Deformable CT image registration via a dual feasible neural network. Issue 12 (3rd August 2022)

• Record Type: Journal Article
• Authors: Lei, Yang; Fu, Yabo; Tian, Zhen; Wang, Tonghe; Dai, Xianjin; Roper, Justin; Yu, David S.; McDonald, Mark; Bradley, Jeffrey D.; Liu, Tian; Zhou, Jun; Yang, Xiaofeng
• Is Part Of: Medical physics. Volume 49:Issue 12(2022)
• Page Start: 7545
• Page End: 7554
• Publication Date: 2022-08-03
• Subjects: CT -- deep learning -- deformable image registration -- radiotherapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15875
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: A quality assurance (QA) CT scans are usually acquired during cancer radiotherapy to assess for any anatomical changes, which may cause an unacceptable dose deviation and therefore warrant a replan. Accurate and rapid deformable image registration (DIR) is needed to support contour propagation from the planning CT (pCT) to the QA CT to facilitate dose volume histogram (DVH) review. Further, the generated deformation maps are used to track the anatomical variations throughout the treatment course and calculate the corresponding accumulated dose from one or more treatment plans. Methods: In this study, we aim to develop a deep learning (DL)‐based method for automatic deformable registration to align the pCT and the QA CT. Our proposed method, named dual‐feasible framework, was implemented by a mutual network that functions as both a forward module and a backward module. The mutual network was trained to predict two deformation vector fields (DVFs) simultaneously, which were then used to register the pCT and QA CT in both directions. A novel dual feasible loss was proposed to train the mutual network. The dual‐feasible framework was able to provide additional DVF regularization during network training, which preserves the topology and reduces folding problems. We conducted experiments on 65 head‐and‐neck cancer patients (228 CTs in total), each with 1 pCT and 2–6 QA CTs. For evaluations, we calculated the mean absolute error (MAE), peak‐signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), target registration error (TRE) between the deformed and target images and the Jacobian determinant of the predicted DVFs. Results: Within the body contour, the mean MAE, PSNR, SSIM, and TRE are 122.7 HU, 21.8 dB, 0.62 and 4.1 mm before registration and are 40.6 HU, 30.8 dB, 0.94, and 2.0 mm after registration using the proposed method. These results demonstrate the feasibility and efficacy of our proposed method for pCT and QA CT DIR. Conclusion: In summary, we proposed a DL‐based method for automatic DIR to match the pCT to the QA CT. Such DIR method would not only benefit current workflow of evaluating DVHs on QA CTs but may also facilitate studies of treatment response assessment and radiomics that depend heavily on the accurate localization of tissues across longitudinal images.