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SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction
Nivetha Jayakumar1, Tonmoy Hossain2, Miaomiao Zhang1,2
1Department of Electrical and Computer Engineering, School of Engineering and Applied Science, University of Virginia, VA, USA.
Shape in Medical Imaging : International Workshop, Shapemi 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. Shapemi (Workshop) (2023 : Vancouver, B.C.)
|March 29, 2024
Summary
This study introduces SADIR, a novel shape-aware network for 3D image reconstruction. It improves accuracy and preserves object topology by integrating shape priors into diffusion models, outperforming existing methods.
Area of Science:
- Computer Vision
- Medical Image Analysis
- Deep Learning
Background:
- 3D image reconstruction from limited 2D views is challenging.
- Current deep learning methods struggle with preserving object topology and shape structure.
- Artifacts like holes and discontinuities often occur in reconstructions.
Purpose of the Study:
- To propose a shape-aware network for improved 3D image reconstruction.
- To address limitations in topology preservation and artifact reduction.
- To leverage shape priors for more accurate 3D image generation.
Main Methods:
- Developed SADIR, a shape-aware network utilizing diffusion models.
- Integrated shape priors learned from training data to guide reconstruction.
- Employed a joint learning network to learn a mean shape under deformation models.
Main Results:
- SADIR demonstrated superior performance compared to baseline methods.
- Achieved lower reconstruction error in experiments.
- Showcased enhanced preservation of object shape structure.
Conclusions:
- The proposed SADIR model effectively reconstructs 3D images with improved shape and topology preservation.
- Leveraging shape priors within diffusion models offers a promising direction for 3D reconstruction.
- SADIR shows potential for applications in medical imaging, such as brain and cardiac MRIs.
