Accelerated Optimization of Implicit Neural Representations for CT Reconstruction
Inspired by their success in solving challenging inverse prob-lems in computer vision, implicit neural representations (INRs) have been recently proposed for reconstruction in low-dose/sparse-view X-ray computed tomography (CT). An INR represents a CT image as a small-scale neural network that takes...
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| Vydané v: | Proceedings (International Symposium on Biomedical Imaging) s. 1 - 5 |
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14.04.2025
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| Abstract | Inspired by their success in solving challenging inverse prob-lems in computer vision, implicit neural representations (INRs) have been recently proposed for reconstruction in low-dose/sparse-view X-ray computed tomography (CT). An INR represents a CT image as a small-scale neural network that takes spatial coordinates as inputs and outputs attenuation values. Fitting an INR to sinogram data is similar to classical model-based iterative reconstruction methods. However, training INRs with losses and gradient-based algorithms can be prohibitively slow, taking many thousands of iterations to converge. This paper investigates strategies to accelerate the optimization of INRs for CT reconstruction. In particular, we propose two approaches: (1) using a modified loss function with improved conditioning, and (2) an algorithm based on the alternating direction method of multipliers. We illustrate that both of these approaches significantly accelerate INR-based reconstruction of a synthetic breast CT phantom in a sparse-view setting. |
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| AbstractList | Inspired by their success in solving challenging inverse prob-lems in computer vision, implicit neural representations (INRs) have been recently proposed for reconstruction in low-dose/sparse-view X-ray computed tomography (CT). An INR represents a CT image as a small-scale neural network that takes spatial coordinates as inputs and outputs attenuation values. Fitting an INR to sinogram data is similar to classical model-based iterative reconstruction methods. However, training INRs with losses and gradient-based algorithms can be prohibitively slow, taking many thousands of iterations to converge. This paper investigates strategies to accelerate the optimization of INRs for CT reconstruction. In particular, we propose two approaches: (1) using a modified loss function with improved conditioning, and (2) an algorithm based on the alternating direction method of multipliers. We illustrate that both of these approaches significantly accelerate INR-based reconstruction of a synthetic breast CT phantom in a sparse-view setting. |
| Author | Ongie, Gregory Najaf, Mahrokh |
| Author_xml | – sequence: 1 givenname: Mahrokh surname: Najaf fullname: Najaf, Mahrokh organization: Marquette University,Department of Mathematical and Statistical Sciences,Milwaukee,WI,USA – sequence: 2 givenname: Gregory surname: Ongie fullname: Ongie, Gregory organization: Marquette University,Department of Mathematical and Statistical Sciences,Milwaukee,WI,USA |
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| Snippet | Inspired by their success in solving challenging inverse prob-lems in computer vision, implicit neural representations (INRs) have been recently proposed for... |
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| SubjectTerms | Computed tomography Coor-dinate Based Neural Networks CT Reconstruction Fitting Image reconstruction Implicit Neural Representations Iterative methods Model-based Iterative Reconstruction Neural networks Optimization Phantoms Reconstruction algorithms Training X-ray imaging |
| Title | Accelerated Optimization of Implicit Neural Representations for CT Reconstruction |
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