Quantitative magnetic resonance imaging for cancer is an invaluable noninvasive imaging tool with superior tissue contrast and greater specificity for disease than other imaging modalities; however, MRI is costly and lengthy, making it difficult to deploy quantitative imaging techniques alongside existing clinical protocols. Moreover, radiologists require high spatial resolution images for anatomical interpretation, which is not often reconcilable with the need for high temporal resolution in dynamic quantitative imaging schemes. The purpose of this dissertation is to develop novel analysis and acquisition methods that support fast quantitative imaging for substantiating clinical protocols without necessitating significantly longer scan times, thereby providing opportunity for clinical deployment of quantitative MRI protocols. This purpose is achieved through three distinct approaches. First, we take high temporal resolution dynamic contrast-enhanced MRI data from women with locally advanced breast cancer and retrospectively abbreviate the data to varying degrees. We subsequently analyze the full-length and abbreviated datasets with perfusion models and assess the agreement between the quantitative parameters from the full-length and abbreviated analyses, showing that scans can be shortened by up to 58%. Second, using fully sampled variable-flip angle raw data of the brain from healthy volunteers, we retrospectively accelerate the raw data by 8- to 36-fold and apply an untrained deep learning method with physics-based regularization to reconstruct the anatomical images while simultaneously computing the relevant quantitative parameter map. We achieve images with high spatial resolution and quantitative maps that agree strongly with the quantitative information computed from the fully sampled data. Finally, with the purpose of noninvasively describing tumor heterogeneity, we identify three distinct tumor habitats by clustering multiparametric quantitative information from diffusion-weighted and dynamic contrast-enhanced MRI data from a murine model of glioma collected at multiple imaging visits. We subsequently model the growth of the tumor habitats over time with a system of ordinary differential equations, thereby establishing a computationally fast pipeline for noninvasive tumor habitat growth prediction. Overall, these approaches yield novel methods that enable fast acquisition of quantitative MRI data as well as pipelines for retrospective abbreviation and analysis of existing quantitative MRI data

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

Among medical imaging modalities, magnetic resonance imaging (MRI) stands out for its excellent soft-tissue contrast, anatomical detail, and high sensitivity for disease detection. However, as proven by the continuous and vast effort to develop new MRI techniques, limitations and open challenges remain. The primary source of contrast in MRI images are the various relaxation parameters associated with the nuclear magnetic resonance (NMR) phenomena upon which MRI is based. Although it is possible to quantify these relaxation parameters (qMRI) they are rarely used in the clinic, and radiological interpretation of images is primarily based upon images that are relaxation time weighted. The clinical adoption of qMRI is mainly limited by the long acquisition times required to quantify each relaxation parameter as well as questions around their accuracy and reliability. More specifically, the main limitations of qMRI methods have been the difficulty in dealing with the high inter-parameter correlations and a high sensitivity to MRI system imperfections. Recently, new methods for rapid qMRI have been proposed. The multi-parametric models at the heart of these techniques have the main advantage of accounting for the correlations between the parameters of interest as well as system imperfections. This holistic view on the MR signal makes it possible to regress many individual parameters at once, potentially with a higher accuracy. Novel, accurate techniques promise a fast estimation of relevant MRI quantities, including but not limited to longitudinal (T1) and transverse (T2) relaxation times. Among these emerging methods, MR Fingerprinting (MRF), synthetic MR (syMRI or MAGIC), and T1‒T2 Shuffling are making their way into the clinical world at a very fast pace. However, the main underlying assumptions and algorithms used are sometimes different from those found in the conventional MRI literature, and can be elusive at times. In this book, we take the opportunity to study and describe the main assumptions, theoretical background, and methods that are the basis of these emerging techniques. Quantitative transient state imaging provides an incredible, transformative opportunity for MRI. There is huge potential to further extend the physics, in conjunction with the underlying physiology, toward a better theoretical description of the underlying models, their application, and evaluation to improve the assessment of disease and treatment efficacy.

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.

Dynamic contrast-enhanced MRI is now established as the methodology of choice for the assessment of tumor microcirculation in vivo. The method assists clinical practitioners in the management of patients with solid tumors and is finding prominence in the assessment of tumor treatments, including anti-angiogenics, chemotherapy, and radiotherapy. Here, leading authorities discuss the principles of the methods, their practical implementation, and their application to specific tumor types. The text is an invaluable single-volume reference that covers all the latest developments in contrast-enhanced oncological MRI.

