, due to the longer acquisition times [3]. Parallel imaging can be used to accelerate the acquisition, but acceleration is limited by noise amplification. An alternative acceleration technique is compressed sensing (CS) [4], where image compressibility can be exploited to undersample k-space without losing image information. 3D imaging is a natural candidate for CS, since higher dimensional data sets increase sparsity. We propose to combine CS and parallel imaging to increase the acceleration rate for 3D carotid imaging. Purpose: Evaluate the feasibility of highly-accelerated 3D carotid MRI using CS and parallel imaging. Methods: 3D carotid MRI was performed in a healthy volunteer on a 3 T scanner (Siemens; Tim-Trio) using a custom 8-channel carotid coil array. Fully-sampled 3D fast spin echo data were acquired with T1-weighting. The relevant imaging parameters include: TE = 12 ms, TR = 800 ms, scan-time = 15 min, FOV = 190 mm x143 mm x 44 mm, image-resolution = 0.3 mm x 0.3 mm x 2 mm. Acceleration was simulated by decimating the fully-sampled data along the phase-encoding (ky) and partition-encoding (kz) dimensions by factors R = 4, 6 and 8, using a random undersampling pattern to generate the required incoherence for CS. Combination of CS and parallel imaging was performed using a single joint reconstruction algorithm (JOCS: joint CS [5]) by enforcing joint sparsity on the multicoil images in order to exploit k-space redundancy and incoherence along the coil dimension. Finite differences along x, y and z were employed to sparsify the 3D data set. A standard GRAPPA reconstruction with simulated acceleration R = 4(2 x 2) was also performed for comparison purposes. Results: Fig. 1 shows reconstructed images in an axial view and Table 1 shows the corresponding root-mean-square-error (RMSE) values. JOCS presented improved image quality over GRAPPA, which yielded more noise. Compared with R = 4, acceleration factors R = 6 and R = 8 presented more blurring and change of contrast in regions with low-value finitedifferences, which are challenging for JOCS reconstruction. Fig. 2 shows intensity profiles through a carotid vessel. JOCS with (Figure Presented) (Figure Presented) R = 4 and R = 6 presented adequate profiles, whereas for R = 8 the epithelium-tissue border was considerably blurred. Conclusion: JOCS enables higher accelerations than GRAPPA for 3D carotid imaging, which may markedly reduce sensitivity to motion. Future work will explore the use of geometricallyoriented wavelets to further improve image sparsity

Standard parallel magnetic resonance imaging (MRI) techniques suffer from residual aliasing artifacts when the coil sensitivities vary within the image voxel. In this work, a parallel MRI approach known as Superresolution SENSE (SURE-SENSE) is presented in which acceleration is performed by acquiring only the central region of k-space instead of increasing the sampling distance over the complete k-space matrix and reconstruction is explicitly based on intra-voxel coil sensitivity variation. In SURE-SENSE, parallel MRI reconstruction is formulated as a superresolution imaging problem where a collection of low resolution images acquired with multiple receiver coils are combined into a single image with higher spatial resolution using coil sensitivities acquired with high spatial resolution. The effective acceleration of conventional gradient encoding is given by the gain in spatial resolution, which is dictated by the degree of variation of the different coil sensitivity profiles within the low resolution image voxel. Since SURE-SENSE is an ill-posed inverse problem, Tikhonov regularization is employed to control noise amplification. Unlike standard SENSE, for which acceleration is constrained to the phase-encoding dimension/s, SURE-SENSE allows acceleration along all encoding directions--for example, two-dimensional acceleration of a 2D echo-planar acquisition. SURE-SENSE is particularly suitable for low spatial resolution imaging modalities such as spectroscopic imaging and functional imaging with high temporal resolution. Application to echo-planar functional and spectroscopic imaging in human brain is presented using two-dimensional acceleration with a 32-channel receiver coil

The promise of increased signal-to-noise ratio and spatial/spectral resolution continues to drive MR technology toward higher magnetic field strengths. SAR management and B1 inhomogeneity correction become critical issues at the high frequencies associated with high field MR. In recent years, multiple coil excitation techniques have been recognized as potentially powerful tools for controlling specific absorption rate (SAR) while simultaneously compensating for B1 inhomogeneities. This work explores electrodynamic constraints on transmit homogeneity and SAR, for both fully parallel transmission and its time-independent special case known as radiofrequency shimming. Ultimate intrinsic SAR--the lowest possible SAR consistent with electrodynamics for a particular excitation profile but independent of transmit coil design--is studied for different field strengths, object sizes, and pulse acceleration factors. The approach to the ultimate intrinsic limit with increasing numbers of finite transmit coils is also studied, and the tradeoff between homogeneity and SAR is explored for various excitation strategies. In the case of fully parallel transmission, ultimate intrinsic SAR shows flattening or slight reduction with increasing field strength, in contradiction to the traditionally cited quadratic dependency, but consistent with established electrodynamic principles

Parallel imaging reconstructions result in spatially varying noise amplification characterized by the g-factor, precluding conventional measurements of noise from the final image. A simple Monte Carlo based method is proposed for all linear image reconstruction algorithms, which allows measurement of signal-to-noise ratio and g-factor and is demonstrated for SENSE and GRAPPA reconstructions for accelerated acquisitions that have not previously been amenable to such assessment. Only a simple 'prescan' measurement of noise amplitude and correlation in the phased-array receiver, and a single accelerated image acquisition are required, allowing robust assessment of signal-to-noise ratio and g-factor. The 'pseudo multiple replica' method has been rigorously validated in phantoms and in vivo, showing excellent agreement with true multiple replica and analytical methods. This method is universally applicable to the parallel imaging reconstruction techniques used in clinical applications and will allow pixel-by-pixel image noise measurements for all parallel imaging strategies, allowing quantitative comparison between arbitrary k-space trajectories, image reconstruction, or noise conditioning techniques

Cardiovascular magnetic resonance imaging (CVMRI) is of proven clinical value in the non-invasive imaging of cardiovascular diseases. CVMRI requires rapid image acquisition, but acquisition speed is fundamentally limited in conventional MRI. Parallel imaging provides a means for increasing acquisition speed and efficiency. However, signal-to-noise (SNR) limitations and the limited number of receiver channels available on most MR systems have in the past imposed practical constraints, which dictated the use of moderate accelerations in CVMRI. High levels of acceleration, which were unattainable previously, have become possible with many-receiver MR systems and many-element, cardiac-optimized RF-coil arrays. The resulting imaging speed improvements can be exploited in a number of ways, ranging from enhancement of spatial and temporal resolution to efficient whole heart coverage to streamlining of CVMRI work flow. In this review, examples of these strategies are provided, following an outline of the fundamentals of the highly accelerated imaging approaches employed in CVMRI. Topics discussed include basic principles of parallel imaging; key requirements for MR systems and RF-coil design; practical considerations of SNR management, supported by multi-dimensional accelerations, 3D noise averaging and high field imaging; highly accelerated clinical state-of-the art cardiovascular imaging applications spanning the range from SNR-rich to SNR-limited; and current trends and future directions