Over the last 25 years, the number of papers written that involve image guidance, liver, and registration has, on average, doubled every 6-8 years. While image guidance has a long history within the neurosurgical theatre, it’s translation to other soft-tissue organs such as the liver has been slower given the inherent difficulty in image-to-physical registration. More specifically, deformations have been recognized to compromise image guidance fidelity in virtually all soft-tissue image guidance applications. As a result, an active area of investigation is the development of sparse-data-driven nonrigid image-to-physical liver registration techniques to compensate for deformation and provide accurate localization for image guided liver surgery. In this work we have leveraged our extensive human-to-phantom registration testing framework based on the work in  and Amazon Web Services to create a sparse data challenge for the image guided liver surgery community (https://sparsedatachallenge.org/). Our sparse data challenge will allow research groups from across the world to extensively test their approaches on common data and have quantitative accuracy measurements provided for assessment of fidelity. Welcome to the Sparse Data Challenge for image-to-physical liver registration assessment.
Although resection and transplantation are primary curative methods of treatment for hepatocellular carcinoma, many patients are not candidates. In these cases, other treatment methods such as selective internal radiation therapy, chemotherapy, or external beam radiation are used. While these treatments are effective, patient-specific customization of treatment could be beneficial. Recent advances in personalized medicine are making this possible, but often there are multiple phenotypes within a proliferating tumor. While not standard, one could envision a serial longitudinal biopsy approach with more phenotypically-targeted therapeutics if one could detect responding and non-responding regions of tumor over time. This work proposes a method to determine active regions of the tumor that differentially respond to treatment to better guide biopsy for longitudinal personalization of treatment. While PET may serve this purpose, it is not easily used for real-time image guidance, is not effective for many types of tumors, and can be confounded by inflammatory responses. In this work, ten total patients with imaging sequences from before and after treatment were retrospectively obtained. Five of these were selected for analysis based on the total liver volume change. A two-phase alignment process comprised of an intensity-based rigid registration followed by a nonrigid refining process driven by bulk deformation of the organ surface was performed. To assess the accuracy of the registration, two metrics were used for preliminary results. The mean closest point surface distance was used to quantify how well the surfaces of the registered livers match and was found to be 2.65±3.54mm. Anatomical features visible in pre- and post-treatment images were also identified. After registration, the mean Euclidean distance between features was found to be 5.22±4.06mm. To assess potential areas of tumor change, the registered tumor pre- and post-treatment were overlaid.
Sparse surface digitization with an optically tracked stylus for use in an organ surface-based image-to-physical registration is an established approach for image-guided open liver surgery procedures. However, variability in sparse data collections during open hepatic procedures can produce disparity in registration alignments. In part, this variability arises from inconsistencies with the patterns and fidelity of collected intraoperative data. The liver lacks distinct landmarks and experiences considerable soft tissue deformation. Furthermore, data coverage of the organ is often incomplete or unevenly distributed. While more robust feature-based registration methodologies have been developed for image-guided liver surgery, it is still unclear how variation in sparse intraoperative data affects registration. In this work, we have developed an application to allow surgeons to study the performance of surface digitization patterns on registration. Given the intrinsic nature of soft-tissue, we incorporate realistic organ deformation when assessing fidelity of a rigid registration methodology. We report the construction of our application and preliminary registration results using four participants. Our preliminary results indicate that registration quality improves as users acquire more experience selecting patterns of sparse intraoperative surface data.