Abstract #0955
Vendor-Neutral Development and Cross-Center Validation of Flip Angle Modulated 2D Sequential CSE-MRI Technique for Liver Fat Quantification
Jiayi Tang1,2, Daiki Tamada2, Xingwang Yong3,4, Yuting Chen4,5, Shohei Fujita4,6, Jitka Starekova2, Jeff Kammerman7, Jean H Brittain7, Alan McMillan1,2,8,9,10, Jon-Fredrik Nielsen11, Maxim Zaitsev12, Scott B Reeder1,2,8,13,14, Berkin Bilgic4, and Diego Hernando1,2
1Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 2Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, 4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 5State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China, 6Radiology, Harvard Medical School, Boston, MA, United States, 7Calimetrix, LLC, Madison, WI, United States, 8Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, 9Data Science Institute, University of Wisconsin-Madison, Madison, WI, United States, 10Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, United States, 11Radiology, University of Michigan, Ann Arbor, MI, United States, 12Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, 13Medicine, University of Wisconsin-Madison, Madison, WI, United States, 14Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States
Synopsis
Keywords: Pulse Sequence Design, Fat, fat/water separation, data acquisition, liver, pulse sequence design, software tools
Motivation: 2D sequential chemical-shift-encoded acquisitions with centric encoding and flip-angle modulation (FAM) enables motion-robust and high-SNR liver fat quantification. Originally developed in a single vendor, the performance and relative simplicity of FAM motivate vendor-neutral implementation and validation.
Goal(s): Implement FAM in the vendor-neutral framework Pulseq, and determine its feasibility, bias, and reproducibility in a multi-center, multi-vendor study.
Approach: Pulseq-FAM was applied in two centers with two vendors on a phantom with controlled PDFF/T1water values, and in volunteers during free breathing.
Results: At both centers, Pulseq-FAM shows low bias and good reproducibility in the phantom, and excellent motion robustness and image quality in volunteers.
Impact: A vendor-neutral implementation of motion-robust liver fat quantification, as demonstrated in this study, may enable detection, staging, and treatment monitoring of steatotic liver disease with improved availability and standardization.
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