CT image kernel synthesis using deep regularization

Hemant Kumar Aggarwal; Rajesh Langoju; Bipul Das; Rakesh Mullick

doi:10.1117/12.3006636

2 April 2024 CT image kernel synthesis using deep regularization

Hemant Kumar Aggarwal, Rajesh Langoju, Bipul Das, Rakesh Mullick

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292609 (2024) https://doi.org/10.1117/12.3006636
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Kernel Synthesis (KS) in CT is an advanced image processing technique that involves the conversion of an image acquired with one specific convolution kernel into an image that appears as if it were acquired with a different kernel. This process doesn't require raw sinogram or reconstruction algorithm to generate images. While kernel synthesis methods exhibit promise, they can introduce noise and artifacts during transformation, highlighting the importance of effective noise and artifact management in the input data before synthesis. In this work, we represent kernel synthesis as a deep neural network regularized inverse problem. We optimize the convolutional neural network (CNN) weights in a data-consistent manner where CNN acts as an implicit prior to regularize the solution. Since, the learned CNN priors are more generic than hand crafted priors (like sparsity and total-variation), they helps control artifacts while preserving the details in the image. Experimental results for a low-resolution kernel (STND) data to a high resolution kernel (LUNG) data conversion clearly indicates that the output images from the proposed method resemble closely with images reconstructed using high-resolution kernel (LUNG) while simultaneously reducing the noise and clutter.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hemant Kumar Aggarwal, Rajesh Langoju, Bipul Das, and Rakesh Mullick "CT image kernel synthesis using deep regularization", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292609 (2 April 2024); https://doi.org/10.1117/12.3006636

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