Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface

2025-05-28

AI Summary

This study addresses the challenge of generating anatomically accurate, high-fidelity 3D thoracic and abdominal CT volumes non-invasively—without internal imaging. We propose a novel surface-to-CT synthesis framework that takes only a body surface scan and demographic inputs (age, sex, height, weight). Methodologically, we design a three-stage cascaded flow-matching architecture integrating signed distance function (SDF)-based surface completion, coarse CT generation, and patch-based super-resolution, all built upon a conditional 3D-adapted EDM2 backbone for end-to-end modeling. Evaluated on 700 test cases, our method achieves organ volume errors within ±11.1%, strong correlations (r = 0.67–0.96) with ground-truth body composition metrics, a Chamfer distance of 2.7 mm for surface reconstruction, and an IoU of 0.98. This work establishes the first “surface-to-visceral-CT” paradigm, enabling low-radiation, cost-effective, personalized medical imaging.

Technology Category

• Computer Vision: 3D Computer Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements

Abstract

We present Surf2CT, a novel cascaded flow matching framework that synthesizes full 3D computed tomography (CT) volumes of the human torso from external surface scans and simple demographic data (age, sex, height, weight). This is the first approach capable of generating realistic volumetric internal anatomy images solely based on external body shape and demographics, without any internal imaging. Surf2CT proceeds through three sequential stages: (1) Surface Completion, reconstructing a complete signed distance function (SDF) from partial torso scans using conditional 3D flow matching; (2) Coarse CT Synthesis, generating a low-resolution CT volume from the completed SDF and demographic information; and (3) CT Super-Resolution, refining the coarse volume into a high-resolution CT via a patch-wise conditional flow model. Each stage utilizes a 3D-adapted EDM2 backbone trained via flow matching. We trained our model on a combined dataset of 3,198 torso CT scans (approximately 1.13 million axial slices) sourced from Massachusetts General Hospital (MGH) and the AutoPET challenge. Evaluation on 700 paired torso surface-CT cases demonstrated strong anatomical fidelity: organ volumes exhibited small mean percentage differences (range from -11.1% to 4.4%), and muscle/fat body composition metrics matched ground truth with strong correlation (range from 0.67 to 0.96). Lung localization had minimal bias (mean difference -2.5 mm), and surface completion significantly improved metrics (Chamfer distance: from 521.8 mm to 2.7 mm; Intersection-over-Union: from 0.87 to 0.98). Surf2CT establishes a new paradigm for non-invasive internal anatomical imaging using only external data, opening opportunities for home-based healthcare, preventive medicine, and personalized clinical assessments without the risks associated with conventional imaging techniques.

Problem

• Synthesize 3D torso CT volumes from external surface scans
• Generate internal anatomy images without internal imaging
• Improve non-invasive healthcare using external data only

Innovation

• Cascaded flow matching for CT synthesis
• Surface completion via conditional 3D flow
• Patch-wise super-resolution for high-resolution CT