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Temporally downsampled cerebral CT perfusion image restoration using deep residual learning.

Abstract

PURPOSE

Acute ischemic stroke is one of the most causes of death all over the world. Onset to treatment time is critical in stroke diagnosis and treatment. Considering the time consumption and high price of MR imaging, CT perfusion (CTP) imaging is strongly recommended for acute stroke. However, too much CT radiation during CTP imaging may increase the risk of health problems. How to reduce CT radiation dose in CT perfusion imaging has drawn our great attention.

METHODS

In this study, the original 30-pass CTP images are downsampled to 15 passes in time sequence, which equals to 50% radiation dose reduction. Then, a residual deep convolutional neural network (DCNN) model is proposed to restore the downsampled 15-pass CTP images to 30 passes to calculate the parameters such as cerebral blood flow, cerebral blood volume, mean transit time, time to peak for stroke diagnosis and treatment. The deep restoration CNN is implemented simply and effectively with 16 successive convolutional layers which form a wide enough receptive field for input image data. 18 patients' CTP images are employed as training set and the other six patients' CTP images are treated as test dataset in this study.

RESULTS

Experiments demonstrate that our CNN can restore high-quality CTP images in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The average SSIM and PSNR for test images are 0.981 and 56.25, and the SSIM and PSNR of regions of interest are 0.915 and 42.44, respectively, showing promising quantitative level. In addition, we compare the perfusion maps calculated from the restored images and from the original images, and the average perfusion results of them are extremely close. Areas of hypoperfusion of six test cases could be detected with comparable accuracy by radiologists.

CONCLUSION

The trained model can restore the temporally downsampled 15-pass CTP to 30 passes very well. According to the contrast test, sufficient information cannot be restored with, e.g., simple interpolation method and deep convolutional generative adversarial network, but can be restored with the proposed CNN model. This method can be an optional way to reduce radiation dose during CTP imaging.

Authors+Show Affiliations

Lab of Image Science and Technology, Key Laboratory of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing, 210096, China.Department of Radiology, The First Hospital of Jilin University, Changchun, 130021, China.IETR, CNRS, UMR 6164, INSA Rennes, 35708, Rennes, France.Lab of Image Science and Technology, Key Laboratory of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing, 210096, China.Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing, 400044, China.Lab of Image Science and Technology, Key Laboratory of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing, 210096, China.Lab of Image Science and Technology, Key Laboratory of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing, 210096, China. chenyang.list@seu.edu.cn. Centre de Recherche en Information Biomedicale, Sino-Francais (LIA CRIBs), 35000, Rennes, France. chenyang.list@seu.edu.cn. School of Cyber Science and Engineering, Southeast University, Nanjing, 210096, China. chenyang.list@seu.edu.cn.Lab of Image Science and Technology, Key Laboratory of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing, 210096, China.Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, 250117, China. zhujian.cn@163.com.Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, 250117, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31673961

Citation

Zhu, Haichen, et al. "Temporally Downsampled Cerebral CT Perfusion Image Restoration Using Deep Residual Learning." International Journal of Computer Assisted Radiology and Surgery, 2019.
Zhu H, Tong D, Zhang L, et al. Temporally downsampled cerebral CT perfusion image restoration using deep residual learning. Int J Comput Assist Radiol Surg. 2019.
Zhu, H., Tong, D., Zhang, L., Wang, S., Wu, W., Tang, H., ... Li, B. (2019). Temporally downsampled cerebral CT perfusion image restoration using deep residual learning. International Journal of Computer Assisted Radiology and Surgery, doi:10.1007/s11548-019-02082-1.
Zhu H, et al. Temporally Downsampled Cerebral CT Perfusion Image Restoration Using Deep Residual Learning. Int J Comput Assist Radiol Surg. 2019 Oct 31; PubMed PMID: 31673961.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Temporally downsampled cerebral CT perfusion image restoration using deep residual learning. AU - Zhu,Haichen, AU - Tong,Dan, AU - Zhang,Lu, AU - Wang,Shijie, AU - Wu,Weiwen, AU - Tang,Hui, AU - Chen,Yang, AU - Luo,Limin, AU - Zhu,Jian, AU - Li,Baosheng, Y1 - 2019/10/31/ PY - 2019/01/17/received PY - 2019/10/18/accepted PY - 2019/11/2/entrez KW - Acute ischemic stroke KW - CT perfusion imaging KW - Deep residual CNN KW - Low-dose CT JF - International journal of computer assisted radiology and surgery JO - Int J Comput Assist Radiol Surg N2 - PURPOSE: Acute ischemic stroke is one of the most causes of death all over the world. Onset to treatment time is critical in stroke diagnosis and treatment. Considering the time consumption and high price of MR imaging, CT perfusion (CTP) imaging is strongly recommended for acute stroke. However, too much CT radiation during CTP imaging may increase the risk of health problems. How to reduce CT radiation dose in CT perfusion imaging has drawn our great attention. METHODS: In this study, the original 30-pass CTP images are downsampled to 15 passes in time sequence, which equals to 50% radiation dose reduction. Then, a residual deep convolutional neural network (DCNN) model is proposed to restore the downsampled 15-pass CTP images to 30 passes to calculate the parameters such as cerebral blood flow, cerebral blood volume, mean transit time, time to peak for stroke diagnosis and treatment. The deep restoration CNN is implemented simply and effectively with 16 successive convolutional layers which form a wide enough receptive field for input image data. 18 patients' CTP images are employed as training set and the other six patients' CTP images are treated as test dataset in this study. RESULTS: Experiments demonstrate that our CNN can restore high-quality CTP images in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The average SSIM and PSNR for test images are 0.981 and 56.25, and the SSIM and PSNR of regions of interest are 0.915 and 42.44, respectively, showing promising quantitative level. In addition, we compare the perfusion maps calculated from the restored images and from the original images, and the average perfusion results of them are extremely close. Areas of hypoperfusion of six test cases could be detected with comparable accuracy by radiologists. CONCLUSION: The trained model can restore the temporally downsampled 15-pass CTP to 30 passes very well. According to the contrast test, sufficient information cannot be restored with, e.g., simple interpolation method and deep convolutional generative adversarial network, but can be restored with the proposed CNN model. This method can be an optional way to reduce radiation dose during CTP imaging. SN - 1861-6429 UR - https://www.unboundmedicine.com/medline/citation/31673961/Temporally_downsampled_cerebral_CT_perfusion_image_restoration_using_deep_residual_learning_ L2 - https://dx.doi.org/10.1007/s11548-019-02082-1 DB - PRIME DP - Unbound Medicine ER -