Relevant bibliographies by topics / Fat-Water Imaging

Academic literature on the topic 'Fat-Water Imaging'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Journal articles on the topic "Fat-Water Imaging":

1

Bley, Thorsten A., Oliver Wieben, Christopher J. François, Jean H. Brittain, and Scott B. Reeder. "Fat and water magnetic resonance imaging." Journal of Magnetic Resonance Imaging 31, no. 1 (December 20, 2009): 4–18. http://dx.doi.org/10.1002/jmri.21895.

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Ma, Jingfei. "Dixon techniques for water and fat imaging." Journal of Magnetic Resonance Imaging 28, no. 3 (September 2008): 543–58. http://dx.doi.org/10.1002/jmri.21492.

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Xiang, Qing-San, and Li An. "Water-fat imaging with direct phase encoding." Journal of Magnetic Resonance Imaging 7, no. 6 (November 1997): 1002–15. http://dx.doi.org/10.1002/jmri.1880070612.

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Bauer, Daniel R., Xiong Wang, Jeff Vollin, Hao Xin, and Russell S. Witte. "Spectroscopic thermoacoustic imaging of water and fat composition." Applied Physics Letters 101, no. 3 (July 16, 2012): 033705. http://dx.doi.org/10.1063/1.4737414.

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Reeder, Scott B., Charles A. McKenzie, Angel R. Pineda, Huanzhou Yu, Ann Shimakawa, Anja C. Brau, Brian A. Hargreaves, Garry E. Gold, and Jean H. Brittain. "Water–fat separation with IDEAL gradient-echo imaging." Journal of Magnetic Resonance Imaging 25, no. 3 (2007): 644–52. http://dx.doi.org/10.1002/jmri.20831.

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Goldfarb, James W. "Fat-water separated delayed hyperenhanced myocardial infarct imaging." Magnetic Resonance in Medicine 60, no. 3 (September 2008): 503–9. http://dx.doi.org/10.1002/mrm.21685.

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Salvati, Roberto, Eric Hitti, Jean-Jacques Bellanger, Hervé Saint-Jalmes, and Giulio Gambarota. "Fat ViP MRI: Virtual Phantom Magnetic Resonance Imaging of water–fat systems." Magnetic Resonance Imaging 34, no. 5 (June 2016): 617–23. http://dx.doi.org/10.1016/j.mri.2015.12.002.

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SIMON, JACK H., and JERZY SZUMOWSKI. "Proton (Fat/Water) Chemical Shift Imaging in Medical Magnetic Resonance Imaging." Investigative Radiology 27, no. 10 (October 1992): 865–74. http://dx.doi.org/10.1097/00004424-199210000-00018.

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Wiens, Curtis N., Colin M. McCurdy, Jacob D. Willig-Onwuachi, and Charles A. McKenzie. "R2*-corrected water-fat imaging using compressed sensing and parallel imaging." Magnetic Resonance in Medicine 71, no. 2 (March 8, 2013): 608–16. http://dx.doi.org/10.1002/mrm.24699.

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Yu, Huanzhou, Scott B. Reeder, Ann Shimakawa, Charles A. McKenzie, and Jean H. Brittain. "Robust multipoint water-fat separation using fat likelihood analysis." Magnetic Resonance in Medicine 67, no. 4 (August 12, 2011): 1065–76. http://dx.doi.org/10.1002/mrm.23087.

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Journal articles Dissertations / Theses

Dissertations / Theses on the topic "Fat-Water Imaging":

1

An, Li. "Water-fat imaging and general chemical shift imaging with spectrum modeling." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0032/NQ38848.pdf.

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Huang, Fangping. "Water and Fat Image Reconstruction in Magnetic Resonance Imaging." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1309791802.

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Mehemed, Taha Mohamed M. "Fat-Water Interface on Susceptibility-Weighted Imaging and Gradient-Echo Imaging: Comparison of Phantoms to Intracranial Lipomas." Kyoto University, 2014. http://hdl.handle.net/2433/193572.

