Automated craniofacial biometry with 3D T2w fetal MRI.
<h4>Objectives</h4>Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (S...
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-12-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000663 |
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| author | Jacqueline Matthew Alena Uus Alexia Egloff Collado Aysha Luis Sophie Arulkumaran Abi Fukami-Gartner Vanessa Kyriakopoulou Daniel Cromb Robert Wright Kathleen Colford Maria Deprez Jana Hutter Jonathan O'Muircheartaigh Christina Malamateniou Reza Razavi Lisa Story Joseph V Hajnal Mary A Rutherford |
| author_facet | Jacqueline Matthew Alena Uus Alexia Egloff Collado Aysha Luis Sophie Arulkumaran Abi Fukami-Gartner Vanessa Kyriakopoulou Daniel Cromb Robert Wright Kathleen Colford Maria Deprez Jana Hutter Jonathan O'Muircheartaigh Christina Malamateniou Reza Razavi Lisa Story Joseph V Hajnal Mary A Rutherford |
| author_sort | Jacqueline Matthew |
| collection | DOAJ |
| description | <h4>Objectives</h4>Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements.<h4>Methods</h4>A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29-36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers.<h4>Results</h4>Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25-35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19-39 weeks), based on 280 control fetuses, were produced for future research.<h4>Conclusion</h4>This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation. |
| format | Article |
| id | doaj-art-e3b1bd939a024bb1b3b26289867b372a |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-e3b1bd939a024bb1b3b26289867b372a2025-01-18T05:31:44ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702024-12-01312e000066310.1371/journal.pdig.0000663Automated craniofacial biometry with 3D T2w fetal MRI.Jacqueline MatthewAlena UusAlexia Egloff ColladoAysha LuisSophie ArulkumaranAbi Fukami-GartnerVanessa KyriakopoulouDaniel CrombRobert WrightKathleen ColfordMaria DeprezJana HutterJonathan O'MuircheartaighChristina MalamateniouReza RazaviLisa StoryJoseph V HajnalMary A Rutherford<h4>Objectives</h4>Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements.<h4>Methods</h4>A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29-36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers.<h4>Results</h4>Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25-35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19-39 weeks), based on 280 control fetuses, were produced for future research.<h4>Conclusion</h4>This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.https://doi.org/10.1371/journal.pdig.0000663 |
| spellingShingle | Jacqueline Matthew Alena Uus Alexia Egloff Collado Aysha Luis Sophie Arulkumaran Abi Fukami-Gartner Vanessa Kyriakopoulou Daniel Cromb Robert Wright Kathleen Colford Maria Deprez Jana Hutter Jonathan O'Muircheartaigh Christina Malamateniou Reza Razavi Lisa Story Joseph V Hajnal Mary A Rutherford Automated craniofacial biometry with 3D T2w fetal MRI. PLOS Digital Health |
| title | Automated craniofacial biometry with 3D T2w fetal MRI. |
| title_full | Automated craniofacial biometry with 3D T2w fetal MRI. |
| title_fullStr | Automated craniofacial biometry with 3D T2w fetal MRI. |
| title_full_unstemmed | Automated craniofacial biometry with 3D T2w fetal MRI. |
| title_short | Automated craniofacial biometry with 3D T2w fetal MRI. |
| title_sort | automated craniofacial biometry with 3d t2w fetal mri |
| url | https://doi.org/10.1371/journal.pdig.0000663 |
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