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Magnetic Resonance Image Reconstruction: Theory, Methods and Applications presents the fundamental concepts of MR image reconstruction, including its formulation as an inverse problem, as well as the most common models and optimization methods for reconstructing MR images. The book discusses approaches for specific applications such as non-Cartesian imaging, under sampled reconstruction, motion correction, dynamic imaging and quantitative MRI. This unique resource is suitable for physicists, engineers, technologists and clinicians with an interest in medical image reconstruction and MRI. - Explains the underlying principles of MRI reconstruction, along with the latest research - Gives example codes for some of the methods presented - Includes updates on the latest developments, including compressed sensing, tensor-based reconstruction and machine learning based reconstruction
This textbook introduces the essential concepts of tomography in the field of medical imaging. The medical imaging modalities include x-ray CT (computed tomography), PET (positron emission tomography), SPECT (single photon emission tomography) and MRI. In these modalities, the measurements are not in the image domain and the conversion from the measurements to the images is referred to as the image reconstruction. The work covers various image reconstruction methods, ranging from the classic analytical inversion methods to the optimization-based iterative image reconstruction methods. As machine learning methods have lately exhibited astonishing potentials in various areas including medical imaging the author devotes one chapter to applications of machine learning in image reconstruction. Based on college level in mathematics, physics, and engineering the textbook supports students in understanding the concepts. It is an essential reference for graduate students and engineers with electrical engineering and biomedical background due to its didactical structure and the balanced combination of methodologies and applications,
The field of magnetic resonance imaging (MRI) has developed rapidly over the past decade, benefiting greatly from the newly developed framework of compressed sensing and its ability to drastically reduce MRI scan times. MRI: Physics, Image Reconstruction, and Analysis presents the latest research in MRI technology, emphasizing compressed sensing-based image reconstruction techniques. The book begins with a succinct introduction to the principles of MRI and then: Discusses the technology and applications of T1rho MRI Details the recovery of highly sampled functional MRIs Explains sparsity-based techniques for quantitative MRIs Describes multi-coil parallel MRI reconstruction techniques Examines off-line techniques in dynamic MRI reconstruction Explores advances in brain connectivity analysis using diffusion and functional MRIs Featuring chapters authored by field experts, MRI: Physics, Image Reconstruction, and Analysis delivers an authoritative and cutting-edge treatment of MRI reconstruction techniques. The book provides engineers, physicists, and graduate students with a comprehensive look at the state of the art of MRI.
Magnetic resonance imaging (MRI) can provide high-quality multi-contrast diagnostic images. It is non-invasive and does not use ionizing radiation. Therefore, it is safe for young patients. MRI exams follow a procedure consisting of preparation, scan prescription, data collection (i.e., the actual scanning), image reconstruction, and checking the result. Unfortunately, MRI has significantly longer scan times compared to other modalities such as computed tomography (CT). These long scan times are especially challenging for children who may struggle to stay still. In this dissertation, we aim to expedite the whole MRI exam procedure by automating and accelerating four parts of the scanning process: prescription, data collection, reconstruction, and the after-scan check. This is done through a series of three projects. First, we present a method for region-of-interest (ROI) prediction and field-of-view (FOV) prescription. Manual prescription of the field of view by MRI technologists is variable and prolongs the scanning process. Often, the FOV is either too large or crops critical anatomy. We propose a deep-learning framework, trained with radiologists' supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate scalars defining the location of a rectangular ROI. The attention mechanism is used to make the model focus on a small number of informative slices in a stack. Then the smallest FOV that makes the neural network predicted an ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. The framework's performance is examined quantitatively with intersection over union (IoU) and pixel error on position, and qualitatively with a reader study. The framework's prescription is clinically acceptable 92\% of the time as rated by an experienced radiologist. Second, we present a learning-based model for reconstructing undersampled data using unpaired adversarial training. The lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this work leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space data and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network -- a cascade of convolutional and data consistency layers. The discriminator is also a multilayer Convolutional Neural Network (CNN) that plays the role of a critic scoring the quality of reconstructed images based on the Wasserstein distance metric. Our experiments with knee MRI datasets demonstrate that the proposed unpaired training enables diagnostic-quality reconstruction when high-quality image labels are not available, or when the amount of label data is small. In addition, our adversarial training scheme can achieve better image quality (as rated by expert radiologists) compared with the paired training methods using pixel-wise loss. Finally, we present a no-reference image quality assessment (IQA) framework that checks the exam outcome. In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate the patient may have to return for an additional scan, or a suboptimal interpretation is rendered. Automatic IQA would enable real-time remediation. Existing IQA methods for MRI give only a general quality score. These are agnostic to the cause of the low-quality scan and the solution for improvement. Furthermore, radiologists' image quality requirements vary with the scan type and diagnostic task. Therefore, the same score may have different implications for different scans. We propose a framework with a multi-task CNN model trained with calibrated labels and measured with image rulers. Labels calibrated by human inputs follow a well-defined and efficient labeling task. Image rulers address varying quality standards and provide a concrete way of interpreting raw scores from the CNN. The model supports assessments of perceptual noise level, rigid motion, and peristaltic motion. Our experiments show that label calibration, image rulers, and multi-task training improve the model's performance and ability to generalize.
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.
This book presents an overview of the guidelines and strategies for transitioning an image or video processing algorithm from a research environment into a real-time constrained environment. Such guidelines and strategies are scattered in the literature of various disciplines including image processing, computer engineering, and software engineering, and thus have not previously appeared in one place. By bringing these strategies into one place, the book is intended to serve the greater community of researchers, practicing engineers, industrial professionals, who are interested in taking an image or video processing algorithm from a research environment to an actual real-time implementation on a resource constrained hardware platform. These strategies consist of algorithm simplifications, hardware architectures, and software methods. Throughout the book, carefully selected representative examples from the literature are presented to illustrate the discussed concepts. After reading the book, the readers are exposed to a wide variety of techniques and tools, which they can then employ to design a real-time image or video processing system.
Emerging technologies in parallel magnetic resonance imaging (MRI) with massive receiver arrays have paved the way for ultra-fast imaging at increasingly high frame rates. With the increase in the number of receiver channels used to implement parallel imaging techniques, there is a corresponding increase in the amount of data that needs to be processed, slowing down the process of image reconstruction. To develop a complete reconstruction system which is easy to assemble in a single computer for a real-time rendition of images is a relevant challenge demanding dedicated resources for high speed digital data transfer and computation. We have enhanced a 64 channel parallel receiver system designed for single echo acquisition (SEA) MRI into a real-time imaging system by interfacing it with two commercially available digital signal processor (DSP) boards which are capable of transferring large amounts of digital data via a dedicated bus from two high performance digitizer boards. The resulting system has been used to demodulate raw image data in real-time data and store them at rates of 200 frames per second (fps) and subsequently display the processed data at rates of 26 fps. A further interest in realtime reconstruction techniques is to reduce the data handling issues. Novel ways to minimize the digitized data are presented using reduced sampling rate techniques. The proposed techniques reduce the amount of data generated by a factor of 5 without compromising the SNR and with no additional hardware. Finally, the usability of this tool is demonstrated by investigating fast imaging applications. Of particular interest among them are MR elastography applications. An exploratory study of SEA MRE was done to study the temperature dependency of shear stiffness in an agarose gel and the results correlate well with existing literature. With the ability to make MRE images in a single echo, the SEA MRE technique has an advantage over the conventional MRE techniques.
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.