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Aviles Verdera J, Tomi-Tricot R, Story L, Rutherford MA, Ourselin S, Hajnal JV, Malik SJ, Hutter J. Characterizing T1 in the fetal brain and placenta over gestational age at 0.55T. Magn Reson Med 2024; 92:2101-2111. [PMID: 38968093 DOI: 10.1002/mrm.30193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/10/2024] [Accepted: 05/24/2024] [Indexed: 07/07/2024]
Abstract
PURPOSE T1 mapping and T1-weighted contrasts have a complimentary but currently under utilized role in fetal MRI. Emerging clinical low field scanners are ideally suited for fetal T1 mapping. The advantages are lower T1 values which results in higher efficiency and reduced field inhomogeneities resulting in a decreased requirement for specialist tools. In addition the increased bore size associated with low field scanners provides improved patient comfort and accessibility. This study aims to demonstrate the feasibility of fetal brain T1 mapping at 0.55T. METHODS An efficient slice-shuffling inversion-recovery echo-planar imaging (EPI)-based T1-mapping and postprocessing was demonstrated for the fetal brain at 0.55T in a cohort of 38 fetal MRI scans. Robustness analysis was performed and placental measurements were taken for validation. RESULTS High-quality T1 maps allowing the investigation of subregions in the brain were obtained and significant correlation with gestational age was demonstrated for fetal brain T1 maps (p < 0 . 05 $$ p<0.05 $$ ) as well as regions-of-interest in the deep gray matter and white matter. CONCLUSIONS Efficient, quantitative T1 mapping in the fetal brain was demonstrated on a clinical 0.55T MRI scanner, providing foundations for both future research and clinical applications including low-field specific T1-weighted acquisitions.
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Affiliation(s)
- Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Raphael Tomi-Tricot
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | | | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sebastien Ourselin
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Shaihan J Malik
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Erlangen, Germany
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Skelton E, Cromb D, Smith A, Harrison G, Rutherford M, Malamateniou C, Ayers S. The influence of antenatal imaging on prenatal bonding in uncomplicated pregnancies: a mixed methods analysis. BMC Pregnancy Childbirth 2024; 24:265. [PMID: 38605314 PMCID: PMC11007968 DOI: 10.1186/s12884-024-06469-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 03/30/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Prenatal bonding describes the emotional connection expectant parents form to their unborn child. Research acknowledges the association between antenatal imaging and enhanced bonding, but the influencing factors are not well understood, particularly for fathers or when using advanced techniques like fetal magnetic resonance imaging (MRI). This study aimed to identify variables which may predict increased bonding after imaging. METHODS First-time expectant parents (mothers = 58, fathers = 18) completed a two-part questionnaire (QualtricsXM™) about their expectations and experiences of ultrasound (n = 64) or fetal MRI (n = 12) scans in uncomplicated pregnancies. A modified version of the Prenatal Attachment Inventory (PAI) was used to measure bonding. Qualitative data were collected through open-ended questions. Multivariate linear regression models were used to identify significant parent and imaging predictors for bonding. Qualitative content analysis of free-text responses was conducted to further understand the predictors' influences. RESULTS Bonding scores were significantly increased after imaging for mothers and fathers (p < 0.05). MRI-parents reported significantly higher bonding than ultrasound-parents (p = 0.02). In the first regression model of parent factors (adjusted R2 = 0.17, F = 2.88, p < 0.01), employment status (β = -0.38, p < 0.05) was a significant predictor for bonding post-imaging. The second model of imaging factors (adjusted R2 = 0.19, F = 3.85, p < 0.01) showed imaging modality (β = -0.53), imaging experience (β = 0.42) and parental excitement after the scan (β = 0.29) were significantly (p < 0.05) associated with increased bonding. Seventeen coded themes were generated from the qualitative content analysis, describing how scans offered reassurance about fetal wellbeing and the opportunity to connect with the baby through quality interactions with imaging professionals. A positive scan experience helped parents to feel excited about parenthood. Fetal MRI was considered a superior modality to ultrasound. CONCLUSIONS Antenatal imaging provides reassurance of fetal development which affirms parents' emotional investment in the pregnancy and supports the growing connection. Imaging professionals are uniquely positioned to provide parent-centred experiences which may enhance parental excitement and facilitate bonding.
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Affiliation(s)
- Emily Skelton
- Division of Radiography and Midwifery, School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, UK.
| | - Daniel Cromb
- Perinatal Imaging and Health, King's College London, London, SE1 7EH, UK
- Guy's & St Thomas' NHS Foundation Trust, London, SE1 7EH, UK
| | - Alison Smith
- Guy's & St Thomas' NHS Foundation Trust, London, SE1 7EH, UK
| | - Gill Harrison
- Society and College of Radiographers, London, SE1 2EW, UK
| | - Mary Rutherford
- Perinatal Imaging and Health, King's College London, London, SE1 7EH, UK
| | - Christina Malamateniou
- Division of Radiography and Midwifery, School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, UK
| | - Susan Ayers
- Centre for Maternal and Child Health Research, School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, UK
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3
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Uus AU, Hall M, Grigorescu I, Avena Zampieri C, Egloff Collado A, Payette K, Matthew J, Kyriakopoulou V, Hajnal JV, Hutter J, Rutherford MA, Deprez M, Story L. Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI. Sci Rep 2024; 14:6637. [PMID: 38503833 PMCID: PMC10950851 DOI: 10.1038/s41598-024-57087-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range.
