@inproceedings{a058b648980c49ccb0a03751245f04b5,
title = "Deep-learning lesion and noise insertion for virtual clinical trial in chest ct",
abstract = "Accurate and objective image quality assessment is essential for the task of radiation dose optimization in clinical CT. Standard method relies on multi-reader multi-case (MRMC) studies in which radiologists are tasked to interpret diagnostic image quality of many carefully-collected positive and negative cases. The efficiency of such MRMC studies is frequently challenged by the lengthy and expensive procedure of case collection and the establishment of clinical reference standard. To address this challenge, multiple methods of virtual clinical trial to synthesize patient cases at different conditions have been proposed. Projection-domain lesion- / noise-insertion methods require the access to patient raw data and vendor-specific proprietary tools which are frequently not accessible to most users. The conventional image-domain noise-insertion methods are often challenged by the over-simplified lesion models and CT system models which may not represent conditions in real scans. In this work, we developed deep-learning lesion and noise insertion techniques that can synthesize chest CT images at different dose levels with and without lung nodules using existing patient cases. The proposed method involved a nodule-insertion convolutional neural network (CNN) and a noise-insertion CNN. Both CNNs demonstrated comparable quality to our previously-validated projection domain lesion- / noise-insertion techniques: mean structural similarity index (SSIM) of inserted nodules 0.94 (routine dose), and mean percent noise difference ∼5% (50% of routine dose). The proposed deep-learning techniques for chest CT virtual clinical trial operate directly on image domain, which is more widely applicable than projection-domain techniques.",
keywords = "Chest ct, Deep learning, Image domain, Lesion insertion, Noise insertion, Virtual clinical trial",
author = "Hao Gong and Marsh, {Jeffrey F.} and Jamison Thorne and Shuai Leng and McCollough, {Cynthia H.} and Fletcher, {Joel G.} and Lifeng Yu",
note = "Funding Information: Dr. McCollough receives industrial grant support from Siemens Healthcare Inc., which is not related to the presented work. Other authors do not have any conflict of interest to disclose. Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Award Numbers EB017095. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2021: Physics of Medical Imaging ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2582106",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Hilde Bosmans and Wei Zhao and Lifeng Yu",
booktitle = "Medical Imaging 2021",
}