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Optogenetic functional magnetic resonance imaging (ofMRI) [1] is a powerful new technology that enables precise control of brain circuit elements while monitoring their causal outputs. To bring ofMRI to its full potential, it is essential to achieve high-spatial resolution with minimal distortions. With our proposed compressed sensing (CS) enabled method, high-spatial resolution ofMRI images can be obtained with a large field of view (FOV) without increasing spatial distortions and the amount of acquired data. The ofMRI data were sampled with passband balanced steady-state free precession (b-SSFP) [8, 17] fast stack-of-spiral sequence in order to achieve ultra-high-spatial resolution images in a short amount of time. Interleaves of data were randomly collected. The images were recovered from the undersampled k-space data by solving an unconstrained convex optimization problem, which balances the trade-off between data consistency and sparsity. The optimization problem can be solved by gradient descent combined with backtracking line search algorithms. Discrete cosine transform (DCT) were chosen as a sparsifying transform. The ofMRI image reconstruction was processed in parallel on a graphics processing unit (GPU) using C/C++ language supported by NVIDIA CUDA engine in order to achieve short reconstruction time. An existing nonequispaced fast Fourier transform (NFFT) algorithm [13, 14] was modified for our GPU parallel processing purpose. The results demonstrate that the compressed sensing reconstructed image has higher resolution while maintaining a precise activation map, compared to a fully sampled low-resolution image with the same amount of data and scan time. A 4-D image can be reconstructed in less than fifteen minutes, which allows compressed sensing ofMRI to become a practical application.
Functional magnetic resonance imaging (fMRI) is a technique that enables non-invasive monitoring of brain activity by detecting changes in blood oxygenation levels. With recent advancements in high performance computing and MRI hardware, real-time fMRI has become possible and the spatiotemporal resolution of fMRI has been significantly improved. However, there are still many challenges for fMRI to achieve its full potential. First, because many basic real-time fMRI modules still uses a large portion of the available processing time, there is insufficient time for the integration of advanced real-time fMRI techniques. Second, current high-resolution fMRI techniques do not provide the resolution needed for imaging activity of small but critical brain regions, such as cortical layers and hippocampal sub-regions. Third, it is still not trivial to achieve the high-resolution and real-time fMRI at once because significant higher computation power is needed. To address these challenges, three projects were conducted and illustrated in this dissertation. In the first project, a high-throughput real-time fMRI system is designed on the graphics processing unit (GPU) to overcome computation barriers associated with reconstruction of non-uniformly sampled image, motion correction and statistical analysis. This system achieves an overall processing speed of 15.01 ms per 3D image, which is more than 49-fold faster than widely used software packages. The high processing speed also enables sliding window reconstruction, which improves the temporal resolution. With this ultra high speed fMRI system, integration of CS reconstruction for real-time and high spatiotemporal resolution fMRI becomes possible. The second project explores the feasibility of CS fMRI and demonstrates a High SPAtial Resolution compressed SEnsing (HSPARSE) fMRI method. HSPARSE fMRI enables a 6-fold spatial resolution improvement with contrast to noise ratio (CNR) increase and no loss of temporal resolution. A novel randomly under-sampled, variable density spiral data acquisition trajectory is designed to achieve an imaging speed acceleration factor of 5.3, which is 32 \% higher than previously reported CS fMRI methods. HSPARSE fMRI also achieves high sensitivity and low false positive rate. Importantly, its high spatial resolution enables localization of brain regions that cannot be resolved using the highest spatial resolution fully-sampled reconstruction. The third project combines the methods in the previous two into a real-time high-resolution CS fMRI system. A random stack of variable density spiral trajectory is first designed to achieve highly incoherent CS sampling and 3.2 times imaging speed acceleration. An optimized CS reconstruction algorithm using wavelet regularization is then implemented on GPU, which achieves a reconstruction speed of 605 ms per 3D image. This method also achieves a 4-fold spatial resolution improvement, with increased CNR, high sensitivity, low false positive rate and no loss of temporal resolution. Notably, this is the first system that achieves the real-time 3D non-uniformly sampled image CS fMRI reconstruction.
