Fully automated planning for anatomical fetal brain MRI on 0.55T
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913200315826176 |
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| author | Silva, Sara Neves McElroy, Sarah Verdera, Jordina Aviles Colford, Kathleen Clair, Kamilah St Tomi-Tricot, Raphael Uus, Alena Ozenne, Valery Hall, Megan Story, Lisa Pushparajah, Kuberan Rutherford, Mary A Hajnal, Joseph V Hutter, Jana |
| author_facet | Silva, Sara Neves McElroy, Sarah Verdera, Jordina Aviles Colford, Kathleen Clair, Kamilah St Tomi-Tricot, Raphael Uus, Alena Ozenne, Valery Hall, Megan Story, Lisa Pushparajah, Kuberan Rutherford, Mary A Hajnal, Joseph V Hutter, Jana |
| contents | Purpose: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. Methods: Deep learning-based detection of key brain landmarks on a whole-uterus EPI scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. Comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality and the resulting image quality was conducted. Results: Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.21+-2.56 mm for the fetal eyes and 6.47+-3.23 for the cerebellum, planning quality was 2.44/3 (compared to 2.56/3 for manual planning) and diagnostic image quality was 2.14 compared to 2.07 for manual planning. Conclusions: Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way towards simplifying the acquisition of fetal MRI thereby widening the availability of this modality in non-specialist centres. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10441 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fully automated planning for anatomical fetal brain MRI on 0.55T Silva, Sara Neves McElroy, Sarah Verdera, Jordina Aviles Colford, Kathleen Clair, Kamilah St Tomi-Tricot, Raphael Uus, Alena Ozenne, Valery Hall, Megan Story, Lisa Pushparajah, Kuberan Rutherford, Mary A Hajnal, Joseph V Hutter, Jana Medical Physics Purpose: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. Methods: Deep learning-based detection of key brain landmarks on a whole-uterus EPI scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. Comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality and the resulting image quality was conducted. Results: Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.21+-2.56 mm for the fetal eyes and 6.47+-3.23 for the cerebellum, planning quality was 2.44/3 (compared to 2.56/3 for manual planning) and diagnostic image quality was 2.14 compared to 2.07 for manual planning. Conclusions: Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way towards simplifying the acquisition of fetal MRI thereby widening the availability of this modality in non-specialist centres. |
| title | Fully automated planning for anatomical fetal brain MRI on 0.55T |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2401.10441 |