Space-Variant Total Variation boosted by learning techniques in few-view tomographic imaging
This work addresses medical imaging reconstruction from limited data, representing an incremental improvement over existing Total Variation methods.
The paper tackled the problem of reconstructing medical images from few-view tomographic noisy data by developing a space-variant regularization model that balances denoising with preservation of details and edges, demonstrating promising results for high-quality reconstructions from low-sampled data.
This paper focuses on the development of a space-variant regularization model for solving an under-determined linear inverse problem. The case study is a medical image reconstruction from few-view tomographic noisy data. The primary objective of the proposed optimization model is to achieve a good balance between denoising and the preservation of fine details and edges, overcoming the performance of the popular and largely used Total Variation (TV) regularization through the application of appropriate pixel-dependent weights. The proposed strategy leverages the role of gradient approximations for the computation of the space-variant TV weights. For this reason, a convolutional neural network is designed, to approximate both the ground truth image and its gradient using an elastic loss function in its training. Additionally, the paper provides a theoretical analysis of the proposed model, showing the uniqueness of its solution, and illustrates a Chambolle-Pock algorithm tailored to address the specific problem at hand. This comprehensive framework integrates innovative regularization techniques with advanced neural network capabilities, demonstrating promising results in achieving high-quality reconstructions from low-sampled tomographic data.