Download or read book Improvements to Highly Accelerated Parallel Magnetic Resonance Imaging written by Richard Winkelmann and published by . This book was released on 2007 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improvement in High Acceleration Parallel Magnetic Resonance Imaging Using Efficient Graph based Energy Minimization Methods written by Gurmeet Singh and published by . This book was released on 2008 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Parallel Imaging in Clinical MR Applications written by Stefan O. Schönberg and published by Springer Science & Business Media. This book was released on 2007-01-11 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the first in-depth introduction to parallel imaging techniques and, in particular, to the application of parallel imaging in clinical MRI. It will provide readers with a broader understanding of the fundamental principles of parallel imaging and of the advantages and disadvantages of specific MR protocols in clinical applications in all parts of the body at 1.5 and 3 Tesla.

Download or read book Accelerated Imaging Techniques for Chemical Shift Magnetic Resonance Imaging written by Curtis N. Wiens and published by . This book was released on 2013 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chemical shift imaging is a magnetic resonance imaging technique that separates the signal from two or more chemical species. The cost of chemical shift encoding is increased acquisition time as multiple acquisitions are required at different echo times. Image accelera tion techniques, typically parallel imaging, are often used to improve coverage and resolution. This thesis describes a new technique for estimating the signal to noise ratio for parallel imaging reconstruction s and proposes new image reconstructions for a ccelerated chemical shift imaging using compressed sensing and/or parallel imaging for two applications: water- at separation and metabolic imaging of hyperpolarized [1-13C] pyruvate. Spatially varying noise in parallel imaging reconstructions makes measurements of the signal to noise ratio, a commonly used metric for image for image quality, difficult. Existing approaches have limitations: they are not applicable to all reconstructions, require significant computation time, or rely on repeated image acquisitions. A signal to noise ratio estimation technique is proposed that does not exhibit these limitations. Water-fat imaging of highly undersampled datasets from the liver, calf, knee, and abdominal cavity are demonstrated using a customized IDEAL-SPGR pulse sequence and an integrated compressed sensing, parallel imaging, water-fat reconstruction. This method offer s image quality comparable to fully sampled reference images for a range of acceleration factors. At high acceleration factors, this method offers improved image quality when compared to the current standard of parallel imaging. Accelerated metabolic imaging of hyperpolarized [1-13C] pyruvate and its metabolic by-products lactate, alanine, and bicarbonate is demonstrated using an integrated compressed sensing, metabolite separation reconstruction. Phantoms are used to validate this technique while retrospectively and prospectively accelerated 3D in vivo datasets are used to demonstrate feasibility. An alternative approach to accelerated metabolic imaging is demonstrated using high performance magnetic field gradient set. This thesis addresses the inherently slow acquisition times of chemical shift imaging by examining the role compressed sensing and parallel imaging can play in chemical shift imaging. An approach to SNR assessment for parallel imaging reconstruction is proposed and approaches to accelerated chemical shift imaging are described for applications in water-fat imaging and metabolic imaging of hyperpolarized [1-13C] pyruvate.

Download or read book Application Tailored Accelerated Magnetic Resonance Imaging Methods written by Ziwu Zhou and published by . This book was released on 2018 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Regularized Image Reconstruction in Parallel MRI with MATLAB written by Joseph Suresh Paul and published by CRC Press. This book was released on 2019-11-05 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features: Provides details for optimizing regularization parameters in each type of reconstruction. Presents comparison of regularization approaches for each type of pMRI reconstruction. Includes discussion of case studies using clinically acquired data. MATLAB codes are provided for each reconstruction type. Contains method-wise description of adapting regularization to optimize speed and accuracy. This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.

Download or read book Improved Image Reconstruction Methods for Parallel Magnetic Resonance Imaging written by Kaiyu Zheng and published by . This book was released on 2010 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Acceleration of Parallel Magnetic Resonance Imaging written by Stephanie Tsao and published by . This book was released on 2008 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Highly Parallel Magnetic Resonance Imaging with a Fourth Gradient Channel for Compensation of RF Phase Patterns written by John Carl Bosshard and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A fourth gradient channel was implemented to provide slice dependent RF coil phase compensation for arrays in dual-sided or "sandwich" configurations. The use of highly parallel arrays for single echo acquisition magnetic resonance imaging allows both highly accelerated imaging and capture of dynamic and single shot events otherwise inaccessible to MRI. When using RF coils with dimensions on the order of the voxel size, the array coil element phase patterns adversely affect image acquisition, requiring correction. This has previously been accomplished using a pulse of the gradient coil, imparting a linear phase gradient across the sample opposite of that due to the RF coil elements. However, the phase gradient due to the coil elements reverses on opposite sides of the coils, preventing gradient-based phase compensation with sandwich arrays. To utilize such arrays, which extend the imaging field of view of this technique, a fourth gradient channel and coil were implemented to simultaneously provide phase compensation of opposite magnitude to the lower and upper regions of a sample, imparting opposite phase gradients to compensate for the opposite RF coil phase patterns of the arrays. The fourth gradient coil was designed using a target field approach and constructed using printed circuit boards. This coil was integrated with an RF excitation coil, dual-sided receive array, and sample loading platform to form a single imaging probe capable of both ultra-fast and high resolution magnetic resonance imaging. By employing the gradient coil, this probe was shown to simultaneously provide improved phase compensation throughout a sample, enabling simultaneous SEA imaging using arrays placed below and above a sample. The fourth gradient coil also improves the acquisition efficiency of highly accelerated imaging using both arrays for receive. The same imaging probe was shown to facilitate accelerated MR microscopy over the field of view of the entire array with no changes to the hardware configuration. The spatio-temporal imaging capabilities of this system were explored with magnetic resonance elastography. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148045

