Efficient Registration of Longitudinal Studies for Follow-Up Lesion Assessment by Exploiting Redundancy and Composition of Deformations

Follow-up assessment of lesions for cancer patients is an important part of radiologists' work. Image registration is a key technology to facilitate this task, as it allows for the automatic establishment of correspondences between previous findings and current observations. However, as the number of examinations increases, more registrations must be computed to allow full correspondence assessment between longitudinal studies. We address the challenge of increased computational time and complexity by identifying and eliminating redundant registration procedures and composing deformations from previously performed registrations, thereby significantly reducing the number of registrations required. We evaluate our proposed methods on a dataset consisting of oncological thoracic follow-up CT scans from 260 patients. By grouping series within a study and identifying reference series, we can reduce the total number of registrations required for a patient by an average factor of 27.5 while maintaining comparable registration quality. Additionally composing deformations further reduces the number of registrations by a factor of 1.86, resulting in an overall average reduction factor of 51.4. Since the number of registrations is directly related to the time required to process the input data, the information is available more quickly and subsequent examinations can be performed sooner. For a single subject, this results in an exemplary reduction of total computation time from 37.4 to 1.3 min.