Application of Spherical Convolutional Neural Networks to Image Reconstruction and Denoising in Nuclear Medicine
This addresses the problem of high training requirements in medical imaging for practitioners, though it appears incremental as an adaptation of existing equivariant networks to a specific domain.
This work tackled the limitations of conventional CNNs in nuclear medicine imaging by applying spherical CNNs (SCNNs) to image reconstruction and denoising, achieving superior quality and computational efficiency with significant cost reductions.
This work investigates use of equivariant neural networks as efficient and high-performance frameworks for image reconstruction and denoising in nuclear medicine. Our work aims to tackle limitations of conventional Convolutional Neural Networks (CNNs), which require significant training. We investigated equivariant networks, aiming to reduce CNN's dependency on specific training sets. Specifically, we implemented and evaluated equivariant spherical CNNs (SCNNs) for 2- and 3-dimensional medical imaging problems. Our results demonstrate superior quality and computational efficiency of SCNNs in both image reconstruction and denoising benchmark problems. Furthermore, we propose a novel approach to employ SCNNs as a complement to conventional image reconstruction tools, enhancing the outcomes while reducing reliance on the training set. Across all cases, we observed significant decrease in computational cost by leveraging the inherent inclusion of equivariant representatives while achieving the same or higher quality of image processing using SCNNs compared to CNNs. Additionally, we explore the potential of SCNNs for broader tomography applications, particularly those requiring rotationally variant representation.