Towards automatic US-MR fetal brain image registration with learning-based methods
Fetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological abnormalities. Despite the great potential synerg...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925001065 |
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| author | Qi Zeng Weide Liu Bo Li Ryne Didier P. Ellen Grant Davood Karimi |
| author_facet | Qi Zeng Weide Liu Bo Li Ryne Didier P. Ellen Grant Davood Karimi |
| author_sort | Qi Zeng |
| collection | DOAJ |
| description | Fetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological abnormalities. Despite the great potential synergy of combined fetal brain US and MR imaging to enhance diagnostic accuracy, little effort has been made to integrate these modalities. An essential step towards this integration is accurate automatic spatial alignment, which is technically very challenging due to the inherent differences in contrast and modality-specific imaging artifacts. In this work, we present a novel atlas-assisted multi-task learning technique to address this problem. Instead of training the registration model solely with intra-subject US-MR image pairs, our approach enables the network to also learn from domain-specific image-to-atlas registration tasks. This leads to an end-to-end multi-task learning framework with superior registration performance. Our proposed method was validated using a dataset of same-day intra-subject 3D US-MR image pairs. The results show that our method outperforms conventional optimization-based methods and recent learning-based techniques for rigid image registration. Specifically, the average target registration error for our method is less than 4 mm, which is significantly better than existing methods. Extensive experiments have also shown that our method has a much wider capture range and is robust to brain abnormalities. Given these advantages over existing techniques, our method is more suitable for deployment in clinical workflows and may contribute to streamlined multimodal imaging pipelines for fetal brain assessment. |
| format | Article |
| id | doaj-art-2c016501e3b743348bfa72fc85bbf498 |
| institution | OA Journals |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-2c016501e3b743348bfa72fc85bbf4982025-08-20T02:09:07ZengElsevierNeuroImage1095-95722025-04-0131012110410.1016/j.neuroimage.2025.121104Towards automatic US-MR fetal brain image registration with learning-based methodsQi Zeng0Weide Liu1Bo Li2Ryne Didier3P. Ellen Grant4Davood Karimi5Correspondence to: Landmark Center, 401 Park Dr, Boston, MA 02215, USA.; Department of Radiology, Boston Children’s Hospital, USA; Harvard Medical School, USADepartment of Radiology, Boston Children’s Hospital, USA; Harvard Medical School, USADepartment of Radiology, Boston Children’s Hospital, USA; Harvard Medical School, USADepartment of Radiology, Boston Children’s Hospital, USA; Harvard Medical School, USADepartment of Radiology, Boston Children’s Hospital, USA; Harvard Medical School, USADepartment of Radiology, Boston Children’s Hospital, USA; Harvard Medical School, USAFetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological abnormalities. Despite the great potential synergy of combined fetal brain US and MR imaging to enhance diagnostic accuracy, little effort has been made to integrate these modalities. An essential step towards this integration is accurate automatic spatial alignment, which is technically very challenging due to the inherent differences in contrast and modality-specific imaging artifacts. In this work, we present a novel atlas-assisted multi-task learning technique to address this problem. Instead of training the registration model solely with intra-subject US-MR image pairs, our approach enables the network to also learn from domain-specific image-to-atlas registration tasks. This leads to an end-to-end multi-task learning framework with superior registration performance. Our proposed method was validated using a dataset of same-day intra-subject 3D US-MR image pairs. The results show that our method outperforms conventional optimization-based methods and recent learning-based techniques for rigid image registration. Specifically, the average target registration error for our method is less than 4 mm, which is significantly better than existing methods. Extensive experiments have also shown that our method has a much wider capture range and is robust to brain abnormalities. Given these advantages over existing techniques, our method is more suitable for deployment in clinical workflows and may contribute to streamlined multimodal imaging pipelines for fetal brain assessment.http://www.sciencedirect.com/science/article/pii/S1053811925001065Fetal brain imagingUltrasoundMRIImage registrationMachine learning |
| spellingShingle | Qi Zeng Weide Liu Bo Li Ryne Didier P. Ellen Grant Davood Karimi Towards automatic US-MR fetal brain image registration with learning-based methods NeuroImage Fetal brain imaging Ultrasound MRI Image registration Machine learning |
| title | Towards automatic US-MR fetal brain image registration with learning-based methods |
| title_full | Towards automatic US-MR fetal brain image registration with learning-based methods |
| title_fullStr | Towards automatic US-MR fetal brain image registration with learning-based methods |
| title_full_unstemmed | Towards automatic US-MR fetal brain image registration with learning-based methods |
| title_short | Towards automatic US-MR fetal brain image registration with learning-based methods |
| title_sort | towards automatic us mr fetal brain image registration with learning based methods |
| topic | Fetal brain imaging Ultrasound MRI Image registration Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925001065 |
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