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Bhattacharya S, Price AN, Uus A, Sousa HS, Marenzana M, Colford K, Murkin P, Lee M, Cordero-Grande L, Teixeira RPAG, Malik SJ, Deprez M. In vivo T2 measurements of the fetal brain using single-shot fast spin echo sequences. Magn Reson Med 2024; 92:715-729. [PMID: 38623934 DOI: 10.1002/mrm.30094] [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: 10/26/2023] [Revised: 02/18/2024] [Accepted: 03/08/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE We propose a quantitative framework for motion-corrected T2 fetal brain measurements in vivo and validate the single-shot fast spin echo (SS-FSE) sequence to perform these measurements. METHODS Stacks of two-dimensional SS-FSE slices are acquired with different echo times (TE) and motion-corrected with slice-to-volume reconstruction (SVR). The quantitative T2 maps are obtained by a fit to a dictionary of simulated signals. The sequence is selected using simulated experiments on a numerical phantom and validated on a physical phantom scanned on a 1.5T system. In vivo quantitative T2 maps are obtained for five fetuses with gestational ages (GA) 21-35 weeks on the same 1.5T system. RESULTS The simulated experiments suggested that a TE of 400 ms combined with the clinically utilized TEs of 80 and 180 ms were most suitable for T2 measurements in the fetal brain. The validation on the physical phantom confirmed that the SS-FSE T2 measurements match the gold standard multi-echo spin echo measurements. We measured average T2s of around 200 and 280 ms in the fetal brain grey and white matter, respectively. This was slightly higher than fetal T2* and the neonatal T2 obtained from previous studies. CONCLUSION The motion-corrected SS-FSE acquisitions with varying TEs offer a promising practical framework for quantitative T2 measurements of the moving fetus.
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Affiliation(s)
- Suryava Bhattacharya
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anthony N Price
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Alena Uus
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Helena S Sousa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | - Kathleen Colford
- Centre for the Developing Brain, King's College London, London, UK
| | - Peter Murkin
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maggie Lee
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Biomedical Image Technologies, ETSI Telecomunicración, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Rui Pedro A G Teixeira
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Shaihan J Malik
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maria Deprez
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
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2
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Hufnagel S, Schuenke P, Schulz-Menger J, Schaeffter T, Kolbitsch C. 3D whole heart k-space-based super-resolution cardiac T1 mapping using rotated stacks. Phys Med Biol 2024; 69:085027. [PMID: 38479021 DOI: 10.1088/1361-6560/ad33b6] [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: 11/06/2023] [Accepted: 03/13/2024] [Indexed: 04/10/2024]
Abstract
Objective. To provide three-dimensional (3D) whole-heart high-resolution isotropic cardiac T1 maps using a k-space-based through-plane super-resolution reconstruction (SRR) with rotated multi-slice stacks.Approach. Due to limited SNR and cardiac motion, often only 2D T1 maps with low through-plane resolution (4-8 mm) can be obtained. Previous approaches used SRR to calculate 3D high-resolution isotropic cardiac T1 maps. However, they were limited to the ventricles. The proposed approach acquires rotated stacks in long-axis orientation with high in-plane resolution but low through-plane resolution. This results in radially overlapping stacks from which high-resolution T1 maps of the whole heart are reconstructed using a k-space-based SRR framework considering the complete acquisition model. Cardiac and residual respiratory motion between different breath holds is estimated and incorporated into the reconstruction. The proposed approach was evaluated in simulations and phantom experiments and successfully applied to ten healthy subjects.Main results. 3D T1 maps of the whole heart were obtained in the same acquisition time as previous methods covering only the ventricles. T1 measurements were possible even for small structures, such as the atrial wall. The proposed approach provided accurate (P> 0.4;R2> 0.99) and precise T1 values (SD of 64.32 ± 22.77 ms in the proposed approach, 44.73 ± 31.9 ms in the reference). The edge sharpness of the T1 maps was increased by 6.20% and 4.73% in simulation and phantom experiments, respectively. Contrast-to-noise ratios between the septum and blood pool increased by 14.50% inin vivomeasurements with a k-space compared to an image-space-based SRR.Significance. The proposed approach provided whole-heart high-resolution 1.3 mm isotropic T1 maps in an overall acquisition time of approximately three minutes. Small structures, such as the atrial and right ventricular walls, could be visualized in the T1 maps.
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Affiliation(s)
- Simone Hufnagel
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Patrick Schuenke
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jeanette Schulz-Menger
- Charité Medical Faculty University Medicine, Berlin, Germany
- Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center (ECRC), Charité Humboldt University Berlin, DZHK partner site Berlin, Berlin, Germany
- Department of Cardiology and Nephrology, HELIOS Klinikum Berlin Buch, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Department of Biomedical Engineering, Technical University of Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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Lin J, Miao QI, Surawech C, Raman SS, Zhao K, Wu HH, Sung K. High-Resolution 3D MRI With Deep Generative Networks via Novel Slice-Profile Transformation Super-Resolution. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2023; 11:95022-95036. [PMID: 37711392 PMCID: PMC10501177 DOI: 10.1109/access.2023.3307577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
High-resolution magnetic resonance imaging (MRI) sequences, such as 3D turbo or fast spin-echo (TSE/FSE) imaging, are clinically desirable but suffer from long scanning time-related blurring when reformatted into preferred orientations. Instead, multi-slice two-dimensional (2D) TSE imaging is commonly used because of its high in-plane resolution but is limited clinically by poor through-plane resolution due to elongated voxels and the inability to generate multi-planar reformations due to staircase artifacts. Therefore, multiple 2D TSE scans are acquired in various orthogonal imaging planes, increasing the overall MRI scan time. In this study, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic super-resolution. Compared to SMORE SR method and the networks trained by conventional downsampling, our SPTSR framework demonstrated the best overall image quality from 50 testing cases, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.
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Affiliation(s)
- Jiahao Lin
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Department of Electrical and Computer Engineering, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Q I Miao
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Chuthaporn Surawech
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
- Division of Diagnostic Radiology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok 10330, Thailand
| | - Steven S Raman
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Kai Zhao
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Holden H Wu
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Kyunghyun Sung
- Department of Radiological Sciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
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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 2023; 96:20220071. [PMID: 35834425 PMCID: PMC7614695 DOI: 10.1259/bjr.20220071] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/27/2022] [Accepted: 05/11/2022] [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
| | | | | | - 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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Ciceri T, Squarcina L, Pigoni A, Ferro A, Montano F, Bertoldo A, Persico N, Boito S, Triulzi FM, Conte G, Brambilla P, Peruzzo D. Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester. Neuroinformatics 2023; 21:549-563. [PMID: 37284977 PMCID: PMC10406722 DOI: 10.1007/s12021-023-09635-5] [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] [Accepted: 05/20/2023] [Indexed: 06/08/2023]
Abstract
Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandro Pigoni
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Adele Ferro
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Florian Montano
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy
- Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nicola Persico
- Department of Woman, Child and Newborn, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Simona Boito
- Department of Woman, Child and Newborn, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Maria Triulzi
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Services and Preventive Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio Conte
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Services and Preventive Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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Xu J, Moyer D, Gagoski B, Iglesias JE, Grant PE, Golland P, Adalsteinsson E. NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1707-1719. [PMID: 37018704 PMCID: PMC10287191 DOI: 10.1109/tmi.2023.3236216] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e. g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness to subject motion and other image artifacts, we adopt a continuous and comprehensive slice acquisition model that takes into account rigid inter-slice motion, point spread function, and bias fields. NeSVoR also estimates pixel-wise and slice-wise variances of image noise and enables removal of outliers during reconstruction and visualization of uncertainty. Extensive experiments are performed on both simulated and in vivo data to evaluate the proposed method. Results show that NeSVoR achieves state-of-the-art reconstruction quality while providing two to ten-fold acceleration in reconstruction times over the state-of-the-art algorithms.
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7
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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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Kyriakopoulou V, Davidson A, Chew A, Gupta N, Arichi T, Nosarti C, Rutherford MA. Characterisation of ASD traits among a cohort of children with isolated fetal ventriculomegaly. Nat Commun 2023; 14:1550. [PMID: 36941265 PMCID: PMC10027681 DOI: 10.1038/s41467-023-37242-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 03/09/2023] [Indexed: 03/23/2023] Open
Abstract
Fetal ventriculomegaly is the most common antenatally-diagnosed brain abnormality. Imaging studies in antenatal isolated ventriculomegaly demonstrate enlarged ventricles and cortical overgrowth which are also present in children with autism-spectrum disorder/condition (ASD). We investigate the presence of ASD traits in a cohort of children (n = 24 [20 males/4 females]) with isolated fetal ventriculomegaly, compared with 10 controls (n = 10 [6 males/4 females]). Neurodevelopmental outcome at school age included IQ, ASD traits (ADOS-2), sustained attention, neurological functioning, behaviour, executive function, sensory processing, co-ordination, and adaptive behaviours. Pre-school language development was assessed at 2 years. 37.5% of children, all male, in the ventriculomegaly cohort scored above threshold for autism/ASD classification. Pre-school language delay predicted an ADOS-2 autism/ASD classification with 73.3% specificity/66.7% sensitivity. Greater pre-school language delay was associated with more ASD symptoms. In this study, the neurodevelopment of children with isolated fetal ventriculomegaly, associated with altered cortical development, includes ASD traits, difficulties in sustained attention, working memory and sensation-seeking behaviours.
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Affiliation(s)
- Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | - Alice Davidson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Nidhi Gupta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Paediatric Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Paediatric Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Department of Bioengineering, Imperial College London, London, UK
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
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9
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Hadjidekov G, Haynatzki G, Chaveeva P, Nikolov M, Masselli G, Rossi A. Concordance between US and MRI Two-Dimensional Measurement and Volumetric Segmentation in Fetal Ventriculomegaly. Diagnostics (Basel) 2023; 13:diagnostics13061183. [PMID: 36980491 PMCID: PMC10047855 DOI: 10.3390/diagnostics13061183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 03/03/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
We provide a study comparison between two-dimensional measurement and volumetric (3D) segmentation of the lateral ventricles and brain structures in fetuses with isolated and non-isolated ventriculomegaly with 3D virtual organ computer-aided analysis (VOCAL) ultrasonography vs. magnetic resonance imaging (MRI) analyzed with 3D-Slicer software. In this cross-sectional study, 40 fetuses between 20 and 38 gestational weeks with various degrees of ventriculomegaly were included. A total of 71 ventricles were measured with ultrasound (US) and with MRI. A total of 64 sonographic ventricular volumes, 80 ventricular and 40 fetal brain MR volumes were segmented and analyzed using both imaging modalities by three observers. Sizes and volumes of the ventricles and brain parenchyma were independently analyzed by two radiologists, and interobserver correlation of the results with 3D fetal ultrasound data was performed. The semiautomated rotational multiplanar 3D VOCAL technique was performed for ultrasound volumetric measurements. Results were compared to manually extracted ventricular and total brain volumes in 3D-Slicer. Segmentation of fetal brain structures (cerebral and cerebellar hemispheres, brainstem, ventricles) performed independently by two radiologists showed high interobserver agreement. An excellent agreement between VOCAL and MRI volumetric and two-dimensional measurements was established, taking into account the intraclass correlation coefficients (ICC), and a Bland-Altman plot was established. US and MRI are valuable tools for performing fetal brain and ventricular volumetry for clinical prognosis and patient counseling. Our datasets could provide the backbone for further construction of quantitative normative trajectories of fetal intracranial structures and support earlier detection of abnormal brain development and ventriculomegaly, its timing and progression during gestation.
