CT-based synthetic contrast-enhanced dual-energy CT generation using conditional denoising diffusion probabilistic model Journal Article
| Authors: | Gao, Y.; Qiu, R. L. J.; Xie, H.; Chang, C. W.; Wang, T.; Ghavidel, B.; Roper, J.; Zhou, J.; Yang, X. |
| Article Title: | CT-based synthetic contrast-enhanced dual-energy CT generation using conditional denoising diffusion probabilistic model |
| Abstract: | Objective. The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients. Approach. A conditional denoising diffusion probabilistic model (C-DDPM) was utilized to create synthetic images. Imaging data were collected from 130 head-and-neck (HN) cancer patients who had undergone both non-contrast SECT and CE-DECT scans. Main Results. The performance of the C-DDPM was evaluated using Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). The results showed MAE values of 27.37±3.35 Hounsfield Units (HU) for high-energy CT (H-CT) and 24.57±3.35HU for low-energy CT (L-CT), SSIM values of 0.74±0.22 for H-CT and 0.78±0.22 for L-CT, and PSNR values of 18.51±4.55 decibels (dB) for H-CT and 18.91±4.55 dB for L-CT. Significance. The study demonstrates the efficacy of the deep learning model in producing high-quality synthetic CE-DECT images, which significantly benefits radiation therapy planning. This approach provides a valuable alternative imaging solution for facilities lacking DECT scanners and for patients who are unsuitable for iodine contrast imaging, thereby enhancing the reach and effectiveness of advanced imaging in cancer treatment planning. © 2024 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd. |
| Keywords: | radiotherapy; diffusion; signal noise ratio; tomography, x-ray computed; diagnostic imaging; image enhancement; computerized tomography; head and neck neoplasms; models, statistical; contrast medium; contrast media; image processing, computer-assisted; image processing; energy; statistical model; diseases; head and neck tumor; health risks; signal to noise ratio; procedures; contrast-enhanced; signal-to-noise ratio; ct image; humans; human; x-ray computed tomography; de-noising; deep learning; dual-energy ct; probabilistic models; single-energy ct; diffusion probabilistic model; lower energies; cordless telephones |
| Journal Title: | Physics in Medicine and Biology |
| Volume: | 69 |
| Issue: | 16 |
| ISSN: | 0031-9155 |
| Publisher: | IOP Publishing Ltd |
| Date Published: | 2024-08-21 |
| Start Page: | 165015 |
| Language: | English |
| DOI: | 10.1088/1361-6560/ad67a1 |
| PUBMED: | 39053511 |
| PROVIDER: | scopus |
| PMCID: | PMC11294926 |
| DOI/URL: | |
| Notes: | Source: Scopus |
Altmetric
Citation Impact
BMJ Impact Analytics
Related MSK Work