This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI) is able to reduce the scan time of MRI considerably as it is possible to reconstruct MR images from only a few measurements in the k-space; far below the requirements of the Nyquist sampling rate. L1-norm-based regularization problems can be solved efficiently using the state-of-the-art convex optimization techniques, which in general outperform the greedy techniques in terms of quality of reconstructions. Recently, fast convex optimization based reconstruction algorithms have been developed which are also able to achieve the benchmarks for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of medical image processing, particularly in MRI to understand the need for the CS in MRI, and thereby how it could revolutionize the soft tissue imaging to benefit healthcare technology without making major changes in the existing scanner hardware. It would be particularly useful for researchers who have just entered into the exciting field of CS-MRI and would like to quickly go through the developments to date without diving into the detailed mathematical analysis. Finally, it also discusses recent trends and future research directions for implementation of CS-MRI in clinical practice, particularly in Bio- and Neuro-informatics applications.
Expecting the reader to have some basic training in liner algebra and optimization, the book begins with a general discussion on CS techniques and algorithms. It moves on to discussing single channel static MRI, the most common modality in clinical studies. It then takes up multi-channel MRI and the interesting challenges consequently thrown up in signal reconstruction. Off-line and on-line techniques in dynamic MRI reconstruction are visited. Towards the end the book broadens the subject by discussing how CS is being applied to other areas of biomedical signal processing like X-ray, CT and EEG acquisition. The emphasis throughout is on qualitative understanding of the subject rather than on quantitative aspects of mathematical forms. The book is intended for MRI engineers interested in the brass tacks of image formation; medical physicists interested in advanced techniques in image reconstruction; and mathematicians or signal processing engineers.
The popularity of magnetic resonance (MR) imaging in medicine is no mystery: it is non-invasive, it produces high quality structural and functional image data, and it is very versatile and flexible. Research into MR technology is advancing at a blistering pace, and modern engineers must keep up with the latest developments. This is only possible with a firm grounding in the basic principles of MR, and Advanced Image Processing in Magnetic Resonance Imaging solidly integrates this foundational knowledge with the latest advances in the field. Beginning with the basics of signal and image generation and reconstruction, the book covers in detail the signal processing techniques and algorithms, filtering techniques for MR images, quantitative analysis including image registration and integration of EEG and MEG techniques with MR, and MR spectroscopy techniques. The final section of the book explores functional MRI (fMRI) in detail, discussing fundamentals and advanced exploratory data analysis, Bayesian inference, and nonlinear analysis. Many of the results presented in the book are derived from the contributors' own work, imparting highly practical experience through experimental and numerical methods. Contributed by international experts at the forefront of the field, Advanced Image Processing in Magnetic Resonance Imaging is an indispensable guide for anyone interested in further advancing the technology and capabilities of MR imaging.
The foundation for understanding the function and dynamics of biological systems is not only knowledge of their structure, but the new methodologies and applications used to determine that structure. This volume in Biological Magnetic Resonance emphasizes the methods that involve Ultra High Field Magnetic Resonance Imaging. It will interest researchers working in the field of imaging.
Quantitative Magnetic Resonance Imaging is a 'go-to' reference for methods and applications of quantitative magnetic resonance imaging, with specific sections on Relaxometry, Perfusion, and Diffusion. Each section will start with an explanation of the basic techniques for mapping the tissue property in question, including a description of the challenges that arise when using these basic approaches. For properties which can be measured in multiple ways, each of these basic methods will be described in separate chapters. Following the basics, a chapter in each section presents more advanced and recently proposed techniques for quantitative tissue property mapping, with a concluding chapter on clinical applications. The reader will learn: - The basic physics behind tissue property mapping - How to implement basic pulse sequences for the quantitative measurement of tissue properties - The strengths and limitations to the basic and more rapid methods for mapping the magnetic relaxation properties T1, T2, and T2* - The pros and cons for different approaches to mapping perfusion - The methods of Diffusion-weighted imaging and how this approach can be used to generate diffusion tensor - maps and more complex representations of diffusion - How flow, magneto-electric tissue property, fat fraction, exchange, elastography, and temperature mapping are performed - How fast imaging approaches including parallel imaging, compressed sensing, and Magnetic Resonance - Fingerprinting can be used to accelerate or improve tissue property mapping schemes - How tissue property mapping is used clinically in different organs - Structured to cater for MRI researchers and graduate students with a wide variety of backgrounds - Explains basic methods for quantitatively measuring tissue properties with MRI - including T1, T2, perfusion, diffusion, fat and iron fraction, elastography, flow, susceptibility - enabling the implementation of pulse sequences to perform measurements - Shows the limitations of the techniques and explains the challenges to the clinical adoption of these traditional methods, presenting the latest research in rapid quantitative imaging which has the possibility to tackle these challenges - Each section contains a chapter explaining the basics of novel ideas for quantitative mapping, such as compressed sensing and Magnetic Resonance Fingerprinting-based approaches
This book presents a comprehensive review of the recent developments in fast L1-norm regularization-based compressed sensing (CS) magnetic resonance image reconstruction algorithms. Compressed sensing magnetic resonance imaging (CS-MRI) is able to reduce the scan time of MRI considerably as it is possible to reconstruct MR images from only a few measurements in the k-space; far below the requirements of the Nyquist sampling rate. L1-norm-based regularization problems can be solved efficiently using the state-of-the-art convex optimization techniques, which in general outperform the greedy techniques in terms of quality of reconstructions. Recently, fast convex optimization based reconstruction algorithms have been developed which are also able to achieve the benchmarks for the use of CS-MRI in clinical practice. This book enables graduate students, researchers, and medical practitioners working in the field of medical image processing, particularly in MRI to understand the need for the CS in MRI, and thereby how it could revolutionize the soft tissue imaging to benefit healthcare technology without making major changes in the existing scanner hardware. It would be particularly useful for researchers who have just entered into the exciting field of CS-MRI and would like to quickly go through the developments to date without diving into the detailed mathematical analysis. Finally, it also discusses recent trends and future research directions for implementation of CS-MRI in clinical practice, particularly in Bio- and Neuro-informatics applications.
Magnetic resonance imaging (MRI) is a powerful diagnostic medical imaging technique that provides very high spatial resolution. By manipulating the signal evolution through careful imaging sequence design, MRI can generate a wide range of soft-tissue contrast unique to individual application. However, imaging speed remains an issue for many applications. In order to increase scan output without compromising the image quality, the data acquisition and image reconstruction methods need to be designed to fit each application to achieve maximum efficiency. This dissertation concerns several application-tailored accelerated imaging methods through improved sequence design, efficient k-space traverse, as well as tailored image reconstruction algorithm, all together aiming to exploit the full potential of data acquisition and image reconstruction in each application. The first application is ferumoxtyol-enhanced 4D multi-phase cardiovascular MRI on pediatric patients with congenital heart disease. By taking advantage of the high signal-to-noise ratio (SNR) results from contrast enhancement, we introduced two methods to improve the scan efficiency with maintained clinical utility: one with reduced scan time and one with improved temporal resolution. The first method used prospective Poisson-disc under-sampling in combination with graphics processing unit accelerated parallel imaging and compressed sensing combined reconstruction algorithm to reduce scan time by approximately 50% while maintaining highly comparable image quality to un-accelerated acquisition in a clinically practical reconstruction time. The second method utilized a motion weighted reconstruction technique to