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Affiliation(s)
- Alena U Uus
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
| | - Megan Hall
- Centre for the Developing Brain, King's College London, London, UK
- Department of Women and Children's Health, King's College London, London, UK
- Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Irina Grigorescu
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Carla Avena Zampieri
- Centre for the Developing Brain, King's College London, London, UK
- Department of Women and Children's Health, King's College London, London, UK
| | | | - Kelly Payette
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Jacqueline Matthew
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | | | - Joseph V Hajnal
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Jana Hutter
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
- Smart Imaging Lab, Radiological Institute, University Hospital Erlangen, Erlangen, Germany
| | | | - Maria Deprez
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Lisa Story
- Centre for the Developing Brain, King's College London, London, UK
- Department of Women and Children's Health, King's College London, London, UK
- Fetal Medicine Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
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Matthew J, Uus A, De Souza L, Wright R, Fukami-Gartner A, Priego G, Saija C, Deprez M, Collado AE, Hutter J, Story L, Malamateniou C, Rhode K, Hajnal J, Rutherford MA. Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models. BMC Med Imaging 2024; 24:52. [PMID: 38429666 PMCID: PMC10905839 DOI: 10.1186/s12880-024-01230-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/19/2024] [Indexed: 03/03/2024] Open
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.
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Affiliation(s)
- Jacqueline Matthew
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
- Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Alena Uus
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Leah De Souza
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Robert Wright
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Abi Fukami-Gartner
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Gema Priego
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Barking, Havering and Redbridge University Hospitals NHS Trust, London, UK
| | - Carlo Saija
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Maria Deprez
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Alexia Egloff Collado
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Jana Hutter
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Lisa Story
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Kawal Rhode
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Jo Hajnal
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Mary A Rutherford
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
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5
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Mufti N, Aertsen M, Thomson D, De Vloo P, Demaerel P, Deprest J, Melbourne A, David AL. Longitudinal MRI in the context of in utero surgery for open spina bifida: A descriptive study. Acta Obstet Gynecol Scand 2024; 103:322-333. [PMID: 37984808 PMCID: PMC10823411 DOI: 10.1111/aogs.14711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/17/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023]
Abstract
INTRODUCTION Fetal surgery for open spina bifida (OSB) requires comprehensive preoperative assessment using imaging for appropriate patient selection and to evaluate postoperative efficacy and complications. We explored patient access and conduct of fetal magnetic resonance imaging (MRI) for prenatal assessment of OSB patients eligible for fetal surgery. We compared imaging acquisition and reporting to the International Society of Ultrasound in Obstetrics and Gynecology MRI performance guidelines. MATERIAL AND METHODS We surveyed access to fetal MRI for OSB in referring fetal medicine units (FMUs) in the UK and Ireland, and two NHS England specialist commissioned fetal surgery centers (FSCs) at University College London Hospital, and University Hospitals KU Leuven Belgium. To study MRI acquisition protocols, we retrospectively analyzed fetal MRI images before and after fetal surgery for OSB. RESULTS MRI for fetal OSB was accessible with appropriate specialists available to supervise, perform, and report scans. The average time to arrange a fetal MRI appointment from request was 4 ± 3 days (range, 0-10), the average scan time available was 37 ± 16 min (range, 20-80 min), with 15 ± 11 min (range, 0-30 min) extra time to repeat sequences as required. Specific MRI acquisition protocols, and MRI reporting templates were available in only 32% and 18% of units, respectively. Satisfactory T2-weighted (T2W) brain imaging acquired in three orthogonal planes was achieved preoperatively in all centers, and 6 weeks postoperatively in 96% of FSCs and 78% of referring FMUs. However, for T2W spine image acquisition referring FMUs were less able to provide three orthogonal planes presurgery (98% FSC vs. 50% FMU, p < 0.001), and 6 weeks post-surgery (100% FSC vs. 48% FMU, p < 0.001). Other standard imaging recommendations such as T1-weighted (T1W), gradient echo (GE) or echoplanar fetal brain and spine imaging in one or two orthogonal planes were more likely available in FSCs compared to FMUs pre- and post-surgery (p < 0.001). CONCLUSIONS There was timely access to supervised MRI for OSB fetal surgery assessment. However, the provision of images of the fetal brain and spine in sufficient orthogonal planes, which are required for determining eligibility and to determine the reversal of hindbrain herniation after fetal surgery, were less frequently acquired. Our evidence suggests the need for specific guidance in relation to fetal MRI for OSB. We propose an example guidance for MRI acquisition and reporting.
