Noise characteristics in low to high kVp domain translation with Deep Regression Network

Vanika Singhal; Rajesh Langoju; Sidharth Abrol; Utkarsh Agrawal; Bhushan D. Patil; Sandeep Dutta; Bipul Das

doi:10.1117/12.2581736

15 February 2021 Noise characteristics in low to high kVp domain translation with Deep Regression Network

Vanika Singhal, Rajesh Langoju, Sidharth Abrol, Utkarsh Agrawal, Bhushan D. Patil, Sandeep Dutta, Bipul Das

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115963E (2021) https://doi.org/10.1117/12.2581736
Event: SPIE Medical Imaging, 2021, Online Only

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Abstract

In Computed Tomography (CT), problems like low to high dose image conversion, denoising and super-resolution using deep regression network have gained a lot of importance. This led to the study of multiple denoising approaches with these networks, as non-linear characteristics of the network often changes the noise pattern. Noise2Noise (N2N) and similar studies have shown the impact of these networks, where one can use noisy image pairs and exploit the uncorrelated nature of noise to generate denoised images. In this paper, we study the behaviour of regression network for a domain translation between one energy image to another energy and its impact on noise. Inter-kVp translation leads to change in attenuation values in CT image. A design of experiments is set up using CATSim phantom with different tissue types at varying levels of density and proportions of materials, for heart, soft tissue, fat, calcification and bone. The intent is to understand the impact of inter and intra-tissue dynamic ranges, as this network learns intensity translation of the image between 2 kVp levels and noise characteristics. The results demonstrate ability of regression network to change intensty values from low kVp image to high kVp image. It also shows impact on noise level in a tissue is proportional to (a) intra-tissue variability and (b) the desired mean shift of the tissue between two energy levels. Simultaneously, there is a marked change in the artifacts and resultant image quality, as expected, through the learning method.

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Citation Download Citation

Vanika Singhal, Rajesh Langoju, Sidharth Abrol, Utkarsh Agrawal, Bhushan D. Patil, Sandeep Dutta, and Bipul Das "Noise characteristics in low to high kVp domain translation with Deep Regression Network", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115963E (15 February 2021); https://doi.org/10.1117/12.2581736

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