increase temporal resolution of acquired data, and thus permits improved cardiac functional assessment. Compared with existing acceleration method, the proposed method has nearly three times lower computation burden and six times faster reconstruction speed, all with equal image quality. The second application is noncontrast-enhanced 4D intracranial MR angiography with arterial spin labeling (ASL). Considering the inherently low SNR of ASL signal, we proposed to sample k-space with the efficient golden-angle stack-of-stars trajectory and reconstruct images using compressed sensing with magnitude subtraction as regularization. The acquisition and reconstruction strategy in combination produces images with detailed vascular structures and clean background. At the same time, it allows a reduced temporal blurring delineation of the fine distal arteries when compared with the conventional k-space weighted image contrast (KWIC) reconstruction. Stands upon on this, we further developed an improved stack-of-stars radial sampling strategy for reducing streaking artifacts in general volumetric MRI. By rotating the radial spokes in a golden angle manner along the partition-encoding direction, the aliasing pattern due to under-sampling is modified, resulting in improved image quality for gridding and more advanced reconstruction methods. The third application is low-latency real-time imaging. To achieve sufficient frame rate, real-time MRI typically requires significant k-space under-sampling to accelerate the data acquisition. At the same time, many real-time application, such as interventional MRI, requires user interaction or decision making based on image feedback. Therefore, low-latency on-the-fly reconstruction is highly desirable. We proposed a parallel imaging and convolutional neural network combined image reconstruction framework for low-latency and high quality reconstruction. This is achieved by compacting gradient descent steps resolved from conventional parallel imaging reconstruction as network layers and interleaved with convolutional layers in a general convolutional neural network. Once all parameters of the network are determined during the off-line training process, it can be applied to unseen data with less than 100ms reconstruction time per frame, while more than 1s is usually needed for conventional parallel imaging and compressed sensing combined reconstruction.
Magnetic resonance imaging (MRI) is an increasingly versatile diagnostic tool for a variety of medical purposes. During a conventional MRI scan, samples are acquired along a trajectory in the spatial Fourier transform domain (called k-space) and the image is reconstructed using an inverse discrete Fourier transform. The affordability, availability, and applications of MRI remain limited by the time required to sample enough points of k-space for the desired field of view (FOV), resolution, and signal-to-noise ratio (SNR). GRAPPA, an accelerated parallel imaging method, and compressed sensing (CS) have been successfully employed to accelerate the acquisition process by reducing the number of k-space samples required. GRAPPA leverages the different spatial weightings of each receiver coil to undo the aliasing from the reduction in FOV induced by undersampling k-space. However, accelerated parallel imaging reconstruction methods like GRAPPA amplify the noise present in the data, reducing the SNR by a factor greater than that due to only the level of undersampling. Completely separate from accelerated parallel imaging, which capitalizes on observing data with multiple receivers, CS leverages the sparsity of the object along with incoherent sampling and nonlinear reconstruction algorithms to recover the image from fewer samples. In contrast to parallel imaging, CS actually denoises the result, because noise typically is not sparse. When reconstructing brain images, the discrete wavelet transform and finite differences are effective in producing an approximately sparse representation of the image. Because parallel imaging utilizes the multiple receiver coils and CS takes advantage of the sparsity of the image itself, these methods are complementary, and a combination of these methods would be expected to enable further acceleration beyond what is achievable using parallel imaging or CS alone. This thesis investigates three approaches to leveraging both multiple receiver coils and image sparsity. The first approach involves an optimization framework for jointly optimizing the fidelity to the GRAPPA result and the sparsity of the image. This technique operates in the nullspace of the data observation matrix, preserving the acquired data without resorting to techniques for constrained optimization. While this framework is presented generally, the effectiveness of the implementation depends on the choice of sparsifying transform, sparsity penalty function, and undersampling pattern. The second approach involves modifying the kernel estimation step of GRAPPA to promote sparsity in the reconstructed image and mitigate the noise amplification typically encountered with parallel imaging. The third approach involves imposing a sparsity prior on the coil images and estimating the full k-space from the observations using Bayesian techniques. This third method is extended to jointly estimate the GRAPPA kernel weights and the full k-space together. These approaches represent different frameworks for accelerating MRI imaging beyond current methods. The results presented suggest that these practical reconstruction and post-processing methods allow for greater acceleration with conventional Cartesian acquisitions.