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Affiliation(s)
- Nada Mufti
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - Michael Aertsen
- Department of RadiologyUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
| | - Dominic Thomson
- Pediatric Neurosurgery DepartmentGreat Ormond Street Hospital for ChildrenLondonUK
| | - Phillippe De Vloo
- Department of NeurosurgeryUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
| | - Philippe Demaerel
- Department of RadiologyUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
| | - Jan Deprest
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- Department of Obstetrics and GynecologyUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
- Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Anna L. David
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- Department of Obstetrics and GynecologyUniversity Hospitals Katholieke Universiteit (KU)LeuvenBelgium
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6
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Uus AU, Hall M, Grigorescu I, Zampieri CA, Collado AE, Payette K, Matthew J, Kyriakopoulou V, Hajnal JV, Hutter J, Rutherford MA, Deprez M, Story L. 3D T2w fetal body MRI: automated organ volumetry, growth charts and population-averaged atlas. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.31.23290751. [PMID: 37398121 PMCID: PMC10312818 DOI: 10.1101/2023.05.31.23290751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range. In addition, the results of comparison between 60 normal and 12 fetal growth restriction datasets revealed significant differences in organ volumes.
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Affiliation(s)
- Alena U. Uus
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Megan Hall
- Centre for the Developing Brain, King’s College London, London, UK
- Department of Women and Children’s Health, King’s College London, London, UK
- Fetal Medicine Unit, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Irina Grigorescu
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Carla Avena Zampieri
- Centre for the Developing Brain, King’s College London, London, UK
- Department of Women and Children’s Health, King’s College London, London, UK
| | | | - Kelly Payette
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
- Centre for the Developing Brain, King’s College London, London, UK
| | - Jacqueline Matthew
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
- Centre for the Developing Brain, King’s College London, London, UK
| | | | - Joseph V. Hajnal
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
- Centre for the Developing Brain, King’s College London, London, UK
| | - Jana Hutter
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
- Centre for the Developing Brain, King’s College London, London, UK
| | | | - Maria Deprez
- School of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Lisa Story
- Centre for the Developing Brain, King’s College London, London, UK
- Department of Women and Children’s Health, King’s College London, London, UK
- Fetal Medicine Unit, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
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Pagliaccio D, Cao X, Sussman TJ. No Meta-analytic Evidence for Risks due to Prenatal Magnetic Resonance Imaging in Animal Models. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:592-598. [PMID: 36773800 PMCID: PMC10257767 DOI: 10.1016/j.bpsc.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is a powerful, noninvasive tool for both clinical practice and research. Though the safety of MRI has been endorsed by many professional societies and government bodies, some concerns have remained about potential risk from prenatal MRI. Case-control animal studies of MRI scanning during gestation and effects on offspring are the most direct test available for potential risks. We performed a meta-analysis of extant animal studies of prenatal MRI examining reproductive and offspring outcomes. METHODS Relevant articles were identified through PubMed search and citation searching of known articles and review papers. Eighteen relevant studies were identified with case-control designs of prenatal scanning conducted in vivo with mammalian species using MRI-relevant field strength. Standardized mean difference effect sizes were analyzed across k = 81 outcomes assessed across 649 unexposed dams, 622 exposed dams, 3024 unexposed offspring, and 3328 exposed offspring using a multilevel meta-analytic approach that clustered effect sizes within publications. RESULTS The meta-analysis indicated no significant evidence for a deleterious effects of prenatal MRI (standardized mean difference = 0.17, 95% CI [-0.19, 0.54], t80 = 0.94, p = .35) across outcomes. Similarly, no effects were observed when separately examining the 4 most commonly assessed outcomes: birth weight, litter size, fetal viability, and physical malformations (p > .05). CONCLUSIONS Case-control mammalian animal studies indicate no significant known risks of prenatal MRI to reproductive outcomes or offspring development. This finding is largely mirrored in human research, though the lack of randomized case-control designs limits direct comparison. The current findings provide additional support to the prevailing consensus that prenatal MRI poses no known risk to offspring.
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Affiliation(s)
- David Pagliaccio
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, New York; Department of Psychiatry, Columbia University, New York, New York.
| | - Xiaohe Cao
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, New York; Department of Psychiatry, Columbia University, New York, New York
| | - Tamara J Sussman
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, New York; Department of Psychiatry, Columbia University, New York, New York
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8
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Uus AU, Kyriakopoulou V, Makropoulos A, Fukami-Gartner A, Cromb D, Davidson A, Cordero-Grande L, Price AN, Grigorescu I, Williams LZJ, Robinson EC, Lloyd D, Pushparajah K, Story L, Hutter J, Counsell SJ, Edwards AD, Rutherford MA, Hajnal JV, Deprez M. BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.18.537347. [PMID: 37131820 PMCID: PMC10153133 DOI: 10.1101/2023.04.18.537347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is a lack of universally accepted protocols for fetal brain parcellation and segmentation. Published clinical studies tend to use different segmentation approaches that also reportedly require significant amounts of time-consuming manual refinement. In this work, we propose to address this challenge by developing a new robust deep learning-based fetal brain segmentation pipeline for 3D T2w motion corrected brain images. At first, we defined a new refined brain tissue parcellation protocol with 19 regions-of-interest using the new fetal brain MRI atlas from the Developing Human Connectome Project. This protocol design was based on evidence from histological brain atlases, clear visibility of the structures in individual subject 3D T2w images and the clinical relevance to quantitative studies. It was then used as a basis for developing an automated deep learning brain tissue parcellation pipeline trained on 360 fetal MRI datasets with different acquisition parameters using semi-supervised approach with manually refined labels propagated from the atlas. The pipeline demonstrated robust performance for different acquisition protocols and GA ranges. Analysis of tissue volumetry for 390 normal participants (21-38 weeks gestational age range), scanned with three different acquisition protocols, did not reveal significant differences for major structures in the growth charts. Only minor errors were present in < 15% of cases thus significantly reducing the need for manual refinement. In addition, quantitative comparison between 65 fetuses with ventriculomegaly and 60 normal control cases were in agreement with the findings reported in our earlier work based on manual segmentations. These preliminary results support the feasibility of the proposed atlas-based deep learning approach for large-scale volumetric analysis. The created fetal brain volumetry centiles and a docker with the proposed pipeline are publicly available online at https://hub.docker.com/r/fetalsvrtk/segmentation (tag brain_bounti_tissue).
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Affiliation(s)
- Alena U Uus
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | | | | | - Daniel Cromb
- Centre for the Developing Brain, King's College London, London, UK
| | - Alice Davidson
- Centre for the Developing Brain, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politécnica de Madrid and CIBER-BBN, ISCII, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, King's College London, London, UK
| | - Irina Grigorescu
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Logan Z J Williams
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emma C Robinson
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - David Lloyd
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Kuberan Pushparajah
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Lisa Story
- Centre for the Developing Brain, King's College London, London, UK
| | - Jana Hutter
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | - A David Edwards
- Centre for the Developing Brain, King's College London, London, UK
| | | | - Joseph V Hajnal
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maria Deprez
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
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9
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Deprest T, Fidon L, De Keyzer F, Ebner M, Deprest J, Demaerel P, De Catte L, Vercauteren T, Ourselin S, Dymarkowski S, Aertsen M. Application of Automatic Segmentation on Super-Resolution Reconstruction MR Images of the Abnormal Fetal Brain. AJNR Am J Neuroradiol 2023; 44:486-491. [PMID: 36863845 PMCID: PMC10084897 DOI: 10.3174/ajnr.a7808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 02/06/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND AND PURPOSE Fetal brain MR imaging is clinically used to characterize fetal brain abnormalities. Recently, algorithms have been proposed to reconstruct high-resolution 3D fetal brain volumes from 2D slices. By means of these reconstructions, convolutional neural networks have been developed for automatic image segmentation to avoid labor-intensive manual annotations, usually trained on data of normal fetal brains. Herein, we tested the performance of an algorithm specifically developed for segmentation of abnormal fetal brains. MATERIALS AND METHODS This was a single-center retrospective study on MR images of 16 fetuses with severe CNS anomalies (gestation, 21-39 weeks). T2-weighted 2D slices were converted to 3D volumes using a super-resolution reconstruction algorithm. The acquired volumetric data were then processed by a novel convolutional neural network to perform segmentations of white matter and the ventricular system and cerebellum. These were compared with manual segmentation using the Dice coefficient, Hausdorff distance (95th percentile), and volume difference. Using interquartile ranges, we identified outliers of these metrics and further analyzed them in detail. RESULTS The mean Dice coefficient was 96.2%, 93.7%, and 94.7% for white matter and the ventricular system and cerebellum, respectively. The Hausdorff distance was 1.1, 2.3, and 1.6 mm, respectively. The volume difference was 1.6, 1.4, and 0.3 mL, respectively. Of the 126 measurements, there were 16 outliers among 5 fetuses, discussed on a case-by-case basis. CONCLUSIONS Our novel segmentation algorithm obtained excellent results on MR images of fetuses with severe brain abnormalities. Analysis of the outliers shows the need to include pathologies underrepresented in the current data set. Quality control to prevent occasional errors is still needed.
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Affiliation(s)
- T Deprest
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
| | - L Fidon
- School of Biomedical Engineering and Imaging Sciences (L.F., M.E., T.V., S.O.), King's College London, London, UK
| | - F De Keyzer
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
| | - M Ebner
- School of Biomedical Engineering and Imaging Sciences (L.F., M.E., T.V., S.O.), King's College London, London, UK
- Department of Medical Physics and Biomedical Engineering (M.E., T.V.), University College London, London, UK
| | - J Deprest
- Gynaecology and Obstetrics (J.D., L.D.C., T.V.), University Hospitals Leuven, Belgium
- Institute for Women's Health (J.D.)
| | - P Demaerel
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
| | - L De Catte
- Gynaecology and Obstetrics (J.D., L.D.C., T.V.), University Hospitals Leuven, Belgium
| | - T Vercauteren
- Gynaecology and Obstetrics (J.D., L.D.C., T.V.), University Hospitals Leuven, Belgium
- School of Biomedical Engineering and Imaging Sciences (L.F., M.E., T.V., S.O.), King's College London, London, UK
- Department of Medical Physics and Biomedical Engineering (M.E., T.V.), University College London, London, UK
| | - S Ourselin
- School of Biomedical Engineering and Imaging Sciences (L.F., M.E., T.V., S.O.), King's College London, London, UK
| | - S Dymarkowski
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
| | - M Aertsen
- From the Department of Radiology (T.D., F.D.K., P.D., S.D., M.A.)
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10
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Fet-Net Algorithm for Automatic Detection of Fetal Orientation in Fetal MRI. Bioengineering (Basel) 2023; 10:bioengineering10020140. [PMID: 36829634 PMCID: PMC9952178 DOI: 10.3390/bioengineering10020140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/06/2023] [Accepted: 01/13/2023] [Indexed: 01/22/2023] Open
Abstract
Identifying fetal orientation is essential for determining the mode of delivery and for sequence planning in fetal magnetic resonance imaging (MRI). This manuscript describes a deep learning algorithm named Fet-Net, composed of convolutional neural networks (CNNs), which allows for the automatic detection of fetal orientation from a two-dimensional (2D) MRI slice. The architecture consists of four convolutional layers, which feed into a simple artificial neural network. Compared with eleven other prominent CNNs (different versions of ResNet, VGG, Xception, and Inception), Fet-Net has fewer architectural layers and parameters. From 144 3D MRI datasets indicative of vertex, breech, oblique and transverse fetal orientations, 6120 2D MRI slices were extracted to train, validate and test Fet-Net. Despite its simpler architecture, Fet-Net demonstrated an average accuracy and F1 score of 97.68% and a loss of 0.06828 on the 6120 2D MRI slices during a 5-fold cross-validation experiment. This architecture outperformed all eleven prominent architectures (p < 0.05). An ablation study proved each component's statistical significance and contribution to Fet-Net's performance. Fet-Net demonstrated robustness in classification accuracy even when noise was introduced to the images, outperforming eight of the 11 prominent architectures. Fet-Net's ability to automatically detect fetal orientation can profoundly decrease the time required for fetal MRI acquisition.
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11
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Gai S, Wang L, Zheng W. Comparison of prenatal ultrasound with MRI in the evaluation and prediction of fetal orofacial clefts. BMC Med Imaging 2022; 22:213. [PMID: 36471263 PMCID: PMC9720929 DOI: 10.1186/s12880-022-00929-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 11/05/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Orofacial clefts (OFCs) are common craniofacial abnormalities. This study aimed to compare the diagnostic and predictive values of prenatal ultrasonography (US) and magnetic resonance imaging (MRI). METHODS We reviewed the newborn physical examinations or fetal autopsy data with OFCs. Between January 2013 and December 2018, the diagnoses resulting from prenatal US and MRI examination were compared retrospectively with the postpartum diagnoses. The diagnostic prediction of prenatal imaging was then determined. RESULTS 334 infants were identified with OFCs by either newborn physical exam or stillborn autopsy. For detection of OFCs by US, the total accuracy (ACC), true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) were 99.9% (111,178/110,286), 81.9% (230/281), 99.9% (109,948/110,005), 80.1% (230/287), and 99.9% (109,948/109,999), respectively. For MRI, the ACC, TPR, TNR, PPV, and NPV were 99.8% (4,125/4,132), 89.8% (44/49), 99.9% (4,081/4,083), 95.7% (44/46), and 99.9% (4,081/4,086), respectively. When we compared the predictive values between prenatal US and MRI, there were significant differences in the PPV of OFCs (P < 0.05), NPV of OFCs (P < 0.05), TPR of CLO (P < 0.001), PPV of CLP (P < 0.05), and TPR of CPO (P < 0.05). CONCLUSION Our results suggest that prenatal US could be effective for diagnosing and ruling out fetal OFCs. Diagnostic confidence is significantly improved when fetal MRI is used to assess fetal OFCs as an adjunct to US examination.
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Affiliation(s)
- Shuangshuang Gai
- grid.13402.340000 0004 1759 700XDepartment of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Rd No.88, Hangzhou, 310029 Zhejiang People’s Republic of China ,grid.13402.340000 0004 1759 700XDepartment of Ultrasound, Women’s Hospital, Zhejiang University School of Medicine, Xueshi Rd No.1, Hangzhou, 310006 Zhejiang People’s Republic of China
| | - Lixiu Wang
- grid.13402.340000 0004 1759 700XDepartment of Ultrasound, Women’s Hospital, Zhejiang University School of Medicine, Xueshi Rd No.1, Hangzhou, 310006 Zhejiang People’s Republic of China
| | - Weizeng Zheng
- grid.13402.340000 0004 1759 700XDepartment of Radiology, Women’s Hospital, Zhejiang University School of Medicine, Xueshi Rd No.1, Hangzhou, 310006 Zhejiang People’s Republic of China
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12
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Uus AU, Egloff Collado A, Roberts TA, Hajnal JV, Rutherford MA, Deprez M. Retrospective motion correction in foetal MRI for clinical applications: existing methods, applications and integration into clinical practice. Br J Radiol 2022:20220071. [PMID: 35834425 DOI: 10.1259/bjr.20220071] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Foetal MRI is a complementary imaging method to antenatal ultrasound. It provides advanced information for detection and characterisation of foetal brain and body anomalies. Even though modern single shot sequences allow fast acquisition of 2D slices with high in-plane image quality, foetal MRI is intrinsically corrupted by motion. Foetal motion leads to loss of structural continuity and corrupted 3D volumetric information in stacks of slices. Furthermore, the arbitrary and constantly changing position of the foetus requires dynamic readjustment of acquisition planes during scanning.
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Affiliation(s)
- Alena U Uus
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom
| | - Alexia Egloff Collado
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom
| | - Thomas A Roberts
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom.,Clinical Scientific Computing, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Joseph V Hajnal
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom.,Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom
| | - Maria Deprez
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom
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13
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Aertsen M, Dymarkowski S, Vander Mijnsbrugge W, Cockmartin L, Demaerel P, De Catte L. Anatomical and diffusion-weighted imaging of brain abnormalities in third-trimester fetuses with cytomegalovirus infection. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2022; 60:68-75. [PMID: 35018680 DOI: 10.1002/uog.24856] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 12/16/2021] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVES In this study of cytomegalovirus (CMV)-infected fetuses with first-trimester seroconversion, we aimed to evaluate the detection of brain abnormalities using magnetic resonance imaging (MRI) and neurosonography (NSG) in the third trimester, and compare the grading systems of the two modalities. We also evaluated the feasibility of routine use of diffusion-weighted imaging (DWI) fetal MRI and compared the regional apparent diffusion coefficient (ADC) values between CMV-infected fetuses and presumed normal, non-infected fetuses in the third trimester. METHODS This was a retrospective review of MRI and NSG scans in fetuses with confirmed first-trimester CMV infection performed between September 2015 and August 2019. Brain abnormalities were recorded and graded using fetal MRI and NSG grading systems to compare the two modalities. To investigate feasibility of DWI, a four-point rating scale (poor, suboptimal, good, excellent) was applied to assess the quality of the images. Quantitative assessment was performed by placing a freehand drawn region of interest in the white matter of the frontal, parietal, temporal and occipital lobes and the basal ganglia, pons and cerebellum to calculate ADC values. Regional ADC measurements were obtained similarly in a control group of fetuses with negative maternal CMV serology in the first trimester, normal brain findings on fetal MRI and normal genetic testing. RESULTS Fifty-three MRI examinations of 46 fetuses with confirmed first-trimester CMV infection were included. NSG detected 24 of 27 temporal cysts seen on MRI scans, with a sensitivity of 78% and an accuracy of 83%. NSG did not detect abnormal gyration visible on two (4%) MRI scans. Periventricular calcifications were detected on two MRI scans compared with 10 NSG scans. While lenticulostriate vasculopathy was detected on 11 (21%) NSG scans, no fetus demonstrated this finding on MRI. MRI grading correlated significantly with NSG grading of brain abnormalities (P < 0.0001). Eight (15%) of the DWI scans in the CMV cohort were excluded from further analysis because of insufficient quality. The ADC values of CMV-infected fetuses were significantly increased in the frontal (both sides, P < 0.0001), temporal (both sides, P < 0.0001), parietal (left side, P = 0.0378 and right side, P = 0.0014) and occipital (left side, P = 0.0002 and right side, P < 0.0001) lobes and decreased in the pons (P = 0.0085) when compared with non-infected fetuses. The ADC values in the basal ganglia and the cerebellum were not significantly different in CMV-infected fetuses compared with normal controls (all P > 0.05). Temporal and frontal ADC values were higher in CMV-infected fetuses with more severe brain abnormalities compared to fetuses with mild abnormalities. CONCLUSIONS Ultrasound and MRI are complementary during the third trimester in the assessment of brain abnormalities in CMV-infected fetuses, with a significant correlation between the grading systems of the two modalities. On DWI in the third trimester, the ADC values in several brain regions are abnormal in CMV-infected fetuses compared with normal controls. Furthermore, they seem to correlate in the temporal area and, to a lesser extent, frontal area with the severity of brain abnormalities associated with CMV infection. Larger prospective studies are needed for further investigation of the microscopic nature of diffusion abnormalities and correlation of different imaging findings with postnatal outcome. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- M Aertsen
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - S Dymarkowski
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | | | - L Cockmartin
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - P Demaerel
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - L De Catte
- Division Woman and Child, Fetal Medicine Unit, Clinical Department of Obstetrics and Gynecology, University Hospital Gasthuisberg, Leuven, Belgium
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14
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Mufti N, Sacco A, Aertsen M, Ushakov F, Ourselin S, Thomson D, Deprest J, Melbourne A, David AL. What brain abnormalities can magnetic resonance imaging detect in foetal and early neonatal spina bifida: a systematic review. Neuroradiology 2022; 64:233-245. [PMID: 34792623 PMCID: PMC8789702 DOI: 10.1007/s00234-021-02853-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/03/2021] [Indexed: 12/02/2022]
Abstract
PURPOSE Open spina bifida (OSB) encompasses a wide spectrum of intracranial abnormalities. With foetal surgery as a new treatment option, robust intracranial imaging is important for comprehensive preoperative evaluation and prognostication. We aimed to determine the incidence of infratentorial and supratentorial findings detected by magnetic resonance imaging (MRI) alone and MRI compared to ultrasound. METHODS Two systematic reviews comparing MRI to ultrasound and MRI alone were conducted on MEDLINE, EMBASE, and Cochrane databases identifying studies of foetal OSB from 2000 to 2020. Intracranial imaging findings were analysed at ≤ 26 or > 26 weeks gestation and neonates (≤ 28 days). Data was independently extracted by two reviewers and meta-analysis was performed where possible. RESULTS Thirty-six studies reported brain abnormalities detected by MRI alone in patients who previously had an ultrasound. Callosal dysgenesis was identified in 4/29 cases (2 foetuses ≤ 26 weeks, 1 foetus under any gestation, and 1 neonate ≤ 28 days) (15.1%, CI:5.7-34.3%). Heterotopia was identified in 7/40 foetuses ≤ 26 weeks (19.8%, CI:7.7-42.2%), 9/36 foetuses > 26 weeks (25.3%, CI:13.7-41.9%), and 64/250 neonates ≤ 28 days (26.9%, CI:15.3-42.8%). Additional abnormalities included aberrant cortical folding and other Chiari II malformation findings such as lower cervicomedullary kink level, tectal beaking, and hypoplastic tentorium. Eight studies compared MRI directly to ultrasound, but due to reporting inconsistencies, it was not possible to meta-analyse. CONCLUSION MRI is able to detect anomalies hitherto underestimated in foetal OSB which may be important for case selection. In view of increasing prenatal OSB surgery, further studies are required to assess developmental consequences of these findings.
