Magnetic resonance imaging contrast enhancement synthesis using cascade networks with local supervision.
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| Title: | Magnetic resonance imaging contrast enhancement synthesis using cascade networks with local supervision. |
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| Authors: | Xie, Huiqiao1 (AUTHOR), Lei, Yang1 (AUTHOR), Wang, Tonghe1 (AUTHOR), Roper, Justin1 (AUTHOR), Axente, Marian1 (AUTHOR), Bradley, Jeffrey D.1 (AUTHOR), Liu, Tian1 (AUTHOR), Yang, Xiaofeng1 (AUTHOR) xiaofeng.yang@emory.edu |
| Source: | Medical Physics. May2022, Vol. 49 Issue 5, p3278-3287. 10p. |
| Subjects: | Magnetic resonance imaging, Cascade connections, Image intensifiers, Pearson correlation (Statistics), Retina, Deep learning |
| Abstract: | Purpose: Gadolinium‐based contrast agents (GBCAs) are widely administrated in MR imaging for diagnostic studies and treatment planning. Although GBCAs are generally thought to be safe, various health and environmental concerns have been raised recently about their use in MR imaging. The purpose of this work is to derive synthetic contrast enhance MR images from unenhanced counterpart images, thereby eliminating the need for GBCAs, using a cascade deep learning workflow that incorporates contour information into the network. Methods and materials: The proposed workflow consists of two sequential networks: (1) a retina U‐Net, which is first trained to derive semantic features from the non‐contrast MR images in representing the tumor regions; and (2) a synthesis module, which is trained after the retina U‐Net to take the concatenation of the semantic feature maps and non‐contrast MR image as input and to generate the synthetic contrast enhanced MR images. After network training, only the non‐contrast enhanced MR images are required for the input in the proposed workflow. The MR images of 369 patients from the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were used in this study to evaluate the proposed workflow for synthesizing contrast enhanced MR images (200 patients for five‐fold cross‐validation and 169 patients for hold‐out test). Quantitative evaluations were conducted by calculating the normalized mean absolute error (NMAE), structural similarity index measurement (SSIM), and Pearson correlation coefficient (PCC). The original contrast enhanced MR images were considered as the ground truth in this analysis. Results: The proposed cascade deep learning workflow synthesized contrast enhanced MR images that are not visually differentiable from the ground truth with and without supervision of the tumor contours during the network training. Difference images and profiles of the synthetic contrast enhanced MR images revealed that intensity differences could be observed in the tumor region if the contour information was not incorporated in network training. Among the hold‐out test patients, mean values and standard deviations of the NMAE, SSIM, and PCC were 0.063±0.022, 0.991±0.007 and 0.995±0.006, respectively, for the whole brain; and were 0.050±0.025, 0.993±0.008 and 0.999±0.003, respectively, for the tumor contour regions. Quantitative evaluations with five‐fold cross‐validation and hold‐out test showed that the calculated metrics can be significantly enhanced (p‐values ≤ 0.002) with the tumor contour supervision in network training. Conclusion: The proposed workflow was able to generate synthetic contrast enhanced MR images that closely resemble the ground truth images from non‐contrast enhanced MR images when the network training included tumor contours. These results suggest that it may be possible to minimize the use of GBCAs in cranial MR imaging studies. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Magnetic resonance imaging contrast enhancement synthesis using cascade networks with local supervision. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xie%2C+Huiqiao%22">Xie, Huiqiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lei%2C+Yang%22">Lei, Yang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Tonghe%22">Wang, Tonghe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Roper%2C+Justin%22">Roper, Justin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Axente%2C+Marian%22">Axente, Marian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bradley%2C+Jeffrey+D%2E%22">Bradley, Jeffrey D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Tian%22">Liu, Tian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xiaofeng%22">Yang, Xiaofeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xiaofeng.yang@emory.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. May2022, Vol. 49 Issue 5, p3278-3287. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Cascade+connections%22">Cascade connections</searchLink><br /><searchLink fieldCode="DE" term="%22Image+intensifiers%22">Image intensifiers</searchLink><br /><searchLink fieldCode="DE" term="%22Pearson+correlation+%28Statistics%29%22">Pearson correlation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Retina%22">Retina</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: Gadolinium‐based contrast agents (GBCAs) are widely administrated in MR imaging for diagnostic studies and treatment planning. Although GBCAs are generally thought to be safe, various health and environmental concerns have been raised recently about their use in MR imaging. The purpose of this work is to derive synthetic contrast enhance MR images from unenhanced counterpart images, thereby eliminating the need for GBCAs, using a cascade deep learning workflow that incorporates contour information into the network. Methods and materials: The proposed workflow consists of two sequential networks: (1) a retina U‐Net, which is first trained to derive semantic features from the non‐contrast MR images in representing the tumor regions; and (2) a synthesis module, which is trained after the retina U‐Net to take the concatenation of the semantic feature maps and non‐contrast MR image as input and to generate the synthetic contrast enhanced MR images. After network training, only the non‐contrast enhanced MR images are required for the input in the proposed workflow. The MR images of 369 patients from the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were used in this study to evaluate the proposed workflow for synthesizing contrast enhanced MR images (200 patients for five‐fold cross‐validation and 169 patients for hold‐out test). Quantitative evaluations were conducted by calculating the normalized mean absolute error (NMAE), structural similarity index measurement (SSIM), and Pearson correlation coefficient (PCC). The original contrast enhanced MR images were considered as the ground truth in this analysis. Results: The proposed cascade deep learning workflow synthesized contrast enhanced MR images that are not visually differentiable from the ground truth with and without supervision of the tumor contours during the network training. Difference images and profiles of the synthetic contrast enhanced MR images revealed that intensity differences could be observed in the tumor region if the contour information was not incorporated in network training. Among the hold‐out test patients, mean values and standard deviations of the NMAE, SSIM, and PCC were 0.063±0.022, 0.991±0.007 and 0.995±0.006, respectively, for the whole brain; and were 0.050±0.025, 0.993±0.008 and 0.999±0.003, respectively, for the tumor contour regions. Quantitative evaluations with five‐fold cross‐validation and hold‐out test showed that the calculated metrics can be significantly enhanced (p‐values ≤ 0.002) with the tumor contour supervision in network training. Conclusion: The proposed workflow was able to generate synthetic contrast enhanced MR images that closely resemble the ground truth images from non‐contrast enhanced MR images when the network training included tumor contours. These results suggest that it may be possible to minimize the use of GBCAs in cranial MR imaging studies. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.15578 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 3278 Subjects: – SubjectFull: Magnetic resonance imaging Type: general – SubjectFull: Cascade connections Type: general – SubjectFull: Image intensifiers Type: general – SubjectFull: Pearson correlation (Statistics) Type: general – SubjectFull: Retina Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Magnetic resonance imaging contrast enhancement synthesis using cascade networks with local supervision. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xie, Huiqiao – PersonEntity: Name: NameFull: Lei, Yang – PersonEntity: Name: NameFull: Wang, Tonghe – PersonEntity: Name: NameFull: Roper, Justin – PersonEntity: Name: NameFull: Axente, Marian – PersonEntity: Name: NameFull: Bradley, Jeffrey D. – PersonEntity: Name: NameFull: Liu, Tian – PersonEntity: Name: NameFull: Yang, Xiaofeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 49 – Type: issue Value: 5 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