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Sun, Ling. "3D Mellisa : a new three dimensional fat/water image acquisition technique for magnetic resonance imaging /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487854314873059.

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Berglund, Johan. "Separation of Water and Fat Signal in Magnetic Resonance Imaging : Advances in Methods Based on Chemical Shift." Doctoral thesis, Uppsala universitet, Enheten för radiologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-158111.

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Abstract:
Magnetic resonance imaging (MRI) is one of the most important diagnostic tools of modern healthcare. The signal in medical MRI predominantly originates from water and fat molecules. Separation of the two components into water-only and fat-only images can improve diagnosis, and is the premier non-invasive method for measuring the amount and distribution of fatty tissue. Fat-water imaging (FWI) enables fast fat/water separation by model-based estimation from chemical shift encoded data, such as multi-echo acquisitions. Qualitative FWI is sufficient for visual separation of the components, while quantitative FWI also offers reliable estimates of the fat percentage in each pixel. The major problems of current FWI methods are long acquisition times, long reconstruction times, and reconstruction errors that degrade image quality. In this thesis, existing FWI methods were reviewed, and novel fully automatic methods were developed and evaluated, with a focus on fast 3D image reconstruction. All MRI data was acquired on standard clinical scanners. A triple-echo qualitative FWI method was developed for the specific application of 3D whole-body imaging. The method was compared with two reference methods, and demonstrated superior image quality when evaluated in 39 volunteers. The problem of qualitative FWI by dual-echo data with unconstrained echo times was solved, allowing faster and more flexible image acquisition than conventional FWI. Feasibility of the method was demonstrated in three volunteers and the noise performance was evaluated. Further, a quantitative multi-echo FWI method was developed. The signal separation was based on discrete whole-image optimization. Fast 3D image reconstruction with few reconstruction errors was demonstrated by abdominal imaging of ten volunteers. Lastly, a method was proposed for quantitative mapping of average fatty acid chain length and degree of saturation. The method was validated by imaging different oils, using gas-liquid chromatography (GLC) as the reference. The degree of saturation agreed well with GLC, and feasibility of the method was demonstrated in the thigh of a volunteer. The developed methods have applications in clinical settings, and are already being used in several research projects, including studies of obesity, dietary intervention, and the metabolic syndrome.
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Shibahara, Eriko, Hiroshi Fukatsu, Shinji Naganawa, Tokiko Ito, Eriko Iwayama, Takeo Ishigaki, Toru Segawa, and Waguo Zhang. "Water fat separation using the single acquisition "sandwich" type 3-point Dixon method to optimize knee joint scans." Nagoya University School of Medicine, 2000. http://hdl.handle.net/2237/5354.

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Bookwalter, Candice Anne. "CONTINUOUS SAMPLING IN MAGNETIC RESONANCE IMAGING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1194049081.

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Mendoza, Michael A. "Water Fat Separation with Multiple-Acquisition Balanced Steady-State Free Precession MRI." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/4304.

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Magnetic resonance imaging (MRI) is an important medical imaging technique for visualizing soft tissue structures in the body. It has the advantages of being noninvasive and, unlike x-ray, does not rely on ionizing radiation for imaging. In traditional hydrogen-based MRI, the strongest measured signals are generated from the hydrogen nuclei contained in water and fat molecules.Reliable and uniform water fat separation can be used to improve medical diagnosis. In many applications the water component is the primary signal of interest, while the fat component represents a signal which can obscure the underlying pathology or other features of interest. In other applications the fat signal is the signal of interest. There currently exist many techniques for water fat separation. Dixon reconstruction techniques take multiple images acquired at select echo times with specific phase properties. Linear combinations of these images produce separate water and fat images. In MR imaging, images with high signal-to-noise ratio (SNR), that can be generated in a short time, are desired. Balanced steady-state free precession (bSSFP) MRI is a technique capable of producing images with high SNR in a short imaging time but suffers from signal voids or banding artifacts due to magnetic field inhomogeneity and susceptibly variations. These signal voids degrade image quality. Several methods have been developed to remove these banding effects. The simplest methods combine images across multiple bSSFP image acquisitions. This thesis describes a technique in water fat separation I developed which combines the advantages of bSSFP with Dixon reconstruction in order to produce robust water fat decomposition with high SNR in a short imaging time, while simultaneously reducing banding artifacts which traditionally degrade image quality. This algorithm utilizes four phased-cycled bSSFP acquisitions at specific echo times. Phase sensitive post-processing and a field map are used to prepare the data and reduce the effects of field inhomogeneities. Dixon reconstruction is then used to generate separate water and fat images.
9

Salvati, Roberto. "Development of Magnetic Resonance Imaging (MRI) methods for in vivo quantification of lipids in preclinical models." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1B026/document.

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L'obésité est associée à une augmentation de la morbidité et de la mortalité liée à de nombreuses maladies, y compris le diabète de type 2, l'hypertension et des pathologies hépatiques menant à une surcharge lipidique d’origine non alcoolique. Récemment, l’imagerie par résonance magnétique (IRM) est devenue la méthode de choix pour la quantification non invasive de la graisse. Dans cette thèse, les méthodes d'IRM ont été étudiées sur un scanner préclinique de 4.7T in vitro (fantômes MR) et in vivo (souris). Deux algorithmes de quantifications de la graisse -la méthode de Dixon et l’algorithme IDEAL- ont été considérés. Les performances de l'algorithme IDEAL ont été analysées en fonction de propriétés des tissus (T2*, fraction de graisse et modèle spectral de la graisse), de paramètres d'acquisition IRM (temps d’écho, nombre d'échos) et de paramètres expérimentaux (SNR et carte de champ). Sur les fantômes, l'approche standard single-T2* IDEAL a montré certaines limites qui pourraient être surmontées en optimisant le nombre d'échos. Une nouvelle méthode, pour déterminer les valeurs de vérité terrain pour T2* de l'eau et pour T2* de la graisse, a été proposée. Pour les mesures in vivo, différentes analyses ont été effectuées en utilisant l'algorithme IDEAL sur le foie et les muscles. L'analyse statistique sur les mesures de ROI a montré que le choix optimal du nombre d'échos est égal à trois pour la quantification de la graisse et six ou plus pour la quantification du T2*. Les valeurs de la fraction de graisse, calculées avec l'algorithme IDEAL, étaient statistiquement comparables aux valeurs obtenues avec la méthode de Dixon. Enfin, un procédé pour générer des signaux de référence mimant les systèmes eau-graisse (Fat Virtual Phantom MRI), sans l'aide d'objets physiques, a été proposé. Ces fantômes virtuels, qui présentent des caractéristiques de bruit réalistes, représentent une alternative intéressante aux fantômes physiques pour fournir un signal de référence dans les mesures IRM
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
10

Belbaisi, Adham. "Deep Learning-Based Skeleton Segmentation for Analysis of Bone Marrow and Cortical Bone in Water-Fat Magnetic Resonance Imaging." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297528.

