UnWave-Net: Unrolled Wavelet Network for Compton Tomography Image Reconstruction

TL;DR

UnWave-Net is a novel wavelet-based unrolled neural network designed for efficient and high-quality image reconstruction in Compton scatter tomography, overcoming computational challenges and outperforming existing methods.

Contribution

The paper introduces UnWave-Net, a new wavelet-based unrolled network that improves CST image reconstruction efficiency and quality, with a non-local wavelet regularization component.

Findings

Outperforms existing CST reconstruction methods in SSIM and PSNR.

Achieves state-of-the-art reconstruction quality with improved computational efficiency.

Effectively captures multi-scale image features using wavelet-based regularization.

Abstract

Computed tomography (CT) is a widely used medical imaging technique to scan internal structures of a body, typically involving collimation and mechanical rotation. Compton scatter tomography (CST) presents an interesting alternative to conventional CT by leveraging Compton physics instead of collimation to gather information from multiple directions. While CST introduces new imaging opportunities with several advantages such as high sensitivity, compactness, and entirely fixed systems, image reconstruction remains an open problem due to the mathematical challenges of CST modeling. In contrast, deep unrolling networks have demonstrated potential in CT image reconstruction, despite their computationally intensive nature. In this study, we investigate the efficiency of unrolling networks for CST image reconstruction. To address the important computational cost required for training, we propose UnWave-Net, a novel unrolled wavelet-based reconstruction network. This architecture includes a non-local regularization term based on wavelets, which captures long-range dependencies within images and emphasizes the multi-scale components of the wavelet transform. We evaluate our approach using a CST of circular geometry which stays completely static during data acquisition, where UnWave-Net facilitates image reconstruction in the absence of a specific reconstruction formula. Our method outperforms existing approaches and achieves state-of-the-art performance in terms of SSIM and PSNR, and offers an improved computational efficiency compared to traditional unrolling networks.