MRI to CT synthesis using contrastive learning
ID：51
Submission ID：62 View Protection：ATTENDEE
Updated Time：2021-10-30 17:37:08 Hits：1048
Poster Presentation
Start Time：2021-11-13 10:15 （Asia/Shanghai）
Duration：5min
Session：[Pos] Poster » [Pos] Poster Session
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Abstract
Compared with CT, MRI enables more accurate delineation of target and organs-at-risk. But unlike CT, MRI images is not related with electron density for radiotherapy planning. The purpose of this paper is to generate pseudo CT for radiotherapy planning from MRI using deep learning method. Twenty-nine brain cancer patients with planning CT and diagnostic MRI were selected, of which 23 were used for training and 6 for testing. We use a new neural network based on contrast learning, called CUT. Meanwhile, we change the residual block to nine dense blocks and add a structural similarity to the loss function of the generator, the latter network is called denseCUT. We compare Hounsfield Unit (HU) discrepancies between pseudo-CT and original CT images. The mean absolute (MAE) errors were 72.0±6.9 HU，72.5±8.0 HU and 65.7±8.0 HU for the cycleGAN, CUT and denseCUT, respectively. Meanwhile, the structure similarity index (SSIM) were 0.91±0.01, 0.91±0.01 and 0.93±0.01, the peak signal-to-noise ratio (PSNR) were 28.5±0.7 dB, 28.5±0.7 dB and 29.4±0.8 dB, respectively. Experimental results show that the proposed denseCUT network is more accurate, robust, and efficient for predicting synthetic CT from MR images for MRI-only radiotherapy.
Keywords
MRI, contrastive learning, pseudo CT
Speaker
江涛 王
中国科学技术大学Submission Author
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Important Dates
| Abstract submission date: |
2021-10-25 |
| Full paper submission date: |
2021-10-25 |
| Notification of acceptance date: |
2021-11-01 |
| Conference date: | 2021-11-12~2021-11-14 |
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