A fat fraction phantom for establishing new convolutional neural network to determine the pancreatic fat deposition. Issue 12 (December 2022) - Yang, John Zhiyong; Murphy, Rinki; Lu, Jun
Abstract: The determination of fat fraction based on Magnetic Resonance Imaging (MRI) requires extremely accurate data reconstruction for the assessment of pancreatic fat accumulation in medical diagnostics and biological research. In this study, the signal model of the oil and water emulsion was created with a 3.0 T field strength. We examined the quantification of the fat fraction from phantom and the intrapancreatic fat fraction using the techniques of magnetic resonance spectroscopy (MRS) and Iterative Decomposition with Echo Asymmetry and Least-Squares estimate (IDEAL) in magnetic resonance imaging (MRI). Additionally, we contrasted expert manual pancreatic fat assessment with MRS and IDEAL pancreatic fat fraction quantification. There was a strong connection between the true fat volume fraction and the fat fraction from IDEAL and MRS (R 2 = 0.99 and 0.99, respectively). For both phantom and in vivo measurements, Pearson's correlation and linear regression analysis were used. The findings of the in vivo assessment revealed a variable correlation between the pancreatic fat fraction MRI readings. We also used MR-opsy for manual pancreatic fat fraction segmentation since it read pancreatic fat fractions more accurately than IDEAL and MRS, which aided in the development of machine learning's ability to assess pancreatic fat automatically. Abstract : MRI; Phantom; Intra-pancreatic fat; Automatic measurement; Machine learning.