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Kebiri H, Gholipour A, Lin R, Vasung L, Calixto C, Krsnik Ž, Karimi D, Bach Cuadra M. Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study. Med Image Anal 2024; 95:103186. [PMID: 38701657 DOI: 10.1016/j.media.2024.103186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/06/2024] [Accepted: 04/22/2024] [Indexed: 05/05/2024]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.
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Affiliation(s)
- Hamza Kebiri
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rizhong Lin
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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Lautarescu A, Bonthrone AF, Bos B, Barratt B, Counsell SJ. Advances in fetal and neonatal neuroimaging and everyday exposures. Pediatr Res 2024:10.1038/s41390-024-03294-1. [PMID: 38877283 DOI: 10.1038/s41390-024-03294-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 06/16/2024]
Abstract
The complex, tightly regulated process of prenatal brain development may be adversely affected by "everyday exposures" such as stress and environmental pollutants. Researchers are only just beginning to understand the neural sequelae of such exposures, with advances in fetal and neonatal neuroimaging elucidating structural, microstructural, and functional correlates in the developing brain. This narrative review discusses the wide-ranging literature investigating the influence of parental stress on fetal and neonatal brain development as well as emerging literature assessing the impact of exposure to environmental toxicants such as lead and air pollution. These 'everyday exposures' can co-occur with other stressors such as social and financial deprivation, and therefore we include a brief discussion of neuroimaging studies assessing the effect of social disadvantage. Increased exposure to prenatal stressors is associated with alterations in the brain structure, microstructure and function, with some evidence these associations are moderated by factors such as infant sex. However, most studies examine only single exposures and the literature on the relationship between in utero exposure to pollutants and fetal or neonatal brain development is sparse. Large cohort studies are required that include evaluation of multiple co-occurring exposures in order to fully characterize their impact on early brain development. IMPACT: Increased prenatal exposure to parental stress and is associated with altered functional, macro and microstructural fetal and neonatal brain development. Exposure to air pollution and lead may also alter brain development in the fetal and neonatal period. Further research is needed to investigate the effect of multiple co-occurring exposures, including stress, environmental toxicants, and socioeconomic deprivation on early brain development.
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Affiliation(s)
- Alexandra Lautarescu
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alexandra F Bonthrone
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Brendan Bos
- MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Ben Barratt
- MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Serena J Counsell
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
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Arichi T. Characterizing Large-Scale Human Circuit Development with In Vivo Neuroimaging. Cold Spring Harb Perspect Biol 2024; 16:a041496. [PMID: 38438187 PMCID: PMC11146311 DOI: 10.1101/cshperspect.a041496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Large-scale coordinated patterns of neural activity are crucial for the integration of information in the human brain and to enable complex and flexible human behavior across the life span. Through recent advances in noninvasive functional magnetic resonance imaging (fMRI) methods, it is now possible to study this activity and how it emerges in the living fetal brain across the second half of human gestation. This work has demonstrated that functional activity in the fetal brain has several features in keeping with highly organized networks of activity, which are undergoing a highly programmed and rapid sequence of development before birth, in which long-range connections emerge and core features of the mature functional connectome (such as hub regions and a gradient organization) are established. In this review, the findings of these studies are summarized, their relationship to the known changes in developmental neurobiology is considered, and considerations for future work in the context of limitations to the fMRI approach are presented.
