1
|
Calixto C, Taymourtash A, Karimi D, Snoussi H, Velasco-Annis C, Jaimes C, Gholipour A. Advances in Fetal Brain Imaging. Magn Reson Imaging Clin N Am 2024; 32:459-478. [PMID: 38944434 PMCID: PMC11216711 DOI: 10.1016/j.mric.2024.03.004] [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] [Indexed: 07/01/2024]
Abstract
Over the last 20 years, there have been remarkable developments in fetal brain MR imaging analysis methods. This article delves into the specifics of structural imaging, diffusion imaging, functional MR imaging, and spectroscopy, highlighting the latest advancements in motion correction, fetal brain development atlases, and the challenges and innovations. Furthermore, this article explores the clinical applications of these advanced imaging techniques in comprehending and diagnosing fetal brain development and abnormalities.
Collapse
Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
| | - Athena Taymourtash
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Spitalgasse 23, Wien 1090, Austria
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Haykel Snoussi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Camilo Jaimes
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02215, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| |
Collapse
|
2
|
George E, Jaimes C, Xu D, Kasprian G, Glenn OA. How to Perform Fetal MR Imaging. Magn Reson Imaging Clin N Am 2024; 32:443-457. [PMID: 38944433 DOI: 10.1016/j.mric.2024.02.008] [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] [Indexed: 07/01/2024]
Abstract
This article provides the readers with practical guidance on how to perform fetal MR imaging, including technical considerations such as scanner field strength and use of appropriate radiofrequency receive coils, and summarizes the role, strengths, and limitations of the various MR imaging sequences. The authors review the various factors to consider in scan preparation, including study indication, timing, maternal preparation, and the creation of an institutional fetal imaging protocol. Additional factors that go into scan optimization during acquisition including prioritizing maternal comfort and ways to troubleshoot various artifacts that maybe encountered in fetal imaging are discussed.
Collapse
Affiliation(s)
- Elizabeth George
- Department of Radiology and Biomedical Imaging, University of California, 505 Parnassus Avenue, San Francisco, CA 94143, USA.
| | - Camilo Jaimes
- Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, 1700 4th Street BH303B, San Francisco, CA 94143, USA
| | - Gregor Kasprian
- Division of Neuroradiology, Department of Radiology and Biomedical Imaging, Medical University of Vienna, Währinger Gürtel 18-21, Vienna 1090, Austria
| | - Orit A Glenn
- Department of Radiology and Biomedical Imaging, University of California, 505 Parnassus Avenue, San Francisco, CA 94143, USA
| |
Collapse
|
3
|
Sanchez T, Esteban O, Gomez Y, Pron A, Koob M, Dunet V, Girard N, Jakab A, Eixarch E, Auzias G, Bach Cuadra M. FetMRQC: A robust quality control system for multi-centric fetal brain MRI. Med Image Anal 2024; 97:103282. [PMID: 39053168 DOI: 10.1016/j.media.2024.103282] [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: 12/15/2023] [Revised: 06/28/2024] [Accepted: 07/15/2024] [Indexed: 07/27/2024]
Abstract
Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts' ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC's predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain.
Collapse
Affiliation(s)
- Thomas Sanchez
- CIBM - Center for Biomedical Imaging, Switzerland; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
| | - Oscar Esteban
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Yvan Gomez
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Spain; Department Woman-Mother-Child, CHUV, Lausanne, Switzerland
| | - Alexandre Pron
- Aix-Marseille Université, CNRS, Institut de Neurosciences de La Timone, Marseilles, France
| | - Mériam Koob
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Vincent Dunet
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nadine Girard
- Aix-Marseille Université, CNRS, Institut de Neurosciences de La Timone, Marseilles, France; Service de Neuroradiologie Diagnostique et Interventionnelle, Hôpital Timone, AP-HM, Marseilles, France
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland; Research Priority Project Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
| | - Elisenda Eixarch
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Spain; IDIBAPS and CIBERER, Barcelona, Spain
| | - Guillaume Auzias
- Aix-Marseille Université, CNRS, Institut de Neurosciences de La Timone, Marseilles, France
| | - Meritxell Bach Cuadra
- CIBM - Center for Biomedical Imaging, Switzerland; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| |
Collapse
|
4
|
Xu Y, Li J, Feng X, Qing K, Wu D, Zhao L. Efficient segmentation of fetal brain MRI based on the physical resolution. Med Phys 2024. [PMID: 39008780 DOI: 10.1002/mp.17306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/25/2024] [Accepted: 05/28/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND The image resolution of fetal brain magnetic resonance imaging (MRI) is a critical factor in brain development measures, which is mainly determined by the physical resolution configured in the MRI sequence. However, fetal brain MRI are commonly reconstructed to 3D images with a higher apparent resolution, compared to the original physical resolution. PURPOSE This work is to demonstrate that accurate segmentation can be achieved based on the MRI physical resolution, and the high apparent resolution segmentation can be achieved by a simple deep learning module. METHODS This retrospective study included 150 adult and 80 fetal brain MRIs. The adult brain MRIs were acquired at a high physical resolution, which were downsampled to visualize and quantify its impacts on the segmentation accuracy. The physical resolution of fetal images was estimated based on MRI acquisition settings and the images were downsampled accordingly before segmentation and restored using multiple upsampling strategies. Segmentation accuracy of ConvNet models were evaluated on the original and downsampled images. Dice coefficients were calculated, and compared to the original data. RESULTS When the apparent resolution was higher than the physical resolution, the accuracy of fetal brain segmentation had negligible degradation (accuracy reduced by 0.26%, 1.1%, and 1.8% with downsampling factors of 4/3, 2, and 4 in each dimension, without significant differences from the original data). Using a downsampling factor of 4 in each dimension, the proposed method provided 7× smaller and 10× faster models. CONCLUSION Efficient and accurate fetal brain segmentation models can be developed based on the physical resolution of MRI acquisitions.
Collapse
Affiliation(s)
- Yunzhi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiaxin Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Xue Feng
- Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Kun Qing
- Department of Radiation Oncology, City of Hope National Center, Duarte, California, USA
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| |
Collapse
|
5
|
Faghihpirayesh R, Karimi D, Erdoğmuş D, Gholipour A. Fetal-BET: Brain Extraction Tool for Fetal MRI. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:551-562. [PMID: 39157057 PMCID: PMC11329220 DOI: 10.1109/ojemb.2024.3426969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/09/2024] [Accepted: 07/07/2024] [Indexed: 08/20/2024] Open
Abstract
Goal: In this study, we address the critical challenge of fetal brain extraction from MRI sequences. Fetal MRI has played a crucial role in prenatal neurodevelopmental studies and in advancing our knowledge of fetal brain development in-utero. Fetal brain extraction is a necessary first step in most computational fetal brain MRI pipelines. However, it poses significant challenges due to 1) non-standard fetal head positioning, 2) fetal movements during examination, and 3) vastly heterogeneous appearance of the developing fetal brain and the neighboring fetal and maternal anatomy across gestation, and with various sequences and scanning conditions. Development of a machine learning method to effectively address this task requires a large and rich labeled dataset that has not been previously available. Currently, there is no method for accurate fetal brain extraction on various fetal MRI sequences. Methods: In this work, we first built a large annotated dataset of approximately 72,000 2D fetal brain MRI images. Our dataset covers the three common MRI sequences including T2-weighted, diffusion-weighted, and functional MRI acquired with different scanners. These data include images of normal and pathological brains. Using this dataset, we developed and validated deep learning methods, by exploiting the power of the U-Net style architectures, the attention mechanism, feature learning across multiple MRI modalities, and data augmentation for fast, accurate, and generalizable automatic fetal brain extraction. Results: Evaluations on independent test data, including data available from other centers, show that our method achieves accurate brain extraction on heterogeneous test data acquired with different scanners, on pathological brains, and at various gestational stages. Conclusions:By leveraging rich information from diverse multi-modality fetal MRI data, our proposed deep learning solution enables precise delineation of the fetal brain on various fetal MRI sequences. The robustness of our deep learning model underscores its potential utility for fetal brain imaging.
Collapse
Affiliation(s)
- Razieh Faghihpirayesh
- Electrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
- Radiology DepartmentBoston Children's Hospital, and Harvard Medical SchoolBostonMA02115USA
| | - Davood Karimi
- Radiology DepartmentBoston Children's Hospital, and Harvard Medical SchoolBostonMA02115USA
| | - Deniz Erdoğmuş
- Electrical and Computer Engineering DepartmentNortheastern UniversityBostonMA02115USA
| | - Ali Gholipour
- Radiology DepartmentBoston Children's Hospital, and Harvard Medical SchoolBostonMA02115USA
| |
Collapse
|
6
|
Snoussi H, Karimi D, Afacan O, Utkur M, Gholipour A. HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI. ARXIV 2024:arXiv:2406.20042v1. [PMID: 38979484 PMCID: PMC11230346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Diffusion magnetic resonance imaging (dMRI) is pivotal for probing the microstructure of the rapidly-developing fetal brain. However, fetal motion during scans and its interaction with magnetic field inhomogeneities result in artifacts and data scattering across spatial and angular domains. The effects of those artifacts are more pronounced in high-angular resolution fetal dMRI, where signal-to-noise ratio is very low. Those effects lead to biased estimates and compromise the consistency and reliability of dMRI analysis. This work presents HAITCH, the first and the only publicly available tool to correct and reconstruct multi-shell high-angular resolution fetal dMRI data. HAITCH offers several technical advances that include a blip-reversed dual-echo acquisition for dynamic distortion correction, advanced motion correction for model-free and robust reconstruction, optimized multi-shell design for enhanced information capture and increased tolerance to motion, and outlier detection for improved reconstruction fidelity. The framework is open-source, flexible, and can be used to process any type of fetal dMRI data including single-echo or single-shell acquisitions, but is most effective when used with multi-shell multi-echo fetal dMRI data that cannot be processed with any of the existing tools. Validation experiments on real fetal dMRI scans demonstrate significant improvements and accurate correction across diverse fetal ages and motion levels. HAITCH successfully removes artifacts and reconstructs high-fidelity fetal dMRI data suitable for advanced diffusion modeling, including fiber orientation distribution function estimation. These advancements pave the way for more reliable analysis of the fetal brain microstructure and tractography under challenging imaging conditions.
Collapse
Affiliation(s)
- Haykel Snoussi
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Davood Karimi
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Onur Afacan
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Mustafa Utkur
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Ali Gholipour
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| |
Collapse
|
7
|
Ciceri T, Casartelli L, Montano F, Conte S, Squarcina L, Bertoldo A, Agarwal N, Brambilla P, Peruzzo D. Fetal brain MRI atlases and datasets: A review. Neuroimage 2024; 292:120603. [PMID: 38588833 DOI: 10.1016/j.neuroimage.2024.120603] [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: 11/03/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024] Open
Abstract
Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.
Collapse
Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Florian Montano
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Stefania Conte
- Psychology Department, State University of New York at Binghamton, New York, USA
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, 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 Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| |
Collapse
|
8
|
Zhang W, Zhang X, Li L, Liao L, Zhao F, Zhong T, Pei Y, Xu X, Yang C, Zhang H, Li G. A joint brain extraction and image quality assessment framework for fetal brain MRI slices. Neuroimage 2024; 290:120560. [PMID: 38431181 DOI: 10.1016/j.neuroimage.2024.120560] [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: 10/13/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024] Open
Abstract
Brain extraction and image quality assessment are two fundamental steps in fetal brain magnetic resonance imaging (MRI) 3D reconstruction and quantification. However, the randomness of fetal position and orientation, the variability of fetal brain morphology, maternal organs around the fetus, and the scarcity of data samples, all add excessive noise and impose a great challenge to automated brain extraction and quality assessment of fetal MRI slices. Conventionally, brain extraction and quality assessment are typically performed independently. However, both of them focus on the brain image representation, so they can be jointly optimized to ensure the network learns more effective features and avoid overfitting. To this end, we propose a novel two-stage dual-task deep learning framework with a brain localization stage and a dual-task stage for joint brain extraction and quality assessment of fetal MRI slices. Specifically, the dual-task module compactly contains a feature extraction module, a quality assessment head and a segmentation head with feature fusion for simultaneous brain extraction and quality assessment. Besides, a transformer architecture is introduced into the feature extraction module and the segmentation head. We utilize a multi-step training strategy to guarantee a stable and successful training of all modules. Finally, we validate our method by a 5-fold cross-validation and ablation study on a dataset with fetal brain MRI slices in different qualities, and perform a cross-dataset validation in addition. Experiments show that the proposed framework achieves very promising performance.
Collapse
Affiliation(s)
- Wenhao Zhang
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Xin Zhang
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China.
| | - Lingyi Li
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Lufan Liao
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Tao Zhong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Yuchen Pei
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Xiangmin Xu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
| | - Chaoxiang Yang
- Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
| |
Collapse
|
9
|
Lamon S, de Dumast P, Sanchez T, Dunet V, Pomar L, Vial Y, Koob M, Bach Cuadra M. Assessment of fetal corpus callosum biometry by 3D super-resolution reconstructed T2-weighted magnetic resonance imaging. Front Neurol 2024; 15:1358741. [PMID: 38595845 PMCID: PMC11002102 DOI: 10.3389/fneur.2024.1358741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
Objective To assess the accuracy of corpus callosum (CC) biometry, including sub-segments, using 3D super-resolution fetal brain MRI (SR) compared to 2D or 3D ultrasound (US) and clinical low-resolution T2-weighted MRI (T2WS). Method Fetal brain biometry was conducted by two observers on 57 subjects [21-35 weeks of gestational age (GA)], including 11 cases of partial CC agenesis. Measures were performed by a junior observer (obs1) on US, T2WS and SR and by a senior neuroradiologist (obs2) on T2WS and SR. CC biometric regression with GA was established. Statistical analysis assessed agreement within and between modalities and observers. Results This study shows robust SR to US concordance across gestation, surpassing T2WS. In obs1, SR aligns with US, except for genu and CC length (CCL), enhancing splenium visibility. In obs2, SR closely corresponds to US, differing in rostrum and CCL. The anterior CC (rostrum and genu) exhibits higher variability. SR's regression aligns better with literature (US) for CCL, splenium and body than T2WS. SR is the method with the least missing values. Conclusion SR yields CC biometry akin to US (excluding anterior CC). Thanks to superior 3D visualization and better through plane spatial resolution, SR allows to perform CC biometry more frequently than T2WS.
Collapse
Affiliation(s)
- Samuel Lamon
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Ultrasound and Fetal Medicine, Department Woman-Mother-Child, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Thomas Sanchez
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Vincent Dunet
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Léo Pomar
- Ultrasound and Fetal Medicine, Department Woman-Mother-Child, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Yvan Vial
- Ultrasound and Fetal Medicine, Department Woman-Mother-Child, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Mériam Koob
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| |
Collapse
|
10
|
Specktor-Fadida B, Link-Sourani D, Rabinowich A, Miller E, Levchakov A, Avisdris N, Ben-Sira L, Hiersch L, Joskowicz L, Ben-Bashat D. Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility. Eur Radiol 2024; 34:2072-2083. [PMID: 37658890 DOI: 10.1007/s00330-023-10038-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity to detect fetuses with growth restriction (FGR). METHODS Retrospective data of 348 fetuses with gestational age (GA) of 19-39 weeks were included: 249 normal appropriate for GA (AGA), 19 FGR, and 80 Other (having various imaging abnormalities). A fetal whole-body segmentation model with a quality estimation module was developed and evaluated in 169 cases. The method was evaluated for its repeatability (repeated scans within the same scanner, n = 22), reproducibility (different scanners, n = 6), and accuracy (compared with birth weight, n = 7). A normal MRI-based growth chart was derived. RESULTS The method achieved a Dice = 0.973, absolute volume difference ratio (VDR) = 1.8% and VDR mean difference = 0.75% ([Formula: see text]: - 3.95%, 5.46), and high agreement with the gold standard. The method achieved a repeatability coefficient = 4.01%, ICC = 0.99, high reproducibility with a mean difference = 2.21% ([Formula: see text]: - 1.92%, 6.35%), and high accuracy with a mean difference between estimated fetal weight (EFW) and birth weight of - 0.39% ([Formula: see text]: - 8.23%, 7.45%). A normal growth chart (n = 246) was consistent with four ultrasound charts. EFW based on MRI correctly predicted birth-weight percentiles for all 18 fetuses ≤ 10thpercentile and for 14 out of 17 FGR fetuses below the 3rd percentile. Six fetuses referred to MRI as AGA were found to be < 3rd percentile. CONCLUSIONS The proposed method for automatic MRI-based EFW demonstrated high performance and sensitivity to identify FGR fetuses. CLINICAL RELEVANCE STATEMENT Results from this study support the use of the automatic fetal weight estimation method based on MRI for the assessment of fetal development and to detect fetuses at risk for growth restriction. KEY POINTS • An AI-based segmentation method with a quality assessment module for fetal weight estimation based on MRI was developed, achieving high repeatability, reproducibility, and accuracy. • An MRI-based fetal weight growth chart constructed from a large cohort of normal and appropriate gestational-age fetuses is proposed. • The method showed a high sensitivity for the diagnosis of small fetuses suspected of growth restriction.
Collapse
Affiliation(s)
- Bella Specktor-Fadida
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | | | - Aviad Rabinowich
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Elka Miller
- Department of Medical Imaging, The Hospital for Sick Children, University of Toronto, Toronto, Canada
- Department of Medical Imaging, CHEO, University of Ottawa, Ottawa, Canada
| | - Anna Levchakov
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Netanell Avisdris
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Liat Ben-Sira
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Liran Hiersch
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Obstetrics and Gynecology, Lis Hospital for Women's Health, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dafna Ben-Bashat
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| |
Collapse
|
11
|
Zhang M, Arango N, Arefeen Y, Guryev G, Stockmann JP, White J, Adalsteinsson E. Stochastic-offset-enhanced restricted slice excitation and 180° refocusing designs with spatially non-linear ΔB 0 shim array fields. Magn Reson Med 2023; 90:2572-2591. [PMID: 37667645 PMCID: PMC10699120 DOI: 10.1002/mrm.29827] [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: 01/19/2023] [Revised: 06/30/2023] [Accepted: 07/26/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Developing a general framework with a novel stochastic offset strategy for the design of optimized RF pulses and time-varying spatially non-linear ΔB0 shim array fields for restricted slice excitation and refocusing with refined magnetization profiles within the intervals of the fixed voxels. METHODS Our framework uses the decomposition property of the Bloch equations to enable joint design of RF-pulses and shim array fields for restricted slice excitation and refocusing with auto-differentiation optimization. Bloch simulations are performed independently on orthogonal basis vectors, Mx, My, and Mz, which enables designs for arbitrary initial magnetizations. Requirements for refocusing pulse designs are derived from the extended phase graph formalism obviating time-consuming sub-voxel isochromatic simulations to model the effects of crusher gradients. To refine resultant slice-profiles because of voxelwise optimization functions, we propose an algorithm that stochastically offsets spatial points at which loss is computed during optimization. RESULTS We first applied our proposed design framework to standard slice-selective excitation and refocusing pulses in the absence of non-linear ΔB0 shim array fields and compared them against pulses designed with Shinnar-Le Roux algorithm. Next, we demonstrated our technique in a simulated setup of fetal brain imaging in pregnancy for restricted-slice excitation and refocusing of the fetal brain. CONCLUSIONS Our proposed framework for optimizing RF pulse and time-varying spatially non-linear ΔB0 shim array fields achieve high fidelity restricted-slice excitation and refocusing for fetal MRI, which could enable zoomed fast-spin-echo-MRI and other applications.
Collapse
Affiliation(s)
- Molin Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicolas Arango
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yamin Arefeen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Georgy Guryev
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jason P. Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Charlestown, MA, USA
| | - Jacob White
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
12
|
Mufti N, Chappell J, O'Brien P, Attilakos G, Irzan H, Sokolska M, Narayanan P, Gaunt T, Humphries PD, Patel P, Whitby E, Jauniaux E, Hutchinson JC, Sebire NJ, Atkinson D, Kendall G, Ourselin S, Vercauteren T, David AL, Melbourne A. Use of super resolution reconstruction MRI for surgical planning in Placenta accreta spectrum disorder: Case series. Placenta 2023; 142:36-45. [PMID: 37634372 PMCID: PMC10937261 DOI: 10.1016/j.placenta.2023.08.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/23/2023] [Accepted: 08/17/2023] [Indexed: 08/29/2023]
Abstract
INTRODUCTION Comprehensive imaging using ultrasound and MRI of placenta accreta spectrum (PAS) aims to prevent catastrophic haemorrhage and maternal death. Standard MRI of the placenta is limited by between-slice motion which can be mitigated by super-resolution reconstruction (SRR) MRI. We applied SRR in suspected PAS cases to determine its ability to enhance anatomical placental assessment and predict adverse maternal outcome. METHODS Suspected PAS patients (n = 22) underwent MRI at a gestational age (weeks + days) of (32+3±3+2, range (27+1-38+6)). SRR of the placental-myometrial-bladder interface involving rigid motion correction of acquired MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume, was achieved in twelve. 2D MRI or SRR images alone, and paired data were assessed by four radiologists in three review rounds. All radiologists were blinded to results of the ultrasound, original MR image reports, case outcomes, and PAS diagnosis. A Random Forest Classification model was used to highlight the most predictive pathological MRI markers for major obstetric haemorrhage (MOH), bladder adherence (BA), and placental attachment depth (PAD). RESULTS At delivery, four patients had placenta praevia with no abnormal attachment, two were clinically diagnosed with PAS, and six had histopathological PAS confirmation. Pathological MRI markers (T2-dark intraplacental bands, and loss of retroplacental T2-hypointense line) predicting MOH were more visible using SRR imaging (accuracy 0.73), in comparison to 2D MRI or paired imaging. Bladder wall interruption, predicting BA, was only easily detected by paired imaging (accuracy 0.72). Better detection of certain pathological markers predicting PAD was found using 2D MRI (placental bulge and myometrial thinning (accuracy 0.81)), and SRR (loss of retroplacental T2-hypointense line (accuracy 0.82)). DISCUSSION The addition of SRR to 2D MRI potentially improved anatomical assessment of certain pathological MRI markers of abnormal placentation that predict maternal morbidity which may benefit surgical planning.
Collapse
Affiliation(s)
- Nada Mufti
- Elizabeth Garret Anderson Institute for Women's Health, University College London, UK; School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK.
| | - Joanna Chappell
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| | | | | | - Hassna Irzan
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| | - Magda Sokolska
- Department of Medical Physics and Biomedical Engineering, University College London Hospitals, UK
| | | | - Trevor Gaunt
- University College London Hospital NHS Foundation Trust, UK
| | | | | | | | - Eric Jauniaux
- Elizabeth Garret Anderson Institute for Women's Health, University College London, UK; University College London Hospital NHS Foundation Trust, UK
| | | | | | - David Atkinson
- Centre for Medical Imaging, University College London, UK
| | - Giles Kendall
- Elizabeth Garret Anderson Institute for Women's Health, University College London, UK; University College London Hospital NHS Foundation Trust, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| | - Anna L David
- Elizabeth Garret Anderson Institute for Women's Health, University College London, UK; University College London Hospital NHS Foundation Trust, UK; NIHR, University College London Hospitals BRC, UK
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences (BMEIS), King's College London, UK
| |
Collapse
|
13
|
Cavalheiro S, da Costa MDS, Barbosa MM, Suriano IC, Ottaiano AC, de Andrade Lourenção Freddi T, Ferreira NPFD, Kusano CU, Dastoli PA, Nicácio JM, Sarmento SGP, Moron AF. Fetal neurosurgery. Childs Nerv Syst 2023; 39:2899-2927. [PMID: 37606832 DOI: 10.1007/s00381-023-06109-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 07/28/2023] [Indexed: 08/23/2023]
Abstract
Among fetal surgical procedures, neurosurgery stands out due to the number of cases and the possibility of developing new procedures that can be performed in the fetal period. To perform fetal neurosurgical procedures, there is a need for specialized centers that have experts in the diagnosis of fetal pathologies and a highly complex obstetrics service with specialized maternal-fetal teams associated with a pediatric neurosurgery center with expertise in the diverse pathologies of the fetus and the central nervous system that offers multidisciplinary follow-up during postnatal life. Services that do not have these characteristics should refer their patients to these centers to obtain better treatment results. It is essential that the fetal neurosurgical procedure be performed by a pediatric neurosurgeon with extensive experience, as he will be responsible for monitoring these patients in the postnatal period and for several years. The objective of this manuscript is to demonstrate the diagnostic and treatment possibilities, in the fetal period, of some neurosurgical diseases such as hydrocephalus, tumors, occipital encephalocele, and myelomeningocele.
Collapse
Affiliation(s)
- Sergio Cavalheiro
- Department of Neurology and Neurosurgery, Universidade Federal de São Paulo, Rua Napoleão de Barros 715, 6th Floor, São Paulo, SP, 04024-002, Brazil
| | - Marcos Devanir Silva da Costa
- Department of Neurology and Neurosurgery, Universidade Federal de São Paulo, Rua Napoleão de Barros 715, 6th Floor, São Paulo, SP, 04024-002, Brazil.
| | | | - Italo Capraro Suriano
- Department of Neurology and Neurosurgery, Universidade Federal de São Paulo, Rua Napoleão de Barros 715, 6th Floor, São Paulo, SP, 04024-002, Brazil
| | - Ana Carolina Ottaiano
- Diagnostic Imaging Department, Neuroradiology Subdivision, Hospital do Coração, São Paulo, SP, Brazil
- Teleimaging, São Paulo, SP, Brazil
| | - Tomás de Andrade Lourenção Freddi
- Diagnostic Imaging Department, Neuroradiology Subdivision, Hospital do Coração, São Paulo, SP, Brazil
- Teleimaging, São Paulo, SP, Brazil
| | - Nelson Paes Fortes Diniz Ferreira
- Diagnostic Imaging Department, Neuroradiology Subdivision, Hospital do Coração, São Paulo, SP, Brazil
- Teleimaging, São Paulo, SP, Brazil
| | - Cid Ura Kusano
- Department of Fetal Medicine, Hosptial e Maternidade Santa Joana, São Paulo, SP, Brazil
| | - Patricia Alessandra Dastoli
- Department of Neurology and Neurosurgery, Universidade Federal de São Paulo, Rua Napoleão de Barros 715, 6th Floor, São Paulo, SP, 04024-002, Brazil
| | - Jardel Mendonça Nicácio
- Department of Neurology and Neurosurgery, Universidade Federal de São Paulo, Rua Napoleão de Barros 715, 6th Floor, São Paulo, SP, 04024-002, Brazil
| | | | | |
Collapse
|
14
|
Gowland PA. The Potential of Low-Field-Strength MRI for Simpler Fetal Scanning. Radiology 2023; 309:e232579. [PMID: 37847132 PMCID: PMC10623202 DOI: 10.1148/radiol.232579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/18/2023]
Affiliation(s)
- Penny A. Gowland
- From the Sir Peter Mansfield Imaging Centre, School of Physics and
Astronomy and Nottingham Biomedical Research Centre, University of Nottingham,
University Park, Nottingham NG7 2RD, England
| |
Collapse
|
15
|
Ciceri T, Squarcina L, Giubergia A, Bertoldo A, Brambilla P, Peruzzo D. Review on deep learning fetal brain segmentation from Magnetic Resonance images. Artif Intell Med 2023; 143:102608. [PMID: 37673558 DOI: 10.1016/j.artmed.2023.102608] [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: 12/21/2022] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 09/08/2023]
Abstract
Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images.
Collapse
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
| | - Alice Giubergia
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; University of Padua, Padova Neuroscience Center, Padua, 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
| |
Collapse
|
16
|
Payette K, Li HB, de Dumast P, Licandro R, Ji H, Siddiquee MMR, Xu D, Myronenko A, Liu H, Pei Y, Wang L, Peng Y, Xie J, Zhang H, Dong G, Fu H, Wang G, Rieu Z, Kim D, Kim HG, Karimi D, Gholipour A, Torres HR, Oliveira B, Vilaça JL, Lin Y, Avisdris N, Ben-Zvi O, Bashat DB, Fidon L, Aertsen M, Vercauteren T, Sobotka D, Langs G, Alenyà M, Villanueva MI, Camara O, Fadida BS, Joskowicz L, Weibin L, Yi L, Xuesong L, Mazher M, Qayyum A, Puig D, Kebiri H, Zhang Z, Xu X, Wu D, Liao K, Wu Y, Chen J, Xu Y, Zhao L, Vasung L, Menze B, Cuadra MB, Jakab A. Fetal brain tissue annotation and segmentation challenge results. Med Image Anal 2023; 88:102833. [PMID: 37267773 DOI: 10.1016/j.media.2023.102833] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 03/16/2023] [Accepted: 04/20/2023] [Indexed: 06/04/2023]
Abstract
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.
Collapse
Affiliation(s)
- Kelly Payette
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.
| | - Hongwei Bran Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Roxane Licandro
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, United States; Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab (CIR), Medical University of Vienna, Vienna, Austria
| | - Hui Ji
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | | | | | | | - Hao Liu
- Shanghai Jiaotong University, China
| | | | | | - Ying Peng
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Huiquan Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Guiming Dong
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Fu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - ZunHyan Rieu
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Hyun Gi Kim
- Department of Radiology, The Catholic University of Korea, Eunpyeong St. Mary's Hospital, Seoul 06247, South Korea
| | - Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Ali Gholipour
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Helena R Torres
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - Bruno Oliveira
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - João L Vilaça
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Yang Lin
- Department of Computer Science, Hong Kong University of Science and Technology, China
| | - Netanell Avisdris
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel; Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel
| | - Ori Ben-Zvi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel
| | - Dafna Ben Bashat
- Sagol School of Neuroscience, Tel Aviv University, Israel; Sackler Faculty of Medicine, Tel Aviv University, Israel
| | - Lucas Fidon
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Michael Aertsen
- Department of Radiology, University Hospitals Leuven, Leuven 3000, Belgium
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Mireia Alenyà
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Maria Inmaculada Villanueva
- Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Oscar Camara
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Bella Specktor Fadida
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Liao Weibin
- School of Computer Science, Beijing Institute of Technology, China
| | - Lv Yi
- School of Computer Science, Beijing Institute of Technology, China
| | - Li Xuesong
- School of Computer Science, Beijing Institute of Technology, China
| | - Moona Mazher
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | | | - Domenec Puig
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Zelin Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | | | - Yixuan Wu
- Zhejiang University, Hangzhou, China
| | | | - Yunzhi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Lana Vasung
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, United States; Department of Pediatrics, Harvard Medical School, United States
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland; University Research Priority Project Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
van Amerom JFP, Goolaub DS, Schrauben EM, Sun L, Macgowan CK, Seed M. Fetal cardiovascular blood flow MRI: techniques and applications. Br J Radiol 2023; 96:20211096. [PMID: 35687661 PMCID: PMC10321246 DOI: 10.1259/bjr.20211096] [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: 09/30/2021] [Revised: 05/11/2022] [Accepted: 05/24/2022] [Indexed: 11/05/2022] Open
Abstract
Fetal cardiac MRI is challenging due to fetal and maternal movements as well as the need for a reliable cardiac gating signal and high spatiotemporal resolution. Ongoing research and recent technical developments to address these challenges show the potential of MRI as an adjunct to ultrasound for the assessment of the fetal heart and great vessels. MRI measurements of blood flow have enabled the assessment of normal fetal circulation as well as conditions with disrupted circulations, such as congenital heart disease, along with associated organ underdevelopment and hemodynamic instability. This review provides details of the techniques used in fetal cardiovascular blood flow MRI, including single slice and volumetric imaging sequences, post-processing and analysis, along with a summary of applications in human studies and animal models.
Collapse
Affiliation(s)
- Joshua FP van Amerom
- Division of Translational Medicine, SickKids Research Institute, Toronto, Canada
| | | | - Eric M Schrauben
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | | | | | | |
Collapse
|
19
|
Whitby E, Gaunt T. Fetal lung MRI and features predicting post-natal outcome: a scoping review of the current literature. Br J Radiol 2023:20220344. [PMID: 37314838 DOI: 10.1259/bjr.20220344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023] Open
Abstract
The outcome for infants with fetal lung pathologies not only depends on the nature of the pathology, but the impact it has on the developing lungs. The main prognostic factor is the degree of pulmonary hypoplasia, but this is not detectable pre-natally. Imaging techniques aim to simulate these features with a variety of surrogate measurements, including lung volume and MRI signal intensity. Despite the complexity of the various research studies and lack of consistent methodology, this scoping review aims to summarise current applications, and promising techniques requiring further investigation.
Collapse
Affiliation(s)
- Elspeth Whitby
- University of Sheffield and Sheffield Teaching Hospitals NHS foundation Trust, England, United Kingdom
| | - Trevor Gaunt
- Department of Maternal and Fetal Medicine, University College London, London, United Kingdom
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
20
|
Pagliaccio D, Cao X, Sussman TJ. No Meta-analytic Evidence for Risks due to Prenatal Magnetic Resonance Imaging in Animal Models. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:592-598. [PMID: 36773800 PMCID: PMC10257767 DOI: 10.1016/j.bpsc.2023.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is a powerful, noninvasive tool for both clinical practice and research. Though the safety of MRI has been endorsed by many professional societies and government bodies, some concerns have remained about potential risk from prenatal MRI. Case-control animal studies of MRI scanning during gestation and effects on offspring are the most direct test available for potential risks. We performed a meta-analysis of extant animal studies of prenatal MRI examining reproductive and offspring outcomes. METHODS Relevant articles were identified through PubMed search and citation searching of known articles and review papers. Eighteen relevant studies were identified with case-control designs of prenatal scanning conducted in vivo with mammalian species using MRI-relevant field strength. Standardized mean difference effect sizes were analyzed across k = 81 outcomes assessed across 649 unexposed dams, 622 exposed dams, 3024 unexposed offspring, and 3328 exposed offspring using a multilevel meta-analytic approach that clustered effect sizes within publications. RESULTS The meta-analysis indicated no significant evidence for a deleterious effects of prenatal MRI (standardized mean difference = 0.17, 95% CI [-0.19, 0.54], t80 = 0.94, p = .35) across outcomes. Similarly, no effects were observed when separately examining the 4 most commonly assessed outcomes: birth weight, litter size, fetal viability, and physical malformations (p > .05). CONCLUSIONS Case-control mammalian animal studies indicate no significant known risks of prenatal MRI to reproductive outcomes or offspring development. This finding is largely mirrored in human research, though the lack of randomized case-control designs limits direct comparison. The current findings provide additional support to the prevailing consensus that prenatal MRI poses no known risk to offspring.
Collapse
Affiliation(s)
- David Pagliaccio
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, New York; Department of Psychiatry, Columbia University, New York, New York.
| | - Xiaohe Cao
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, New York; Department of Psychiatry, Columbia University, New York, New York
| | - Tamara J Sussman
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, New York; Department of Psychiatry, Columbia University, New York, New York
| |
Collapse
|
21
|
Moscatelli S, Leo I, Lisignoli V, Boyle S, Bucciarelli-Ducci C, Secinaro A, Montanaro C. Cardiovascular Magnetic Resonance from Fetal to Adult Life-Indications and Challenges: A State-of-the-Art Review. CHILDREN (BASEL, SWITZERLAND) 2023; 10:children10050763. [PMID: 37238311 DOI: 10.3390/children10050763] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023]
Abstract
Cardiovascular magnetic resonance (CMR) imaging offers a comprehensive, non-invasive, and radiation-free imaging modality, which provides a highly accurate and reproducible assessment of cardiac morphology and functions across a wide spectrum of cardiac conditions spanning from fetal to adult life. It minimises risks to the patient, particularly the risks associated with exposure to ionising radiation and the risk of complications from more invasive haemodynamic assessments. CMR utilises high spatial resolution and provides a detailed assessment of intracardiac and extracardiac anatomy, ventricular and valvular function, and flow haemodynamic and tissue characterisation, which aid in the diagnosis, and, hence, with the management of patients with cardiac disease. This article aims to discuss the role of CMR and the indications for its use throughout the different stages of life, from fetal to adult life.
Collapse
Affiliation(s)
- Sara Moscatelli
- Inherited Cardiovascular Diseases, Great Ormond Street, Children NHS Foundation Trust, London WC1N 3JH, UK
- Paediatric Cardiology Department, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London SW3 5NP, UK
| | - Isabella Leo
- Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy
- CMR Unit, Cardiology Department, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London SW3 5NP, UK
| | - Veronica Lisignoli
- Department of Cardiac Surgery, Cardiology, Heart and Lung Transplantation, Bambino Gesù Children's Hospital IRCCS, 00165 Rome, Italy
- Adult Congenital Heart Disease Department, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London SW3 5NP, UK
| | - Siobhan Boyle
- Adult Congenital Heart Disease Department, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London SW3 5NP, UK
- Cardiology Department, Logan Hospital, Loganlea Rd, Meadowbrook, QLD 4131, Australia
| | - Chiara Bucciarelli-Ducci
- CMR Unit, Cardiology Department, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London SW3 5NP, UK
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College University, London SW7 2BX, UK
| | - Aurelio Secinaro
- Radiology Department, Bambino Gesù Children's Hospital IRCCS, 00165 Rome, Italy
| | - Claudia Montanaro
- CMR Unit, Cardiology Department, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London SW3 5NP, UK
- Adult Congenital Heart Disease Department, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London SW3 5NP, UK
- National Heart and Lung Institute, Imperial Collage London, Dovehouse St, London SW3 6LY, UK
| |
Collapse
|
22
|
Faruk Topaloğlu Ö, Koplay M, Kılınçer A, Örgül G, Sedat Durmaz M. Quantitative measurements and morphological evaluation of fetal cardiovascular structures with fetal cardiac MRI. Eur J Radiol 2023; 163:110828. [PMID: 37059007 DOI: 10.1016/j.ejrad.2023.110828] [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: 01/11/2023] [Revised: 03/17/2023] [Accepted: 04/04/2023] [Indexed: 04/16/2023]
Abstract
PURPOSE Fetal cardiac magnetic resonance imaging (FCMR) can be used as an imaging modality in fetal cardiovascular evaluation as studied in recent years. We aimed to evaluate cardiovascular morphology using FCMR and to observe the development of cardiovascular structures according to gestational age (GA) in pregnant women. METHOD In our prospective study, 120 pregnant women between 19 and 37 weeks of gestation in whom absence of cardiac anomaly could not be excluded by ultrasonography (US) or, who were referred to us for magnetic resonance imaging (MRI) for suspected non-cardiovascular system pathology, were included. According to the axis of the fetal heart, axial, coronal, and sagittal multiplanar steady-state free precession (SSFP) and 'real time' untriggered SSFP sequence, respectively, were obtained. The morphology of the cardiovascular structures and their relationships with each other were evaluated, and their sizes were measured. RESULTS Seven cases (6.3%) contained motion artefacts that did not allow the assessment and measurement of cardiovascular morphology, and three (2.9%) cases with cardiac pathology in the analysed images were excluded from the study. The study included a total of 100 cases. Cardiac chamber diameter, heart diameter, heart length, heart area, thoracic diameter, and thoracic area were measured in all fetuses. The diameters of the aorta ascendens (Aa), aortic isthmus (Ai), aorta descendens (Ad), main pulmonary artery (MPA), ductus arteriosus (DA, superior vena cava (SVC), and inferior vena cava (IVC) were measured in all fetuses. The left pulmonary artery (LPA) was visualised in 89 patients (89%). The right PA (RPA) was visualised in 99 (99%) cases. Four pulmonary veins (PVs) were seen in 49 (49%) cases, three in 33 (33%), and two in 18 (18%). High correlation values were found for all diameter measurements performed with GW. CONCLUSION In cases where US cannot achieve adequate image quality, FCMR can contribute to diagnosis. The very short acquisition time and parallel imaging technique with the SSFP sequence allow for adequate image quality without maternal or fetal sedation.
Collapse
Affiliation(s)
| | - Mustafa Koplay
- Department of Radiology, Selcuk University, Faculty of Medicine, Konya, Turkey
| | - Abidin Kılınçer
- Department of Radiology, Selcuk University, Faculty of Medicine, Konya, Turkey
| | - Gökçen Örgül
- Department of Obstetrics and Gynecology, Selcuk University, Faculty of Medicine, Konya, Turkey
| | - Mehmet Sedat Durmaz
- Department of Radiology, Selcuk University, Faculty of Medicine, Konya, Turkey
| |
Collapse
|
23
|
Machado-Rivas F, Cortes-Albornoz MC, Afacan O, Bedoya MA, Calixto C, Choi JJ, Ruggiero M, Gholipour A, Jaimes C. Fetal MRI at 3 T: Principles to Optimize Success. Radiographics 2023; 43:e220141. [PMID: 36995947 PMCID: PMC10091224 DOI: 10.1148/rg.220141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 03/31/2023]
Abstract
Fetal MRI has emerged as a cornerstone of prenatal imaging, helping to establish the correct diagnosis in pregnancies affected by congenital anomalies. In the past decade, 3 T imaging was introduced as an alternative to increase the signal-to-noise ratio (SNR) of the pulse sequences and improve anatomic detail. However, imaging at a higher field strength is not without challenges. Many artifacts that are barely appreciable at 1.5 T are amplified at 3 T. A systematic approach to imaging at 3 T that incorporates appropriate patient positioning, a thoughtful protocol design, and sequence optimization minimizes the impact of these artifacts and allows radiologists to reap the benefits of the increased SNR. The sequences used are the same at both field strengths and include single-shot T2-weighted, balanced steady-state free-precession, three-dimensional T1-weighted spoiled gradient-echo, and echo-planar imaging. Synergistic use of these acquisitions to sample various tissue contrasts and in various planes provides valuable information about fetal anatomy and pathologic conditions. In the authors' experience, fetal imaging at 3 T outperforms imaging at 1.5 T for most indications when performed under optimal circumstances. The authors condense the cumulative experience of fetal imaging specialists and MRI technologists who practice at a large referral center into a guideline covering all major aspects of fetal MRI at 3 T, from patient preparation to image interpretation. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.
Collapse
Affiliation(s)
- Fedel Machado-Rivas
- From the Department of Radiology, Boston Children’s Hospital,
300 Longwood Ave, Boston, MA 02215 (F.M.R., M.C.C.A., O.A., M.A.B., C.C., M.R.,
A.G., C.J.); Department of Radiology, Harvard Medical School, Boston, Mass
(J.J.C.); and Department of Radiology, Cincinnati Children’s Hospital,
Cincinnati, Ohio (F.M.R., M.C.C.A., O.A., M.A.B., C.C., A.G., C.J.)
| | - Maria Camila Cortes-Albornoz
- From the Department of Radiology, Boston Children’s Hospital,
300 Longwood Ave, Boston, MA 02215 (F.M.R., M.C.C.A., O.A., M.A.B., C.C., M.R.,
A.G., C.J.); Department of Radiology, Harvard Medical School, Boston, Mass
(J.J.C.); and Department of Radiology, Cincinnati Children’s Hospital,
Cincinnati, Ohio (F.M.R., M.C.C.A., O.A., M.A.B., C.C., A.G., C.J.)
| | - Onur Afacan
- From the Department of Radiology, Boston Children’s Hospital,
300 Longwood Ave, Boston, MA 02215 (F.M.R., M.C.C.A., O.A., M.A.B., C.C., M.R.,
A.G., C.J.); Department of Radiology, Harvard Medical School, Boston, Mass
(J.J.C.); and Department of Radiology, Cincinnati Children’s Hospital,
Cincinnati, Ohio (F.M.R., M.C.C.A., O.A., M.A.B., C.C., A.G., C.J.)
| | - Maria Alejandra Bedoya
- From the Department of Radiology, Boston Children’s Hospital,
300 Longwood Ave, Boston, MA 02215 (F.M.R., M.C.C.A., O.A., M.A.B., C.C., M.R.,
A.G., C.J.); Department of Radiology, Harvard Medical School, Boston, Mass
(J.J.C.); and Department of Radiology, Cincinnati Children’s Hospital,
Cincinnati, Ohio (F.M.R., M.C.C.A., O.A., M.A.B., C.C., A.G., C.J.)
| | - Camilo Calixto
- From the Department of Radiology, Boston Children’s Hospital,
300 Longwood Ave, Boston, MA 02215 (F.M.R., M.C.C.A., O.A., M.A.B., C.C., M.R.,
A.G., C.J.); Department of Radiology, Harvard Medical School, Boston, Mass
(J.J.C.); and Department of Radiology, Cincinnati Children’s Hospital,
Cincinnati, Ohio (F.M.R., M.C.C.A., O.A., M.A.B., C.C., A.G., C.J.)
| | - Jungwhan John Choi
- From the Department of Radiology, Boston Children’s Hospital,
300 Longwood Ave, Boston, MA 02215 (F.M.R., M.C.C.A., O.A., M.A.B., C.C., M.R.,
A.G., C.J.); Department of Radiology, Harvard Medical School, Boston, Mass
(J.J.C.); and Department of Radiology, Cincinnati Children’s Hospital,
Cincinnati, Ohio (F.M.R., M.C.C.A., O.A., M.A.B., C.C., A.G., C.J.)
| | - Matthew Ruggiero
- From the Department of Radiology, Boston Children’s Hospital,
300 Longwood Ave, Boston, MA 02215 (F.M.R., M.C.C.A., O.A., M.A.B., C.C., M.R.,
A.G., C.J.); Department of Radiology, Harvard Medical School, Boston, Mass
(J.J.C.); and Department of Radiology, Cincinnati Children’s Hospital,
Cincinnati, Ohio (F.M.R., M.C.C.A., O.A., M.A.B., C.C., A.G., C.J.)
| | - Ali Gholipour
- From the Department of Radiology, Boston Children’s Hospital,
300 Longwood Ave, Boston, MA 02215 (F.M.R., M.C.C.A., O.A., M.A.B., C.C., M.R.,
A.G., C.J.); Department of Radiology, Harvard Medical School, Boston, Mass
(J.J.C.); and Department of Radiology, Cincinnati Children’s Hospital,
Cincinnati, Ohio (F.M.R., M.C.C.A., O.A., M.A.B., C.C., A.G., C.J.)
| | - Camilo Jaimes
- From the Department of Radiology, Boston Children’s Hospital,
300 Longwood Ave, Boston, MA 02215 (F.M.R., M.C.C.A., O.A., M.A.B., C.C., M.R.,
A.G., C.J.); Department of Radiology, Harvard Medical School, Boston, Mass
(J.J.C.); and Department of Radiology, Cincinnati Children’s Hospital,
Cincinnati, Ohio (F.M.R., M.C.C.A., O.A., M.A.B., C.C., A.G., C.J.)
| |
Collapse
|
24
|
Zhang X. Magnetic resonance imaging of the monkey fetal brain in utero. INVESTIGATIVE MAGNETIC RESONANCE IMAGING 2022; 26:177-190. [PMID: 36937817 PMCID: PMC10019598 DOI: 10.13104/imri.2022.26.4.177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Non-human primates (NHPs) are the closest living relatives of the human and play a critical role in investigating the effects of maternal viral infection and consumption of medicines, drugs, and alcohol on fetal development. With the advance of contemporary fast MRI techniques with parallel imaging, fetal MRI is becoming a robust tool increasingly used in clinical practice and preclinical studies to examine congenital abnormalities including placental dysfunction, congenital heart disease (CHD), and brain abnormalities non-invasively. Because NHPs are usually scanned under anesthesia, the motion artifact is reduced substantially, allowing multi-parameter MRI techniques to be used intensively to examine the fetal development in a single scanning session or longitudinal studies. In this paper, the MRI techniques for scanning monkey fetal brains in utero in biomedical research are summarized. Also, a fast imaging protocol including T2-weighted imaging, diffusion MRI, resting-state functional MRI (rsfMRI) to examine rhesus monkey fetal brains in utero on a clinical 3T scanner is introduced.
Collapse
Affiliation(s)
- Xiaodong Zhang
- EPC Imaging Center and Division of Neuropharmacology and Neurologic Diseases, Emory National Primate Research Center, Emory University, Atlanta, Georgia, 30329, USA
| |
Collapse
|
25
|
Tsujimura K, Shiohama T, Takahashi E. microRNA Biology on Brain Development and Neuroimaging Approach. Brain Sci 2022; 12:brainsci12101366. [PMID: 36291300 PMCID: PMC9599180 DOI: 10.3390/brainsci12101366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 11/22/2022] Open
Abstract
Proper brain development requires the precise coordination and orchestration of various molecular and cellular processes and dysregulation of these processes can lead to neurological diseases. In the past decades, post-transcriptional regulation of gene expression has been shown to contribute to various aspects of brain development and function in the central nervous system. MicroRNAs (miRNAs), short non-coding RNAs, are emerging as crucial players in post-transcriptional gene regulation in a variety of tissues, such as the nervous system. In recent years, miRNAs have been implicated in multiple aspects of brain development, including neurogenesis, migration, axon and dendrite formation, and synaptogenesis. Moreover, altered expression and dysregulation of miRNAs have been linked to neurodevelopmental and psychiatric disorders. Magnetic resonance imaging (MRI) is a powerful imaging technology to obtain high-quality, detailed structural and functional information from the brains of human and animal models in a non-invasive manner. Because the spatial expression patterns of miRNAs in the brain, unlike those of DNA and RNA, remain largely unknown, a whole-brain imaging approach using MRI may be useful in revealing biological and pathological information about the brain affected by miRNAs. In this review, we highlight recent advancements in the research of miRNA-mediated modulation of neuronal processes that are important for brain development and their involvement in disease pathogenesis. Also, we overview each MRI technique, and its technological considerations, and discuss the applications of MRI techniques in miRNA research. This review aims to link miRNA biological study with MRI analytical technology and deepen our understanding of how miRNAs impact brain development and pathology of neurological diseases.
Collapse
Affiliation(s)
- Keita Tsujimura
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Group of Brain Function and Development, Nagoya University Neuroscience Institute of the Graduate School of Science, Nagoya 4648602, Japan
- Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Nagoya 4648602, Japan
- Correspondence: (K.T.); (E.T.)
| | - Tadashi Shiohama
- Department of Pediatrics, Chiba University Hospital, Chiba 2608677, Japan
| | - Emi Takahashi
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Correspondence: (K.T.); (E.T.)
| |
Collapse
|
26
|
A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Sci Rep 2022; 12:8682. [PMID: 35606398 PMCID: PMC9127105 DOI: 10.1038/s41598-022-10335-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 04/05/2022] [Indexed: 11/28/2022] Open
Abstract
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation.
Collapse
|
27
|
Gagoski B, Xu J, Wighton P, Tisdall MD, Frost R, Lo WC, Golland P, van der Kouwe A, Adalsteinsson E, Grant PE. Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T. Magn Reson Med 2022; 87:1914-1922. [PMID: 34888942 PMCID: PMC8810713 DOI: 10.1002/mrm.29106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/19/2021] [Accepted: 11/12/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE Fetal brain Magnetic Resonance Imaging suffers from unpredictable and unconstrained fetal motion that causes severe image artifacts even with half-Fourier single-shot fast spin echo (HASTE) readouts. This work presents the implementation of a closed-loop pipeline that automatically detects and reacquires HASTE images that were degraded by fetal motion without any human interaction. METHODS A convolutional neural network that performs automatic image quality assessment (IQA) was run on an external GPU-equipped computer that was connected to the internal network of the MRI scanner. The modified HASTE pulse sequence sent each image to the external computer, where the IQA convolutional neural network evaluated it, and then the IQA score was sent back to the sequence. At the end of the HASTE stack, the IQA scores from all the slices were sorted, and only slices with the lowest scores (corresponding to the slices with worst image quality) were reacquired. RESULTS The closed-loop HASTE acquisition framework was tested on 10 pregnant mothers, for a total of 73 acquisitions of our modified HASTE sequence. The IQA convolutional neural network, which was successfully employed by our modified sequence in real time, achieved an accuracy of 85.2% and area under the receiver operator characteristic of 0.899. CONCLUSION The proposed acquisition/reconstruction pipeline was shown to successfully identify and automatically reacquire only the motion degraded fetal brain HASTE slices in the prescribed stack. This minimizes the overall time spent on HASTE acquisitions by avoiding the need to repeat the entire stack if only few slices in the stack are motion-degraded.
Collapse
Affiliation(s)
- Borjan Gagoski
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Junshen Xu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Paul Wighton
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert Frost
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Wei-Ching Lo
- Siemens Medical Solutions USA, Inc, Charlestown, Massachusetts, USA
| | - Polina Golland
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Andre van der Kouwe
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - P. Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
28
|
Attention-guided deep learning for gestational age prediction using fetal brain MRI. Sci Rep 2022; 12:1408. [PMID: 35082346 PMCID: PMC8791965 DOI: 10.1038/s41598-022-05468-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/05/2022] [Indexed: 12/18/2022] Open
Abstract
Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R2 score of 0.945, mean absolute error of 6.7 days, and concordance correlation coefficient of 0.970. The convolutional neural network was trained on a heterogeneous dataset of 741 developmentally normal fetal brain images ranging from 19 to 39 weeks in gestational age. We also demonstrate model performance and generalizability using independent datasets from four academic institutions across the U.S. and Turkey with R2 scores of 0.81–0.90 after minimal fine-tuning. The proposed regression algorithm provides an automated machine-enabled tool with the potential to better characterize in utero neurodevelopment and guide real-time gestational age estimation after the first trimester.
Collapse
|
29
|
Liao Y, Li X, Jia F, Ye Z, Ning G, Liu S, Li P, Fu C, Li Q, Wang S, Zhang H, Qu H. Optimization of the image contrast for the developing fetal brain using 3D radial VIBE sequence in 3 T magnetic resonance imaging. BMC Med Imaging 2022; 22:11. [PMID: 35057733 PMCID: PMC8780316 DOI: 10.1186/s12880-022-00737-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/13/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Faster and motion robust magnetic resonance imaging (MRI) sequences are desirable in fetal brain MRI. T1-weighted images are essential for evaluating fetal brain development. We optimized the radial volumetric interpolated breath-hold examination (VIBE) sequence for qualitative T1-weighted images of the fetal brain with improved image contrast and reduced motion sensitivity. MATERIALS AND METHODS This was an institutional review board-approved prospective study. Thirty-five pregnant subjects underwent fetal brain scan at 3 Tesla MRI. T1-weighted images were acquired using a 3D radial VIBE sequence with flip angles of 6º, 9º, 12º, and 15º. T1-weighted images of Cartesian VIBE sequence were acquired in three of the subjects. Qualitative assessments including image quality and motion artifact severity were evaluated. The image contrast ratio between gray and white matter were measured. Interobserver reliability and intraobserver repeatability were assessed using intraclass correlation coefficient (ICC). RESULTS Interobserver reliability and intraobserver repeatability universally revealed almost perfect agreement (ICC > 0.800). Significant differences in image quality were detected in basal ganglia (P = 0.023), central sulcus (P = 0.028), myelination (P = 0.007) and gray matter (P = 0.023) among radial VIBE with flip angles 6º, 9º, 12º, 15º. Image quality at the 9º flip angle in radial VIBE was generally better than flip angle of 15º. Radial VIBE sequence with 9º flip angle of gray matter was significantly different by gestational age (GA) before and after 28 weeks (P = 0.036). Quantified image contrast was significantly different among different flip angles, consistent with qualitative analysis of image quality. CONCLUSIONS Three-dimensional radial VIBE with 9º flip angle provides optimal, stable T1-weighted images of the fetal brain. Fetal brain structure and development can be evaluated using high-quality images obtained using this angle. However, different scanners will achieve different TRs and so the FA should be re-optimized each time a new protocol is employed.
Collapse
Affiliation(s)
- Yi Liao
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Xuesheng Li
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Fenglin Jia
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Zhijun Ye
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Gang Ning
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Sai Liu
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Pei Li
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Chuan Fu
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Qing Li
- MR Collaborations, Siemens Healthineers, Shanghai, People's Republic of China
| | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, People's Republic of China
| | - Huapeng Zhang
- MR Application, Xi'an Branch of Siemens Healthineers, Shanxi, People's Republic of China
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China.
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
| |
Collapse
|
30
|
Zhang M, Arango N, Stockmann JP, White J, Adalsteinsson E. Selective RF excitation designs enabled by time-varying spatially non-linear ΔB 0 fields with applications in fetal MRI. Magn Reson Med 2021; 87:2161-2177. [PMID: 34931714 PMCID: PMC8847339 DOI: 10.1002/mrm.29114] [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: 06/30/2021] [Revised: 10/22/2021] [Accepted: 11/17/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE To demonstrate, through numerical simulations, novel designs of spatially selective radiofrequency (RF) excitations of the fetal brain by both a restricted 2D slice and 3D inner-volume selection. These designs exploit a single-channel RF pulse, conventional gradient fields, and the spatially non-linear ΔB0 fields of a multi-coil shim array, using an auto-differentiation optimization algorithm. METHODS The design algorithm jointly optimizes the RF pulse and the time-varying ΔB0 fields, which is produced by a 64-channel multi-coil ΔB0 body array to augment the RF and the linear gradient fields, using an auto-differentiation approach. Two design targets were specified, one a 4-mm thick slice with a limited in-slice extent in one dimension ("restricted slice"), and the other a 3D inner-volume selection encompassing the fetal brain ("inner volume"). The RF duration was limited to 2 ms for the restricted slice excitation and 6 ms for the inner-volume excitation. RESULTS Excitation profiles were achieved for both the restricted slice excitation task (one-minus-minimum magnitude, 8%) within the region of interest (ROI) and (maximum-minus-zero magnitude, 8%) in the suppressed regions and the fetal brain volume excitation task (13% and 9%, respectively). CONCLUSIONS The proposed joint design of RF and time-varying, spatially non-linear ΔB0 fields achieves the target excitation profiles with short RF pulse durations and demonstrates the potential to enhance fetal MRI with multi-channel body shim arrays.
Collapse
Affiliation(s)
- Molin Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Nicolas Arango
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jason P Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Jacob White
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| |
Collapse
|
31
|
Mufti N, Ebner M, Patel P, Aertsen M, Gaunt T, Humphries PD, Bredaki FE, Hewitt R, Butler C, Sokolska M, Kendall GS, Atkinson D, Vercauteren T, Ourselin S, Pandya PP, Deprest J, Melbourne A, David AL. Super-resolution Reconstruction MRI Application in Fetal Neck Masses and Congenital High Airway Obstruction Syndrome. OTO Open 2021; 5:2473974X211055372. [PMID: 34723053 PMCID: PMC8549475 DOI: 10.1177/2473974x211055372] [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: 09/20/2021] [Accepted: 10/06/2021] [Indexed: 11/21/2022] Open
Abstract
Objective Reliable airway patency diagnosis in fetal tracheolaryngeal obstruction is crucial to select and plan ex utero intrapartum treatment (EXIT) surgery. We compared the clinical utility of magnetic resonance imaging (MRI) super-resolution reconstruction (SRR) of the trachea, which can mitigate unpredictable fetal motion effects, with standard 2-dimensional (2D) MRI for airway patency diagnosis and assessment of fetal neck mass anatomy. Study Design A single-center case series of 7 consecutive singleton pregnancies with complex upper airway obstruction (2013-2019). Setting A tertiary fetal medicine unit performing EXIT surgery. Methods MRI SRR of the trachea was performed involving rigid motion correction of acquired 2D MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume. SRR, 2D MRI, and paired data were blindly assessed by 3 radiologists in 3 experimental rounds. Results Airway patency was correctly diagnosed in 4 of 7 cases (57%) with 2D MRI as compared with 2 of 7 cases (29%) with SRR alone or paired 2D MRI and SRR. Radiologists were more confident (P = .026) in airway patency diagnosis when using 2D MRI than SRR. Anatomic clarity was higher with SRR (P = .027) or paired data (P = .041) in comparison with 2D MRI alone. Radiologists detected further anatomic details by using paired images versus 2D MRI alone (P < .001). Cognitive load, as assessed by the NASA Task Load Index, was increased with paired or SRR data in comparison with 2D MRI. Conclusion The addition of SRR to 2D MRI does not increase fetal airway patency diagnostic accuracy but does provide improved anatomic information, which may benefit surgical planning of EXIT procedures.
Collapse
Affiliation(s)
- Nada Mufti
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Michael Ebner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Premal Patel
- Radiology Department, Great Ormond Street Hospital for Children, London, UK
| | - Michael Aertsen
- Department of Radiology, University Hospitals Katholieke Universiteit, Leuven, Belgium
| | - Trevor Gaunt
- Radiology Department, Great Ormond Street Hospital for Children, London, UK.,Women's Health Division, University College London Hospitals, London, UK
| | - Paul D Humphries
- Radiology Department, Great Ormond Street Hospital for Children, London, UK
| | | | - Richard Hewitt
- Ear, Nose and Throat Department, Great Ormond Street Hospital for Children, London, UK
| | - Colin Butler
- Ear, Nose and Throat Department, Great Ormond Street Hospital for Children, London, UK
| | - Magdalena Sokolska
- Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Giles S Kendall
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK.,Women's Health Division, University College London Hospitals, London, UK
| | - David Atkinson
- Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK.,Centre for Medical Imaging, University College London, London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Pranav P Pandya
- Women's Health Division, University College London Hospitals, London, UK
| | - Jan Deprest
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK.,Department of Obstetrics and Gynaecology, University Hospitals Katholieke Universiteit, Leuven, Belgium
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK
| | - Anna L David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, London, UK.,Department of Obstetrics and Gynaecology, University Hospitals Katholieke Universiteit, Leuven, Belgium
| |
Collapse
|
32
|
Karimi D, Jaimes C, Machado-Rivas F, Vasung L, Khan S, Warfield SK, Gholipour A. Deep learning-based parameter estimation in fetal diffusion-weighted MRI. Neuroimage 2021; 243:118482. [PMID: 34455242 PMCID: PMC8573718 DOI: 10.1016/j.neuroimage.2021.118482] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 08/03/2021] [Accepted: 08/17/2021] [Indexed: 11/24/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a result, accurate and robust parameter estimation in fetal DW-MRI remains an open problem. Recently, deep learning techniques have been successfully used for DW-MRI parameter estimation in non-fetal subjects. However, none of those prior works has addressed the fetal brain because obtaining reliable fetal training data is challenging. To address this problem, in this work we propose a novel methodology that utilizes fetal scans as well as scans from prematurely-born infants. High-quality newborn scans are used to estimate accurate maps of the parameter of interest. These parameter maps are then used to generate DW-MRI data that match the measurement scheme and noise distribution that are characteristic of fetal data. In order to demonstrate the effectiveness and reliability of the proposed data generation pipeline, we used the generated data to train a convolutional neural network (CNN) to estimate color fractional anisotropy (CFA). We evaluated the trained CNN on independent sets of fetal data in terms of reconstruction accuracy, precision, and expert assessment of reconstruction quality. Results showed significantly lower reconstruction error (n=100,p<0.001) and higher reconstruction precision (n=20,p<0.001) for the proposed machine learning pipeline compared with standard estimation methods. Expert assessments on 20 fetal test scans showed significantly better overall reconstruction quality (p<0.001) and more accurate reconstruction of 11 regions of interest (p<0.001) with the proposed method.
Collapse
Affiliation(s)
- Davood Karimi
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.
| | - Camilo Jaimes
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Fedel Machado-Rivas
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Lana Vasung
- Department of Pediatrics at Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Shadab Khan
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| |
Collapse
|
33
|
Xu J, Turk EA, Grant PE, Golland P, Adalsteinsson E. STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12907:197-206. [PMID: 37103468 PMCID: PMC10129053 DOI: 10.1007/978-3-030-87234-2_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Fetal motion is unpredictable and rapid on the scale of conventional MR scan times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and dynamics of fetal function, is limited to fast imaging techniques with compromises in image quality and resolution. Super-resolution for dynamic fetal MRI is still a challenge, especially when multi-oriented stacks of image slices for oversampling are not available and high temporal resolution for recording the dynamics of the fetus or placenta is desired. Further, fetal motion makes it difficult to acquire high-resolution images for supervised learning methods. To address this problem, in this work, we propose STRESS (Spatio-Temporal Resolution Enhancement with Simulated Scans), a self-supervised super-resolution framework for dynamic fetal MRI with interleaved slice acquisitions. Our proposed method simulates an interleaved slice acquisition along the high-resolution axis on the originally acquired data to generate pairs of low- and high-resolution images. Then, it trains a super-resolution network by exploiting both spatial and temporal correlations in the MR time series, which is used to enhance the resolution of the original data. Evaluations on both simulated and in utero data show that our proposed method outperforms other self-supervised super-resolution methods and improves image quality, which is beneficial to other downstream tasks and evaluations.
Collapse
Affiliation(s)
- Junshen Xu
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Esra Abaci Turk
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Polina Golland
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
| |
Collapse
|
34
|
Hoffmann M, Abaci Turk E, Gagoski B, Morgan L, Wighton P, Tisdall MD, Reuter M, Adalsteinsson E, Grant PE, Wald LL, van der Kouwe AJW. Rapid head-pose detection for automated slice prescription of fetal-brain MRI. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021; 31:1136-1154. [PMID: 34421216 PMCID: PMC8372849 DOI: 10.1002/ima.22563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 01/29/2021] [Accepted: 02/09/2021] [Indexed: 06/13/2023]
Abstract
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment. As motion limits acquisitions to thick slices that preclude retrospective resampling, technologists repeat ~55-second stack-of-slices scans (HASTE) with incrementally reoriented field of view numerous times, deducing the head pose from previous stacks. To address this inefficient workflow, we propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take ~5 seconds to acquire. Our ~2-second procedure automatically locates the fetal brain and eyes, which we derive from maximally stable extremal regions (MSERs). The success rate of the method exceeds 94% in the third trimester, outperforming a trained technologist by up to 20%. The pipeline may be used to automatically orient the anatomical sequence, removing the need to estimate the head pose from 2D views and reducing delays during which motion can occur.
Collapse
Affiliation(s)
- Malte Hoffmann
- Department of Radiology, Massachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | - Esra Abaci Turk
- Fetal‐Neonatal Neuroimaging and Developmental Science Center, Boston Children's HospitalBostonMassachusettsUSA
- Electrical Engineering and Computer ScienceMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Borjan Gagoski
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Fetal‐Neonatal Neuroimaging and Developmental Science Center, Boston Children's HospitalBostonMassachusettsUSA
| | - Leah Morgan
- Department of Radiology, Massachusetts General HospitalBostonMassachusettsUSA
| | - Paul Wighton
- Department of Radiology, Massachusetts General HospitalBostonMassachusettsUSA
| | - Matthew Dylan Tisdall
- Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Martin Reuter
- Department of Radiology, Massachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- German Center for Neurodegenerative DiseasesBonnGermany
| | - Elfar Adalsteinsson
- Electrical Engineering and Computer ScienceMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
- Institute for Medical Engineering and ScienceMassachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Patricia Ellen Grant
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
- Fetal‐Neonatal Neuroimaging and Developmental Science Center, Boston Children's HospitalBostonMassachusettsUSA
| | - Lawrence L. Wald
- Department of Radiology, Massachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | - André J. W. van der Kouwe
- Department of Radiology, Massachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| |
Collapse
|
35
|
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.
Collapse
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
| |
Collapse
|
36
|
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
| |
Collapse
|
37
|
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.
