Develop a large-scale library of comprehensive deformable image registration (DIR) benchmark datasets and an integrated framework for quantifying accuracy of patient-specific DIR results

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Summary Deformable image registration (DIR) between different image sets acquired from the same patient is a key enabling technology for many important diagnostic and therapeutic tasks, e.g. tumor diagnosis, evaluation of tumor response to treatment, and image-guided surgery. DIR algorithms compute tissue deformation by maximizing intensity and/or structural similarity between moving and target images, and regularity of deformation. DIR accuracy, which is the voxel-level positional correspondence between the two images, is not guaranteed, often inadequate, unpredictable and patient specific. DIR accuracy is largely dependent on anatomical site, image modality and quality, algorithm designs and implementations, operator skills and workflow selections. Inaccurate DIRs can have significant deleterious impact clinical decisions, treatment quality and patient safety. Lack of confidence in current registration tools has significantly limited the broader use of DIR in automating clinical decision-making tasks and improving diagnostic and therapeutic outcomes. We posit that lack of accurate or robust performance arises from the fact that current DIR algorithms are based upon overly simplistic models of tissue deformation and failure to accommodate the reality of CT image quality. Currently, no method exists for quantitatively and automatically evaluating patient- specific DIR accuracy. We are therefore motivated to conduct two studies: 1) Build a large and comprehensive library of DIR benchmark datasets to support DIR algorithm validation in challenging settings. Each new DIR benchmark dataset will consist of automatically and precisely detected landmark pairs, small blood vessel section pairs, and segmentation of organs and large blood vessels. Currently no such DIR benchmark dataset exist. These datasets will spur development of new and advanced DIR algorithms able to support complex, patient-specific tissue deformation. These datasets will be immensely valuable for applications beyond DIR such as semantic segmentation and vessels extraction, etc. 2) Develop integrated methods for quantitative verification of patient-specific DIRs. The automatic DIR verification procedure will use multiple novel deep-learning models for automatic organ segmentation, vessel bifurcation detection and direct prediction of 3D vector field of TREs (target registration error). These to-be-developed deep-learning-based image processing procedures are robust with respect to image noise and intensity variations, and will naturally support many anatomical sites. This DIR verification procedure will provide quality assurance for patient-specific DIRs for supporting clinical applications.