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Affiliation(s)
- George Hadjidekov
- Department of Radiology, University Hospital Lozenetz, Koziak 1 Str., 1407 Sofia, Bulgaria
- Department of Physics, Biophysics and Radiology, Faculty of Medicine, Sofia University "St Kliment Ohridski", 1504 Sofia, Bulgaria
| | - Gleb Haynatzki
- Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Petya Chaveeva
- Department of Fetal Medicine, Shterev Hospital, 1330 Sofia, Bulgaria
| | - Miroslav Nikolov
- Department of Theoretical Electrical Engineering, Technical University, 1156 Sofia, Bulgaria
| | - Gabriele Masselli
- Radiology Department, Umberto 1 Hospital Sapienza University, 00161 Rome, Italy
| | - Andrea Rossi
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini 5, 16147 Genoa, Italy
- Department of Health Sciences, University of Genoa, 16126 Genoa, Italy
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10
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Shi W, Xu H, Sun C, Sun J, Li Y, Xu X, Zheng T, Zhang Y, Wang G, Wu D. AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:209-219. [PMID: 36129858 DOI: 10.1109/tmi.2022.3208277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%.
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11
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Corroenne R, Arthuis C, Kasprian G, Mahallati H, Ville Y, Millischer Bellaiche AE, Henry C, Grevent D, Salomon LJ. Diffusion tensor imaging of fetal brain: principles, potential and limitations of promising technique. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2022; 60:470-476. [PMID: 35561129 DOI: 10.1002/uog.24935] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 04/24/2022] [Accepted: 04/29/2022] [Indexed: 06/15/2023]
Abstract
Human brain development is a complex process that begins in the third week of gestation. During early development, the fetal brain undergoes dynamic morphological changes. These changes result from events such as neurogenesis, neuronal migration, synapse formation, axonal growth and myelination. Disruption of any of these processes is thought to be responsible for a wide array of different pathologies. Recent advances in magnetic resonance imaging, especially diffusion-weighted imaging and diffusion tensor imaging (DTI), have enabled characterization and evaluation of brain development in utero. In this review, aimed at practitioners involved in fetal medicine and high-risk pregnancies, we provide a comprehensive overview of fetal DTI studies focusing on characterization of early normal brain development as well as evaluation of brain pathology in utero. We also discuss the reliability and limitations of fetal brain DTI. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- R Corroenne
- Department of Obstetrics, Fetal Medicine and Surgery, Necker-Enfants Malades Hospital, APHP, Paris, France
- EA FETUS 7328 and LUMIERE Platform, University of Paris, Paris, France
| | - C Arthuis
- EA FETUS 7328 and LUMIERE Platform, University of Paris, Paris, France
- Department of Obstetrics, University Hospital of Nantes, Nantes, France
| | - G Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - H Mahallati
- Department of Radiology, University of Calgary, Calgary, Canada
| | - Y Ville
- Department of Obstetrics, Fetal Medicine and Surgery, Necker-Enfants Malades Hospital, APHP, Paris, France
| | | | - C Henry
- EA FETUS 7328 and LUMIERE Platform, University of Paris, Paris, France
| | - D Grevent
- EA FETUS 7328 and LUMIERE Platform, University of Paris, Paris, France
- Department of Radiology, Necker-Enfants Malades Hospital, APHP, Paris, France
| | - L J Salomon
- Department of Obstetrics, Fetal Medicine and Surgery, Necker-Enfants Malades Hospital, APHP, Paris, France
- EA FETUS 7328 and LUMIERE Platform, University of Paris, Paris, France
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12
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Sui Y, Afacan O, Jaimes C, Gholipour A, Warfield SK. Scan-Specific Generative Neural Network for MRI Super-Resolution Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1383-1399. [PMID: 35020591 PMCID: PMC9208763 DOI: 10.1109/tmi.2022.3142610] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The interpretation and analysis of Magnetic resonance imaging (MRI) benefit from high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is time-consuming and costly, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio (SNR). Super-resolution reconstruction (SRR) is one of the most widely used methods in MRI since it allows for the trade-off between high spatial resolution, high SNR, and reduced scan times. Deep learning has emerged for improved SRR as compared to conventional methods. However, current deep learning-based SRR methods require large-scale training datasets of high-resolution images, which are practically difficult to obtain at a suitable SNR. We sought to develop a methodology that allows for dataset-free deep learning-based SRR, through which to construct images with higher spatial resolution and of higher SNR than can be practically obtained by direct Fourier encoding. We developed a dataset-free learning method that leverages a generative neural network trained for each specific scan or set of scans, which in turn, allows for SRR tailored to the individual patient. With the SRR from three short duration scans, we achieved high quality brain MRI at an isotropic spatial resolution of 0.125 cubic mm with six minutes of imaging time for T2 contrast and an average increase of 7.2 dB (34.2%) in SNR to these short duration scans. Motion compensation was achieved by aligning the three short duration scans together. We assessed our technique on simulated MRI data and clinical data acquired from 15 subjects. Extensive experimental results demonstrate that our approach achieved superior results to state-of-the-art methods, while in parallel, performed at reduced cost as scans delivered with direct high-resolution acquisition.
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13
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Ren JY, Zhu M, Wang G, Gui Y, Jiang F, Dong SZ. Quantification of Intracranial Structures Volume in Fetuses Using 3-D Volumetric MRI: Normal Values at 19 to 37 Weeks' Gestation. Front Neurosci 2022; 16:886083. [PMID: 35645723 PMCID: PMC9133784 DOI: 10.3389/fnins.2022.886083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveThe purpose of this study is to establish a reference of intracranial structure volumes in normal fetuses ranging from 19 to 37 weeks' gestation (mean 27 weeks).Materials and MethodsA retrospective analysis of 188 MRI examinations (1.5 T) of fetuses with a normal brain appearance (19–37 gestational weeks) from January 2018 to December 2021 was included in this study. Three dimensional (3-D) volumetric parameters from slice-to-volume reconstructed (SVR) images, such as total brain volume (TBV), cortical gray matter volume (GMV), subcortical brain tissue volume (SBV), intracranial cavity volume (ICV), lateral ventricles volume (VV), cerebellum volume (CBV), brainstem volume (BM), and extra-cerebrospinal fluid volume (e-CSFV), were quantified by manual segmentation from two experts. The mean, SD, minimum, maximum, median, and 25th and 75th quartiles for intracranial structures volume were calculated per gestational week. A linear regression analysis was used to determine the gestational weekly age-related change adjusted for sex. A t-test was used to compare the mean TBV and ICV values to previously reported values at each gestational week. The formulas to calculate intracranial structures volume derived from our data were created using a regression model. In addition, we compared the predicted mean TBV values derived by our formula with the expected mean TBV predicted by the previously reported Jarvis' formula at each time point. For intracranial volumes, the intraclass correlation coefficient (ICC) was calculated to convey association within and between observers.ResultsThe intracranial volume data are shown in graphs and tabular summaries. The male fetuses had significantly larger VV compared with female fetuses (p = 0.01). Measured mean ICV values at 19 weeks are significantly different from those published in the literature (p < 0.05). Means were compared with the expected TBV generated by the previously reported formula, showing statistically differences at 22, 26, 29, and 30 weeks' gestational age (GA) (all p < 0.05). A comparison between our data-derived formula and the previously reported formula for TBV showed very similar values at every GA. The predicted TBV means derived from the previously reported formula were all within the 95% confidence interval (CI) of the predicted means of this study. Intra- and inter-observer agreement was high, with an intraclass correlation coefficient larger than 0.98.ConclusionWe have shown that the intracranial structural volume of the fetal brain can be reliably quantified using 3-D volumetric MRI with a high degree of reproducibility and reinforces the existing data with more robust data in the earlier second and third stages of pregnancy.
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Affiliation(s)
- Jing-Ya Ren
- Department of Radiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ming Zhu
- Department of Radiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guanghai Wang
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, School of Medicine, Xinhua Hospital, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Yiding Gui
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, School of Medicine, Xinhua Hospital, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Fan Jiang
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, School of Medicine, Xinhua Hospital, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Su-Zhen Dong
- Department of Radiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Su-Zhen Dong
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14
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Beirinckx Q, Jeurissen B, Nicastro M, Poot DH, Verhoye M, Dekker AJD, Sijbers J. Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping. Comput Med Imaging Graph 2022; 100:102071. [DOI: 10.1016/j.compmedimag.2022.102071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 04/07/2022] [Accepted: 04/29/2022] [Indexed: 01/18/2023]
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15
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Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Okan Irfanoglu M. What's new and what's next in diffusion MRI preprocessing. Neuroimage 2022; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
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Affiliation(s)
- Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre, School of Physics and Astronomy, Cardiff University, UK.
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, New York University Grossman School of Medicine, NY, USA
| | | | - M Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
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16
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Rajagopalan V, Deoni S, Panigrahy A, Thomason ME. Is fetal MRI ready for neuroimaging prime time? An examination of progress and remaining areas for development. Dev Cogn Neurosci 2021; 51:100999. [PMID: 34391003 PMCID: PMC8365463 DOI: 10.1016/j.dcn.2021.100999] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 11/25/2022] Open
Abstract
A major challenge in designing large-scale, multi-site studies is developing a core, scalable protocol that retains the innovation of scientific advances while also lending itself to the variability in experience and resources across sites. In the development of a common Healthy Brain and Child Development (HBCD) protocol, one of the chief questions is "is fetal MRI ready for prime-time?" While there is agreement about the value of prenatal data obtained non-invasively through MRI, questions about practicality abound. There has been rapid progress over the past years in fetal and placental MRI methodology but there is uncertainty about whether the gains afforded outweigh the challenges in supporting fetal MRI protocols at scale. Here, we will define challenges inherent in building a common protocol across sites with variable expertise and will propose a tentative framework for evaluation of design decisions. We will compare and contrast various design considerations for both normative and high-risk populations, in the setting of the post-COVID era. We will conclude with articulation of the benefits of overcoming these challenges and would lend to the primary questions articulated in the HBCD initiative.
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Affiliation(s)
- Vidya Rajagopalan
- Department of Radiology, Keck School of Medicine, University of Southern California and Childrens Hospital of Los Angeles, United States.
| | - Sean Deoni
- Department of Pediatrics, Memorial Hospital of Rhode Island, United States
| | - Ashok Panigrahy
- Department of Radiology, University of Pittsburgh Medical School and Children's Hospital of Pittsburgh, United States
| | - Moriah E Thomason
- Departments of Child and Adolescent Psychiatry and Population Health, Hassenfeld Children's Hospital at NYU Langone, United States
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17
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Askin Incebacak NC, Sui Y, Gui Levy L, Merlini L, Sa de Almeida J, Courvoisier S, Wallace TE, Klauser A, Afacan O, Warfield SK, Hüppi P, Lazeyras F. Super-resolution reconstruction of T2-weighted thick-slice neonatal brain MRI scans. J Neuroimaging 2021; 32:68-79. [PMID: 34506677 PMCID: PMC8752487 DOI: 10.1111/jon.12929] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/23/2021] [Accepted: 08/20/2021] [Indexed: 11/28/2022] Open
Abstract
Background and Purpose Super‐resolutionreconstruction (SRR) can be used to reconstruct 3‐dimensional (3D) high‐resolution (HR) volume from several 2‐dimensional (2D) low‐resolution (LR) stacks of MRI slices. The purpose is to compare lengthy 2D T2‐weighted HR image acquisition of neonatal subjects with 3D SRR from several LR stacks in terms of image quality for clinical and morphometric assessments. Methods LR brain images were acquired from neonatal subjects to reconstruct isotropic 3D HR volumes by using SRR algorithm. Quality assessments were done by an experienced pediatric radiologist using scoring criteria adapted to newborn anatomical landmarks. The Wilcoxon signed‐rank test was used to compare scoring results between HR and SRR images. For quantitative assessments, morphology‐based segmentation was performed on both HR and SRR images and Dice coefficients between the results were computed. Additionally, simple linear regression was performed to compare the tissue volumes. Results No statistical difference was found between HR and SRR structural scores using Wilcoxon signed‐rank test (p = .63, Z = .48). Regarding segmentation results, R2 values for the volumes of gray matter, white matter, cerebrospinal fluid, basal ganglia, cerebellum, and total brain volume including brain stem ranged between .95 and .99. Dice coefficients between the segmented regions from HR and SRR ranged between .83 ± .04 and .96 ± .01. Conclusion Qualitative and quantitative assessments showed that 3D SRR of several LR images produces images that are of comparable quality to standard 2D HR image acquisition for healthy neonatal imaging without loss of anatomical details with similar edge definition allowing the detection of fine anatomical structures and permitting comparable morphometric measurement.
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Affiliation(s)
| | - Yao Sui
- CRL, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Laura Gui Levy
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Laura Merlini
- Pediatric Radiology Unit, Division of Radiology, University Hospitals of Geneva, Geneva, Switzerland
| | - Joana Sa de Almeida
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Sebastien Courvoisier
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.,CIBM, Center of Biomedical Imaging, Geneva, Switzerland
| | - Tess E Wallace
- CRL, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Antoine Klauser
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.,CIBM, Center of Biomedical Imaging, Geneva, Switzerland
| | - Onur Afacan
- CRL, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- CRL, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Petra Hüppi
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Francois Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.,CIBM, Center of Biomedical Imaging, Geneva, Switzerland
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18
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Payette K, de Dumast P, Kebiri H, Ezhov I, Paetzold JC, Shit S, Iqbal A, Khan R, Kottke R, Grehten P, Ji H, Lanczi L, Nagy M, Beresova M, Nguyen TD, Natalucci G, Karayannis T, Menze B, Bach Cuadra M, Jakab A. An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. Sci Data 2021; 8:167. [PMID: 34230489 PMCID: PMC8260784 DOI: 10.1038/s41597-021-00946-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 05/13/2021] [Indexed: 11/09/2022] Open
Abstract
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open datasets of segmented fetal brains. Here we introduce a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter, brainstem/spinal cord). In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. Four research groups participated, submitting a total of 10 algorithms, demonstrating the benefits the dataset for the development of automatic algorithms.
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Affiliation(s)
- Kelly Payette
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland.
| | - Priscille de Dumast
- CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
- Medical Image Analysis Laboratory, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Hamza Kebiri
- CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
- Medical Image Analysis Laboratory, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ivan Ezhov
- Image-Based Biomedical Imaging Group, Technical University of Munich, München, Germany
| | - Johannes C Paetzold
- Image-Based Biomedical Imaging Group, Technical University of Munich, München, Germany
| | - Suprosanna Shit
- Image-Based Biomedical Imaging Group, Technical University of Munich, München, Germany
| | - Asim Iqbal
- Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Center for Intelligent Systems & Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Romesa Khan
- Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, UZH/ETH Zurich, Zurich, Switzerland
| | - Raimund Kottke
- Department of Diagnostic Imaging, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Patrice Grehten
- Department of Diagnostic Imaging, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Hui Ji
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Levente Lanczi
- Faculty of Medicine, Department of Medical Imaging, University of Debrecen, Debrecen, Hajdú-Bihar, Hungary
| | - Marianna Nagy
- Faculty of Medicine, Department of Medical Imaging, University of Debrecen, Debrecen, Hajdú-Bihar, Hungary
| | - Monika Beresova
- Faculty of Medicine, Department of Medical Imaging, University of Debrecen, Debrecen, Hajdú-Bihar, Hungary
| | - Thi Dao Nguyen
- Newborn Research, Department of Neonatology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Giancarlo Natalucci
- Newborn Research, Department of Neonatology, University Hospital and University of Zurich, Zurich, Switzerland
- Larsson-Rosenquist Center for Neurodevelopment, Growth and Nutrition of the Newborn, Department of Neonatology, University Hospital and University of Zurich, Zurich, Switzerland
| | | | - Bjoern Menze
- Image-Based Biomedical Imaging Group, Technical University of Munich, München, Germany
| | - Meritxell Bach Cuadra
- CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
- Medical Image Analysis Laboratory, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland
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19
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Jakab A, Payette K, Mazzone L, Schauer S, Muller CO, Kottke R, Ochsenbein-Kölble N, Tuura R, Moehrlen U, Meuli M. Emerging magnetic resonance imaging techniques in open spina bifida in utero. Eur Radiol Exp 2021; 5:23. [PMID: 34136989 PMCID: PMC8209133 DOI: 10.1186/s41747-021-00219-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/01/2021] [Indexed: 11/25/2022] Open
Abstract
Magnetic resonance imaging (MRI) has become an essential diagnostic modality for congenital disorders of the central nervous system. Recent advancements have transformed foetal MRI into a clinically feasible tool, and in an effort to find predictors of clinical outcomes in spinal dysraphism, foetal MRI began to unveil its potential. The purpose of our review is to introduce MRI techniques to experts with diverse backgrounds, who are involved in the management of spina bifida. We introduce advanced foetal MRI postprocessing potentially improving the diagnostic work-up. Importantly, we discuss how postprocessing can lead to a more efficient utilisation of foetal or neonatal MRI data to depict relevant anatomical characteristics. We provide a critical perspective on how structural, diffusion and metabolic MRI are utilised in an endeavour to shed light on the correlates of impaired development. We found that the literature is consistent about the value of MRI in providing morphological cues about hydrocephalus development, hindbrain herniation or outcomes related to shunting and motor functioning. MRI techniques, such as foetal diffusion MRI or diffusion tractography, are still far from clinical use; however, postnatal studies using these methods revealed findings that may reflect early neural correlates of upstream neuronal damage in spinal dysraphism.
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Affiliation(s)
- Andras Jakab
- Center for MR-Research, University Children's Hospital Zürich, Zürich, Switzerland. .,Neuroscience Center Zürich, University of Zürich, Zürich, Switzerland.
| | - Kelly Payette
- Center for MR-Research, University Children's Hospital Zürich, Zürich, Switzerland.,Neuroscience Center Zürich, University of Zürich, Zürich, Switzerland
| | - Luca Mazzone
- Department of Pediatric Surgery, University Children's Hospital Zurich, Zürich, Switzerland.,The Zurich Center for Fetal Diagnosis and Therapy, Zürich, Switzerland
| | - Sonja Schauer
- Department of Pediatric Surgery, University Children's Hospital Zurich, Zürich, Switzerland
| | | | - Raimund Kottke
- Department of Diagnostic Imaging, University Children's Hospital Zurich, Zurich, Switzerland
| | | | - Ruth Tuura
- Center for MR-Research, University Children's Hospital Zürich, Zürich, Switzerland
| | - Ueli Moehrlen
- Department of Pediatric Surgery, University Children's Hospital Zurich, Zürich, Switzerland.,The Zurich Center for Fetal Diagnosis and Therapy, Zürich, Switzerland.,University of Zurich, Zürich, Switzerland
| | - Martin Meuli
- Department of Pediatric Surgery, University Children's Hospital Zurich, Zürich, Switzerland.,The Zurich Center for Fetal Diagnosis and Therapy, Zürich, Switzerland.,University of Zurich, Zürich, Switzerland
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20
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Sui Y, Afacan O, Gholipour A, Warfield SK. Fast and High-Resolution Neonatal Brain MRI Through Super-Resolution Reconstruction From Acquisitions With Variable Slice Selection Direction. Front Neurosci 2021; 15:636268. [PMID: 34220414 PMCID: PMC8242183 DOI: 10.3389/fnins.2021.636268] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 05/19/2021] [Indexed: 12/18/2022] Open
Abstract
The brain of neonates is small in comparison to adults. Imaging at typical resolutions such as one cubic mm incurs more partial voluming artifacts in a neonate than in an adult. The interpretation and analysis of MRI of the neonatal brain benefit from a reduction in partial volume averaging that can be achieved with high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is slow, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio. The purpose of this study is thus that using super-resolution reconstruction in conjunction with fast imaging protocols to construct neonatal brain MRI images at a suitable signal-to-noise ratio and with higher spatial resolution than can be practically obtained by direct Fourier encoding. We achieved high quality brain MRI at a spatial resolution of isotropic 0.4 mm with 6 min of imaging time, using super-resolution reconstruction from three short duration scans with variable directions of slice selection. Motion compensation was achieved by aligning the three short duration scans together. We applied this technique to 20 newborns and assessed the quality of the images we reconstructed. Experiments show that our approach to super-resolution reconstruction achieved considerable improvement in spatial resolution and signal-to-noise ratio, while, in parallel, substantially reduced scan times, as compared to direct high-resolution acquisitions. The experimental results demonstrate that our approach allowed for fast and high-quality neonatal brain MRI for both scientific research and clinical studies.
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Affiliation(s)
- Yao Sui
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Simon K. Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
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21
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Li H, Yan G, Luo W, Liu T, Wang Y, Liu R, Zheng W, Zhang Y, Li K, Zhao L, Limperopoulos C, Zou Y, Wu D. Mapping fetal brain development based on automated segmentation and 4D brain atlasing. Brain Struct Funct 2021; 226:1961-1972. [PMID: 34050792 DOI: 10.1007/s00429-021-02303-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022]
Abstract
Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23 and 36 weeks of gestation. The proposed pipeline enabled the fully automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for the quantitative characterization of fetal brain development, especially in the Chinese population.
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Affiliation(s)
- Haotian Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Guohui Yan
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wanrong Luo
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yan Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ruibin Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Neurology, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Kui Li
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Li Zhao
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Catherine Limperopoulos
- Center for the Developing Brain, Diagnostic Imaging and Radiology, Children's National Medical Center, Washington, DC, USA
| | - Yu Zou
- Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
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22
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Lu Q. Editorial for "Data Quality Assessment for Super-Resolution Fetal Brain MR Imaging: A Retrospective 1.5 T Study". J Magn Reson Imaging 2021; 54:1361-1362. [PMID: 33982827 DOI: 10.1002/jmri.27699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 05/05/2021] [Indexed: 11/10/2022] Open
Affiliation(s)
- Quin Lu
- Philips Healthcare North America, San Francisco, California, USA
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23
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Rubert N, Bardo DME, Vaughn J, Cornejo P, Goncalves LF. Data Quality Assessment for Super-Resolution Fetal Brain MR Imaging: A Retrospective 1.5 T Study. J Magn Reson Imaging 2021; 54:1349-1360. [PMID: 33949725 DOI: 10.1002/jmri.27665] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Super-resolution is a promising technique to create isotropic image volumes from stacks of two-dimensional (2D) motion-corrupted images in fetal magnetic resonance imaging (MRI). PURPOSE To determine an acquisition quality metric and correlate that metric with radiologist perception of three-dimensional (3D) image quality. STUDY TYPE Retrospective. SUBJECTS Eighty-seven patients, mean gestational age 29 ± 6 weeks. FIELD STRENGTH/SEQUENCE 1.5 T/2D fast spin-echo. ASSESSMENT Four radiologists (L.G., D.M.E.B., P.C., and J.V.; 31, 21, 7, and 7 years' experience, respectively) graded reconstructions on a 0 to 4 scale for overall appearance and visibility of specific anatomy. During reconstruction, slices were labeled as inliers based on correlation between a simulated vs. actual acquisition. The fraction of brain voxels in inlier slicers vs. total brain voxels was measured for each acquisition. STATISTICAL TESTS Paired sample t test, Pearson's correlation, intra-class correlation. RESULTS The average brain mask inlier fraction for all acquisitions was 0.8. There was a statistically significant correlation (0.71) between overall reconstruction appearance and number of acquisitions with inlier fraction above 0.73. There was low correlation (0.21, P = 0.05) between the number of acquisitions used in the reconstruction and overall score when no data quality measure was considered. Similar results were found for ratings of specific anatomy. Statistically significant differences in overall perception of image quality were found when using three vs. four, four vs. five, and three vs. five high-quality acquisitions in the reconstruction. Five high-quality acquisitions were sufficient to yield an average radiologist rating of 3.59 out of 4.0 for overall image quality. DATA CONCLUSION Reconstruction quality can be reliably predicted using the brain mask inlier fraction. Real-time super-resolution protocols could exploit this to terminate acquisition when enough high-quality acquisitions have been collected. To achieve consistent 3D image quality it may be necessary to acquire more than five scans to compensate for severely motion-corrupted acquisitions. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: 1.
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Affiliation(s)
- Nicholas Rubert
- Department of Radiology, Phoenix Children's Hospital, Phoenix, Arizona, USA
| | - Dianna M E Bardo
- Department of Radiology, Phoenix Children's Hospital, Phoenix, Arizona, USA.,Departments of Radiology and Child Health, University of Arizona, Phoenix, Arizona, USA.,Department of Radiology, Mayo Clinic, Scottsdale, Arizona, USA.,Department of Radiology, Creighton University, Phoenix, Arizona, USA.,Department of Neuroradiology, Barrows Neurological Institute, Phoenix, Arizona, USA
| | - Jennifer Vaughn
- Department of Radiology, Phoenix Children's Hospital, Phoenix, Arizona, USA.,Departments of Radiology and Child Health, University of Arizona, Phoenix, Arizona, USA.,Department of Radiology, Creighton University, Phoenix, Arizona, USA.,Department of Neuroradiology, Barrows Neurological Institute, Phoenix, Arizona, USA
| | - Patricia Cornejo
- Department of Radiology, Phoenix Children's Hospital, Phoenix, Arizona, USA.,Departments of Radiology and Child Health, University of Arizona, Phoenix, Arizona, USA.,Department of Radiology, Mayo Clinic, Scottsdale, Arizona, USA.,Department of Radiology, Creighton University, Phoenix, Arizona, USA.,Department of Neuroradiology, Barrows Neurological Institute, Phoenix, Arizona, USA
| | - Luis F Goncalves
- Department of Radiology, Phoenix Children's Hospital, Phoenix, Arizona, USA.,Departments of Radiology and Child Health, University of Arizona, Phoenix, Arizona, USA
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24
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Park S, Gach HM, Kim S, Lee SJ, Motai Y. Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2021; 9:1800113. [PMID: 34168920 PMCID: PMC8216682 DOI: 10.1109/jtehm.2021.3076152] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 04/14/2021] [Accepted: 04/24/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. METHOD & MATERIALS Previous CNN-based MRI super-resolution methods cause loss of input image information due to the pooling layer. An Autoencoder-inspired Convolutional Network-based Super-resolution (ACNS) method was developed with the deconvolution layer that extrapolates the missing spatial information by the convolutional neural network-based nonlinear mapping between LR and HR features of MRI. Simulation experiments were conducted with virtual phantom images and thoracic MRIs from four volunteers. The Peak Signal-to-Noise Ratio (PSNR), Structure SIMilarity index (SSIM), Information Fidelity Criterion (IFC), and computational time were compared among: ACNS; Super-Resolution Convolutional Neural Network (SRCNN); Fast Super-Resolution Convolutional Neural Network (FSRCNN); Deeply-Recursive Convolutional Network (DRCN). RESULTS ACNS achieved comparable PSNR, SSIM, and IFC results to SRCNN, FSRCNN, and DRCN. However, the average computation speed of ACNS was 6, 4, and 35 times faster than SRCNN, FSRCNN, and DRCN, respectively under the computer setup used with the actual average computation time of 0.15 s per [Formula: see text].
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Affiliation(s)
- Seonyeong Park
- Department of BioengineeringUniversity of Illinois at Urbana-ChampaignUrbanaIL61820USA
| | - H. Michael Gach
- Department of Radiation OncologyWashington University in St. LouisSt. LouisMO63130USA
| | - Siyong Kim
- Department of Radiation OncologyDivision of Medical PhysicsVirginia Commonwealth UniversityRichmondVA23284USA
| | - Suk Jin Lee
- TSYS School of Computer ScienceColumbus State UniversityColumbusGA31907USA
| | - Yuichi Motai
- Department of Electrical and Computer EngineeringVirginia Commonwealth UniversityRichmondVA23284USA
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25
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Largent A, Kapse K, Barnett SD, De Asis-Cruz J, Whitehead M, Murnick J, Zhao L, Andersen N, Quistorff J, Lopez C, Limperopoulos C. Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods. J Magn Reson Imaging 2021; 54:818-829. [PMID: 33891778 DOI: 10.1002/jmri.27649] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/09/2021] [Accepted: 04/12/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Due to random motion of fetuses and maternal respirations, image quality of fetal brain MRIs varies considerably. To address this issue, visual inspection of the images is performed during acquisition phase and after 3D-reconstruction, and the images are re-acquired if they are deemed to be of insufficient quality. However, this process is time-consuming and subjective. Multi-instance (MI) deep learning methods (DLMs) may perform this task automatically. PURPOSE To propose an MI count-based DLM (MI-CB-DLM), an MI vote-based DLM (MI-VB-DLM), and an MI feature-embedding DLM (MI-FE-DLM) for automatic assessment of 3D fetal-brain MR image quality. To quantify influence of fetal gestational age (GA) on DLM performance. STUDY TYPE Retrospective. SUBJECTS Two hundred and seventy-one MR exams from 211 fetuses (mean GA ± SD = 30.9 ± 5.5 weeks). FIELD STRENGTH/SEQUENCE T2 -weighted single-shot fast spin-echo acquired at 1.5 T. ASSESSMENT The T2 -weighted images were reconstructed in 3D. Then, two fetal neuroradiologists, a clinical neuroscientist, and a fetal MRI technician independently labeled the reconstructed images as 1 or 0 based on image quality (1 = high; 0 = low). These labels were fused and served as ground truth. The proposed DLMs were trained and evaluated using three repeated 10-fold cross-validations (training and validation sets of 244 and 27 scans). To quantify GA influence, this variable was included as an input of the DLMs. STATISTICAL TESTS DLM performance was evaluated using precision, recall, F-score, accuracy, and AUC values. RESULTS Precision, recall, F-score, accuracy, and AUC averaged over the three cross validations were 0.85 ± 0.01, 0.85 ± 0.01, 0.85 ± 0.01, 0.85 ± 0.01, 0.93 ± 0.01, for MI-CB-DLM (without GA); 0.75 ± 0.03, 0.75 ± 0.03, 0.75 ± 0.03, 0.75 ± 0.03, 0.81 ± 0.03, for MI-VB-DLM (without GA); 0.81 ± 0.01, 0.81 ± 0.01, 0.81 ± 0.01, 0.81 ± 0.01, 0.89 ± 0.01, for MI-FE-DLM (without GA); and 0.86 ± 0.01, 0.86 ± 0.01, 0.86 ± 0.01, 0.86 ± 0.01, 0.93 ± 0.01, for MI-CB-DLM with GA. DATA CONCLUSION MI-CB-DLM performed better than other DLMs. Including GA as an input of MI-CB-DLM improved its performance. MI-CB-DLM may potentially be used to objectively and rapidly assess fetal MR image quality. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Axel Largent
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Kushal Kapse
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Scott D Barnett
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Josepheen De Asis-Cruz
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Matthew Whitehead
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA.,Department of Neurology, Children's National Hospital, Washington, District of Columbia, USA
| | - Jonathan Murnick
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Li Zhao
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Nicole Andersen
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Jessica Quistorff
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Catherine Lopez
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, District of Columbia, USA.,Department of Radiology, Pediatrics, George Washington University, Washington, District of Columbia, USA.,Neurology School of Medicine and Health Sciences, George Washington University, Washington, District of Columbia, USA
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26
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A Sparse Volume Reconstruction Method for Fetal Brain MRI Using Adaptive Kernel Regression. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6685943. [PMID: 33748279 PMCID: PMC7960018 DOI: 10.1155/2021/6685943] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/25/2021] [Accepted: 02/18/2021] [Indexed: 11/18/2022]
Abstract
Slice-to-volume reconstruction (SVR) method can deal well with motion artifacts and provide high-quality 3D image data for fetal brain MRI. However, the problem of sparse sampling is not well addressed in the SVR method. In this paper, we mainly focus on the sparse volume reconstruction of fetal brain MRI from multiple stacks corrupted with motion artifacts. Based on the SVR framework, our approach includes the slice-to-volume 2D/3D registration, the point spread function- (PSF-) based volume update, and the adaptive kernel regression-based volume update. The adaptive kernel regression can deal well with the sparse sampling data and enhance the detailed preservation by capturing the local structure through covariance matrix. Experimental results performed on clinical data show that kernel regression results in statistical improvement of image quality for sparse sampling data with the parameter setting of the structure sensitivity 0.4, the steering kernel size of 7 × 7 × 7 and steering smoothing bandwidth of 0.5. The computational performance of the proposed GPU-based method can be over 90 times faster than that on CPU.
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27
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Borisch EA, Grimm RC, Kargar S, Kawashima A, Rossman PJ, Riederer SJ. Cross correlation-based misregistration correction for super resolution T 2 -weighted spin-echo images: application to prostate. Magn Reson Med 2021; 85:1350-1363. [PMID: 32970892 PMCID: PMC7718320 DOI: 10.1002/mrm.28518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/12/2020] [Accepted: 08/23/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE The purpose is to develop a retrospective correction for subtle slice-to-slice positional inconsistencies that can occur when overlapped slices are acquired for super resolution in T2 -weighted spin-echo multislice imaging. METHODS Spin-echo acquisition of overlapped slices is typically done using multiple passes. After the passes are assembled into the final slice set, consecutive slices are correlated due to their overlap. Cross correlation was used to measure slice-to-slice displacement. After Z-dependent filtering to preserve true object shape, the displacements were used to correct slice position. The method was tested in a phantom moved slowly (0.16-0.63 mm/pass) under computer control and in vivo in 16 patients having prostate MRI. RESULTS Over the motion range, the correlation method had an accuracy within 0.03 mm/pass and precision ± 0.20 mm (ie, subpixel). Corrected images visually resemble the true object. Over the patient studies, the mean range of motion in the anterior-posterior direction was 1.63 mm. Motion-corrected axial images and the sagittal reformats were evaluated as significantly superior over those formed without motion correction. CONCLUSION The retrospective correlation-based motion-correction method provides significant improvement in the slice-to-slice registration necessary for effective super resolution using overlapped slices.
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Affiliation(s)
| | | | - Soudabeh Kargar
- Department of Radiology, Mayo Clinic, Rochester MN
- Department of Radiology, University of Wisconsin, Madison WI
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28
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Yeung PH, Aliasi M, Papageorghiou AT, Haak M, Xie W, Namburete AIL. Learning to map 2D ultrasound images into 3D space with minimal human annotation. Med Image Anal 2021; 70:101998. [PMID: 33711741 DOI: 10.1016/j.media.2021.101998] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 01/26/2021] [Accepted: 02/01/2021] [Indexed: 10/22/2022]
Abstract
In fetal neurosonography, aligning two-dimensional (2D) ultrasound scans to their corresponding plane in the three-dimensional (3D) space remains a challenging task. In this paper, we propose a convolutional neural network that predicts the position of 2D ultrasound fetal brain scans in 3D atlas space. Instead of purely supervised learning that requires heavy annotations for each 2D scan, we train the model by sampling 2D slices from 3D fetal brain volumes, and target the model to predict the inverse of the sampling process, resembling the idea of self-supervised learning. We propose a model that takes a set of images as input, and learns to compare them in pairs. The pairwise comparison is weighted by the attention module based on its contribution to the prediction, which is learnt implicitly during training. The feature representation for each image is thus computed by incorporating the relative position information to all the other images in the set, and is later used for the final prediction. We benchmark our model on 2D slices sampled from 3D fetal brain volumes at 18-22 weeks' gestational age. Using three evaluation metrics, namely, Euclidean distance, plane angles and normalized cross correlation, which account for both the geometric and appearance discrepancy between the ground-truth and prediction, in all these metrics, our model outperforms a baseline model by as much as 23%, when the number of input images increases. We further demonstrate that our model generalizes to (i) real 2D standard transthalamic plane images, achieving comparable performance as human annotations, as well as (ii) video sequences of 2D freehand fetal brain scans.
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Affiliation(s)
- Pak-Hei Yeung
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.
| | - Moska Aliasi
- Division of Fetal Medicine, Department of Obstetrics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Aris T Papageorghiou
- Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Oxford, United Kingdom
| | - Monique Haak
- Division of Fetal Medicine, Department of Obstetrics, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Weidi Xie
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom; Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Ana I L Namburete
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
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29
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Ren JY, Zhu M, Dong SZ. Three-Dimensional Volumetric Magnetic Resonance Imaging Detects Early Alterations of the Brain Growth in Fetuses With Congenital Heart Disease. J Magn Reson Imaging 2021; 54:263-272. [PMID: 33559371 DOI: 10.1002/jmri.27526] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/29/2020] [Accepted: 12/30/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Several published studies have shown alterations of brain development in third-trimester fetuses with congenital heart disease (CHD). However, little is known about the timing and pattern of altered brain development in fetuses with CHD. PURPOSE To investigate the changes in the volume of intracranial structures in fetuses with CHD by three-dimensional (3D) volumetric magnetic resonance imaging (MRI) in the earlier stages of pregnancy (median gestational age [GA], 26 weeks). STUDY TYPE Retrospective. POPULATION Forty women carrying a fetus with CHD (including 20 fetuses with GA <26 weeks) and 120 pregnant women carrying a healthy fetus (including 50 fetuses with GA <26 weeks). FIELD STRENGTH/SEQUENCE Two-dimensional single-shot turbo spin echo sequence at 1.5 -T. ASSESSMENT Three-dimensional volumetric parameters from slice-to-volume registered images, including cortical gray matter volume (GMV), subcortical brain tissue volume (SBV), intracranial cavity volume (ICV), lateral ventricles volume (VV), cerebellum, brainstem, and extra-cerebrospinal fluid (e-CSF) were quantified by manual segmentation from one primary and two secondary observers. STATISTICAL TESTS Volumes were presented graphically with quadratic curve fitting. Scatterplots were produced mapping volumes against GA in normal and CHD fetuses. For GA <26 weeks, Z scores were calculated and Student's t-tests were conducted to compare volumes between the normal and CHD fetuses. RESULTS In fetuses with CHD GMV, SBV, cerebellum, and brainstem were significantly reduced (all P < 0.05) in early stages of pregnancy (GA <26 weeks), with differences becoming progressively greater with increasing GA. Compared with normal fetuses, e-CSF, e-CSF to ICV ratio, and VV were higher in fetuses with CHD (all P < 0.05). However, ICV volume and the GMV to SBV ratio were not significantly reduced in the CHD group (P = 0.94 and P = 0.13, respectively) during the middle gestation (GA <26 weeks). DATA CONCLUSION There appear to be alterations of brain development trajectory in CHD fetuses that can be detected by 3D volumetric MRI in the earlier stages of pregnancy. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Jing-Ya Ren
- Department of Radiology, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ming Zhu
- Department of Radiology, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Su-Zhen Dong
- Department of Radiology, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
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30
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Ho A, Hutter J, Slator P, Jackson L, Seed PT, Mccabe L, Al-Adnani M, Marnerides A, George S, Story L, Hajnal JV, Rutherford M, Chappell LC. Placental magnetic resonance imaging in chronic hypertension: A case-control study. Placenta 2021; 104:138-145. [PMID: 33341490 PMCID: PMC7921773 DOI: 10.1016/j.placenta.2020.12.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 11/28/2020] [Accepted: 12/09/2020] [Indexed: 11/21/2022]
Abstract
INTRODUCTION We aimed to explore the use of magnetic resonance imaging (MRI) in vivo as a tool to elucidate the placental phenotype in women with chronic hypertension. METHODS In case-control study, women with chronic hypertension and those with uncomplicated pregnancies were imaged using either a 3T Achieva or 1.5T Ingenia scanner. T2-weighted images, diffusion weighted and T1/T2* relaxometry data was acquired. Placental T2*, T1 and apparent diffusion coefficient (ADC) maps were calculated. RESULTS 129 women (43 with chronic hypertension and 86 uncomplicated pregnancies) were imaged at a median of 27.7 weeks' gestation (interquartile range (IQR) 23.9-32.1) and 28.9 (IQR 26.1-32.9) respectively. Visual analysis of T2-weighted imaging demonstrated placentae to be either appropriate for gestation or to have advanced lobulation in women with chronic hypertension, resulting in a greater range of placental mean T2* values for a given gestation, compared to gestation-matched controls. Both skew and kurtosis (derived from histograms of T2* values across the whole placenta) increased with advancing gestational age at imaging in healthy pregnancies; women with chronic hypertension had values overlapping those in the control group range. Upon visual assessment, the mean ADC declined in the third trimester, with a corresponding decline in placental mean T2* values and showed an overlap of values between women with chronic hypertension and the control group. DISCUSSION A combined placental MR examination including T2 weighted imaging, T2*, T1 mapping and diffusion imaging demonstrates varying placental phenotypes in a cohort of women with chronic hypertension, showing overlap with the control group.
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Affiliation(s)
- Alison Ho
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom.
| | - Jana Hutter
- Centre for the Developing Brain, King's College London, London, United Kingdom; Biomedical Engineering Department, King's College London, London, United Kingdom
| | - Paddy Slator
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Laurence Jackson
- Centre for the Developing Brain, King's College London, London, United Kingdom; Biomedical Engineering Department, King's College London, London, United Kingdom
| | - Paul T Seed
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
| | - Laura Mccabe
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Mudher Al-Adnani
- Department of Cellular Pathology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Andreas Marnerides
- Department of Cellular Pathology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Simi George
- Department of Cellular Pathology, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Lisa Story
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, King's College London, London, United Kingdom; Biomedical Engineering Department, King's College London, London, United Kingdom
| | - Mary Rutherford
- Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Lucy C Chappell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, United Kingdom
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Khawam M, de Dumast P, Deman P, Kebiri H, Yu T, Tourbier S, Lajous H, Hagmann P, Maeder P, Thiran JP, Meuli R, Dunet V, Bach Cuadra M, Koob M. Fetal Brain Biometric Measurements on 3D Super-Resolution Reconstructed T2-Weighted MRI: An Intra- and Inter-observer Agreement Study. Front Pediatr 2021; 9:639746. [PMID: 34447726 PMCID: PMC8383736 DOI: 10.3389/fped.2021.639746] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/07/2021] [Indexed: 11/27/2022] Open
Abstract
We present the comparison of two-dimensional (2D) fetal brain biometry on magnetic resonance (MR) images using orthogonal 2D T2-weighted sequences (T2WSs) vs. one 3D super-resolution (SR) reconstructed volume and evaluation of the level of confidence and concordance between an experienced pediatric radiologist (obs1) and a junior radiologist (obs2). Twenty-five normal fetal brain MRI scans (18-34 weeks of gestation) including orthogonal 3-mm-thick T2WSs were analyzed retrospectively. One 3D SR volume was reconstructed per subject based on multiple series of T2WSs. The two observers performed 11 2D biometric measurements (specifying their level of confidence) on T2WS and SR volumes. Measurements were compared using the paired Wilcoxon rank sum test between observers for each dataset (T2WS and SR) and between T2WS and SR for each observer. Bland-Altman plots were used to assess the agreement between each pair of measurements. Measurements were made with low confidence in three subjects by obs1 and in 11 subjects by obs2 (mostly concerning the length of the corpus callosum on T2WS). Inter-rater intra-dataset comparisons showed no significant difference (p > 0.05), except for brain axial biparietal diameter (BIP) on T2WS and for brain and skull coronal BIP and coronal transverse cerebellar diameter (DTC) on SR. None of them remained significant after correction for multiple comparisons. Inter-dataset intra-rater comparisons showed statistical differences in brain axial and coronal BIP for both observers, skull coronal BIP for obs1, and axial and coronal DTC for obs2. After correction for multiple comparisons, only axial brain BIP remained significantly different, but differences were small (2.95 ± 1.73 mm). SR allows similar fetal brain biometry as compared to using the conventional T2WS while improving the level of confidence in the measurements and using a single reconstructed volume.
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Affiliation(s)
- Marie Khawam
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Pierre Deman
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Thomas Yu
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Sébastien Tourbier
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Hélène Lajous
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Philippe Maeder
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland.,Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Vincent Dunet
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.,CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Mériam Koob
- Department of Radiology, Lausanne University Hospital, University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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32
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Singh A, Salehi SSM, Gholipour A. Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3523-3534. [PMID: 32746102 PMCID: PMC7787194 DOI: 10.1109/tmi.2020.2998600] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient. The current process is highly operator-dependent, increases scanner usage and cost, and significantly increases the length of fetal MRI scans which makes them hard to tolerate for pregnant women. To help build automatic MRI motion tracking and navigation systems to overcome the limitations of the current process and improve fetal imaging, we have developed a new real-time image-based motion tracking method based on deep learning that learns to predict fetal motion directly from acquired images. Our method is based on a recurrent neural network, composed of spatial and temporal encoder-decoders, that infers motion parameters from anatomical features extracted from sequences of acquired slices. We compared our trained network on held-out test sets (including data with different characteristics, e.g. different fetuses scanned at different ages, and motion trajectories recorded from volunteer subjects) with networks designed for estimation as well as methods adopted to make predictions. The results show that our method outperformed alternative techniques, and achieved real-time performance with average errors of 3.5 and 8 degrees for the estimation and prediction tasks, respectively. Our real-time deep predictive motion tracking technique can be used to assess fetal movements, to guide slice acquisitions, and to build navigation systems for fetal MRI.
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33
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Roberts TA, van Amerom JFP, Uus A, Lloyd DFA, van Poppel MPM, Price AN, Tournier JD, Mohanadass CA, Jackson LH, Malik SJ, Pushparajah K, Rutherford MA, Razavi R, Deprez M, Hajnal JV. Fetal whole heart blood flow imaging using 4D cine MRI. Nat Commun 2020; 11:4992. [PMID: 33020487 PMCID: PMC7536221 DOI: 10.1038/s41467-020-18790-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 09/10/2020] [Indexed: 12/26/2022] Open
Abstract
Prenatal detection of congenital heart disease facilitates the opportunity for potentially life-saving care immediately after the baby is born. Echocardiography is routinely used for screening of morphological malformations, but functional measurements of blood flow are scarcely used in fetal echocardiography due to technical assumptions and issues of reliability. Magnetic resonance imaging (MRI) is readily used for quantification of abnormal blood flow in adult hearts, however, existing in utero approaches are compromised by spontaneous fetal motion. Here, we present and validate a novel method of MRI velocity-encoding combined with a motion-robust reconstruction framework for four-dimensional visualization and quantification of blood flow in the human fetal heart and major vessels. We demonstrate simultaneous 4D visualization of the anatomy and circulation, which we use to quantify flow rates through various major vessels. The framework introduced here could enable new clinical opportunities for assessment of the fetal cardiovascular system in both health and disease.
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Affiliation(s)
- Thomas A Roberts
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK.
| | - Joshua F P van Amerom
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Alena Uus
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - David F A Lloyd
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- Department of Congenital Heart Disease, Evelina Children's Hospital, London, SE1 7EH, UK
| | - Milou P M van Poppel
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- Department of Congenital Heart Disease, Evelina Children's Hospital, London, SE1 7EH, UK
| | - Anthony N Price
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Jacques-Donald Tournier
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Chloe A Mohanadass
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Laurence H Jackson
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Shaihan J Malik
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Kuberan Pushparajah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- Department of Congenital Heart Disease, Evelina Children's Hospital, London, SE1 7EH, UK
| | - Mary A Rutherford
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Reza Razavi
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
- Department of Congenital Heart Disease, Evelina Children's Hospital, London, SE1 7EH, UK
| | - Maria Deprez
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Joseph V Hajnal
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EH, UK
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Thomas MSC, Ojinaga Alfageme O, D'Souza H, Patkee PA, Rutherford MA, Mok KY, Hardy J, Karmiloff-Smith A. A multi-level developmental approach to exploring individual differences in Down syndrome: genes, brain, behaviour, and environment. RESEARCH IN DEVELOPMENTAL DISABILITIES 2020; 104:103638. [PMID: 32653761 PMCID: PMC7438975 DOI: 10.1016/j.ridd.2020.103638] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 03/18/2020] [Accepted: 03/20/2020] [Indexed: 05/06/2023]
Abstract
In this article, we focus on the causes of individual differences in Down syndrome (DS), exemplifying the multi-level, multi-method, lifespan developmental approach advocated by Karmiloff-Smith (1998, 2009, 2012, 2016). We evaluate the possibility of linking variations in infant and child development with variations in the (elevated) risk for Alzheimer's disease (AD) in adults with DS. We review the theoretical basis for this argument, considering genetics, epigenetics, brain, behaviour and environment. In studies 1 and 2, we focus on variation in language development. We utilise data from the MacArthur-Bates Communicative Development Inventories (CDI; Fenson et al., 2007), and Mullen Scales of Early Learning (MSEL) receptive and productive language subscales (Mullen, 1995) from 84 infants and children with DS (mean age 2;3, range 0;7 to 5;3). As expected, there was developmental delay in both receptive and expressive vocabulary and wide individual differences. Study 1 examined the influence of an environmental measure (socio-economic status as measured by parental occupation) on the observed variability. SES did not predict a reliable amount of the variation. Study 2 examined the predictive power of a specific genetic measure (apolipoprotein APOE genotype) which modulates risk for AD in adulthood. There was no reliable effect of APOE genotype, though weak evidence that development was faster for the genotype conferring greater AD risk (ε4 carriers), consistent with recent observations in infant attention (D'Souza, Mason et al., 2020). Study 3 considered the concerted effect of the DS genotype on early brain development. We describe new magnetic resonance imaging methods for measuring prenatal and neonatal brain structure in DS (e.g., volumes of supratentorial brain, cortex, cerebellar volume; Patkee et al., 2019). We establish the methodological viability of linking differences in early brain structure to measures of infant cognitive development, measured by the MSEL, as a potential early marker of clinical relevance. Five case studies are presented as proof of concept, but these are as yet too few to discern a pattern.
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Affiliation(s)
- Michael S C Thomas
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London WC1E 7HX, United Kingdom.
| | - Olatz Ojinaga Alfageme
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London WC1E 7HX, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Hana D'Souza
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London WC1E 7HX, United Kingdom; Department of Psychology & Newnham College, University of Cambridge, Cambridge CB3 9DF, United Kingdom
| | - Prachi A Patkee
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Kin Y Mok
- Department of Neurodegenerative Disease, Institute of Neurology, University College London, United Kingdom
| | - John Hardy
- Department of Neurodegenerative Disease, Institute of Neurology, University College London, United Kingdom
| | - Annette Karmiloff-Smith
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London WC1E 7HX, United Kingdom
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35
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Uus A, Zhang T, Jackson LH, Roberts TA, Rutherford MA, Hajnal JV, Deprez M. Deformable Slice-to-Volume Registration for Motion Correction of Fetal Body and Placenta MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2750-2759. [PMID: 32086200 PMCID: PMC7116020 DOI: 10.1109/tmi.2020.2974844] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable SVR scheme to allow high resolution reconstruction of the fetal body and placenta. Additionally, a robust scheme for structure-based rejection of outliers minimises the impact of registration errors. The improved performance of DSVR in comparison to SVR and patch-to-volume registration (PVR) methods is quantitatively demonstrated in simulated experiments and 20 fetal MRI datasets from 28-31 weeks gestational age (GA) range with varying degree of motion corruption. In addition, we present qualitative evaluation of 100 fetal body cases from 20-34 weeks GA range.
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36
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Sui Y, Afacan O, Gholipour A, Warfield SK. SLIMM: Slice localization integrated MRI monitoring. Neuroimage 2020; 223:117280. [PMID: 32853815 PMCID: PMC7735257 DOI: 10.1016/j.neuroimage.2020.117280] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/17/2020] [Accepted: 08/13/2020] [Indexed: 12/17/2022] Open
Abstract
Functional MRI (fMRI) is extremely challenging to perform in subjects who move because subject motion disrupts blood oxygenation level dependent (BOLD) signal measurement. It has become common to use retrospective framewise motion detection and censoring in fMRI studies to eliminate artifacts arising from motion. Data censoring results in significant loss of data and statistical power unless the data acquisition is extended to acquire more data not corrupted by motion. Acquiring more data than is necessary leads to longer than necessary scan duration, which is more expensive and may lead to additional subject non-compliance. Therefore, it is well established that real-time prospective motion monitoring is crucial to ensure data quality and reduce imaging costs. In addition, real-time monitoring of motion allows for feedback to the operator and the subject during the acquisition, to enable intervention to reduce the subject motion. The most widely used form of motion monitoring for fMRI is based on volume-to-volume registration (VVR), which quantifies motion as the misalignment between subsequent volumes. However, motion is not constrained to occur only at the boundaries of volume acquisition, but instead may occur at any time. Consequently, each slice of an fMRI acquisition may be displaced by motion, and assessment of whole volume to volume motion may be insensitive to both intra-volume and inter-volume motion that is revealed by displacement of the slices. We developed the first slice-by-slice self-navigated motion monitoring system for fMRI by developing a real-time slice-to-volume registration (SVR) algorithm. Our real-time SVR algorithm, which is the core of the system, uses a local image patch-based matching criterion along with a Levenberg-Marquardt optimizer, all accelerated via symmetric multi-processing, with interleaved and simultaneous multi-slice acquisition schemes. Extensive experimental results on real motion data demonstrated that our fast motion monitoring system, named Slice Localization Integrated MRI Monitoring (SLIMM), provides more accurate motion measurements than a VVR based approach. Therefore, SLIMM offers improved online motion monitoring which is particularly important in fMRI for challenging patient populations. Real-time motion monitoring is crucial for online data quality control and assurance, for enabling feedback to the subject and the operator to act to mitigate motion, and in adaptive acquisition strategies that aim to ensure enough data of sufficient quality is acquired without acquiring excess data.
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Affiliation(s)
- Yao Sui
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Onur Afacan
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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37
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Torres ER, Tumey TA, Dean DC, Kassahun-Yimer W, Lopez-Lambert ED, Hitchcock ME. Non-pharmacological strategies to obtain usable magnetic resonance images in non-sedated infants: Systematic review and meta-analysis. Int J Nurs Stud 2020; 106:103551. [PMID: 32294563 DOI: 10.1016/j.ijnurstu.2020.103551] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 02/12/2020] [Accepted: 02/14/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Although the use of sedation is commonly practiced to keep infants still while receiving magnetic resonance imaging, non-pharmacological strategies are a potential alternative. OBJECTIVES The purpose of this study was to determine the success rate of obtaining usable magnetic resonance images in infants with the sole use of non-pharmacological strategies. DESIGN Systematic literature review and meta-analysis SETTING: A search was conducted in PubMed, CINAHL and Cochrane Library. PARTICIPANTS Human infants from birth to 24 months of age who did not receive any sedation or anesthesia during magnetic resonance imaging METHOD: Articles that reported the success rate of obtaining usable images were included. RESULTS Of the 521 non-duplicate articles found, 58 articles were included in the systematic review with sample sizes ranging from 2-457, an average success rate of 87.8%, and an average scan time of 30 min. The most common non-pharmacological technique included feeding and swaddling infants before imaging to encourage infants to sleep during the scan. Meta-analysis performed on 53 articles comprising 3,410 infants found a success rate of 87%, but significant heterogeneity was found (I2 = 98.30%). It was more difficult to obtain usable images solely with non-pharmacological techniques if infants were critically ill or a structural magnetic resonance imaging of the brain was required. CONCLUSION Non-pharmacological techniques are effective for obtaining usable magnetic resonance imaging scans in most but not all infants. Tweetable abstract: Non-pharmacological techniques are effective for obtaining usable magnetic resonance imaging scans in most infants.
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Affiliation(s)
- Elisa R Torres
- School of Nursing, University of Mississippi Medical Center, 2500 North State Street, Jackson 39216, MS, United States.
| | - Tyler A Tumey
- Burrell College of Osteopathic Medicine, 3501 Arrowhead Dr Las Cruces, NM 88001, United States.
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison WI 53705, United States.
| | - Wondwosen Kassahun-Yimer
- Department of Data Science, University of Mississippi Medical Center, School of Population Health,2500 North State Street, Jackson, MS 39216, United States.
| | - Eloise D Lopez-Lambert
- School of Nursing, University of Mississippi Medical Center, 2500 North State Street, Jackson 39216, MS, United States
| | - Mary E Hitchcock
- Ebling Library, University of Wisconsin-Madison, 750 Highland Ave, Madison WI 53705, United States.
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Correction of out-of-FOV motion artifacts using convolutional neural network. Magn Reson Imaging 2020; 71:93-102. [PMID: 32464243 DOI: 10.1016/j.mri.2020.05.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 05/14/2020] [Indexed: 11/23/2022]
Abstract
PURPOSE Subject motion during MRI scan can result in severe degradation of image quality. Existing motion correction algorithms rely on the assumption that no information is missing during motions. However, this assumption does not hold when out-of-FOV motion happens. Currently available algorithms are not able to correct for image artifacts introduced by out-of-FOV motion. The purpose of this study is to demonstrate the feasibility of incorporating convolutional neural network (CNN) derived prior image into solving the out-of-FOV motion problem. METHODS AND MATERIALS A modified U-net network was proposed to correct out-of-FOV motion artifacts by incorporating motion parameters into the loss function. A motion model based data fidelity term was applied in combination with the CNN prediction to further improve the motion correction performance. We trained the CNN on 1113 MPRAGE images with simulated oscillating and sudden motion trajectories, and compared our algorithm to a gradient-based autofocusing (AF) algorithm in both 2D and 3D images. Additional experiment was performed to demonstrate the feasibility of transferring the networks to different dataset. We also evaluated the robustness of this algorithm by adding Gaussian noise to the motion parameters. The motion correction performance was evaluated using mean square error (NMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). RESULTS The proposed algorithm outperformed AF-based algorithm for both 2D (NMSE: 0.0066 ± 0.0009 vs 0.0141 ± 0.008, P < .01; PSNR: 29.60 ± 0.74 vs 21.71 ± 0.27, P < .01; SSIM: 0.89 ± 0.014 vs 0.73 ± 0.004, P < .01) and 3D imaging (NMSE: 0.0067 ± 0.0008 vs 0.070 ± 0.021, P < .01; PSNR: 32.40 ± 1.63 vs 22.32 ± 2.378, P < .01; SSIM: 0.89 ± 0.01 vs 0.62 ± 0.03, P < .01). Robust reconstruction was achieved with 20% data missed due to the out-of-FOV motion. CONCLUSION In conclusion, the proposed CNN-based motion correction algorithm can significantly reduce out-of-FOV motion artifacts and achieve better image quality compared to AF-based algorithm.
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39
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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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40
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Motion-corrected foetal cardiac MRI. Nat Biomed Eng 2020; 3:852-854. [PMID: 31645682 DOI: 10.1038/s41551-019-0476-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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41
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Ebner M, Wang G, Li W, Aertsen M, Patel PA, Aughwane R, Melbourne A, Doel T, Dymarkowski S, De Coppi P, David AL, Deprest J, Ourselin S, Vercauteren T. An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. Neuroimage 2020; 206:116324. [PMID: 31704293 PMCID: PMC7103783 DOI: 10.1016/j.neuroimage.2019.116324] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 09/26/2019] [Accepted: 10/29/2019] [Indexed: 12/17/2022] Open
Abstract
High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice.
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Affiliation(s)
- Michael Ebner
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | - Wenqi Li
- Nvidia, Cambridge, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Michael Aertsen
- Department of Radiology, University Hospitals KU Leuven, Leuven, Belgium
| | - Premal A Patel
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Rosalind Aughwane
- Institute for Women's Health, University College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Tom Doel
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Steven Dymarkowski
- Department of Radiology, University Hospitals KU Leuven, Leuven, Belgium
| | - Paolo De Coppi
- Institute of Child Health, University College London, London, UK
| | - Anna L David
- Institute for Women's Health, University College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Department of Obstetrics and Gynaecology, University Hospitals KU Leuven, Leuven, Belgium
| | - Jan Deprest
- Department of Obstetrics and Gynaecology, University Hospitals KU Leuven, Leuven, Belgium; Institute for Women's Health, University College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Sébastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Department of Obstetrics and Gynaecology, University Hospitals KU Leuven, Leuven, Belgium
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Abstract
In utero diffusion magnetic resonance imaging (MRI) provides unique opportunities to noninvasively study the microstructure of tissue during fetal development. A wide range of developmental processes, such as the growth of white matter tracts in the brain, the maturation of placental villous trees, or the fibers in the fetal heart remain to be studied and understood in detail. Advances in fetal interventions and surgery furthermore increase the need for ever more precise antenatal diagnosis from fetal MRI. However, the specific properties of the in utero environment, such as fetal and maternal motion, increased field-of-view, tissue interfaces and safety considerations, are significant challenges for most MRI techniques, and particularly for diffusion. Recent years have seen major improvements, driven by the development of bespoke techniques adapted to these specific challenges in both acquisition and processing. Fetal diffusion MRI, an emerging research tool, is now adding valuable novel information for both research and clinical questions. This paper will highlight specific challenges, outline strategies to target them, and discuss two main applications: fetal brain connectomics and placental maturation.
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43
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Abstract
Magnetic resonance imaging (MRI) is an appealing technology for fetal cardiovascular assessment. It can be used to visualize fetal cardiac and vascular anatomy, to quantify fetal blood flow, and to quantify fetal blood oxygen saturation and hematocrit. However, there are practical limitations to the use of conventional MRI for fetal cardiovascular assessment, including the small size and high heart rate of the human fetus, the lack of conventional cardiac gating methods to synchronize data acquisition, and the potential corruption of MRI data due to maternal respiration and unpredictable fetal movements. In this review, we discuss recent technical advances in accelerated imaging, image reconstruction, cardiac gating, and motion compensation that have enabled dynamic MRI of the fetal heart.
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44
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Patkee PA, Baburamani AA, Kyriakopoulou V, Davidson A, Avini E, Dimitrova R, Allsop J, Hughes E, Kangas J, McAlonan G, Rutherford MA. Early alterations in cortical and cerebellar regional brain growth in Down Syndrome: An in vivo fetal and neonatal MRI assessment. Neuroimage Clin 2020; 25:102139. [PMID: 31887718 DOI: 10.1101/683656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 12/15/2019] [Accepted: 12/21/2019] [Indexed: 05/22/2023]
Abstract
Down Syndrome (DS) is the most frequent genetic cause of intellectual disability with a wide spectrum of neurodevelopmental outcomes. At present, the relationship between structural brain morphology and the spectrum of cognitive phenotypes in DS, is not well understood. This study aimed to quantify the development of the fetal and neonatal brain in DS participants, with and without a congenital cardiac defect compared with a control population using dedicated, optimised and motion-corrected in vivo magnetic resonance imaging (MRI). We detected deviations in development and altered regional brain growth in the fetus with DS from 21 weeks' gestation, when compared to age-matched controls. Reduced cerebellar volume was apparent in the second trimester with significant alteration in cortical growth becoming evident during the third trimester. Developmental abnormalities in the cortex and cerebellum are likely substrates for later neurocognitive impairment, and ongoing studies will allow us to confirm the role of antenatal MRI as an early biomarker for subsequent cognitive ability in DS. In the era of rapidly developing technologies, we believe that the results of this study will assist counselling for prospective parents.
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Affiliation(s)
- Prachi A Patkee
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Ana A Baburamani
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Alice Davidson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Elhaam Avini
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom; Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, United Kingdom
| | - Joanna Allsop
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Johanna Kangas
- Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, United Kingdom
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom.
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45
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Patkee PA, Baburamani AA, Kyriakopoulou V, Davidson A, Avini E, Dimitrova R, Allsop J, Hughes E, Kangas J, McAlonan G, Rutherford MA. Early alterations in cortical and cerebellar regional brain growth in Down Syndrome: An in vivo fetal and neonatal MRI assessment. Neuroimage Clin 2019; 25:102139. [PMID: 31887718 PMCID: PMC6938981 DOI: 10.1016/j.nicl.2019.102139] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 12/15/2019] [Accepted: 12/21/2019] [Indexed: 11/25/2022]
Abstract
Down Syndrome (DS) is the most frequent genetic cause of intellectual disability with a wide spectrum of neurodevelopmental outcomes. At present, the relationship between structural brain morphology and the spectrum of cognitive phenotypes in DS, is not well understood. This study aimed to quantify the development of the fetal and neonatal brain in DS participants, with and without a congenital cardiac defect compared with a control population using dedicated, optimised and motion-corrected in vivo magnetic resonance imaging (MRI). We detected deviations in development and altered regional brain growth in the fetus with DS from 21 weeks' gestation, when compared to age-matched controls. Reduced cerebellar volume was apparent in the second trimester with significant alteration in cortical growth becoming evident during the third trimester. Developmental abnormalities in the cortex and cerebellum are likely substrates for later neurocognitive impairment, and ongoing studies will allow us to confirm the role of antenatal MRI as an early biomarker for subsequent cognitive ability in DS. In the era of rapidly developing technologies, we believe that the results of this study will assist counselling for prospective parents.
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Affiliation(s)
- Prachi A Patkee
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Ana A Baburamani
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Alice Davidson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Elhaam Avini
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom; Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, United Kingdom
| | - Joanna Allsop
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom
| | - Johanna Kangas
- Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, United Kingdom
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Science, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, SE5 8AB, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas's Hospital, London, SE1 7EH, United Kingdom.
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46
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Khalili N, Lessmann N, Turk E, Claessens N, Heus RD, Kolk T, Viergever M, Benders M, Išgum I. Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Magn Reson Imaging 2019; 64:77-89. [DOI: 10.1016/j.mri.2019.05.020] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 05/04/2019] [Accepted: 05/15/2019] [Indexed: 10/26/2022]
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47
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Anatomical and diffusion MRI brain atlases of the fetal rhesus macaque brain at 85, 110 and 135 days gestation. Neuroimage 2019; 206:116310. [PMID: 31669303 PMCID: PMC6980966 DOI: 10.1016/j.neuroimage.2019.116310] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 10/15/2019] [Accepted: 10/22/2019] [Indexed: 01/03/2023] Open
Abstract
Recent advances in image reconstruction techniques have enabled high resolution MRI studies of fetal brain development in human subjects. Rhesus macaques (Macaca mulatta) are valuable animal models for use in studies of fetal brain development due to the similarities between this species and humans in brain development and anatomy. There is a need to develop fetal brain templates for the rhesus macaque to facilitate the characterization of the normal brain growth trajectory and departures from this trajectory in rhesus models of neurodevelopmental disorders. Here we have developed unbiased population-based anatomical T2-weighted, fractional anisotropy (FA) and apparent diffusion coefficient (ADC) templates for fetal brain from MR images scanned at 3 time points over the second and third trimesters of the 168 day gestational term. Specifically, atlas images are constructed for brains at gestational ages of 85 days (G85, N = 18, 9 females), 110 days (G110, N = 10, 7 females) and 135 days (G135, N = 16, 7 females). We utilized this atlas to perform segmentation of fetal brain MR images and fetal brain volumetric and microstructure analysis. The T2-weighted template images facilitated characterization of the growth within six fetal brain regions. The template images of diffusion tensor indices provided information related to the maturation of white matter tracts. These growth trajectories are referenced to human studies of fetal brain development. Similarities in the temporal and regional patterns of brain growth over the corresponding periods of central nervous system development are identified between the two species. Atlas images are available online as a reference for registration, reconstruction, segmentation, and for longitudinal analysis of early fetal brain growth over this unique time window.
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48
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Baburamani AA, Patkee PA, Arichi T, Rutherford MA. New approaches to studying early brain development in Down syndrome. Dev Med Child Neurol 2019; 61:867-879. [PMID: 31102269 PMCID: PMC6618001 DOI: 10.1111/dmcn.14260] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/28/2019] [Indexed: 12/19/2022]
Abstract
Down syndrome is the most common genetic developmental disorder in humans and is caused by partial or complete triplication of human chromosome 21 (trisomy 21). It is a complex condition which results in multiple lifelong health problems, including varying degrees of intellectual disability and delays in speech, memory, and learning. As both length and quality of life are improving for individuals with Down syndrome, attention is now being directed to understanding and potentially treating the associated cognitive difficulties and their underlying biological substrates. These have included imaging and postmortem studies which have identified decreased regional brain volumes and histological anomalies that accompany early onset dementia. In addition, advances in genome-wide analysis and Down syndrome mouse models are providing valuable insight into potential targets for intervention that could improve neurogenesis and long-term cognition. As little is known about early brain development in human Down syndrome, we review recent advances in magnetic resonance imaging that allow non-invasive visualization of brain macro- and microstructure, even in utero. It is hoped that together these advances may enable Down syndrome to become one of the first genetic disorders to be targeted by antenatal treatments designed to 'normalize' brain development. WHAT THIS PAPER ADDS: Magnetic resonance imaging can provide non-invasive characterization of early brain development in Down syndrome. Down syndrome mouse models enable study of underlying pathology and potential intervention strategies. Potential therapies could modify brain structure and improve early cognitive levels. Down syndrome may be the first genetic disorder to have targeted therapies which alter antenatal brain development.
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Affiliation(s)
- Ana A Baburamani
- Centre for the Developing BrainDepartment of Perinatal Imaging and HealthSchool of Biomedical Engineering & Imaging SciencesKing's College LondonKing's Health PartnersSt Thomas’ HospitalLondonUK
| | - Prachi A Patkee
- Centre for the Developing BrainDepartment of Perinatal Imaging and HealthSchool of Biomedical Engineering & Imaging SciencesKing's College LondonKing's Health PartnersSt Thomas’ HospitalLondonUK
| | - Tomoki Arichi
- Centre for the Developing BrainDepartment of Perinatal Imaging and HealthSchool of Biomedical Engineering & Imaging SciencesKing's College LondonKing's Health PartnersSt Thomas’ HospitalLondonUK,Department of BioengineeringImperial College LondonLondonUK,Children's NeurosciencesEvelina London Children's HospitalLondonUK
| | - Mary A Rutherford
- Centre for the Developing BrainDepartment of Perinatal Imaging and HealthSchool of Biomedical Engineering & Imaging SciencesKing's College LondonKing's Health PartnersSt Thomas’ HospitalLondonUK
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49
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Ebner M, Patel PA, Atkinson D, Caselton L, Firmin L, Amin Z, Bainbridge A, De Coppi P, Taylor SA, Ourselin S, Chouhan MD, Vercauteren T. Super-resolution for upper abdominal MRI: Acquisition and post-processing protocol optimization using brain MRI control data and expert reader validation. Magn Reson Med 2019; 82:1905-1919. [PMID: 31264270 PMCID: PMC6742507 DOI: 10.1002/mrm.27852] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/23/2019] [Accepted: 05/20/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE Magnetic resonance (MR) cholangiopancreatography (MRCP) is an established specialist method for imaging the upper abdomen and biliary/pancreatic ducts. Due to limitations of either MR image contrast or low through-plane resolution, patients may require further evaluation with contrast-enhanced computed tomography (CT) images. However, CT fails to offer the high tissue-ductal-vessel contrast-to-noise ratio available on T2-weighted MR imaging. METHODS MR super-resolution reconstruction (SRR) frameworks have the potential to provide high-resolution visualizations from multiple low through-plane resolution single-shot T2-weighted (SST2W) images as currently used during MRCP studies. Here, we (i) optimize the source image acquisition protocols by establishing the ideal number and orientation of SST2W series for MRCP SRR generation, (ii) optimize post-processing protocols for two motion correction candidate frameworks for MRCP SRR, and (iii) perform an extensive validation of the overall potential of upper abdominal SRR, using four expert readers with subspeciality interest in hepato-pancreatico-biliary imaging. RESULTS Obtained SRRs show demonstrable advantages over traditional SST2W MRCP data in terms of anatomical clarity and subjective radiologists' preference scores for a range of anatomical regions that are especially critical for the management of cancer patients. CONCLUSIONS Our results underline the potential of using SRR alongside traditional MRCP data for improved clinical diagnosis.
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Affiliation(s)
- Michael Ebner
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London (UCL), London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Premal A Patel
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London (UCL), London, United Kingdom
| | | | - Lucy Caselton
- Centre for Medical Imaging, UCL, London, United Kingdom
| | - Louisa Firmin
- Centre for Medical Imaging, UCL, London, United Kingdom
| | - Zahir Amin
- Centre for Medical Imaging, UCL, London, United Kingdom
| | - Alan Bainbridge
- Department of Medical Physics and Biomedical Engineering, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | | | | | - Sébastien Ourselin
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London (UCL), London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Tom Vercauteren
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London (UCL), London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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50
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van Amerom JFP, Lloyd DFA, Deprez M, Price AN, Malik SJ, Pushparajah K, van Poppel MPM, Rutherford MA, Razavi R, Hajnal JV. Fetal whole-heart 4D imaging using motion-corrected multi-planar real-time MRI. Magn Reson Med 2019; 82:1055-1072. [PMID: 31081250 PMCID: PMC6617816 DOI: 10.1002/mrm.27798] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 03/24/2019] [Accepted: 04/13/2019] [Indexed: 12/17/2022]
Abstract
Purpose To develop an MRI acquisition and reconstruction framework for volumetric cine visualization of the fetal heart and great vessels in the presence of maternal and fetal motion. Methods Four‐dimensional (4D) depiction was achieved using a highly‐accelerated multi‐planar real‐time balanced steady‐state free precession acquisition combined with retrospective image‐domain techniques for motion correction, cardiac synchronization and outlier rejection. The framework was validated using a numerical phantom and evaluated in a study of 20 mid‐ to late‐gestational age human fetal subjects (23‐33 weeks gestational age). Reconstructed MR data were compared with matched ultrasound. A preliminary assessment of flow‐sensitive reconstruction using the velocity information encoded in the phase of real‐time images is included. Results Reconstructed 4D data could be visualized in any two‐dimensional plane without the need for highly specific scan plane prescription prior to acquisition or for maternal breath hold to minimize motion. Reconstruction was fully automated aside from user‐specified masks of the fetal heart and chest. The framework proved robust when applied to fetal data and simulations confirmed that spatial and temporal features could be reliably recovered. Evaluation suggested the reconstructed framework has the potential to be used for comprehensive assessment of the fetal heart, either as an adjunct to ultrasound or in combination with other MRI techniques. Conclusions The proposed methods show promise as a framework for motion‐compensated 4D assessment of the fetal heart and great vessels.
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Affiliation(s)
- Joshua F P van Amerom
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - David F A Lloyd
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.,Department of Congenital Heart Disease, Evelina Children's Hospital, London, United Kingdom
| | - Maria Deprez
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Shaihan J Malik
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Kuberan Pushparajah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.,Department of Congenital Heart Disease, Evelina Children's Hospital, London, United Kingdom
| | - Milou P M van Poppel
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.,Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Reza Razavi
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.,Department of Congenital Heart Disease, Evelina Children's Hospital, London, United Kingdom
| | - Joseph V Hajnal
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.,Centre for the Developing Brain, King's College London, London, United Kingdom
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