Applications of Deep Learning for Ill-Posed Inverse Problems Within Optical Tomography
This is an incremental review paper that addresses image reconstruction issues in medical imaging, specifically for X-Ray CT, with no new methods or data introduced.
The paper tackles the challenge of reconstructing noisy images from undersampled X-Ray CT data, an ill-posed inverse problem, by reviewing classical methods and analyzing state-of-the-art supervised deep learning approaches, without presenting new experimental results or concrete numbers.
Increasingly in medical imaging has emerged an issue surrounding the reconstruction of noisy images from raw measurement data. Where the forward problem is the generation of raw measurement data from a ground truth image, the inverse problem is the reconstruction of those images from the measurement data. In most cases with medical imaging, classical inverse Radon transforms, such as an inverse Fourier transform for MRI, work well for recovering clean images from the measured data. Unfortunately in the case of X-Ray CT, where undersampled data is very common, more than this is needed to resolve faithful and usable images. In this paper, we explore the history of classical methods for solving the inverse problem for X-Ray CT, followed by an analysis of the state of the art methods that utilize supervised deep learning. Finally, we will provide some possible avenues for research in the future.