Pixel-wise bias approximation and correction for convolutional neural network noise reduction in CT

Nathan R. Huber; Hao Gong; Thomas Huber; David Campeau; Scott Hsieh; Shuai Leng; Lifeng Yu; Cynthia McCollough

doi:10.1117/12.2612703

4 April 2022 Pixel-wise bias approximation and correction for convolutional neural network noise reduction in CT

Nathan R. Huber, Hao Gong, Thomas Huber, David Campeau, Scott Hsieh, Shuai Leng, Lifeng Yu, Cynthia McCollough

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 120311C (2022) https://doi.org/10.1117/12.2612703
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

This study introduces a framework to approximate the bias inflicted by CNN noise reduction of CT exams. First, CNN noise reduction was used to approximate the noise-free image and noise-only image of a CT scan. The noise and signal were then recombined with spatial decoupling to simulate an ensemble of 100 images. CNN noise reduction was applied to the simulated ensemble and pixel-wise bias calculated. This bias approximation technique was validated within natural images and phantoms. The technique was then tested on ten whole-body-low-dose CT (WBLD-CT) patient exams. Bias correction led to improved contrast of lung and bone structures.

Conference Presentation

Citation Download Citation

Nathan R. Huber, Hao Gong, Thomas Huber, David Campeau, Scott Hsieh, Shuai Leng, Lifeng Yu, and Cynthia McCollough "Pixel-wise bias approximation and correction for convolutional neural network noise reduction in CT", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 120311C (4 April 2022); https://doi.org/10.1117/12.2612703

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