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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 00942405 |
| DOI: | 10.1002/mp.14777 |
