Abstract #2760

Voxel-wise hepatocellular function prediction using population-based probability density function

Monchai Phonlakrai¹, Behzad Asadi², Neda Gholizadeh³, Kate Skehan², Liam Hilleary², Jameen Arms⁴, Saadallah Ramadan^5,6, John Simpson^2,3, Jonathan Goodwin^2,3, Jarad Martin^2,7, Yuvnik Trada², Swetha Sridharan^2,7, and Peter Greer^2,3

¹School of Health Sciences, The University of Newcastle, Newcastle, Australia, ²Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, Australia, ³School of Mathematical and Physical Sciences, The University of Newcastle, Newcastle, Australia, ⁴Diagnostic Radiology, Calvary Mater Newcastle Hospital, Newcastle, Australia, ⁵Faculty of Health and Medicine, The University of Newcastle, Newcastle, Australia, ⁶HMRI Imaging Centre, John Hunter Hospital, Newcastle, Australia, ⁷School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia

Dynamic gadoxetate contrast-enhanced MRI yields spatial hepatocellular function through hepatic extraction fraction map. This allows well-functioning hepatocyte sparing in radiotherapy to avoid radiation-induced liver toxicity. However, the major challenge of using this parametric map in a clinical practice for normal function sparing is the lack of standard method to determine liver function at a voxel level within the same patient. As such, population-based kernel density function was proposed to deal with this problem to predict voxel-based probability of liver function. This novel approach also allows derivation of functional probability map that could be used for radiation beam guidance in function-based radiation treatment planning.

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