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

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver.

In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squares solver and the L1 total-variation regularized least absolute deviation (L1TV-LAD) solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of λ= 0.4-1.0. The L1TV-LAD with a regularization strength of λ= 0.4-0.7 was found consistently better than least-squares and vendor-provided reconstruction in BLADE MRI with a SENSitivity Encoding (SENSE) factor of 2. This warrants further development of the integrated computing system with the scanner

Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU)

A Python non-uniform fast Fourier transform (PyNUFFT) package has been developed to accelerate multidimensional non-Cartesian image reconstruction on heterogeneous platforms. Since scientific computing with Python encompasses a mature and integrated environment, the time efficiency of the NUFFT algorithm has been a major obstacle to real-time non-Cartesian image reconstruction with Python. The current PyNUFFT software enables multi-dimensional NUFFT accelerated on a heterogeneous platform, which yields an efficient solution to many non-Cartesian imaging problems. The PyNUFFT also provides several solvers, including the conjugate gradient method, ℓ1 total variation regularized ordinary least square (L1TV-OLS), and ℓ1 total variation regularized least absolute deviation (L1TV-LAD). Metaprogramming libraries have been employed to accelerate PyNUFFT. The PyNUFFT package has been tested on multi-core central processing units (CPUs) and graphic processing units (GPUs), with acceleration factors of 6.3–9.5× on a 32-thread CPU platform and 5.4–13× on a GPU

High Quality Magnetic Resonance Spectroscopy

本論文的目標是研究使用”非水抑制”對人體內的磁振頻譜作代謝物定量。此方法在某些情況下是必須的，例如在嚴重的磁場不均勻處以及動態掃描，此時水抑制不可行。同時，此方法便於針對代謝物濃度定量作絕對定量。 第一，我們使用非水抑制的方法來修正時間上的相位與頻率飄移。這個非水抑制的方法能顯著改善人體磁振頻譜品質，但不需延長掃描時間。我們成功地將此修正方法應用在體內的磁振頻譜。 第二，針對體內的非水抑制的頻譜定量，我們提出時域上的濾波-對角化的方法。根據模擬，此方法與其他時域方法有類似的穩定性與穩健性。並且，我們將此方法應用在仿體實驗與人體內實驗，顯示此方法適合應用於人體的磁振頻譜，並且也適合應用在非水抑制的磁振頻譜。The purpose of this thesis is to investigate the potential to acquire in vivo metabolic information using non-water suppressed magnetic resonance spectroscopy (NWS-MRS) technique. The use of NWS MRS is necessary when water suppression is not applicable for serious susceptibility and motion. As well, NWS MRS is potentially convenient for absolute metabolites concentration quantification. First, we correct the temporal phase and frequency drifts during MRS scans using internal water signal in the NWS MR spectra. The use of NWS method significantly improves the quality of in vivo MRS without prolong scan time. The successful application of the developed techniques to high quality in vivo MRS is demonstrated. Second, a time–domain MRS fitting method “filter-diagonalization method” (FDM), is proposed for the quantification of “non-water suppressed” MR spectra. Based on our simulation studies, the stability and robustness of FDM method is comparable to other time-domain methods. Furthermore, the phantom and in vivo experiments showed that the FDM enables the quantification of metabolic information in water suppressed (WS) and NWS MR spectra respectively.Abstract……………………………………………………………….…1 中文摘要………………………………………………………….……...2 Chapter 1 Introduction..……………………………….……..…….......3 Chapter 2 Non-water suppressed MR Spectroscopy and its Quantification algorithms……………………………………………...........7 2.1. Non-water suppressed MR Spectroscopy (NWS MRS) ……....7 2.1.1. The advantages of NWS MRS ………………………………............7 2.1.2. The difficulties and challenges of NWS MRS…………………........11 2.2 Implementation of Time-Domain Methods for NWS-MR spectrum: the phantom study……………………………...…...16 2.3 Review of frequency-domain methods and time-domain methods for MRS quantification……………………………………………..........17 2.3.1 Frequency domain methods: ……………………..………..…...........17 2.3.2 Time-domain methods………………………….………..……..........18 2.3.3 The time domain fitting algorithms……………….………..………...20 2.4 Quantitative NWS MRS: the Phantom Study………………….26 2.5 Results and Discussion ………………………………………..29 Chapter 3. Correction of motion related signal loss in MR spectroscopy ………………………………………….….30 3.1. Introduction……………………………………………………30 3.2 Correction methods for motion induced phase change………...32 3.3. Correction for motion related artifacts using NWS MRS.….…35 3.4. The procedures of constructive averaging…………………….37 3.4.1. Estimation of phase and frequency shift: matrix-pencil method (MPM) …………………………………………………………….....37 3.4.2 Phase and frequency correction for separate spectra: Klose’s method ………………………………………………………..……...38 3.4.3 Constructive averaging……………………………………….……....38 3.4.4 Summary………………………………………………………...…...38 3.5 Experiments……………….…………………………….….….40 3.6 Results………………………………………………………….44 3.7 Discussion………………………………………...……………45 Chapter 4 Filter diagonalization method (FDM) for quantitative MR spectroscopy……………………………………….……...….46 4.1 Introduction….……………………………………………...….46 4.2. Theory of FDM……………………………………………..…48 4.3 Improvements for FDM……………………………….……….51 4.4 Simulation……………………………………..……….…..…..55 4.4.1 Comparison of FDM with LPSVD and MPM ………….…….....…..55 4.4.2 Different levels of baseline distortion………………….………...…..58 4.4.3 Different levels of water suppression………………….………....…..59 4.4.4 The property of short acquisition time………………….………..…..60 4.5 Phantom study………………………………………………….65 4.6 In vivo spectrum………………………………………………..66 4.7 Using NWS-MRS and FDM for lineshape correction………....67 4.7.1. The effects of macroscopic field inhomogeneity on MRS line-shape distortion………………………………………………………….…..68 4.7.2 Correction methods for field inhomogeneity in MRS……………..…71 4.7.3 Reference deconvolution by FDM (in Time Domain) …………....…72 4.7.4 In vivo experiment……………………………………………………73 4.7.5 Results and discussion………………………..………………………75 Chapter 5 Conclusion………………………………………………….76 References…………………………………………………………..…..7

Gradient-based and wavelet-based compressed sensing approaches for highly undersampled tomographic datasets

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