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Affiliation(s)
- Nada Mufti
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, London, UK
| | - Adalina Sacco
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK
- Fetal Medicine Unit, University College London Hospital NHS Foundation Trust, London, UK
| | - Michael Aertsen
- Department of Radiology, University Hospitals Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Fred Ushakov
- Fetal Medicine Unit, University College London Hospital NHS Foundation Trust, London, UK
| | - Sebastian Ourselin
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, London, UK
| | - Dominic Thomson
- Paediatric Neurosurgery Department, Great Ormond Street Hospital for Children, London, UK
| | - Jan Deprest
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK
- Department of Obstetrics and Gynaecology, University Hospitals Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, London, UK
| | - Anna L David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK
- Department of Obstetrics and Gynaecology, University Hospitals Katholieke Universiteit (KU) Leuven, Leuven, Belgium
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15
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Li K, Yan G, Zheng W, Sun J, Zou Y. Measurement of the Brain Volume/Liver Volume Ratio by Three-Dimensional MRI in Appropriate-for-Gestational Age Fetuses and Those With Fetal Growth Restriction. J Magn Reson Imaging 2021; 54:1796-1801. [PMID: 34156128 DOI: 10.1002/jmri.27792] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/02/2021] [Accepted: 06/03/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Fetal growth restriction (FGR) is associated with a high fetal brain volume/liver volume (FBV/FLV) ratio. Ultrasound may not always be reliable, which has prompted further investigation of MRI techniques. PURPOSE To determine the relationship between FBV/FLV ratio, as measured by MRI, and gestational age (GA) in normal fetuses and those with FGR. STUDY TYPE Retrospective. POPULATION One hundred and forty seven singleton pregnancies including 105 appropriate-for-gestational age (AGA) fetuses and 42 FGR fetuses. FIELD STRENGTH/SEQUENCE Three-dimensional fast imaging employing steady-state acquisition at 1.5 T. ASSESSMENT The FBV and FLV were measured by three radiologists. The inter- and intraobserver agreements, the correlation between FBV/FLV ratio, and advancing GA were evaluated; the diagnostic value of FBV/FLV ratio was evaluated and compared with head circumference/abdominal circumference (HC/AC) ratio measured by ultrasound. STATISTICAL TESTS Intraclass correlation coefficient (ICC) was used to determine inter- and intraobserver agreements. Regression analysis was used to assess the correlation between FBV/FLV ratio and advancing GA. The diagnostic value of the FBV/FLV ratio was examined by the area under the receiver operating characteristic (ROC) curve. RESULTS The inter- and intraobserver agreements were excellent with an interobserver ICC of 0.984 and intra-observer ICCs of 0.989, 0.994, and 0.995. The FBV/FLV ratio in AGA fetuses decreased significantly with advancing GA (Pearson correlation coefficient = -0.844). The FBV/FLV ratio in FGR fetuses was significantly higher than that in AGA fetuses. To identify fetuses at high risk for FGR using the FBV/FLV ratio, the area under the ROC curve was 0.978, with an optimal cut-off value of 4.10. The sensitivity of FBV/FLV ratio in identifying FGR was significantly higher than that of HC/AC ratio (0.929 vs. 0.529). DATA CONCLUSION An inverse correlation exists between FBV/FLV ratio and advancing GA in normal fetuses. A high FBV/FLV ratio may be used to ascertain fetuses at high risk for FGR. LEVEL OF EVIDENCE 3 Technical Efficacy Stage: 3.