Download or read book Parallelism Patterns and Performance in Iterative MRI Reconstruction written by Mark Murphy and published by . This book was released on 2011 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic Resonance Imaging (MRI) is a non-invasive and highly flexible medical imaging modality that does not expose patients ionizing radiation. MR Image acquisitions can be designed by varying a large number of contrast-generation parameters, and many clinical diagnostic applications exist. However, imaging speed is a fundamental limitation to many potential applications. Traditionally, MRI data have been collected at Nyquist sampling rates to produce alias-free images. However, many recent scan acceleration techniques produce sub-Nyquist samplings. For example, Parallel Imaging is a well-established acceleration technique that receives the MR signal simultaneously from multiple receive channels. Compressed sensing leverages randomized undersampling and the compressibility (e.g. via Wavelet transforms or Total-Variation) of medical images to allow more aggressive undersampling. Reconstruction of clinically viable images from these highly accelerated acquisitions requires powerful, usually iterative algorithms. Non-Cartesian pulse sequences that perform non-equispaced sampling of k-space further increase computational intensity of reconstruction, as they preclude direct use of the Fast Fourier Transform (FFT). Most iterative algorithms can be understood by considering the MRI reconstruction as an inverse problem, where measurements of un-observable parameters are made via an observation function that models the acquisition process. Traditional direct reconstruction methods attempt to invert this observation function, whereas iterative methods require its repeated computation and computation of its adjoint. As a result, na\"ive sequential implementations of iterative reconstructions produce unfeasibly long runtimes. Their computational intensity is a substantial barrier to their adoption in clinical MRI practice. A powerful new family of massively parallel microprocessor architectures has emerged simultaneously with the development of these new reconstruction techniques. Due to fundamental limitations in silicon fabrication technology, sequential microprocessors reached the power-dissipation limits of commodity cooling systems in the early 2000's. The techniques used by processor architects to extract instruction-level parallelism from sequential programs face ever-diminishing returns, and further performance improvement of sequential processors via increasing clock-frequency has become impractical. However, circuit density and process feature sizes still improve at Moore's Law rates. With every generation of silicon fabrication technology, a larger number of transistors are available to system architects. Consequently, all microprocessor vendors now exclusively produce multi-core parallel processors. Additionally, the move towards on-chip parallelism has allowed processor architects a larger degree of freedom in the design of multi-threaded pipelines and memory hierarchies. Many of the inefficiencies inherent in superscalar out-of-order design are being replaced by the high efficiency afforded by throughput-oriented designs. The move towards on-chip parallelism has resulted in a vast increase in the amount of computational power available in commodity systems. However, this move has also shifted the burden of computational performance towards software developers. In particular, the highly efficient implementation of MRI reconstructions on these systems requires manual parallelization and optimization. Thus, while ubiquitous parallelism provides a solution to the computational intensity of iterative MRI reconstructions, it also poses a substantial software productivity challenge. In this thesis, we propose that a principled approach to the design and implementation of reconstruction algorithms can ameliorate this software productivity issue. We draw much inspiration from developments in the field of computational science, which has faced similar parallelization and software development challenges for several decades. We propose a Software Architecture for the implementation of reconstruction algorithms, which composes two Design Patterns that originated in the domain of massively parallel scientific computing. This architecture allows for the most computationally intense operations performed by MRI reconstructions to be implemented as re-usable libraries. Thus the software development effort required to produce highly efficient and heavily optimized implementations of these operations can be amortized over many different reconstruction systems. Additionally, the architecture prescribes several different strategies for mapping reconstruction algorithms onto parallel processors, easing the burden of parallelization. We describe the implementation of a complete reconstruction, $\ell_1$-SPIRiT, according to these strategies. $\ell_1$-SPIRiT is a general reconstruction framework that seamlessly integrates all three of the scan acceleration techniques mentioned above. Our implementation achieves substantial performance improvement over baseline, and has enabled substantial clinical evaluation of its approach to combining Parallel Imaging and Compressive Sensing. Additionally, we include an in-depth description of the performance optimization of the non-uniform Fast Fourier Transform (nuFFT), an operation used in all non-Cartesian reconstructions. This discussion complements well our description of $\ell_1$-SPIRiT, which we have only implemented for Cartesian samplings.

Download or read book Application of Parallel Imaging to Murine Magnetic Resonance Imaging written by Chieh-Wei Chang and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of parallel imaging techniques for image acceleration is now common in clinical magnetic resonance imaging (MRI). There has been limited work, however, in translating the parallel imaging techniques to routine animal imaging. This dissertation describes foundational level work to enable parallel imaging of mice on a 4.7 Tesla/40 cm bore research scanner. Reducing the size of the hardware setup associated with typical parallel imaging was an integral part of achieving the work, as animal scanners are typically small-bore systems. To that end, an array element design is described that inherently decouples from a homogenous transmit field, potentially allowing for elimination of typically necessary active detuning switches. The unbalanced feed of this "dual-plane pair" element also eliminates the need for baluns in this case. The use of the element design in a 10-channel adjustable array coil for mouse imaging is presented, styled as a human cardiac top-bottom half-rack design. The design and construction of the homogenous transmit birdcage coil used is also described, one of the necessary components to eliminating the active detuning networks on the array elements. In addition, the design of a compact, modular multi-channel isolation preamplifier board is described, removing the preamplifiers from the elements and saving space in the bore. Several additions/improvements to existing laboratory infrastructure needed for parallel imaging of live mice are also described, including readying an animal preparation area and developing the ability to maintain isoflurane anesthesia delivery during scanning. In addition, the ability to trigger the MRI scanner to the ECG and respiratory signals from the mouse in order to achieve images free from physiological motion artifacts is described. The imaging results from the compact 10-channel mouse array coils are presented, and the challenges associated with the work are described, including difficulty achieving sample-loss dominance and signal-to-noise ratio (SNR) limitations. In conclusion, in vivo imaging of mice with cardiac and respiratory gating has been demonstrated. Compact array coils tailored for mice have been studied and potential future work and design improvements for our lab in this area are discussed. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148107

Download or read book Parallel Magnetic Resonance Imaging written by Florian Wiesinger and published by . This book was released on 2005 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improvements in Magnetic Resonance Imaging Using Information Redundancy written by Ashish Raj and published by . This book was released on 2005 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Accelerating Magnetic Resonance Imaging by Unifying Sparse Models and Multiple Receivers written by Daniel (Daniel Stuart) Weller and published by . This book was released on 2012 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Rapid 3D Phase Contrast Magnetic Resonance Angiography Through High moment Velocity Encoding and 3D Parallel Imaging written by Nicholas R. Zwart and published by . This book was released on 2011 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Phase contrast magnetic resonance angiography (PCMRA) is a non-invasive imaging modality that is capable of producing quantitative vascular flow velocity information. The encoding of velocity information can significantly increase the imaging acquisition and reconstruction durations associated with this technique. The purpose of this work is to provide mechanisms for reducing the scan time of a 3D phase contrast exam, so that hemodynamic velocity data may be acquired robustly and with a high sensitivity. The methods developed in this work focus on the reduction of scan duration and reconstruction computation of a neurovascular PCMRA exam. The reductions in scan duration are made through a combination of advances in imaging and velocity encoding methods. The imaging improvements are explored using rapid 3D imaging techniques such as spiral projection imaging (SPI), Fermat looped orthogonally encoded trajectories (FLORET), stack of spirals and stack of cones trajectories. Scan durations are also shortened through the use and development of a novel parallel imaging technique called Pretty Easy Parallel Imaging (PEPI). Improvements in the computational efficiency of PEPI and in general MRI reconstruction are made in the area of sample density estimation and correction of 3D trajectories. A new method of velocity encoding is demonstrated to provide more efficient signal to noise ratio (SNR) gains than current state of the art methods. The proposed velocity encoding achieves improved SNR through the use of high gradient moments and by resolving phase aliasing through the use measurement geometry and non-linear constraints.

Download or read book Fast Motion robust Magnetic Resonance Imaging written by Feiyu Chen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) is a non-invasive imaging modality with high value in medical imaging. MRI provides multi-contrast structural and functional information for accurate and efficient clinical diagnoses. However, compared to other medical imaging modalities, MRI has a relatively long acquisition process, thus it is usually sensitive to motion, such as respiratory motion, cardiac motion, flow motion, bowel motion, and bulk body motion. Apart from resulting in high motion-sensitivity, long scan times also impact the clinical workflow and patient comfort. Therefore, it is highly desirable to accelerate MRI and improve its motion-robustness. To enable fast motion-robust MRI, approaches for fast motion-robust T1-weighted and T2-weighted imaging were developed. Among these approaches, the proposed T2-weighted single-shot fast spin echo (SSFSE) imaging has been clinically deployed and thoroughly evaluated in clinical practice since 2017, providing useful diagnostic information for over 1,000 clinical patients. A deep-learning-based reconstruction technique was first developed for fast and robust image reconstruction of standard 2D Cartesian variable-density (VD) SSFSE acquisitions. Variational networks (VN) were trained using images reconstructed from 130 abdominal patients with standard parallel imaging and compressed sensing (PICS) and evaluated on another 27 abdominal patients. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts. Results showed that VN achieved improved perceived signal-to-noise ratio (P = 0.01) and improved sharpness (P

Download or read book Quantitative Magnetic Resonance Imaging written by Nicole Seiberlich and published by Academic Press. This book was released on 2020-12-10 with total page 1092 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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