Learned Spectral Computed Tomography
This work addresses the problem of impractical calibration and high computational costs in SPCCT reconstruction for medical imaging, offering a more efficient solution, though it appears incremental by combining deep learning with model-based knowledge.
The paper tackles the challenge of reconstructing high-quality images from Spectral Photon-Counting Computed Tomography (SPCCT) data, which is complex and computationally expensive, especially with limited-angle data. It proposes a deep learning method that achieves fast reconstruction and high imaging performance, as demonstrated through numerical examples in cardiovascular imaging.
Spectral Photon-Counting Computed Tomography (SPCCT) is a promising technology that has shown a number of advantages over conventional X-ray Computed Tomography (CT) in the form of material separation, artefact removal and enhanced image quality. However, due to the increased complexity and non-linearity of the SPCCT governing equations, model-based reconstruction algorithms typically require handcrafted regularisation terms and meticulous tuning of hyperparameters making them impractical to calibrate in variable conditions. Additionally, they typically incur high computational costs and in cases of limited-angle data, their imaging capability deteriorates significantly. Recently, Deep Learning has proven to provide state-of-the-art reconstruction performance in medical imaging applications while circumventing most of these challenges. Inspired by these advances, we propose a Deep Learning imaging method for SPCCT that exploits the expressive power of Neural Networks while also incorporating model knowledge. The method takes the form of a two-step learned primal-dual algorithm that is trained using case-specific data. The proposed approach is characterised by fast reconstruction capability and high imaging performance, even in limited-data cases, while avoiding the hand-tuning that is required by other optimisation approaches. We demonstrate the performance of the method in terms of reconstructed images and quality metrics via numerical examples inspired by the application of cardiovascular imaging.