Deep-ASL enhancement technique in arterial spin labeling MRI – A novel approach for the error reduction of partial volume correction technique with linear regression algorithm
•Reduce CBF quantification error by enhancing ASL image quality with a novel model.•For clinical use, sufficiency of simulated ASL data to train the model is proved.•Developed Deep-ASL ENHANCE as a preprocessing step to Linear Regression algorithm Arterial Spin Labelling MRI is a noninvasive quantit...
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| Published in: | Journal of computational science Vol. 58; p. 101546 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.02.2022
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| ISSN: | 1877-7503, 1877-7511 |
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| Abstract | •Reduce CBF quantification error by enhancing ASL image quality with a novel model.•For clinical use, sufficiency of simulated ASL data to train the model is proved.•Developed Deep-ASL ENHANCE as a preprocessing step to Linear Regression algorithm
Arterial Spin Labelling MRI is a noninvasive quantitative imaging technique for measuring Cerebral Blood Flow (CBF) that plays a vital role in diagnosing different neurological disorders. Limited signal-to-noise ratio and significant partial volume effect due to the low resolution of ASL images make an accurate CBF estimation difficult. This work proposes a deep learning based ASL enhancement algorithm (Deep-ASL ENHANCE), based on the principle of single image super resolution and multi-loss joint strategy with two reconstruction modules and one weighted fusion module that employ residual dense block as the basic building block. Lack of huge amount of low quality and high quality images for training this deep learning network, is addressed by generating simulated ASL images from structural images of ADNI2. The experiment is conducted and results are evaluated on a simulated dataset in terms of different metrics such as RMSE, PSNR and SSIM. The model is also validated using clinical ASL images with the help of two independent radiologists and the results are compared using Visual Quality Score (VQS). The deep learning model trained by using simulated ASL images shows more promising results on clinical ASL data. The effectiveness of using Deep-ASL ENHANCE as a preprocessing step to the partial volume correction technique with Linear Regression algorithm (LR) has been investigated using RMSE score and it is found that CBF quantification accuracy is improved compared to the standalone LR algorithm. |
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| AbstractList | •Reduce CBF quantification error by enhancing ASL image quality with a novel model.•For clinical use, sufficiency of simulated ASL data to train the model is proved.•Developed Deep-ASL ENHANCE as a preprocessing step to Linear Regression algorithm
Arterial Spin Labelling MRI is a noninvasive quantitative imaging technique for measuring Cerebral Blood Flow (CBF) that plays a vital role in diagnosing different neurological disorders. Limited signal-to-noise ratio and significant partial volume effect due to the low resolution of ASL images make an accurate CBF estimation difficult. This work proposes a deep learning based ASL enhancement algorithm (Deep-ASL ENHANCE), based on the principle of single image super resolution and multi-loss joint strategy with two reconstruction modules and one weighted fusion module that employ residual dense block as the basic building block. Lack of huge amount of low quality and high quality images for training this deep learning network, is addressed by generating simulated ASL images from structural images of ADNI2. The experiment is conducted and results are evaluated on a simulated dataset in terms of different metrics such as RMSE, PSNR and SSIM. The model is also validated using clinical ASL images with the help of two independent radiologists and the results are compared using Visual Quality Score (VQS). The deep learning model trained by using simulated ASL images shows more promising results on clinical ASL data. The effectiveness of using Deep-ASL ENHANCE as a preprocessing step to the partial volume correction technique with Linear Regression algorithm (LR) has been investigated using RMSE score and it is found that CBF quantification accuracy is improved compared to the standalone LR algorithm. |
| ArticleNumber | 101546 |
| Author | C., Kesavadas C., Ushadevi Amma John, Ansamma Thomas, Bejoy A, Shyna |
| Author_xml | – sequence: 1 givenname: Shyna surname: A fullname: A, Shyna email: s4shyna@gmail.com organization: Department of Computer Science and Engineering, ThangalKunju Musaliar College of Engineering, APJ Abdul Kalam Technological University, Kerala, India – sequence: 2 givenname: Ushadevi Amma surname: C. fullname: C., Ushadevi Amma organization: Department of Electronics and Communications Engineering, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kerala, India – sequence: 3 givenname: Ansamma surname: John fullname: John, Ansamma organization: Department of Computer Science and Engineering, ThangalKunju Musaliar College of Engineering, APJ Abdul Kalam Technological University, Kerala, India – sequence: 4 givenname: Kesavadas surname: C. fullname: C., Kesavadas organization: Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India – sequence: 5 givenname: Bejoy orcidid: 0000-0003-0355-3375 surname: Thomas fullname: Thomas, Bejoy organization: Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Trivandrum, Kerala, India |
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| Keywords | Residual dense block Linear regression algorithm Deeplearning Partial volume effect Cerebral blood flow Arterial spin labeling |
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