A Data-Driven Reconstruction Technique based on Newton's Method for Emission Tomography
This work addresses image quality issues in emission tomography for medical imaging applications, representing an incremental improvement over existing methods.
The authors tackled the problem of image reconstruction in emission tomography by developing DNR-Net, a hybrid data-driven technique based on Newton's method, which achieved reconstructions comparable to the OSEM method with higher contrast and less noise in SPECT phantom simulations using 24 projections.
In this work, we present the Deep Newton Reconstruction Network (DNR-Net), a hybrid data-driven reconstruction technique for emission tomography inspired by Newton's method, a well-known iterative optimization algorithm. The DNR-Net employs prior information about the tomographic problem provided by the projection operator while utilizing deep learning approaches to a) imitate Newton's method by approximating the Newton descent direction and b) provide data-driven regularisation. We demonstrate that DNR-Net is capable of providing high-quality image reconstructions using data from SPECT phantom simulations by applying it to reconstruct images from noisy sinograms, each one containing 24 projections. The Structural Similarity Index (SSIM) and the Contrast-to-Noise ratio (CNR) were used to quantify the image quality. We also compare our results to those obtained by the OSEM method. According to the quantitative results, the DNR-Net produces reconstructions comparable to the ones produced by OSEM while featuring higher contrast and less noise.