Improving Radiomics Reproducibility Using MR Fingerprinting and Physics-Informed Quantization
Walter Zhao1,2, Zheyuan Hu1, Anahita Fathi Kazerooni3,4, Gregor Körzdörfer5, Matthias Nittka5, Christos Davatzikos3,4, Satish E. Viswanath1, Xiaofeng Wang6, Chaitra Badve7, and Dan Ma1
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Medical Scientist Training Program, Case Western Reserve University, Cleveland, OH, United States, 3Center for Biomedical Image Computing and Analysis, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 4Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 5Siemens Healthineers, Erlangen, Germany, 6Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States, 7Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, OH, United States
MR fingerprinting (MRF) is a rapid, quantitative imaging approach with significant potential for use in clinical studies, including radiomic applications. Due to its quantitative nature, robustness to system imperfections, and requiring fewer image preprocessing steps, we believe MRF radiomics is uniquely positioned to offer improved reproducibility and generalizability compared to conventional MRI. Here we report reproducibility results of MRF T1 and T2 radiomic features in the healthy human brain, and introduce a novel physics-informed quantization approach for improved reproducibility of quantitative image texture features.
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