Owing to its exquisitely sensitive contrast mechanism, diffusion magnetic resonance imaging is a powerful non-invasive approach for studying the microstructural properties of the human brain in vivo. Magnetic resonance images are made sensitive to the microscopic displacements of water molecules that take place in brain tissue as part of the natural, physical diffusion process. Tissue water is used as an intrinsic probe, revealing important clues into the subtle architectural features of normal and pathologic brain tissue. Typical inferences include the intravoxel orientation distribution of neuronal fibers and changes in diffusion resulting from cell swelling in acute stroke. However, despite the many important advances made in the field of diffusion magnetic resonance imaging over the past decade, quantitative inference in the human brain remains somewhat limited due to the lack of direct quantitative validation against realistic biological architectures and practical limitations in data collection due to sub-optimal design parameters and artifacts caused by patient motion during scanning. In addition, current methods to resolve neuronal fiber orientations are unable to disambiguate fiber structures at different microscopic length (size) scales. In this dissertation I present a series of studies addressing each of these important limitations, starting with a general real-time image-based technique for motion correction in magnetic resonance images and ending with a series of studies on inferring complex fiber orientations from diffusion data, addressing issues such as quantitative histological validation, optimal acquisition, and improved multi-scale analysis.

This book offers the concepts of quantitative MRI for kidney imaging. Kidney MRI holds incredible promise for making a quantum leap in improving diagnosis and care of patients with a multitude of diseases, by moving beyond the limitations and restrictions of current routine clinical practice. Clinical kidney MRI is advancing with ever increasing rapidity, and yet, it is still not good enough. Several roadblocks still slow the pace of progress, particularly inefficient education of renal MR researchers, and lack of harmonization of approaches that limits the sharing of results among multiple research groups. With the help of this book, we aim to address these limitations, by providing a comprehensive collection of more chapters on MRI methods that serve as a foundational resource for clinical kidney MRI studies. This includes chapters describing the fundamental principles underlying a variety of kidney MRI methods, step-by-step protocols for executing kidney MRI studies, and detailed guides for post-processing and data analysis. This collection serves as a crucial part of a roadmap towards conducting kidney MRI studies in a robust and reproducible way, that promotes the standardization and sharing of data, and ultimately, clinical translation. Chapters are divided into three parts: MRI physics and acquisition protocols, post-processing and data analysis methods, and clinical applications. The first section includes MRI physics background and describe a detailed step by step MRI acquisition protocol. If a clinician would like to perform a renal MRI – this would include the parameters to set up the acquisition on the scanner. By this section, the reader should have the details to be able to successfully collect human renal MR images. In the second section, expert authors describe methods on how to post-process and analyze the data. By this section, the reader should have the details to be able to successfully generate quantitative data from the human renal MR images. In the final section, chapters show clinical examples of various methods. Authors share examples of multi-parametric renal MRI that are being used in clinical practice. This is an ideal guide for clinicians from radiology, nephrology, physiology, clinical scientists, and as well as basic scientists and experts in imaging sciences and physics of kidney MRI. It also provides an opportunity to students, trainees, and post-doctoral fellows to learn about these kidney MRI techniques.

This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain. The data processing pipelines described rely on R. The book is intended for readers from two communities: Statisticians who are interested in neuroimaging and looking for an introduction to the acquired data and typical scientific problems in the field; and neuroimaging students wanting to learn about the statistical modeling and analysis of MRI data. Offering a practical introduction to the field, the book focuses on those problems in data analysis for which implementations within R are available. It also includes fully worked examples and as such serves as a tutorial on MRI analysis with R, from which the readers can derive their own data processing scripts. The book starts with a short introduction to MRI and then examines the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters cover three common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, and Multi-Parameter Mapping. The book concludes with extended appendices providing details of the non-parametric statistics used and the resources for R and MRI data.The book also addresses the issues of reproducibility and topics like data organization and description, as well as open data and open science. It relies solely on a dynamic report generation with knitr and uses neuroimaging data publicly available in data repositories. The PDF was created executing the R code in the chunks and then running LaTeX, which means that almost all figures, numbers, and results were generated while producing the PDF from the sources.