How can a multimodal 3D reconstruction framework integrating MRI, CT, and other imaging modalities be designed to provide a unified and quantitative representation of tumor-critical-structure relationships for preoperative assessment?

The spatial relationship of a tumor to surrounding critical structures is paramount to safe surgical planning, but the MRI, CT and PET scans are generally examined individually, and surgeons must mentally integrate 3-D relationships from a series of 2-D images. To this end, this paper introduces a multimodal three-dimensional framework called OncoMap3D that can combine these imaging modalities to construct a patient-specific model for preoperative evaluation. The proposed pipeline integrates multimodal image registration, anatomic segmentation, 3D surface reconstruction, quantitative spatial analysis and interactive visualization. OncoMap3D is designed to calculate tumor volumes, surface areas, minimum tumor-to-structure distance, and anatomical contact areas, and the visualization and exploration of each critical structure separately. The framework is not intended as a standalone or autonomous surgical-planning system, and the proposed validation will include segmentation accuracy, registration reliability, geometric-measurement reliability, and a comparison of the 3D visualization with the traditional 2D image review. This includes directions for future development, such as automated segmentation, uncertainty visualization, longitudinal tracking of tumors, updating the model during surgery, and surgical-pathway simulation. OncoMap3D is proposed as a candidate framework to enable multimodal imaging to be used as an interactive, quantitative visualization of tumor–critical-structure relationships for preoperative oncology. A preliminary single-case implementation using the ReMIND dataset produced a tumor volume of 27.28 cm³, a minimum tumor-to-ventricle distance of 14.57 mm, and quantitative surface-proximity measurements, demonstrating the technical feasibility of the proposed spatial-analysis approach. This paper presents the proposed architecture, preliminary technical proof of concept, and a validation plan for a system currently in development.