ACCELERATED FIRST PASS CARDIAC PERFUSION MRI USING IMPROVED k − t SLR

ABSTRACT Routinely trade-offs between the spatio-temporal resolution, volume coverage and SNR are done in first pass cardiac perfusion MRI due to the restricted imaging acquisition window (usually of the order of 300 to 400 msec per heart beat). In this paper, we demonstrate the use a low rank and sparse reconstruction scheme (k − t SLR) in obtaining highly accelerated first pass perfusion MR images and hence aim to reduce the above mentioned trade-offs. We introduce non-convex spectral norms and use a spatio-temporal total variation norm in recovering the dynamic signal matrix. We introduce an augmented Lagrangian optimization scheme in the context of matrix recovery to speed up the convergence of the algorithm. Extensive validations on in-vivo data are done to demonstrate the performance improvement of the proposed frame work

Robust Algorithms for Low-Rank and Sparse Matrix Models

Data in statistical signal processing problems is often inherently matrix-valued, and a natural first step in working with such data is to impose a model with structure that captures the distinctive features of the underlying data. Under the right model, one can design algorithms that can reliably tease weak signals out of highly corrupted data. In this thesis, we study two important classes of matrix structure: low-rankness and sparsity. In particular, we focus on robust principal component analysis (PCA) models that decompose data into the sum of low-rank and sparse (in an appropriate sense) components. Robust PCA models are popular because they are useful models for data in practice and because efficient algorithms exist for solving them. This thesis focuses on developing new robust PCA algorithms that advance the state-of-the-art in several key respects. First, we develop a theoretical understanding of the effect of outliers on PCA and the extent to which one can reliably reject outliers from corrupted data using thresholding schemes. We apply these insights and other recent results from low-rank matrix estimation to design robust PCA algorithms with improved low-rank models that are well-suited for processing highly corrupted data. On the sparse modeling front, we use sparse signal models like spatial continuity and dictionary learning to develop new methods with important adaptive representational capabilities. We also propose efficient algorithms for implementing our methods, including an extension of our dictionary learning algorithms to the online or sequential data setting. The underlying theme of our work is to combine ideas from low-rank and sparse modeling in novel ways to design robust algorithms that produce accurate reconstructions from highly undersampled or corrupted data. We consider a variety of application domains for our methods, including foreground-background separation, photometric stereo, and inverse problems such as video inpainting and dynamic magnetic resonance imaging.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143925/1/brimoor_1.pd

Free-breathing black-blood CINE fast-spin echo imaging for measuring abdominal aortic wall distensibility: a feasibility study.

The paper reports a free-breathing black-blood CINE fast-spin echo (FSE) technique for measuring abdominal aortic wall motion. The free-breathing CINE FSE includes the following MR techniques: (1) variable-density sampling with fast iterative reconstruction; (2) inner-volume imaging; and (3) a blood-suppression preparation pulse. The proposed technique was evaluated in eight healthy subjects. The inner-volume imaging significantly reduced the intraluminal artifacts of respiratory motion (p  =  0.015). The quantitative measurements were a diameter of 16.3  ±  2.8 mm and wall distensibility of 2.0  ±  0.4 mm (12.5  ±  3.4%) and 0.7  ±  0.3 mm (4.1  ±  1.0%) for the anterior and posterior walls, respectively. The cyclic cross-sectional distensibility was 35  ±  15% greater in the systolic phase than in the diastolic phase. In conclusion, we developed a feasible CINE FSE method to measure the motion of the abdominal aortic wall, which will enable clinical scientists to study the elasticity of the abdominal aorta

High-resolution diffusion-weighted brain MRI under motion

Magnetic resonance imaging is one of the fastest developing medical imaging techniques. It provides excellent soft tissue contrast and has been a leading tool for neuroradiology and neuroscience research over the last decades. One of the possible MR imaging contrasts is the ability to visualize diffusion processes. The method, referred to as diffusion-weighted imaging, is one of the most common clinical contrasts but is prone to artifacts and is challenging to acquire at high resolutions. This thesis aimed to improve the resolution of diffusion weighted imaging, both in a clinical and in a research context. While diffusion-weighted imaging traditionally has been considered a 2D technique the manuscripts and methods presented here explore 3D diffusion acquisitions with isotropic resolution. Acquiring multiple small 3D volumes, or slabs, which are combined into one full volume has been the method of choice in this work. The first paper presented explores a parallel imaging driven multi-echo EPI readout to enable high resolution with reduced geometric distortions. The work performed on diffusion phase correction lead to an understanding that was used for the subsequent multi-slab papers. The second and third papers introduce the diffusion-weighted 3D multi-slab echo-planar imaging technique and explore its advantages and performance. As the method requires a slightly increased acquisition time the need for prospective motion correction became apparent. The forth paper suggests a new motion navigator using the subcutaneous fat surrounding the skull for rigid body head motion estimation, dubbed FatNav. The spatially sparse representation of the fat signal allowed for high parallel imaging acceleration factors, short acquisition times, and reduced geometric distortions of the navigator. The fifth manuscript presents a combination of the high-resolution 3D multi-slab technique and a modified FatNav module. Unlike our first FatNav implementation, using a single sagittal slab, this modified navigator acquired orthogonal projections of the head using the fat signal alone. The combined use of both presented methods provides a promising start for a fully motion corrected high-resolution diffusion acquisition in a clinical setting