A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions.

ABSTRACT:

Purpose

A major challenge for accurate quantitative SPECT imaging of some radionuclides is the inadequacy of simple energy window-based scatter estimation methods, widely available on clinic systems. A deep learning approach for SPECT/CT scatter estimation is investigated as an alternative to computationally expensive Monte Carlo (MC) methods for challenging SPECT radionuclides, such as ⁹⁰Y.

Methods

A deep convolutional neural network (DCNN) was trained to separately estimate each scatter projection from the measured ⁹⁰Y bremsstrahlung SPECT emission projection and CT attenuation projection that form the network inputs. The 13-layer deep architecture consisted of separate paths for the emission and attenuation projection that are concatenated before the

SUBMITTER: Xiang H 

PROVIDER: S-EPMC7666660 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature