Range probing as a quality control tool for CBCT-based synthetic CTs: In vivo application for head and neck cancer patients.

ABSTRACT:

Purpose

Cone-beam CT (CBCT)-based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed. This work evaluates the potential of using a range-probing (RP)-based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs.

Methods

A RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectivel

SUBMITTER: Seller Oria C 

PROVIDER: S-EPMC8456797 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature