Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT
CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (p = 0.16). Four radiologists' accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (p < 0.05), and matches real thin-slice CT (p > 0.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (p < 0.001) and comparable to real thin-slice CT (p > 0.05). These findings indicate the potential of our model to generate high-quality synthetic thin-slice CT as a practical alternative when real thin-slice CT is preferred but unavailable.
Authors
- Corey W Arnold (ORCID: https://orcid.org/0000-0002-4119-8143)
- Fereidoun G. Abtin (ORCID: https://orcid.org/0000-0003-2927-0883)
- Haoyue Zhang (ORCID: https://orcid.org/0000-0002-9412-7584)
- Andrea Oh
- John M. Hoffman (ORCID: https://orcid.org/0000-0001-9492-2206)
- Shouliang Qi
- Kathleen Ruchalski (ORCID: https://orcid.org/0000-0001-7988-8484)
- Xinna Lv
- Rongguo Zhang (ORCID: https://orcid.org/0000-0001-6566-8843)
- Huairong Zhang (ORCID: https://orcid.org/0000-0002-2898-182X)
- Lijun Wu (ORCID: https://orcid.org/0000-0002-3120-3121)
- Denise R. Aberle (ORCID: https://orcid.org/0000-0002-8858-3401)
- Ashley E. Prosper (ORCID: https://orcid.org/0000-0001-8615-5246)
- Pengxin Yu (ORCID: https://orcid.org/0000-0003-4065-0377)
- Ye Li (ORCID: https://orcid.org/0000-0001-5620-4246)
- Min Liu (ORCID: https://orcid.org/0000-0003-4591-188X)
- Nana Wang (ORCID: https://orcid.org/0000-0002-6152-8373)
- Dawei Wang (ORCID: https://orcid.org/0000-0001-7547-0689)
- Xiaoxu Liu (ORCID: https://orcid.org/0000-0002-5668-1701)
- Mei Deng
- Chaoyang Liang (ORCID: https://orcid.org/0000-0002-1706-8613)
- Dasheng Li (ORCID: https://orcid.org/0000-0002-5071-5739)
- Shaohong Zhao (ORCID: https://orcid.org/0000-0002-4943-9263)
- Haoyu Yang
Institutions
- National Institutes of Health (US)
- University of California, Los Angeles (US)
- Capital Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking University (CN)
- China-Japan Friendship Hospital (CN)
- Peking Union Medical College Hospital (CN)
- Chinese PLA General Hospital (CN)
- Beijing Chest Hospital (CN)
- InferVision (China) (CN)
- Ningxia Medical University (CN)
- Beijing Haidian Hospital (CN)
- Northeastern University (CN)
- Capital Normal University (CN)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2024-11-23
- DOI
- https://doi.org/10.1038/s41746-024-01338-8
- Citations
- 14
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 5.66
Funders
- National Science and Technology Major Project
- Major Research Plan