Neural network regularization in the problem of few-view computed tomography

Abstract

The computed tomography allows to reconstruct the inner morphological structure of an object without physical destructing. The accuracy of digital image reconstruction directly depends on the measurement conditions of tomographic projections, in particular, on the number of recorded projections. In medicine, to reduce the dose of the patient load there try to reduce the number of measured projections. However, in a few-view computed tomography, when we have a small number of projections, using standard reconstruction algorithms leads to the reconstructed images degradation. The main feature of our approach for few-view tomography is that algebraic reconstruction is being finalized by a neural network with keeping measured projection data because the additive result is in zero space of the forward projection operator. The final reconstruction presents the sum of the additive calculated with the neural network and the algebraic reconstruction. First is an element of zero space of the forward projection operator. The second is an element of orthogonal addition to the zero space. Last is the result of applying the algebraic reconstruction method to a few-angle sinogram. The dependency model between elements of zero space of forward projection operator and algebraic reconstruction is built with neural networks. It demonstrated that realization of the suggested approach allows achieving better reconstruction accuracy and better computation time than state-of-the-art approaches on test data from the Low Dose CT Challenge dataset without increasing reprojection error.

Applicability and potential of monitored reconstruction in computed tomography

“…Non-equidistant projections (random order) or limited angle range (consecutive order) lead to reconstruction corruptions demonstrated in Fig 3 . These cases are usually dealt with by utilizing special slow iterative [ 22 ] or neural network-based methods [ 23 ]. At the same time, the reconstruction speed is a critical factor for MTR implementations and if not even a single reconstruction can be performed for the time of acquisition, no real-time reconstruction would be possible.…”

From tomographic reconstruction to automatic text recognition: the next frontier task for the artificial intelligence

“…We also hope that the published data will be useful for the development of new robust CT-reconstruction methods [58][59][60][61][62][63][64] optimized for speed [65] and memory requirements.…”