Ali Bilgin1,2, Rajagopalan Sundaresan,
Christian G. Graff3, Chuan Huang4, Tomoe Barr1,
Maria I. Altbach5
1Biomedical Engineering, University
of Arizona, Tucson, AZ, United States; 2Electrical & Computer
Engineering, University of Arizona, Tucson, AZ, United States; 3Division
of Imaging & Applied Mathematics, Food & Drug Administration; 4Mathematics,
University of Arizona, Tucson, AZ, United States; 5Radiology,
University of Arizona
Diagnosing diffuse liver disease using parametric imaging requires use of the parameter values for as much of the liver parenchyma as possible due to the diffuse nature of the disease. An automated liver parenchyma and vessel segmentation methodology is proposed for datasets obtained using radial Gradient and Spin-Echo (GRASE). The proposed segmentation strategy is evaluated using in vivo data and an abdominal phantom. The results illustrate that the proposed segmentation is accurate and improves estimation of T2 value of liver parenchyma which can be used to characterize diffuse liver disease.