Compressed sensing techniques for radial Ultra-short Echo Time (UTE) magnetic resonance imaging

This thesis proposes two techniques, namely Compressed Sensing (CS) and self-gating, for pre-clinical (CMRI) to reduce scan time and RF exposure to mouse heart, simply experimental procedures, and improve imaging quality. The proposed CS technique reduces the number of radial trajectories in Ultra-short Echo Time (UTE) CMRI scans on a 7 Tesla MRI machine to acquire 13% to 38% of the fully sampled k-space data. To reconstruct the image, the Non-Uniform Fast Fourier Transform (NUFFT) is utilized in each iteration of the l1-norm optimization algorithm of the CS to reduce error and aliasing. Experimental results with a phantom and a mouse heart samples show that the image quality of the proposed NUFFT-CS reconstructions, measured by the Peak Signal to Noise ratio (PSNR) and structural similarity (SSIM), is obviously better than those of traditional zero-filling method and regridding-CS method. Comparing the images of the CS technique with the reconstructions of fully sampled data, the quality degradation is illegible while the scan time is largely reduced. The proposed self-gating technique extracts the cardiac cycle information directly from the UTE CMRI measurements that are acquired without Electrocardiography (ECG) trigger. The proposed detector filters the k0 lines in the no-trigger UTE MRI scans to extract the cardiac cycle features, and automatically detects the peaks of the filtered signal as the cycle start points. The reconstruct cardiac images based on the self-gating signals reflect the cardiac cycle clearly and the scan time in MRI exams is reduced by 40% to 70%. The proposed self-gating detector differs from existing k0-line detector in the filter design and in the combination with NUFFT image reconstruction. Future research in this direction may include thorough analysis of the detector performance and may combine self-gated MRI with CS reconstruction. --Abstract, page iv

Advanced H-1 Lung Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is one of the widely used medical imaging modality, since it can provide both structural and functional assessment in a single imaging session. However, two major challenges should be considered by using MRI for lung imaging. The first challenge is the intrinsic low SNR of H-1 lung MRI due to the low proton density as well as the fast decay of the lung parenchyma signal. And the second challenge is subject motion. To achieve high resolution structural image, MRI requires a long scan time, usually a few minutes or even longer, which make MRI sensitive to subject motion. To address the first challenge, ultra-short echo time (UTE) MRI sequence is used to capture the lung parenchyma signal before decay. As for subject motion, two major strategies are widely used. One strategy is fast breath-holding scan, the subjects are asked to hold their breaths for a short duration, and the fast 3D MR sequence would be used to acquire data within that duration. This dissertation proposes a new acquisition scheme based on the standard UTE sequence, which largely increases the encoding efficiency and improves the breath-holding scan images. The other is free breathing scan with motion correction. The subjects are allowed to breathe during the MR acquisition. After the acquisition, the motion corrupted data would go through the motion correction step to reconstruct the motion free images. In this dissertation, two novel motion corrected reconstruction strategies are proposed to incorporate the motion modeling and compensation into the reconstruction to get high SNR motion corrected 3D and 4D images. When translating the developed techniques to the clinical studies, specifically for pediatric and neonatal studies, more practical problems need to be considered, such as smaller but finer anatomy to image, the different respiratory patterns of the young subjects etc. This dissertation proposes a 5-minute free breathing UTE MRI strategy to achieve a 3D high resolution motion free lung image for pediatric and neonatal studies

Magnetic Resonance Imaging of Short-T2 Tissues with Applications for Quantifying Cortical Bone Water and Myelin

The human body contains a variety of tissue species with short T2 ranging from a few microseconds to hundreds of microseconds. Detection and quantification of these short-T2 species is of considerable clinical and scientific interest. Cortical bone water and myelin are two of the most important tissue constituents. Quantification of cortical bone water concentration allows for indirect estimation of bone pore volume and noninvasive assessment of bone quality. Myelin is essential for the proper functioning of the central nervous system (CNS). Direct assessment of myelin would reveal CNS abnormalities and enhance our understanding of neurological diseases. However, conventional MRI with echo times of several milliseconds or longer is unable to detect these short-lived MR signals. Recent advances in MRI technology and hardware have enabled development of a number of short-T2 imaging techniques, key among which are ultra-short echo time (UTE) imaging, zero echo time (ZTE) imaging, and sweep imaging with Fourier transform (SWIFT). While these pulse sequences are able to detect short-T2 species, they still suffer from signal interference between different T2 tissue constituents, image artifacts and excessive scan time. These are primary technical hurdles for application to whole-body clinical scanners. In this thesis research, new MRI techniques for improving short-T2 tissue imaging have been developed to address these challenges with a focus on direct detection and quantification of cortical bone water and myelin on a clinical MRI scanner. The first focus of this research was to optimize long-T2 suppression in UTE imaging. Saturation and adiabatic RF pulses were designed to achieve maximum long-T2 suppression while maximizing the signal from short-T2 species. The imaging protocols were optimized by Bloch equation simulations and were validated using phantom and in vivo experiments. The results show excellent short-T2 contrast with these optimized pulse sequences. The problem of blurring artifacts resulting from the inhomogeneous excitation profile of the rectangular pulses in ZTE imaging was addressed. The proposed approach involves quadratic phase-modulated RF excitation and iterative solution of an inverse problem formulated from the signal model of ZTE imaging and is shown to effectively remove the image artifacts. Subsequently image acquisition efficiency was improved in order to attain clinically-feasible scan times. To accelerate the acquisition speed in UTE and ZTE imaging, compressed sensing was applied with a hybrid 3D UTE sequence. Further, the pulse sequence and reconstruction procedure were modified to enable anisotropic field-of-view shape conforming to the geometry of the elongated imaged object. These enhanced acquisition techniques were applied to the detection and quantification of cortical bone water. A new biomarker, the suppression ratio (a ratio image derived from two UTE images, one without and the other with long-T2 suppression), was conceived as a surrogate measure of cortical bone porosity. Experimental data suggest the suppression ratio may be a more direct measure of porosity than previously measured total bone water concentration. Lastly, the feasibility of directly detecting and quantifying spatially-resolved myelin concentration with a clinical imager was explored, both theoretically and experimentally. Bloch equation simulations were conducted to investigate the intrinsic image resolution and the fraction of detectable myelin signal under current scanner hardware constraints. The feasibility of quantitative ZTE imaging of myelin extract and lamb spinal cord at 3T was demonstrated. The technological advances achieved in this dissertation research may facilitate translation of short-T2 MRI methods from the laboratory to the clinic

Ultrashort echo time (UTE) imaging using gradient pre-equalization and compressed sensing.

Ultrashort echo time (UTE) imaging is a well-known technique used in medical MRI, however, the implementation of the sequence remains non-trivial. This paper introduces UTE for non-medical applications and outlines a method for the implementation of UTE to enable accurate slice selection and short acquisition times. Slice selection in UTE requires fast, accurate switching of the gradient and r.f. pulses. Here a gradient "pre-equalization" technique is used to optimize the gradient switching and achieve an effective echo time of 10μs. In order to minimize the echo time, k-space is sampled radially. A compressed sensing approach is used to minimize the total acquisition time. Using the corrections for slice selection and acquisition along with novel image reconstruction techniques, UTE is shown to be a viable method to study samples of cork and rubber with a shorter signal lifetime than can typically be measured. Further, the compressed sensing image reconstruction algorithm is shown to provide accurate images of the samples with as little as 12.5% of the full k-space data set, potentially permitting real time imaging of short T2(*) materials.HTF would like to acknowledge the financial support of the Gates-Cambridge Trust and all authors of the EPSRC (EP/K008218/1). In addition, we would like to thank SoftPoint Industries Inc. for providing samples of rubber.This version is final published version, distributed under a Creative Commons Attribution License 2.0. This can also be viewed on the publisher's website at: http://www.sciencedirect.com/science/article/pii/S1090780714001840

Motion Compensated Self Supervised Deep Learning for Highly Accelerated 3D Ultrashort Echo Time Pulmonary MRI

Purpose: To investigate motion compensated, self-supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D Pulmonary ultrashort echo time (UTE) acquisitions. Theory and Methods: A self-supervised eXtra Dimension MBDL architecture (XD-MBDL) was developed that combined respiratory states to reconstruct a single high-quality 3D image. Non-rigid, GPU based motion fields were incorporated into this architecture by estimating motion fields from a low resolution motion resolved (XD-GRASP) iterative reconstruction. Motion Compensated XD-MBDL was evaluated on lung UTE datasets with and without contrast and was compared to constrained reconstructions and variants of self-supervised MBDL that do not consider respiratory motion. Results: Images reconstructed using XD-MBDL demonstrate improved image quality as measured by apparent SNR, CNR and visual assessment relative to self-supervised MBDL approaches that do not account for dynamic respiratory states, XD-GRASP and a recently proposed motion compensated iterative reconstruction strategy (iMoCo). Additionally, XD-MBDL reduced reconstruction time relative to both XD-GRASP and iMoCo. Conclusion: A method was developed to allow self-supervised MBDL to combine multiple respiratory states to reconstruct a single image. This method was combined with GPU-based image registration to further improve reconstruction quality. This approach showed promising results reconstructing a user-selected respiratory phase from free breathing 3D pulmonary UTE acquisitions

Development and analysis of Magnetic Resonance Imaging acquisition and reconstruction methods for functional and structural investigation of cardiac and lung tissues.

The imaging of the lung and of the heart are often challenging in magnetic resonance due to the motion of the organs. In order to avoid motion artifacts it is possible to make the acquisition fast enough to fit in the breath-hold, or use some motion management methods in free breathing. A fast image acquisition can be obtained with non-Cartesian acquisition schemes, which require specialized reconstruction methods. In this work the least-squares non-uniform fast Fourier transform (LS-NUFFT) was compared to the standard gridding (GR) taking the direct summation method (DS) as reference. LS-NUFFT obtained lower root mean square error (RMSE), but heavier geometric information loss. The performance improvement of the LS-NUFFT was studied using three regularization methods. The truncated SVD reduced the RMSE of the simple regularization-free LS-NUFFT. Alternatively, the scan time can be shortened with some FOV reduction techniques. For cardiac imaging, the inner volume (IV) reduced-FOV selection was explored for the myocardial T2 mapping. The FOV reduction successfully avoided aliasing and provided a scan time reduction from about 23s to 15s. However, undesired stimulated echoes caused an overestimation in the T2 of about 20%. The effects of the inner volume excitation on the T2 mapping were described and clarified. Finally, motion management was explored for lung imaging in free-breathing, using a non-Cartesian acquisition trajectory. The rotating ultra-fast sequence (RUFIS) was demonstrated to be very suitable for the short T2* lung tissue. The respiratory motion was addressed with three methods: prospective triggering (PT), prospective gating (PG) and retrospective gating (RG). All methods were able to reconstruct a 3D high-resolution dataset. PG and RG could achieve 1.2 mm isotropic resolution in clinically reasonable scan time (~6min). The RG sequence could reconstruct multiple phases of the respiration cycle at cost of higher scan time