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Fat-Referenced MRI

Author: Thobias Romu

Publisher: Linköping University Electronic Press

Published: 2018-02-27

Total Pages: 85

ISBN-13: 9176853519

The amount and distribution of adipose and lean tissues has been shown to be predictive of mortality and morbidity in metabolic disease. Traditionally these risks are assessed by anthropometric measurements based on weight, length, girths or the body mass index (BMI). These measurements are predictive of risks on a population level, where a too low or a too high BMI indicates an increased risk of both mortality and morbidity. However, today a large part of the world’s population belongs to a group with an elevated risk according to BMI, many of which will live long and healthy lives. Thus, better instruments are needed to properly direct health-care resources to those who need it the most. Medical imaging method can go beyond anthropometrics. Tomographic modalities, such as magnetic resonance imaging (MRI), can measure how we have stored fat in and around organs. These measurements can eventually lead to better individual risk predictions. For instance, a tendency to store fat as visceral adipose tissue (VAT) is associated with an increased risk of diabetes type 2, cardio-vascular disease, liver disease and certain types of cancer. Furthermore, liver fat is associated with liver disease, diabetes type 2. Brown adipose tissue (BAT), is another emerging component of body-composition analysis. While the normal white adipose tissue stores fat, BAT burns energy to produce heat. This unique property makes BAT highly interesting, from a metabolic point of view. Magnetic resonance imaging can both accurately and safely measure internal adipose tissue compartments, and the fat infiltration of organs. Which is why MRI is often considered the reference method for non-invasive body-composition analysis. The two major challenges of MRI based body-composition analysis are, the between-scanner reproducibility and a cost-effective analysis of the images. This thesis presents a complete implementation of fat-referenced MRI, a technique that produces quantitative images that can increase both inter-scanner and automation of the image analysis. With MRI, it is possible to construct images where water and fat are separated into paired images. In these images, it easy to depict adipose tissue and lean tissue structures. This thesis takes water-fat MRI one step further, by introducing a quantitative framework called fat-referenced MRI. By calibrating the image using the subjects' own adipose tissue (paper II), the otherwise non-quantitative fat images are made quantitative. In these fat-referenced images it is possible to directly measure the amount of adipose tissue in different compartments. This quantitative property makes image analysis easy and accurate, as lean and adipose tissues can be separated on a sub-voxel level. Fat-referenced MRI further allows the quantification and characterization of BAT. This thesis work starts by formulating a method to produce water-fat images (paper I) based on two gradient recall images, i.e. 2-point Dixon images (2PD). It furthers shows that fat-referenced 2PD images can be corrected for T2*, making the 2PD body-composition measurements comparable with confounder-corrected Dixon measurements (paper III}). Both the water-fat separation method and fat image calibration are applied to BAT imaging. The methodology is first evaluated in an animal model, where it is shown that it can detect both BAT browning and volume increase following cold acclimatization (paper IV). It is then applied to postmortem imaging, were it is used to locate interscapular BAT in human infants (paper V). Subsequent analysis of biopsies, taken based on the MRI images, showed that the interscapular BAT was of a type not previously believed to exist in humans. In the last study, fat-referenced MRI is applied to BAT imaging of adults. As BAT structures are difficult to locate in many adults, the methodology was also extended with a multi-atlas segmentation methods (paper VI). In summary, this thesis shows that fat-referenced MRI is a quantitative method that can be used for body-composition analysis. It also shows that fat-referenced MRI can produce quantitative high-resolution images, a necessity for many BAT applications.

Fat-Referenced MRI

Author: Thobias Romu

Publisher:

Published: 2018

Total Pages:

ISBN-13:

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The amount and distribution of adipose and lean tissues has been shown to be predictive of mortality and morbidity in metabolic disease. Traditionally these risks are assessed by anthropometric measurements based on weight, length, girths or the body mass index (BMI). These measurements are predictive of risks on a population level, where a too low or a too high BMI indicates an increased risk of both mortality and morbidity. However, today a large part of the world’s population belongs to a group with an elevated risk according to BMI, many of which will live long and healthy lives. Thus, better instruments are needed to properly direct health-care resources to those who need it the most. Medical imaging method can go beyond anthropometrics. Tomographic modalities, such as magnetic resonance imaging (MRI), can measure how we have stored fat in and around organs. These measurements can eventually lead to better individual risk predictions. For instance, a tendency to store fat as visceral adipose tissue (VAT) is associated with an increased risk of diabetes type 2, cardio-vascular disease, liver disease and certain types of cancer. Furthermore, liver fat is associated with liver disease, diabetes type 2. Brown adipose tissue (BAT), is another emerging component of body-composition analysis. While the normal white adipose tissue stores fat, BAT burns energy to produce heat. This unique property makes BAT highly interesting, from a metabolic point of view. Magnetic resonance imaging can both accurately and safely measure internal adipose tissue compartments, and the fat infiltration of organs. Which is why MRI is often considered the reference method for non-invasive body-composition analysis. The two major challenges of MRI based body-composition analysis are, the between-scanner reproducibility and a cost-effective analysis of the images. This thesis presents a complete implementation of fat-referenced MRI, a technique that produces quantitative images that can increase both inter-scanner and automation of the image analysis. With MRI, it is possible to construct images where water and fat are separated into paired images. In these images, it easy to depict adipose tissue and lean tissue structures. This thesis takes water-fat MRI one step further, by introducing a quantitative framework called fat-referenced MRI. By calibrating the image using the subjects' own adipose tissue (paper II), the otherwise non-quantitative fat images are made quantitative. In these fat-referenced images it is possible to directly measure the amount of adipose tissue in different compartments. This quantitative property makes image analysis easy and accurate, as lean and adipose tissues can be separated on a sub-voxel level. Fat-referenced MRI further allows the quantification and characterization of BAT. This thesis work starts by formulating a method to produce water-fat images (paper I) based on two gradient recall images, i.e.\ 2-point Dixon images (2PD). It furthers shows that fat-referenced 2PD images can be corrected for T 2 *, making the 2PD body-composition measurements comparable with confounder-corrected Dixon measurements (paper III}). Both the water-fat separation method and fat image calibration are applied to BAT imaging. The methodology is first evaluated in an animal model, where it is shown that it can detect both BAT browning and volume increase following cold acclimatization (paper IV). It is then applied to postmortem imaging, were it is used to locate interscapular BAT in human infants (paper V). Subsequent analysis of biopsies, taken based on the MRI images, showed that the interscapular BAT was of a type not previously believed to exist in humans. In the last study, fat-referenced MRI is applied to BAT imaging of adults. As BAT structures are difficult to locate in many adults, the methodology was also extended with a multi-atlas segmentation methods (paper VI). In summary, this thesis shows that fat-referenced MRI is a quantitative method that can be used for body-composition analysis. It also shows that fat-referenced MRI can produce quantitative high-resolution images, a necessity for many BAT applications.

Human Body Composition

Author: Steven Heymsfield

Publisher: Human Kinetics

Published: 2005

Total Pages: 544

ISBN-13: 9780736046558

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The 2nd edition of Human Body Composition includes updated information and new chapters. The editors and 35 contributors are well respected researchers in the field of body composition science. This is one of few texts that provides comprehensive coverage of body composition research. The primary intent is to present current information on research methods. This book can serve as a textbook for those who are students or new researchers. Descriptions of various methods and background information are imparted in great detail with numerous references. New chapters address energy expenditure, animal body composition, molecular genetics and body composition as it relates to disease states of cancer, HIV, obesity and certain inflammatory diseases like rheumatoid arthritis, inflammatory bowel disease, congestive heart failure and chronic obstructive pulmonary disease. This book is recommended for students and new researchers in the field of body composition research who need to learn various methods, histories and practical applications--Publisher's description.

Free-Breathing Radial Magnetic Resonance Imaging Quantification of Fat and R2*

Author: Tess Armstrong

Publisher:

Published: 2018

Total Pages: 185

ISBN-13:

