Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstruction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915206020464640 |
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| author | Momeni, Mohammadhossein Gopalakrishnan, Vivek Dey, Neel Golland, Polina Frisken, Sarah |
| author_facet | Momeni, Mohammadhossein Gopalakrishnan, Vivek Dey, Neel Golland, Polina Frisken, Sarah |
| contents | We present DiffVox, a self-supervised framework for Cone-Beam Computed Tomography (CBCT) reconstruction by directly optimizing a voxelgrid representation using physics-based differentiable X-ray rendering. Further, we investigate how the different implementations of the X-ray image formation model in the renderer affect the quality of 3D reconstruction and novel view synthesis. When combined with our regularized voxel-based learning framework, we find that using an exact implementation of the discrete Beer-Lambert law for X-ray attenuation in the renderer outperforms both widely used iterative CBCT reconstruction algorithms and modern neural field approaches, particularly when given only a few input views. As a result, we reconstruct high-fidelity 3D CBCT volumes from fewer X-rays, potentially reducing ionizing radiation exposure and improving diagnostic utility. Our implementation is available at https://github.com/hossein-momeni/DiffVox. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19224 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstruction Momeni, Mohammadhossein Gopalakrishnan, Vivek Dey, Neel Golland, Polina Frisken, Sarah Image and Video Processing Computer Vision and Pattern Recognition Medical Physics We present DiffVox, a self-supervised framework for Cone-Beam Computed Tomography (CBCT) reconstruction by directly optimizing a voxelgrid representation using physics-based differentiable X-ray rendering. Further, we investigate how the different implementations of the X-ray image formation model in the renderer affect the quality of 3D reconstruction and novel view synthesis. When combined with our regularized voxel-based learning framework, we find that using an exact implementation of the discrete Beer-Lambert law for X-ray attenuation in the renderer outperforms both widely used iterative CBCT reconstruction algorithms and modern neural field approaches, particularly when given only a few input views. As a result, we reconstruct high-fidelity 3D CBCT volumes from fewer X-rays, potentially reducing ionizing radiation exposure and improving diagnostic utility. Our implementation is available at https://github.com/hossein-momeni/DiffVox. |
| title | Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2411.19224 |