Abstract #0558

Improving Radiomics Reproducibility Using MR Fingerprinting and Physics-Informed Quantization

Walter Zhao^1,2, Zheyuan Hu¹, Anahita Fathi Kazerooni^3,4, Gregor Körzdörfer⁵, Matthias Nittka⁵, Christos Davatzikos^3,4, 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

Synopsis

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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