Automated quality assurance of rigid brain CT/MR image registration using a 3D convolutional neural network

Abstract Background Accurate CT/MR registration is important in stereotactic brain radiotherapy, where small spatial errors may affect target localization and treatment planning. In many clinical settings, registration quality is still assessed primarily through manual visual inspection. However, time and workflow constraints may limit the consistency and depth of manual evaluation. Purpose This study aimed to develop a 3D convolutional neural network (3D CNN)‐based quality assurance (QA) method for rigid brain CT/MR registration using paired sub‐volumes and a registration‐level mean probability score ( μ ). The proposed method classifies local CT/MR registration quality, while the final registration‐level assessment is based on μ across the sampled sub‐volumes. Methods The study included 209 patients. Of these, 202 comprised the primary cohort and were divided patient‐wise into 128 training, 33 validation, and 41 test patients. For each patient, 25 skull‐based CT/MR sub‐volumes of 42 ×  42 ×  42 voxels were used as a two‐channel CNN input. High‐quality examples were obtained from clinically approved registrations, while low‐quality examples were generated using rigid perturbations of the complete MR volume. The mean predicted probability across the 25 sub‐volumes was used as the registration‐level score μ , and the validation‐derived threshold of 0.5871 was fixed for testing. Seven additional patients, separate from the 202‐patient cohort, were evaluated after model development and threshold selection were completed using registrations before and after physician correction. Results On the held‐out test set, patch‐level accuracy was 85.90%, with an ROC AUC of 0.929 and average precision of 0.920. At the registration level, accuracy was 97.56% and ROC AUC was 0.999. All 41 low‐quality registrations were correctly flagged, while 39 of 41 high‐quality registrations were correctly accepted. In the additional clinical evaluation, all seven initial registrations requiring correction were classified as low quality, while six of seven corrected registrations were classified as high quality, resulting in an overall accuracy of 92.86%. Conclusions The proposed method combined local CNN predictions into a registration‐level μ score and showed strong performance on held‐out and clinical cases. It may provide a quantitative screening measure for rigid brain CT/MR registration QA while maintaining clinical review as the final decision step.