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Affiliation(s)
- Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King's College London, New Hunt's House, Guy's Campus, London SE1 1UL, United Kingdom
- Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, London SE1 7EH, United Kingdom
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Ercolani G, Capuani S, Maiuro A, Celli V, Grimm R, Di Mascio D, Porpora MG, Catalano C, Brunelli R, Giancotti A, Manganaro L. Diffusion-sensitized magnetic resonance imaging highlights placental microstructural damage in patients with previous SARS-CoV-2 pregnancy infection. Placenta 2024; 145:38-44. [PMID: 38052124 DOI: 10.1016/j.placenta.2023.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 11/22/2023] [Accepted: 11/26/2023] [Indexed: 12/07/2023]
Abstract
INTRODUCTION Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been a major global health problem since December 2019. This work aimed to investigate whether pregnant women's mild and moderate SARS-CoV-2 infection was associated with microstructural and vascular changes in the placenta observable in vivo by Intravoxel Incoherent Motion (IVIM) at different gestational ages (GA). METHODS This was a retrospective, nested case-control of pregnant women during the SARS-CoV-2 pandemic (COVID-19 group, n = 14) compared to pre-pandemic healthy controls (n = 19). MRI IVIM protocol at 1.5T was constituted of diffusion-weighted (DW) images with TR/TE = 3100/76 ms and 10 b-values (0,10,30,50,75,100,200,400,700,1000s/mm2). Differences between IVIM parameters D (diffusion), and f (fractional perfusion) quantified in the two groups were evaluated using the ANOVA test with Bonferroni correction and linear correlation between IVIM metrics and GA, COVID-19 duration, the delay time between a positive SARS-CoV-2 test and MRI examination (delay-time exam+) was studied by Pearson-test. RESULTS D was significantly higher in the COVID-19 placentas compared to that of the age-matched healthy group (p < 0.04 in fetal and p < 0.007 in maternal site). No significant difference between f values was found in the two groups suggesting no-specific microstructural damage with no perfusion alteration (potentially quantified by f) in mild/moderate SARS-Cov-2 placentas. A significant negative correlation was found between D and GA in the COVID-19 placentas whereas no significant correlation was found in the control placentas reflecting a possible accelerated senescence process due to COVID-19. DISCUSSION We report impaired microstructural placental development during pregnancy and the absence of perfusion-IVIM parameter changes that may indicate no perfusion changing through microvessels and microvilli in the placentas of pregnancies with mild/moderate SARS-Cov-2 after reaching negativity.
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Affiliation(s)
- Giada Ercolani
- Department of Radiological, Oncological and Pathological Sciences, Umberto I Hospital, Sapienza University of Rome, Italy
| | | | - Alessandra Maiuro
- CNR ISC Roma Sapienza, Physics Department Rome, Italy; Sapienza University of Rome, Physics Department, Rome, Italy
| | - Veronica Celli
- Department of Radiological, Oncological and Pathological Sciences, Umberto I Hospital, Sapienza University of Rome, Italy
| | | | - Daniele Di Mascio
- Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Italy
| | - Maria Grazia Porpora
- Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Italy
| | - Carlo Catalano
- Department of Radiological, Oncological and Pathological Sciences, Umberto I Hospital, Sapienza University of Rome, Italy
| | - Roberto Brunelli
- Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Italy
| | - Antonella Giancotti
- Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome, Italy
| | - Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences, Umberto I Hospital, Sapienza University of Rome, Italy.
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Kebiri H, Gholipour A, Vasung L, Krsnik Ž, Karimi D, Cuadra MB. Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.01.547351. [PMID: 37425859 PMCID: PMC10327173 DOI: 10.1101/2023.07.01.547351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation of FODs requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results to standard methods such as Constrained Spherical Deconvolution. We demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical datasets of newborns and fetuses. Additionally, we compute agreement metrics within the HARDI newborn dataset, and validate fetal FODs with post-mortem histological data. The results of this study show the advantage of deep learning in inferring the microstructure of the developing brain from in-vivo dMRI measurements that are often very limited due to subject motion and limited acquisition times, but also highlight the intrinsic limitations of dMRI in the analysis of the developing brain microstructure. These findings, therefore, advocate for the need for improved methods that are tailored to studying the early development of human brain.
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Affiliation(s)
- Hamza Kebiri
- CIBM Center for Biomedical Imaging, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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Wilson S, Pietsch M, Cordero-Grande L, Christiaens D, Uus A, Karolis VR, Kyriakopoulou V, Colford K, Price AN, Hutter J, Rutherford MA, Hughes EJ, Counsell SJ, Tournier JD, Hajnal JV, Edwards AD, O’Muircheartaigh J, Arichi T. Spatiotemporal tissue maturation of thalamocortical pathways in the human fetal brain. eLife 2023; 12:e83727. [PMID: 37010273 PMCID: PMC10125021 DOI: 10.7554/elife.83727] [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: 09/26/2022] [Accepted: 03/31/2023] [Indexed: 04/04/2023] Open
Abstract
The development of connectivity between the thalamus and maturing cortex is a fundamental process in the second half of human gestation, establishing the neural circuits that are the basis for several important brain functions. In this study, we acquired high-resolution in utero diffusion magnetic resonance imaging (MRI) from 140 fetuses as part of the Developing Human Connectome Project, to examine the emergence of thalamocortical white matter over the second to third trimester. We delineate developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. We then quantify microstructural tissue components along the tracts in fetal compartments that are critical substrates for white matter maturation, such as the subplate and intermediate zone. We identify patterns of change in the diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester, such as the disassembly of radial glial scaffolding and the lamination of the cortical plate. These maturational trajectories of MR signal in transient fetal compartments provide a normative reference to complement histological knowledge, facilitating future studies to establish how developmental disruptions in these regions contribute to pathophysiology.
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Affiliation(s)
- Siân Wilson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de MadridMadridSpain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)MadridSpain
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Department of Electrical Engineering (ESAT/PSI), Katholieke Universiteit LeuvenLeuvenBelgium
| | - Alena Uus
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas' HospitalLondonUnited Kingdom
| | - Vyacheslav R Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
- Department of Forensic and Neurodevelopmental Sciences, King’s College LondonLondonUnited Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College LondonLondonUnited Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
- Children’s Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation TrustLondonUnited Kingdom
- Department of Bioengineering, Imperial College LondonLondonUnited Kingdom
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Dynamics of T2* and deformation in the placenta and myometrium during pre-labour contractions. Sci Rep 2022; 12:18542. [PMID: 36329074 PMCID: PMC9633703 DOI: 10.1038/s41598-022-22008-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022] Open
Abstract
Pre-labour uterine contractions, occurring throughout pregnancy, are an important phenomenon involving the placenta in addition to the myometrium. They alter the uterine environment and thus potentially the blood supply to the fetus and may thus provide crucial insights into the processes of labour. Assessment in-vivo is however restricted due to their unpredictability and the inaccessible nature of the utero-placental compartment. While clinical cardiotocography (CTG) only allows global, pressure-based assessment, functional magnetic resonance imaging (MRI) provides an opportunity to study contractile activity and its effects on the placenta and the fetus in-vivo. This study aims to provide both descriptive and quantitative structural and functional MR assessments of pre-labour contractions in the human uterus. A total of 226 MRI scans (18-41 weeks gestation) from ongoing research studies were analysed, focusing on free-breathing dynamic quantitative whole uterus dynamic T2* maps. These provide an indirect measure of tissue properties such as oxygenation. 22 contractile events were noted visually and both descriptive and quantitative analysis of the myometrial and placental changes including volumetric and T2* variations were undertaken. Processing and analysis was successfully performed, qualitative analysis shows distinct and highly dynamic contraction related characteristics including; alterations in the thickness of the low T2* in the placental bed and other myometrial areas, high intensity vessel-like structures in the myometrium, low-intensity vessel structures within the placental parenchyma and close to the chorionic plate. Quantitative evaluation shows a significant negative correlation between T2* in both contractile and not-contractile regions with gestational age (p < 0.05) as well as a significant reduction in T2* during contractions. The T2* values in the myometrium were however not correlated to gestational age (p > 0.5). The quantitative and qualitative description of uterine pre-labour contractions including dynamic changes and key characteristics aims to contribute to the sparsely available in-vivo information and to provide an in-vivo tool to study this important phenomenon. Further work is required to analyse the origins of these subclinical contractions, their effects in high-risk pregnancies and their ability to determine the likelihood of a successful labour. Assessing T2* distribution as a marker for placental oxygenation could thus potentially complement clinically used cardiotocography measurements in the future.
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Christiaens D, Cordero-Grande L, Pietsch M, Hutter J, Price AN, Hughes EJ, Vecchiato K, Deprez M, Edwards AD, Hajnal JV, Tournier JD. Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI. Neuroimage 2020; 225:117437. [PMID: 33068713 PMCID: PMC7779423 DOI: 10.1016/j.neuroimage.2020.117437] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 09/22/2020] [Accepted: 10/01/2020] [Indexed: 11/18/2022] Open
Abstract
Subject motion in dMRI leads to a set of scattered slices with unique contrast. We introduce a slice-to-volume reconstruction framework for multi-shell HARDI data Based on a data-driven representation as spherical harmonics and radial decomposition (SHARD). The method is evaluated in test-retest scans and in the neonatal dHCP cohort. Results show robust reconstruction in severely motion-corrupted scans.
