Whole‐brain functional MRI registration based on a semi‐supervised deep learning model.
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| Title: | Whole‐brain functional MRI registration based on a semi‐supervised deep learning model. |
|---|---|
| Authors: | Zhu, QiaoYun1 (AUTHOR), Sun, YuHang1 (AUTHOR), Wu, Yi1 (AUTHOR), Zhu, HuoBiao1 (AUTHOR), Lin, GuoYe1 (AUTHOR), Zhou, YuJia1 (AUTHOR) zyj.shmily08@gmail.com, Feng, QianJin1 (AUTHOR) 1271992826@qq.com |
| Source: | Medical Physics. Jun2021, Vol. 48 Issue 6, p2847-2858. 12p. |
| Subjects: | Image registration, Deep learning, Functional magnetic resonance imaging, Echo-planar imaging, Recording & registration |
| Abstract: | Purpose: Traditional registration of functional magnetic resonance images (fMRI) is typically achieved through registering their coregistered structural MRI. However, it cannot achieve accurate performance in that functional units which are not necessarily located relative to anatomical structures. In addition, registration methods based on functional information focus on gray matter (GM) information but ignore the importance of white matter (WM). To overcome the limitations of exiting techniques, in this paper, we aim to register resting‐state fMRI (rs‐fMRI) based directly on rs‐fMRI data and make full use of GM and WM information to improve the registration performance. Methods: We provide a robust representation of WM functional connectivity features using tissue‐specific patch‐based functional correlation tensors (ts‐PFCTs) as auxiliary information to assist registration. Furthermore, we propose a semi‐supervised deep learning model that uses GM and WM information (GM ts‐PFCTs and WM ts‐PFCTs) during training as a fine tweak to improve registration accuracy when such information is not provided in new test image pairs. We implement our method on the 1000 Functional Connectomes Project dataset. To evaluate our method, a group‐level analysis was implemented in resting‐state brain functional networks after registration, resulting in t maps. Results: Our method increases the peak t values of the t maps of default mode network, visual network, central executive network, and sensorimotor network to 21.4, 20.0, 18.4, and 19.0, respectively. Through comparison with traditional methods (FMRIB Software Library(FSL), Statistical Parametric Mapping _ Echo Planar Image(SPM_EPI), and SPM_T1), our method achieves an average improvement of 67.39%, 12.96%, and 25.14%. Conclusion: We propose a semi‐supervised deep learning network by adding GM and WM information as auxiliary information for resting‐state fMRI registration. GM and WM information is extracted and described as GM ts‐PFCTs and WM ts‐PFCTs. Experimental results show that our method achieves superior registration performance. [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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| Header | DbId: egs DbLabel: Engineering Source An: 151285905 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Whole‐brain functional MRI registration based on a semi‐supervised deep learning model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhu%2C+QiaoYun%22">Zhu, QiaoYun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+YuHang%22">Sun, YuHang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Yi%22">Wu, Yi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+HuoBiao%22">Zhu, HuoBiao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+GuoYe%22">Lin, GuoYe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+YuJia%22">Zhou, YuJia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zyj.shmily08@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Feng%2C+QianJin%22">Feng, QianJin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 1271992826@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Jun2021, Vol. 48 Issue 6, p2847-2858. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+magnetic+resonance+imaging%22">Functional magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Echo-planar+imaging%22">Echo-planar imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Recording+%26+registration%22">Recording & registration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: Traditional registration of functional magnetic resonance images (fMRI) is typically achieved through registering their coregistered structural MRI. However, it cannot achieve accurate performance in that functional units which are not necessarily located relative to anatomical structures. In addition, registration methods based on functional information focus on gray matter (GM) information but ignore the importance of white matter (WM). To overcome the limitations of exiting techniques, in this paper, we aim to register resting‐state fMRI (rs‐fMRI) based directly on rs‐fMRI data and make full use of GM and WM information to improve the registration performance. Methods: We provide a robust representation of WM functional connectivity features using tissue‐specific patch‐based functional correlation tensors (ts‐PFCTs) as auxiliary information to assist registration. Furthermore, we propose a semi‐supervised deep learning model that uses GM and WM information (GM ts‐PFCTs and WM ts‐PFCTs) during training as a fine tweak to improve registration accuracy when such information is not provided in new test image pairs. We implement our method on the 1000 Functional Connectomes Project dataset. To evaluate our method, a group‐level analysis was implemented in resting‐state brain functional networks after registration, resulting in t maps. Results: Our method increases the peak t values of the t maps of default mode network, visual network, central executive network, and sensorimotor network to 21.4, 20.0, 18.4, and 19.0, respectively. Through comparison with traditional methods (FMRIB Software Library(FSL), Statistical Parametric Mapping _ Echo Planar Image(SPM_EPI), and SPM_T1), our method achieves an average improvement of 67.39%, 12.96%, and 25.14%. Conclusion: We propose a semi‐supervised deep learning network by adding GM and WM information as auxiliary information for resting‐state fMRI registration. GM and WM information is extracted and described as GM ts‐PFCTs and WM ts‐PFCTs. Experimental results show that our method achieves superior registration performance. [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.14777 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 2847 Subjects: – SubjectFull: Image registration Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Functional magnetic resonance imaging Type: general – SubjectFull: Echo-planar imaging Type: general – SubjectFull: Recording & registration Type: general Titles: – TitleFull: Whole‐brain functional MRI registration based on a semi‐supervised deep learning model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhu, QiaoYun – PersonEntity: Name: NameFull: Sun, YuHang – PersonEntity: Name: NameFull: Wu, Yi – PersonEntity: Name: NameFull: Zhu, HuoBiao – PersonEntity: Name: NameFull: Lin, GuoYe – PersonEntity: Name: NameFull: Zhou, YuJia – PersonEntity: Name: NameFull: Feng, QianJin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 48 – Type: issue Value: 6 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
