Accelerating MRI fat quantification using a signal model-based dictionary to assess gastric fat volume and distribution of fat fraction |
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| Affiliation: | 1. Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland;2. Division of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland;1. Department of Medical Imaging, Tongji Hospital, Tongji University, No. 389, Xincun Road, Putuo District, Shanghai 200065, China;2. Department of Medical Imaging, Renji Hospital, Medical School of Jiaotong University, No. 160, Pujian Road, Pudong District, Shanghai 200127, China;1. Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran;2. School of Cognitive Sciences, Institute for Research in Fundamental Sciences, 1954851167, Tehran, Iran;1. Department of Radiology and Imaging Sciences, University of Utah, 729 Arapeen Drive, Salt Lake City, UT 84108, USA;2. Department of Bioengineering, University of Utah, 36 S Wasatch Drive, Rm 3100, Salt Lake City, UT 84112, USA;3. Siemens Medical Solutions, Inc., 660 First Avenue, 4th Floor, New York, NY 10016, USA;4. Siemens Medical Solutions, Inc., 729 Arapeen Drive, Salt Lake City, UT 84108, USA;5. Division of Nephrology, Department of Internal Medicine, University of Utah, 30 N 1900 E, Rm 4R312, Salt Lake City, UT 84132, USA;1. Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA;2. Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA;3. Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA;4. AstraZeneca, Alderley Park, MACCLESFIELD, Cheshire SK10 4TG, United Kingdom;5. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA;6. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA;7. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN 37232, USA;1. Medical Physics Laboratory, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;2. Radiology and Diagnostic Imaging Department, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;3. Department of Radiotherapy, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;4. Department of Pathology, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy;5. Department of Otolaryngology & Head and Neck Surgery, Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00144 Rome, Italy |
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| Abstract: | To quantify intragastric fat volume and distribution with accelerated magnetic resonance (MR) imaging using signal model-based dictionaries (DICT) in comparison to conventional parallel imaging (CG-SENSE). This study was approved by the local ethics committee and written informed consent was obtained. Seven healthy subjects were imaged after intake of a lipid emulsion and data at three different time points during the gastric emptying process was acquired in order to cover a range of fat fractions. Fully sampled and prospectively undersampled image data at a reduction factor of 4 were acquired using a multi gradient echo sequence at 1.5T. Retrospectively and prospectively undersampled data were reconstructed with DICT and CG-SENSE. Image quality of the retrospectively undersampled data was assessed relative to the fully sampled reference using the root mean square error (RMSE). In order to assess the agreement of fat volumes and intragastric fat distribution, Bland-Altman analysis and linear regression were performed on the data. The RMSE in intragastric content (ΔRMSE = 0.10 ± 0.01, P < 0.001) decreased significantly with DICT relative to CG-SENSE. CG-SENSE overestimated fat volumes (bias 2.1 ± 1.3 mL; confidence limits 5.4 and − 1.1 mL) in comparison to the prospective DICT reconstruction (bias − 0.1 ± 0.7 mL; confidence limits 1.8 and − 2.0 mL). There was a good agreement in fat distribution between the images reconstructed by retrospective DICT and the reference images (regression slope: 1.01, R2 = 0.961). Accelerating gastric MRI by integrating a dictionary-based signal model allows for improved image quality and increases accuracy of fat quantification during breathholds. |
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