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Range probing as a quality control tool for CBCT‐based synthetic CTs: In vivo application for head and neck cancer patients
Author(s) -
Seller Oria Carmen,
Thummerer Adrian,
Free Jeffrey,
Langendijk Johannes A.,
Both Stefan,
Knopf Antje C.,
Meijers Arturs
Publication year - 2021
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.15020
Subject(s) - workflow , proton therapy , usability , computer science , artificial intelligence , convolutional neural network , head and neck , imaging phantom , range (aeronautics) , quality assurance , outlier , image quality , head and neck cancer , nuclear medicine , medical physics , medicine , radiology , radiation therapy , image (mathematics) , materials science , database , external quality assessment , surgery , pathology , human–computer interaction , composite material
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 retrospectively assessed. CBCT‐based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images. Results Mean relative range errors showed agreement between measured and simulated RP fields, ranging from −1.2% to 1.5% in rCTs, and from −0.7% to 2.7% in sCTs. Conclusions The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.