@inproceedings{a3b523c8a9064d53bcaedd5692690654,
title = "Pixel-wise bias approximation and correction for convolutional neural network noise reduction in CT",
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.",
keywords = "Bias, Bootstrap Approximation, CNN Denoising, Uncertainty, Whole-Body-Low-Dose CT",
author = "Huber, {Nathan R.} and Hao Gong and Thomas Huber and David Campeau and Hsieh Scott and Shuai Leng and Lifeng Yu and Cynthia McCollough",
note = "Funding Information: Research reported in this work was supported by the Department of Radiology at the Mayo Clinic, the CT Clinical Innovation Center, and Mayo Clinic Graduate School of Biomedical Sciences. Authors would like to thank Greg J. Michalak, Ph.D., for contributions to image acquisition. Publisher Copyright: {\textcopyright} 2022 SPIE.; Medical Imaging 2022: Physics of Medical Imaging ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2612703",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Wei Zhao and Lifeng Yu",
booktitle = "Medical Imaging 2022",
}