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Fully automated planning for anatomical fetal brain MRI on 0.55T.

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Title: Fully automated planning for anatomical fetal brain MRI on 0.55T.
Authors: Neves Silva, Sara1,2 (AUTHOR) sara.neves_silva@kcl.ac.uk, McElroy, Sarah1,3 (AUTHOR), Aviles Verdera, Jordina1,2 (AUTHOR), Colford, Kathleen1,2 (AUTHOR), St Clair, Kamilah1,2 (AUTHOR), Tomi‐Tricot, Raphael1,3 (AUTHOR), Uus, Alena1,2 (AUTHOR), Ozenne, Valéry4 (AUTHOR), Hall, Megan2,5 (AUTHOR), Story, Lisa2,5 (AUTHOR), Pushparajah, Kuberan2 (AUTHOR), Rutherford, Mary A.1,2 (AUTHOR), Hajnal, Joseph V.1,2 (AUTHOR), Hutter, Jana1,2,6 (AUTHOR)
Source: Magnetic Resonance in Medicine. Sep2024, Vol. 92 Issue 3, p1263-1276. 14p.
Subjects: Fetal MRI, Fetal brain, Diagnostic imaging, Magnetic resonance imaging, Cerebellum
Abstract: 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 echo planar imaging 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. A 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 4.2 ±$$ \pm $$ 2.6 mm for the fetal eyes and 6.5 ±$$ \pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning. Conclusions: Real‐time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers. [ABSTRACT FROM AUTHOR]
Copyright of Magnetic Resonance in Medicine 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.)
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  Data: Fully automated planning for anatomical fetal brain MRI on 0.55T.
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  Data: <searchLink fieldCode="AR" term="%22Neves+Silva%2C+Sara%22">Neves Silva, Sara</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> sara.neves_silva@kcl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22McElroy%2C+Sarah%22">McElroy, Sarah</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Aviles+Verdera%2C+Jordina%22">Aviles Verdera, Jordina</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Colford%2C+Kathleen%22">Colford, Kathleen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22St+Clair%2C+Kamilah%22">St Clair, Kamilah</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tomi‐Tricot%2C+Raphael%22">Tomi‐Tricot, Raphael</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Uus%2C+Alena%22">Uus, Alena</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ozenne%2C+Valéry%22">Ozenne, Valéry</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hall%2C+Megan%22">Hall, Megan</searchLink><relatesTo>2,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Story%2C+Lisa%22">Story, Lisa</searchLink><relatesTo>2,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pushparajah%2C+Kuberan%22">Pushparajah, Kuberan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rutherford%2C+Mary+A%2E%22">Rutherford, Mary A.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hajnal%2C+Joseph+V%2E%22">Hajnal, Joseph V.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hutter%2C+Jana%22">Hutter, Jana</searchLink><relatesTo>1,2,6</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Sep2024, Vol. 92 Issue 3, p1263-1276. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Fetal+MRI%22">Fetal MRI</searchLink><br /><searchLink fieldCode="DE" term="%22Fetal+brain%22">Fetal brain</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Cerebellum%22">Cerebellum</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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 echo planar imaging 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. A 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 4.2 ±$$ \pm $$ 2.6 mm for the fetal eyes and 6.5 ±$$ \pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning. Conclusions: Real‐time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Magnetic Resonance in Medicine 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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      – Type: doi
        Value: 10.1002/mrm.30122
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 1263
    Subjects:
      – SubjectFull: Fetal MRI
        Type: general
      – SubjectFull: Fetal brain
        Type: general
      – SubjectFull: Diagnostic imaging
        Type: general
      – SubjectFull: Magnetic resonance imaging
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      – SubjectFull: Cerebellum
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      – TitleFull: Fully automated planning for anatomical fetal brain MRI on 0.55T.
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            – D: 01
              M: 09
              Text: Sep2024
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              Y: 2024
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