Abstract #2753

Principal Component Analysis Applied to Magnetic Resonance Fingerprinting Data in Prostate

Debra F. McGivney¹, Alice Yu², Chaitra Badve³, Mark A. Griswold^1,4, and Vikas Gulani^1,3

¹Radiology, Case Western Reserve University, Cleveland, OH, United States, ²School of Medicine, Case Western Reserve University, Cleveland, OH, United States, ³Radiology, University Hospitals, Cleveland, OH, United States, ⁴Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States

MR fingerprinting provides a way to generate quantitative information on tissue parameters, giving a set of multidimensional data at each pixel. Having a method to interpret this multidimensional data will aid in an objective and efficient means for differentiating between normal and healthy tissues, or variations between disease states. We apply principal component analysis (PCA) to MRF data along with the apparent diffusion coefficient (ADC) to differentiate between normal and diseased (prostate cancer, prostatitis) tissue within the prostate.

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