Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models
Abstract This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-024-01230-7 |
| _version_ | 1797272742325649408 |
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| author | Jacqueline Matthew Alena Uus Leah De Souza Robert Wright Abi Fukami-Gartner Gema Priego Carlo Saija Maria Deprez Alexia Egloff Collado Jana Hutter Lisa Story Christina Malamateniou Kawal Rhode Jo Hajnal Mary A. Rutherford |
| author_facet | Jacqueline Matthew Alena Uus Leah De Souza Robert Wright Abi Fukami-Gartner Gema Priego Carlo Saija Maria Deprez Alexia Egloff Collado Jana Hutter Lisa Story Christina Malamateniou Kawal Rhode Jo Hajnal Mary A. Rutherford |
| author_sort | Jacqueline Matthew |
| collection | DOAJ |
| description | Abstract This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented. Qualitative analysis of multiplanar volumes, based on the SVR output and surface segmentations outputs, were assessed with computer and printed models, using standardised protocols that we developed for evaluating image quality and visibility of diagnostic craniofacial features. A test set of 25, postnatally confirmed, Trisomy 21 fetal cases (24–36 weeks gestational age), revealed that 3D reconstructed T2 SVR images provided 66–100% visibility of relevant craniofacial and head structures in the SVR output, and 20–100% and 60–90% anatomical visibility was seen for the baseline and refined 3D computer surface model outputs respectively. Furthermore, 12 of 25 cases, 48%, of refined surface models demonstrated good or excellent overall quality with a further 9 cases, 36%, demonstrating moderate quality to include facial, scalp and external ears. Additional 3D printing of 12 physical real-size models (20–36 weeks gestational age) revealed good/excellent overall quality in all cases and distinguishable features between healthy control cases and cases with confirmed anomalies, with only minor manual adjustments required before 3D printing. Despite varying image quality and data heterogeneity, 3D T2w SVR reconstructions and models provided sufficient resolution for the subjective characterisation of subtle craniofacial features. We also contributed a publicly accessible online 3D T2w MRI atlas of the fetal head, validated for accurate representation of normal fetal anatomy. Future research will focus on quantitative analysis, optimizing the pipeline, and exploring diagnostic, counselling, and educational applications in fetal craniofacial assessment. |
| first_indexed | 2024-03-07T14:34:34Z |
| format | Article |
| id | doaj.art-29f11697c586414ab0fe1af0070f0aaa |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-03-07T14:34:34Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-29f11697c586414ab0fe1af0070f0aaa2024-03-05T20:44:02ZengBMCBMC Medical Imaging1471-23422024-03-0124111610.1186/s12880-024-01230-7Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical modelsJacqueline Matthew0Alena Uus1Leah De Souza2Robert Wright3Abi Fukami-Gartner4Gema Priego5Carlo Saija6Maria Deprez7Alexia Egloff Collado8Jana Hutter9Lisa Story10Christina Malamateniou11Kawal Rhode12Jo Hajnal13Mary A. Rutherford14School of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalDivision of Midwifery and Radiography, City University of LondonSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalSchool of Biomedical Engineering and Imaging Sciences, King’s College London, St Thomas’ HospitalAbstract This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented. Qualitative analysis of multiplanar volumes, based on the SVR output and surface segmentations outputs, were assessed with computer and printed models, using standardised protocols that we developed for evaluating image quality and visibility of diagnostic craniofacial features. A test set of 25, postnatally confirmed, Trisomy 21 fetal cases (24–36 weeks gestational age), revealed that 3D reconstructed T2 SVR images provided 66–100% visibility of relevant craniofacial and head structures in the SVR output, and 20–100% and 60–90% anatomical visibility was seen for the baseline and refined 3D computer surface model outputs respectively. Furthermore, 12 of 25 cases, 48%, of refined surface models demonstrated good or excellent overall quality with a further 9 cases, 36%, demonstrating moderate quality to include facial, scalp and external ears. Additional 3D printing of 12 physical real-size models (20–36 weeks gestational age) revealed good/excellent overall quality in all cases and distinguishable features between healthy control cases and cases with confirmed anomalies, with only minor manual adjustments required before 3D printing. Despite varying image quality and data heterogeneity, 3D T2w SVR reconstructions and models provided sufficient resolution for the subjective characterisation of subtle craniofacial features. We also contributed a publicly accessible online 3D T2w MRI atlas of the fetal head, validated for accurate representation of normal fetal anatomy. Future research will focus on quantitative analysis, optimizing the pipeline, and exploring diagnostic, counselling, and educational applications in fetal craniofacial assessment.https://doi.org/10.1186/s12880-024-01230-7Fetal MRICraniofacial featuresAutomated segmentationFace rendering3D printingSlice to volume reconstruction |
| spellingShingle | Jacqueline Matthew Alena Uus Leah De Souza Robert Wright Abi Fukami-Gartner Gema Priego Carlo Saija Maria Deprez Alexia Egloff Collado Jana Hutter Lisa Story Christina Malamateniou Kawal Rhode Jo Hajnal Mary A. Rutherford Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models BMC Medical Imaging Fetal MRI Craniofacial features Automated segmentation Face rendering 3D printing Slice to volume reconstruction |
| title | Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models |
| title_full | Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models |
| title_fullStr | Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models |
| title_full_unstemmed | Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models |
| title_short | Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models |
| title_sort | craniofacial phenotyping with fetal mri a feasibility study of 3d visualisation segmentation surface rendered and physical models |
| topic | Fetal MRI Craniofacial features Automated segmentation Face rendering 3D printing Slice to volume reconstruction |
| url | https://doi.org/10.1186/s12880-024-01230-7 |
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