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Purpose Magnetic resonance imaging (MRI) can non-invasively quantify fat and the transverse relaxation rate (R2*) in the human body. This quantitative information can provide further insight about diseases such as non-alcoholic fatty liver disease (NAFLD), obesity, and ischemic placental disease (IPD). Conventional MRI methods for quantifying fat and R2* require breath-holding, which limits the spatial resolution, volumetric coverage, and signal-to-noise ratio that may be achieved. Moreover, several subject populations, including sick, elderly, and mentally impaired patients, as well as children, infants, and pregnant women, may have difficulty performing a breath-hold or are unable to breath-hold. The purpose of this work is to develop and evaluate a new free-breathing 3D stack-of-radial MRI technique (FB radial) for fat and R2* quantification at 3 Tesla (T) that overcomes the aforementioned limitations of conventional breath-holding MRI. Methods To enable free-breathing MRI, a multiecho golden-angle ordered 3D stack-of-radial radiofrequency-spoiled gradient echo sequence with gradient calibration and correction (FB radial) was developed. First, to evaluate FB radial without motion, fat quantification accuracy using FB radial was compared to conventional Cartesian and reference single-voxel magnetic resonance spectroscopy (SVS) sequences using a fat fraction phantom and in the pelvis of five healthy subjects at 3 T. To evaluate FB radial fat quantification accuracy in subjects capable of breath-holding, a population consisting of eleven healthy adults were recruited and imaged at 3 T. The fat quantification accuracy of FB radial was compared to conventional breath-held Cartesian (BH Cartesian) MRI and reference breath-held SVS (BH SVS). The feasibility and repeatability of FB radial for hepatic fat quantification was evaluated in children, which represents a population that may have limited breath-hold ability or may have difficulty complying with operator instructions. Ten healthy children and nine overweight children with NAFLD, 7-17 years of age, were imaged at 3 T using FB radial, BH Cartesian and BH SVS. Acquisitions were performed twice to assess repeatability. Images and proton-density fat fraction (PDFF) maps were scored for image quality. Liver coverage was measured. Ten healthy infants aged 2-7 months were recruited to evaluate the feasibility of FB radial for quantifying hepatic fat and body composition in a population incapable of breath-holding. The preparation time and scan time (median i interquartile range) for each non-sedated MRI exam was recorded. Abdominal and head and chest FB radial scans and abdominal Cartesian scans were performed. Abdominal scans were scored for motion artifacts by a radiologist, masked to the trajectory. Visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and brown adipose tissue (BAT) (volume and PDFF) and hepatic PDFF were measured using FB radial. Repeatability of FB radial hepatic PDFF was assessed. To evaluate the quantitative accuracy of FB radial for R2* mapping without motion, FB radial was compared to a conventional Cartesian sequence using a R2* phantom. To evaluate FB radial R2* mapping in the presence of motion, thirty subjects with normal pregnancies and three subjects with ischemic placental disease (IPD) were scanned twice: between 14-18 and 19-23 weeks gestational age (GA). Feasibility and repeatability of FB radial placental R2* mapping was assessed. The mean and spatial coefficient of variation (CV) of placental R2* was determined for all subjects, and separately for anterior and posterior placentas, at each GA range. For all analyses, quantitative accuracy of fat or R2* quantification was evaluated using linear correlation (Pearson's correlation coefficient, r; Lin's concordance correlation coefficient, c) and Bland-Altman analyses (mean difference, MD; limits of agreement, LoA = MD i 1.96 i standard deviation). The repeatability of FB radial between back-to-back scans for fat or R2* quantification was assessed by calculating the within-technique mean difference (MDwithin) and the coefficient of repeatability (CR). To compare image quality between FB radial and BH Cartesian, differences in the distribution of scores between FB radial and Cartesian were determined using McNemar-Bowker tests. For all statistical analyses, a p-value (P) 0.05 was considered significant. Results In a fat fraction phantom, FB radial demonstrated accuracy with r and c 0.995 (P 0.001), absolute MD 2.2 i 4.9% compared to SVS and absolute MD 0.6 i 3.3% compared to Cartesian. In the pelvis of healthy adults, FB radial demonstrated fat quantification accuracy with absolute MD 1.2 i 3.2% in low fat fraction regions ( 5% PDFF) and absolute MD

