Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks.

ABSTRACT:

Objective

We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions.

Methods

The voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise

SUBMITTER: Salimi Y 

PROVIDER: S-EPMC10667156 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature