90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework.

ABSTRACT:

Purpose

90Y SPECT-based dosimetry following radioembolization (RE) in liver malignancies is challenging due to the inherent scatter and the poor spatial resolution of bremsstrahlung SPECT. This study explores a deep-learning-based absorbed dose-rate estimation method for 90Y that mitigates the impact of poor SPECT image quality on dosimetry and the accuracy-efficiency trade-off of Monte Carlo (MC)-based scatter estimation and voxel dosimetry methods.

Methods

Our unified framework consists of three stages: convolutional neural network (CNN)-based bremsstrahlung scatter estimation, SPECT reconstruction with scatter correction (SC) and absorbed dose-rate map generation with a residual learning network (DblurDoseNet). The input to the framework is the measured SPECT projections

SUBMITTER: Jia Y 

PROVIDER: S-EPMC10719178 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature