@article {Chen1401, author = {Gefei Chen and Zhonglin Lu and Greta Mok}, title = {Personalized Voxel-S-Value Methods for Monte-Carlo-like Quantitative Y-90 PET Dosimetry}, volume = {62}, number = {supplement 1}, pages = {1401--1401}, year = {2021}, publisher = {Society of Nuclear Medicine}, abstract = {1401Objectives: Voxel-S-values (VSV) is an efficient and accurate method to convert time-integrated activity (TIA) to absorbed dose in homogeneous media. However, its accuracy is limited in heterogeneous media such as the lung-liver interface for Y-90 [1]. This study aims to develop and evaluate new VSV methods to address this problem on Y-90 absorbed dose calculations. Methods: Ten sets of Y-90 microsphere TOF PET/CT patient data from the University of Michigan Deep Blue Data sharing repository are analyzed [2]. The PET matrix size is 200{\texttimes}200{\texttimes}122 with a voxel size of 4.07{\texttimes}4.07{\texttimes}3 mm3. TIA maps are generated from PET images assuming only physical decay after the acquired time point. Livers, tumors, lungs, and lung-liver interface, i.e., liver_i and lung_i which are 1.5 cm slabs of liver and lungs extended from the interface, are manually delineated from CT images. VSVs (Gy/MBq-s) are generated by GATE v.8.0 for liver, lung-liver interface and lungs with same voxel size as PET images and respective matrix size of 7{\texttimes}7{\texttimes}7, 13{\texttimes}13{\texttimes}13 and 21{\texttimes}21{\texttimes}21 to include \>99.75\% absorbed dose distribution. Four VSV methods are proposed: (i) liver and lung kernel with density correction (LiLuKD); (ii) liver and lung kernel with central voxel scaling (LiLuCK); (iii) lung kernel with central voxel scaling for lung TIA and liver kernel with density correction for liver TIA (LuCK+LiKD); (iv) liver, lung with density correction and interface kernel (LiLuKDIn), which first determines interface voxels for each patient based on a 5{\texttimes}5{\texttimes}5 region search over the whole image with optimized liver voxel ratio criteria then convolved with optimized lung-liver mixture kernel. The TIA maps are convolved with the VSVs to obtain the absorbed dose images, and the absolute absorbed dose errors (\%AADE) of these methods for the target regions which are segmented using the corresponding CT maps are compared with the existing VSV methods: local deposition (LD), constant liver kernel (LiK), liver kernel with density correction (LiKD), liver kernel with central voxel scaling (LiCK), liver kernel for liver TIA and lung kernel for lung TIA (LiLuK), and various kernels (n=120) depending on the central voxel density (VCK), using GATE-based Monte Carlo simulation (MCS) as the gold standard. Results: The density of the interface kernel is determined to be 0.58 g/cm3 and interface selection criteria is based on a liver voxel ratio range of 0.1-0.3 for LiLuKDIn, based on the lowest \%AADE achieved. The mean liver and tumor \%AADE are \<3\% for all VSV methods and can be as low as 0.2\% for LiLuKDIn, while the liver_i absolute absorbed dose error is mostly \<5\% except for LiLuK (13.3\%) and LiLuCK (11.1\%). The mean lung absolute \%AADE are smallest for LiLuKDIn (3.5\%), followed by LiLuCK (4.2\%), LuCK+LiKD (7.2\%), LiLuKD (8.8\%), LiLuK (12.7\%), LD (17.3\%), LiCK (19.5\%), LiKD (23.7\%), VCK (30.8\%) and LiK (67.9\%). The mean lung_i \%AADE are smallest for LiLuKDIn (2.7\%), followed by LuCK+LiKD (6.2\%), LiLuK (10.1\%), LiLuCK (10.3\%), LiLuKD (11.3\%), VCK (24.7\%), LiCK (27.0\%), LD (27.5\%), LiKD (40.8\%) and LiK (59.4\%). The processing time of all VSV methods is \<1 min, comparing to 189 h of MCS for 1 patient. Conclusions: The four proposed VSV methods are superior to the existing VSV methods, particularly reducing \%AADE of lungs to \<10\%, while LiLuKDIn with personalized lung-liver interface remediation achieves MCS-like results for all regions of interest. Evaluation with more clinical data are warranted. Reference: [1] Mikell, JK., et al. "Comparing voxel-based absorbed dosimetry methods in tumors, liver, lung, and at the liver-lung interface for 90 Y microsphere selective internal radiation therapy." EJNMMI Physics 2.1 (2015): 16. [2] Lim, H., Dewaraja, Y. (2019). Y-90 patients PET/CT \& SPECT/CT and corresponding contours dataset [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/v07v-z854 Research Support: FDCT Research Grant (0091/2019/A2)}, issn = {0161-5505}, URL = {https://jnm.snmjournals.org/content/62/supplement_1/1401}, eprint = {https://jnm.snmjournals.org/content}, journal = {Journal of Nuclear Medicine} }