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Abstract:
A major health concern for subjects with diabetes is weaker bones and increased fracture risk. Current clinical assessment of the bone strength is performed by measuring Bone Mineral Density (BMD), where low BMD-values are associated with an increased risk of fracture. However, subjects with Type 2 Diabetes (T2D) have been shown to have normal or higher BMD-levels compared to healthy controls, which does not reflect the recognized bone fragility among diabetics. Thus, there is need for more research about diabetes-related bone fragility to find other factors of impaired bone health. One potential biomarker that has recently been studied is Bone Marrow Fat (BMF). The data in this project consisted of whole-body water-fat Magnetic Resonance Imaging (MRI) volumes from the UK Biobank Imaging study (UKBB). Each subject in this data has a water volume and a fat volume, allowing for a quantitative assessment of water and fat content in the body. To analyze and perform quantitative measurements of the bones specifically, a Deep Learning (DL) model was trained, validated, and tested for performing fully automated and objective skeleton segmentation, where six different bones were segmented: spine, femur, pelvis, scapula, clavicle and humerus. The model was trained and validated on 120 subjects with 6-fold cross-validation and tested on eight subjects. All ground-truth segmentations of the training and test data were generated using two semi-automatic pipelines. The model was evaluated for each bone separately as well as the overall skeleton segmentation and achieved varying accuracy, performing better on larger bones than on smaller ones. The final trained model was applied on a larger dataset of 9562 subjects (16% type 2 diabetics) and the BMF, as well as bone marrow volume (BMV) and cortical bone volume (CBV), were measured in the segmented bones of each subject. The results of the quantified biomarkers were compared between T2D and healthy subjects. The comparison revealed possible differences between healthy and diabetic subjects, suggesting a potential for new findings related to diabetes and associated bone fragility.
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Books on the topic "Fat-Water Imaging":

1

Poon, Colin Shiu On. Relaxation time measurement and fat/water quantification using magnetic resonance imaging : technical development and clinical applications. 1992.

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Book chapters on the topic "Fat-Water Imaging":

1

Lugauer, Felix, Dominik Nickel, Jens Wetzl, Stephan A. R. Kannengiesser, Andreas Maier, and Joachim Hornegger. "Robust Spectral Denoising for Water-Fat Separation in Magnetic Resonance Imaging." In Lecture Notes in Computer Science, 667–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24571-3_80.

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Zhao, Liang, Yiqiang Zhan, Dominik Nickel, Matthias Fenchel, Berthold Kiefer, and Xiang Sean Zhou. "Identification of Water and Fat Images in Dixon MRI Using Aggregated Patch-Based Convolutional Neural Networks." In Patch-Based Techniques in Medical Imaging, 125–32. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47118-1_16.

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"Water/Fat Separation Techniques." In Magnetic Resonance Imaging, 413–45. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118633953.ch17.

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Kozerke, Sebastian, Redha Boubertakh, and Marc Miquel. "Basic pulse sequences." In The EACVI Textbook of Cardiovascular Magnetic Resonance, edited by Massimo Lombardi, Sven Plein, Steffen Petersen, Chiara Bucciarelli-Ducci, Emanuela R. Valsangiacomo Buechel, Cristina Basso, and Victor Ferrari, 17–25. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198779735.003.0005.

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Pulse sequences control the timing of radiofrequency pulses and time-varying gradients that are necessary to create an image. Sequences are divided into different types: gradient echo, spin echo, and hybrid echo sequences. In cardiac imaging, ‘black blood’ spin echo images are used for anatomical imaging, while ‘bright blood’ imaging is used to study function and is based on gradient echo or hybrid echo sequences. Some key applications of those sequences, e.g. water–fat imaging or T2*-mapping to detect iron loading, are discussed. Preparation pulses can be used to modify image contrast to, for example, improve black blood images or for quantitative applications, including T1- and T2-mapping. To help the reader navigate through the sea of sequence acronyms, the chapter ends with a quick guide covering the acronyms used by the main scanner manufacturers.

Conference papers on the topic "Fat-Water Imaging":

1

Pirogov, Yuri A., Nikolai V. Anisimov, and Leonid V. Gubskii. "Simultaneous suppression of water and fat signals in magnetic resonance imaging." In Medical Imaging 2002, edited by Seong K. Mun. SPIE, 2002. http://dx.doi.org/10.1117/12.466968.

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Tisdall, M. Dylan, and M. Stella Atkins. "Fat/water separation in a single MRI image with arbitrary phase shift." In Medical Imaging, edited by Michael J. Flynn and Jiang Hsieh. SPIE, 2006. http://dx.doi.org/10.1117/12.655128.