Magnetic Resonance Imaging

Author: Robert W. Brown

Publisher: John Wiley & Sons

Published: 2014-06-23

Total Pages: 976

ISBN-13: 0471720852

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New edition explores contemporary MRI principles and practices Thoroughly revised, updated and expanded, the second edition of Magnetic Resonance Imaging: Physical Principles and Sequence Design remains the preeminent text in its field. Using consistent nomenclature and mathematical notations throughout all the chapters, this new edition carefully explains the physical principles of magnetic resonance imaging design and implementation. In addition, detailed figures and MR images enable readers to better grasp core concepts, methods, and applications. Magnetic Resonance Imaging, Second Edition begins with an introduction to fundamental principles, with coverage of magnetization, relaxation, quantum mechanics, signal detection and acquisition, Fourier imaging, image reconstruction, contrast, signal, and noise. The second part of the text explores MRI methods and applications, including fast imaging, water-fat separation, steady state gradient echo imaging, echo planar imaging, diffusion-weighted imaging, and induced magnetism. Lastly, the text discusses important hardware issues and parallel imaging. Readers familiar with the first edition will find much new material, including: New chapter dedicated to parallel imaging New sections examining off-resonance excitation principles, contrast optimization in fast steady-state incoherent imaging, and efficient lower-dimension analogues for discrete Fourier transforms in echo planar imaging applications Enhanced sections pertaining to Fourier transforms, filter effects on image resolution, and Bloch equation solutions when both rf pulse and slice select gradient fields are present Valuable improvements throughout with respect to equations, formulas, and text New and updated problems to test further the readers' grasp of core concepts Three appendices at the end of the text offer review material for basic electromagnetism and statistics as well as a list of acquisition parameters for the images in the book. Acclaimed by both students and instructors, the second edition of Magnetic Resonance Imaging offers the most comprehensive and approachable introduction to the physics and the applications of magnetic resonance imaging.

Quantitative Magnetic Resonance Imaging

Author: Nicole Seiberlich

Publisher: Academic Press

Published: 2020-11-18

Total Pages: 1094

ISBN-13: 0128170581

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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

Epicardial Adipose Tissue

Author: Gianluca Iacobellis

Publisher: Springer Nature

Published: 2020-03-25

Total Pages: 191

ISBN-13: 3030405702

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This book is the first authoritative and comprehensive volume dedicated to epicardial adipose tissue (EAT). It provides an up-to-date and highly illustrated synopsis of the anatomical, biomolecular, genetic, imaging features, and clinical applications of EAT and its role in cardiovascular disease. It relays to the reader a contemporary view of the emerging interplay between the heart and adiposity-related diseases. In addition, this volume discusses the clinical implications and therapeutic targets of EAT in atrial fibrillation, heart failure and coronary artery disease. Comprehensive yet focused, Epicardial Adipose Tissue: From Cell to Clinic is an essential resource for physicians, residents, fellows, and medical students in cardiology, endocrinology, primary care, and health promotion and disease prevention.

Development of Magnetic Resonance Imaging (MRI) Methods for in Vivo Quantification of Lipids in Preclinical Models

Author: Roberto Salvati

Publisher:

Published: 2015

Total Pages: 0

ISBN-13:

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Obesity is associated with increased morbidity and mortality linked to many diseases, including type 2 diabetes, hypertension and disease nonalcoholic fatty liver. Recently, 1H magnetic resonance imaging (MRI) has emerged as the method of choice for non-invasive fat quantification. In this thesis, MRI methodologies were investigated for in vitro (MR phantoms) and in vivo (mice) measurements on a 4.7T preclinical scanner. Two algorithms of fat quantifications - the Dixon's method and IDEAL algorithm - were considered. The performances of the IDEAL algorithm were analyzed as a function of tissue properties (T2*, fat fraction and fat spectral model), MRI acquisition parameters (echo times, number of echoes) and experimental parameters (SNR and field map). In phantoms, the standard approach of single-T2* IDEAL showed some limitations that could be overcome by optimizing the number of echoes. A novel method to determine the ground truth values of T2* of water and T2* of fat was here proposed. For in vivo measurements, different analyses were performed using the IDEAL algorithm in liver and muscle. Statistical analysis on ROI measurements showed that the optimal choice of the number of echoes was equal to three for fat quantification and six or more for T2* quantification. The fat fraction values, calculated with IDEAL algorithm, were statistically similar to the values obtained with Dixon's method. Finally, a method for generating reference signals mimicking fat-water systems (Fat Virtual Phantom MRI), without using physical objects, was proposed. These virtual phantoms, which display realistic noise characteristics, represent an attractive alternative to physical phantoms for providing a reference signal in MRI measurements.