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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 Ramadan5,6, John Simpson2,3, Jonathan Goodwin2,3, Jarad Martin2,7, Yuvnik Trada2, Swetha Sridharan2,7, and Peter Greer2,3
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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