Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction
This work addresses the problem of sparse-view computed tomography reconstruction for medical imaging applications, providing an unsupervised method that can handle noisy data and outperform supervised methods in certain scenarios.
The authors tackled the problem of sparse-view computed tomography (SVCT) reconstruction, achieving performance comparable to supervised state-of-the-art methods on in-domain data and outperforming them on out-of-domain datasets, with significant improvements in handling noisy sinograms. Experimental results on multiple CT datasets showed the effectiveness of their approach.
Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inverse problems. Although these INR-based methods perform well in relatively dense SVCT reconstructions, they struggle to achieve comparable performance to supervised methods in sparser SVCT scenarios. They are prone to being affected by noise, limiting their applicability in real clinical settings. Additionally, current methods have not fully explored the use of image domain priors for solving SVCsT inverse problems. In this work, we demonstrate that imperfect reconstruction results can provide effective image domain priors for INRs to enhance performance. To leverage this, we introduce Self-prior embedding neural representation (Spener), a novel unsupervised method for SVCT reconstruction that integrates iterative reconstruction algorithms. During each iteration, Spener extracts local image prior features from the previous iteration and embeds them to constrain the solution space. Experimental results on multiple CT datasets show that our unsupervised Spener method achieves performance comparable to supervised state-of-the-art (SOTA) methods on in-domain data while outperforming them on out-of-domain datasets. Moreover, Spener significantly improves the performance of INR-based methods in handling SVCT with noisy sinograms. Our code is available at https://github.com/MeijiTian/Spener.