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Affiliation(s)
- Kui Li
- Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Guohui Yan
- Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weizeng Zheng
- Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jin Sun
- Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yu Zou
- Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
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16
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The application of in utero magnetic resonance imaging in the study of the metabolic and cardiovascular consequences of the developmental origins of health and disease. J Dev Orig Health Dis 2020; 12:193-202. [PMID: 33308364 PMCID: PMC8162788 DOI: 10.1017/s2040174420001154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Observing fetal development in utero is vital to further the understanding of later-life diseases. Magnetic resonance imaging (MRI) offers a tool for obtaining a wealth of information about fetal growth, development, and programming not previously available using other methods. This review provides an overview of MRI techniques used to investigate the metabolic and cardiovascular consequences of the developmental origins of health and disease (DOHaD) hypothesis. These methods add to the understanding of the developing fetus by examining fetal growth and organ development, adipose tissue and body composition, fetal oximetry, placental microstructure, diffusion, perfusion, flow, and metabolism. MRI assessment of fetal growth, organ development, metabolism, and the amount of fetal adipose tissue could give early indicators of abnormal fetal development. Noninvasive fetal oximetry can accurately measure placental and fetal oxygenation, which improves current knowledge on placental function. Additionally, measuring deficiencies in the placenta’s transport of nutrients and oxygen is critical for optimizing treatment. Overall, the detailed structural and functional information provided by MRI is valuable in guiding future investigations of DOHaD.
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17
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Dovjak GO, Kanbur I, Prayer F, Brugger PC, Gruber GM, Weber M, Stuhr F, Ulm B, Kasprian GJ, Prayer D. Comparison of the colon with T1 breath-hold vs T1 free-breathing-A retrospective fetal MRI study. Eur J Radiol 2020; 134:109457. [PMID: 33302027 DOI: 10.1016/j.ejrad.2020.109457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/26/2020] [Accepted: 11/30/2020] [Indexed: 01/01/2023]
Abstract
OBJECTIVES Fetal magnetic resonance imaging (MRI) plays an increasingly important role in the prenatal diagnosis of gastrointestinal abnormalities. During gestation, the bowel develops T1-weighted hyperintensity due to meconium formation. Currently used T1-weighted sequences are performed in maternal breath-hold (BH) technique, which may take up to 20 s. The free-breathing (FB) T1-weighted 3D radial VIBE (volumetric interpolated breath-hold examination) sequence requires no breath-hold, improving patient comfort. This study aimed to address how well the FB acquisition technique can visualize large bowel structures compared to the routinely performed breath-hold sequence. METHODS Forty-seven fetal MRI studies between 21 and 36 weeks of gestation without abdominal pathologies on prenatal MRI and ultrasound were included. All fetal scans were performed using a Philips Ingenia 1.5 T MRI. Coronal T1-weighted BH and FB sequences without fat suppression were compared. The following acquisition parameters were used (T1, FB): resolution 1.137 mm, 1.004 mm; matrix size 288 × 288, 448 × 448; FOV 328 mm, 450 mm; TR 81-132 ms, 3.47 ms; TE 4.6 ms, 1.47 ms. Due to the necessity of the breath-hold the duration of the sequence could not exceed 20 s (mean duration of the T1-weighted BH sequence 15.17 s, and mean duration of the FB sequence 26.42 s). In all examined fetuses the following structures were evaluated with respect to their visibility (0-not visible, 1-partially visible, 2-clearly visible): rectum, sigmoid, descending, transverse and ascending colon, cecum. Furthermore, motion artifacts were assessed (0-none, 1-intermediate, 2-severe motion artifacts), and the signal intensity (SI) ratio between maternal fat and fetal rectum SI was calculated. RESULTS No significant differences in the visibility of sigmoid and colon between BH and FB were detected, only the cecum could be seen slightly better (in 29.8 % of cases) using BH technique. Motion artifacts were similar between BH and FB. There was a non-significant SI difference (p = 0.68) in the rectum, with a higher SI in the BH sequence. CONCLUSIONS The FB acquisition technique compared to T1 using BH is equal regarding visibility of bowel structures and artifacts. Due to non-inferiority to the BH technique, the FB sequence is a good alternative in cases where BH cannot be performed. As the FB sequence further allows for thinner slices with a good signal, even small bowel loops may be visualized.
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Affiliation(s)
- G O Dovjak
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - I Kanbur
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - F Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - P C Brugger
- Center for Anatomy and Cell Biology, Department of Anatomy, Medical University of Vienna, Austria
| | - G M Gruber
- Department of Anatomy and Biomechanics, Karl Landsteiner University of Health Sciences, Krems, Austria
| | - M Weber
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - F Stuhr
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - B Ulm
- Department of Obstetrics and Feto-Maternal Medicine, Medical University of Vienna, Austria
| | - G J Kasprian
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - D Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria.
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18
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Prayer D, Deprest J. The use of MRI in fetal conditions amenable for antenatal management. Prenat Diagn 2020; 40:3-5. [PMID: 31860748 DOI: 10.1002/pd.5629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 11/16/2019] [Indexed: 11/12/2022]
Affiliation(s)
- Daniela Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna, Austria
| | - Jan Deprest
- Clinical Department of Obstetrics and Gynaecology, University Hospitals Leuven, and Academic Development and Regeneration, Cluster Woman and Child, Leuven, Belgium.,Institute for Women's Health, University College London, London, UK
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