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Pirogov, Yuri A., Nikolai V. Anisimov, and Leonid V. Gubski. "3D visualization of pathological forms from MRI data obtained with simultaneous water and fat signal suppression." In Medical Imaging 2003, edited by Martin J. Yaffe and Larry E. Antonuk. SPIE, 2003. http://dx.doi.org/10.1117/12.479767.

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Jiang, Yun, Michael S. Hansen, and Jeffrey Tsao. "Self-navigated ideal water-fat separation with variable k-space averaging." In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI). IEEE, 2009. http://dx.doi.org/10.1109/isbi.2009.5192998.

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Kotecha, Tushar, Ana Martinez-Naharro, Liza Chacko, James Brown, Dan Knight, Sarah Anderson, James Moon, et al. "17 Fat water imaging for sub-epicardial gadolinium: enhancing the diagnosis of myocarditis." In British Society of Cardiovascular Magnetic Resonance 2019 annual meeting, March 26 – 27th, Oxford UK. BMJ Publishing Group Ltd and British Cardiovascular Society, 2019. http://dx.doi.org/10.1136/heartjnl-2019-bscmr.17.

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Ong, Henry H., Corey D. Webb, Marnie L. Gruen, Alyssa H. Hasty, John C. Gore, and E. B. Welch. "Fat-water MRI is sensitive to local adipose tissue inflammatory changes in a diet-induced obesity mouse model at 15T." In SPIE Medical Imaging, edited by Barjor Gimi and Robert C. Molthen. SPIE, 2015. http://dx.doi.org/10.1117/12.2082333.

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Xu, Jing, Xiaofei Hu, Haiying Tang, Richard Kennan, and Karim Azer. "Water-Fat Decomposition by IDEAL-MRI With Phase Estimation: A Method to Determine Chemical Contents In Vivo." In ASME 2010 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2010. http://dx.doi.org/10.1115/sbc2010-19296.

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High-resolution Magnetic Resonance Imaging (MRI) of humans and animals in vivo is routine and non-invasive. Identifying and quantifying chemical composition of tissue from acquired images is a challenge. MR spectroscopy (MRS) may be used to identify chemical components accurately over a finite volume in the tissue. However, the temporal and spatial resolutions are limited. Multi-spectral MRI exploits the multiple modes of MR such as T1, T2 and proton density maps and classifies voxels into different tissue types, but the chemical identity of the tissue remains unknown. Many fat suppression methods were developed because the unwanted fat signal often compromises image interpretability in clinical MRI, but these techniques are sensitive to MR field inhomogeneity. Multi-point Dixon methods separate MR images into water and fat images and are less sensitive to field inhomogeneity [1] and IDEAL-MRI (iterative decomposition of water and fat with echo asymmetry and least-squares estimation) improved upon the Dixon methods by avoiding the problem of phase unwrapping [2]. However, special care has to be taken when estimating the field map to avoid erroneous solutions to the least-squares estimation problem which lead to artifacts such as swapping of water and fat. The use of region growing schemes (with a reliable seed) mitigates this problem as demonstrated in previous studies [3][4]. However, the seed is not always reliable and growing schemes can be sensitive to phase discontinuities. Moreover, although the technology was successfully demonstrated on many clinical scanners, only limited applications were found in preclinical scanners with high MR field where the field inhomogeneity can be far worse [5]. We developed a robust and accurate algorithm to compute water and fat content on an 11.7T small animal scanner by improving upon existing phase estimation methods through multiple starting pixels and consensus-based region growing. The method, after further validation, has the potential of providing a translatable assay to study disease progression and regression related to fat and water contents in various animal models, such as studying atherosclerotic plaque composition.
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Wollenweber, S. D., S. Ambwani, A. H. R. Lonn, D. D. Shanbhag, S. Thiruvenkadam, S. Kaushik, R. Mullick, F. Wiesinger, H. Qian, and G. Delso. "Comparison of 4-class and continuous fat/water methods for whole-body, MR-based PET attenuation correction." In 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (2012 NSS/MIC). IEEE, 2012. http://dx.doi.org/10.1109/nssmic.2012.6551690.

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