Diffusion MRI offers a unique probe into neural microstructure and connectivity in the developing brain. However, analysis of neonatal brain imaging data is complicated by inevitable subject motion, leading to a series of scattered slices that need to be aligned within and across diffusion-weighted contrasts. Here, we develop a reconstruction method for scattered slice multi-shell high angular resolution diffusion imaging (HARDI) data, jointly estimating an uncorrupted data representation and motion parameters at the slice or multiband excitation level. The reconstruction relies on data-driven representation of multi-shell HARDI data using a bespoke spherical harmonics and radial decomposition (SHARD), which avoids imposing model assumptions, thus facilitating to compare various microstructure imaging methods in the reconstructed output. Furthermore, the proposed framework integrates slice-level outlier rejection, distortion correction, and slice profile correction. We evaluate the method in the neonatal cohort of the developing Human Connectome Project (650 scans). Validation experiments demonstrate accurate slice-level motion correction across the age range and across the range of motion in the population. Results in the neonatal data show successful reconstruction even in severely motion-corrupted subjects. In addition, we illustrate how local tissue modelling can extract advanced microstructure features such as orientation distribution functions from the motion-corrected reconstructions.
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Affiliation(s)
- Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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The impact of cerebral anomalies on cognitive outcome in patients with spina bifida: A systematic review. Eur J Paediatr Neurol 2020; 28:16-28. [PMID: 32771303 DOI: 10.1016/j.ejpn.2020.07.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 06/12/2020] [Accepted: 07/18/2020] [Indexed: 11/21/2022]
Abstract
BACKGROUND Spina bifida is the most common congenital birth defect affecting the central nervous system. Given the frequent association of cerebral anomalies, spina bifida is not a single developmental abnormality of the central nervous system. Patients with spina bifida typically perform below average on cognitive tasks. It has been hypothesized that associated cerebral anomalies as well negatively affect cognition in spina bifida patients. OBJECTIVE This study aims to review the impact of cerebral anomalies on cognitive outcome in patients with spina bifida. METHODS A systematic search of multiple databases, including Pubmed, Embase, Web of Science and The Cochrane Central Register of Controlled Trials, was performed. All relevant primary research articles were included. All included articles were methodologically evaluated using a critical appraisal checklist. RESULTS In total 27 articles were included in this systematic review. A significant impact of different cerebral anomalies on cognition was found. More specifically, hydrocephalus, Chiari malformation type II and anomalies of the corpus callosum, central executive network, default mode network, cortical thickness and gyrification, fornix, grey matter volume and total brain volume were found to have a significant impact on cognitive outcome. The presence of a CSF shunt was also negatively associated with cognition. The results on Chiari malformation type II decompression and CSF shunt complications are inconsistent. CONCLUSION Associated cerebral anomalies have a significant impact on cognitive outcome in patients with spina bifida. The interrelatedness of the different cerebral anomalies makes it difficult to distinguish their individual impact on cognition.
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Deprez M, Price A, Christiaens D, Lockwood Estrin G, Cordero-Grande L, Hutter J, Daducci A, Tournier JD, Rutherford M, Counsell SJ, Cuadra MB, Hajnal JV. Higher Order Spherical Harmonics Reconstruction of Fetal Diffusion MRI With Intensity Correction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1104-1113. [PMID: 31562073 DOI: 10.1109/tmi.2019.2943565] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We present a novel method for higher order reconstruction of fetal diffusion MRI signal that enables detection of fiber crossings. We combine data-driven motion and intensity correction with super-resolution reconstruction and spherical harmonic parametrisation to reconstruct data scattered in both spatial and angular domains into consistent fetal dMRI signal suitable for further diffusion analysis. We show that intensity correction is essential for good performance of the method and identify anatomically plausible fiber crossings. The proposed methodology has potential to facilitate detailed investigation of developing brain connectivity and microstructure in-utero.
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