Collapse
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
| |
Collapse
|
38
|
Hoffmann M, Moyer DC, Zhang L, Golland P, Gagoski B, Grant PE, van der Kouwe AJ. Learning-based automatic field-of-view positioning for fetal-brain MRI. PROCEEDINGS OF THE INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE ... SCIENTIFIC MEETING AND EXHIBITION. INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE. SCIENTIFIC MEETING AND EXHIBITION 2021; 29:1362. [PMID: 36284600 PMCID: PMC9592155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Affiliation(s)
- Malte Hoffmann
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Daniel C Moyer
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States
| | - Lawrence Zhang
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States
| | - P Ellen Grant
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States
| | - André Jw van der Kouwe
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| |
Collapse
|
39
|
Dou H, Karimi D, Rollins CK, Ortinau CM, Vasung L, Velasco-Annis C, Ouaalam A, Yang X, Ni D, Gholipour A. A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1123-1133. [PMID: 33351755 PMCID: PMC8016740 DOI: 10.1109/tmi.2020.3046579] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burden of manual refinement of segmentations, we have developed a new and powerful deep learning segmentation method. Our method exploits new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections. We evaluated our method quantitatively based on several performance measures and expert evaluations. Results show that our method outperforms several state-of-the-art deep models for segmentation, as well as a state-of-the-art multi-atlas segmentation technique. We achieved average Dice similarity coefficient of 0.87, average Hausdorff distance of 0.96 mm, and average symmetric surface difference of 0.28 mm on reconstructed fetal brain MRI scans of fetuses scanned in the gestational age range of 16 to 39 weeks (28.6± 5.3). With a computation time of less than 1 minute per fetal brain, our method can facilitate and accelerate large-scale studies on normal and altered fetal brain cortical maturation and folding.
Collapse
|
40
|
Sun L, Lee FT, van Amerom JFP, Freud L, Jaeggi E, Macgowan CK, Seed M. Update on fetal cardiovascular magnetic resonance and utility in congenital heart disease. JOURNAL OF CONGENITAL CARDIOLOGY 2021. [DOI: 10.1186/s40949-021-00059-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Congenital heart disease (CHD) is the most common birth defect, affecting approximately eight per thousand newborns. Between one and two neonates per thousand have congenital cardiac lesions that require immediate post-natal treatment to stabilize the circulation, and the management of these patients in particular has been greatly enhanced by prenatal detection. The antenatal diagnosis of CHD has been made possible through the development of fetal echocardiography, which provides excellent visualization of cardiac anatomy and physiology and is widely available. However, late gestational fetal echocardiographic imaging can be hampered by suboptimal sonographic windows, particularly in the setting of oligohydramnios or adverse maternal body habitus.
Main body
Recent advances in fetal cardiovascular magnetic resonance (CMR) technology now provide a feasible alternative that could be helpful when echocardiography is inconclusive or limited. Fetal CMR has also been used to study fetal circulatory physiology in human fetuses with CHD, providing new insights into how these common anatomical abnormalities impact the distribution of blood flow and oxygen across the fetal circulation. In combination with conventional fetal and neonatal magnetic resonance imaging (MRI) techniques, fetal CMR can be used to explore the relationship between abnormal cardiovascular physiology and fetal development. Similarly, fetal CMR has been successfully applied in large animal models of the human fetal circulation, aiding in the evaluation of experimental interventions aimed at improving in utero development. With the advent of accelerated image acquisition techniques, post-processing approaches to correcting motion artifacts and commercial MRI compatible cardiotocography units for acquiring gated fetal cardiac imaging, an increasing number of CMR methods including angiography, ventricular volumetry, and the quantification of vessel blood flow and oxygen content are now possible.
Conclusion
Fetal CMR has reached an exciting stage whereby it may now be used to enhance the assessment of cardiac morphology and fetal hemodynamics in the setting of prenatal CHD.
Collapse
|
41
|
Abstract
Imaging plays a leading role in detection and diagnosis of fetal head and neck lesions. These lesions comprise a heterogeneous group of congenital tumors and malformations. Complementary imaging modalities that can be used in prenatal medicine are ultrasound and MRI. The authors discuss imaging characteristics of fetal lesions, assessment of potential complications and pregnancy management options for the most common pathology of the fetal head and neck.
Collapse
|
42
|
Fetal cardiovascular magnetic resonance imaging. Pediatr Radiol 2020; 50:1881-1894. [PMID: 33252756 DOI: 10.1007/s00247-020-04902-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 09/23/2020] [Accepted: 11/02/2020] [Indexed: 12/11/2022]
Abstract
Fetal cardiovascular MRI is showing promise as a clinical diagnostic tool in the setting of congenital heart disease when the cardiac anatomy is unresolved by US or when complementary quantitative data on blood flow, oxygen saturation and hematocrit are required to aid in management. Compared with postnatal cardiovascular MRI, prenatal cardiovascular MRI still has some technical limitations. However, ongoing technical advances continue to improve the robustness and usability of fetal cardiovascular MRI. In this review, we provide an overview of the state of the art of fetal cardiovascular MRI and summarize the current focus of clinical application for this versatile technique.
Collapse
|
43
|
Machado-Rivas F, Jaimes C, Kirsch JE, Gee MS. Image-quality optimization and artifact reduction in fetal magnetic resonance imaging. Pediatr Radiol 2020; 50:1830-1838. [PMID: 33252752 DOI: 10.1007/s00247-020-04672-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 03/09/2020] [Accepted: 03/31/2020] [Indexed: 11/28/2022]
Abstract
Fetal MRI allows for earlier and better detection of complex congenital anomalies. However, its diagnostic utility is often limited by technical barriers that introduce artifacts and reduce image quality. The main determinants of fetal MR image quality are speed of acquisition, spatial resolution and signal-to-noise ratio (SNR). Imaging optimization is a challenge because a change to improve one scan parameter often leads to worsening of another. Moreover, the recent introduction of fetal MRI on 3-tesla (T) scanners to achieve better SNR can amplify some technical issues. Motion, banding artifacts and aliasing artifacts impact the quality of fetal acquisitions at any field strength. High specific absorption rate (SAR) and artifacts from inhomogeneities in the radiofrequency field are important limitations of high-field-strength imaging. We discuss technical barriers that impact image quality and are important limitations to prenatal MRI diagnosis, and propose solutions to improve image quality and reduce artifacts.
Collapse
Affiliation(s)
- Fedel Machado-Rivas
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Camilo Jaimes
- Department of Radiology, Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - John E Kirsch
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Michael S Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA. .,Department of Radiology, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
44
|
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.
Collapse
|
45
|
Luks FI, Collins S, Xia J, Cao SA, Rios M. Combination of volume-rendering 3D surface modeling and medical illustration to capture the living fetus. Prenat Diagn 2020; 41:79-88. [PMID: 33058179 DOI: 10.1002/pd.5844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 09/21/2020] [Accepted: 10/11/2020] [Indexed: 11/09/2022]
Abstract
OBJECTIVE A good medical illustration renders essential aspects of a procedure or condition faithfully, yet idealizes it enough to make it widely applicable. Unfortunately, the live fetus is generally hidden from sight, and illustrating it relies either on autopsy material or manipulated newborn images. High-definition volume rendering of diagnostic imaging data can represent hidden conditions with an almost lifelike realism but is limited by the resolution and artifacts of the data capture. We have combined both approaches to enhance the accuracy and didactic value of illustrations of fetal conditions. METHODS Three examples, of increasing complexity, are presented to demonstrate the creation of medical illustrations of the fetus based on semiautomatic computerized posthoc manipulation of diagnostic images. RESULTS The end product utilizes the diagnostic accuracy of ultrasound and magnetic resonance imaging of the fetuses and the spatial manipulation of 3D models to create a lifelike, accurate and informative image of the fetal anomalies. CONCLUSION Volume-rendering and 3D surface modeling can be combined with medical illustration to create realistic and informative images of the developing fetus, using a level of detail that is tailored to the intended audience.
Collapse
Affiliation(s)
- Francois I Luks
- Division of Pediatric Surgery, Brown University Warren Alpert Medical School, Providence, Rhode Island, USA.,Brown University, Providence, Rhode Island, USA.,Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Scott Collins
- Department of Diagnostic Imaging, Rhode Island Hospital, Providence, Rhode Island, USA
| | - Jimmy Xia
- Brown University, Providence, Rhode Island, USA
| | - Shiliang Alice Cao
- Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Matthew Rios
- Rhode Island School of Design, Providence, Rhode Island, USA
| |
Collapse
|
46
|
Xu J, Lala S, Gagoski B, Turk EA, Grant PE, Golland P, Adalsteinsson E. Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12266:386-395. [PMID: 36383490 PMCID: PMC9652031 DOI: 10.1007/978-3-030-59725-2_37] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Fetal brain MRI is useful for diagnosing brain abnormalities but is challenged by fetal motion. The current protocol for T2-weighted fetal brain MRI is not robust to motion so image volumes are degraded by inter- and intra-slice motion artifacts. Besides, manual annotation for fetal MR image quality assessment are usually time-consuming. Therefore, in this work, a semi-supervised deep learning method that detects slices with artifacts during the brain volume scan is proposed. Our method is based on the mean teacher model, where we not only enforce consistency between student and teacher models on the whole image, but also adopt an ROI consistency loss to guide the network to focus on the brain region. The proposed method is evaluated on a fetal brain MR dataset with 11,223 labeled images and more than 200,000 unlabeled images. Results show that compared with supervised learning, the proposed method can improve model accuracy by about 6% and outperform other state-of-the-art semi-supervised learning methods. The proposed method is also implemented and evaluated on an MR scanner, which demonstrates the feasibility of online image quality assessment and image reacquisition during fetal MR scans.
Collapse
Affiliation(s)
- Junshen Xu
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Sayeri Lala
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Borjan Gagoski
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Esra Abaci Turk
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Polina Golland
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
| |
Collapse
|
47
|
Vasung L, Rollins CK, Velasco-Annis C, Yun HJ, Zhang J, Warfield SK, Feldman HA, Gholipour A, Grant PE. Spatiotemporal Differences in the Regional Cortical Plate and Subplate Volume Growth during Fetal Development. Cereb Cortex 2020; 30:4438-4453. [PMID: 32147720 PMCID: PMC7325717 DOI: 10.1093/cercor/bhaa033] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 01/27/2020] [Accepted: 01/28/2020] [Indexed: 12/16/2022] Open
Abstract
The regional specification of the cerebral cortex can be described by protomap and protocortex hypotheses. The protomap hypothesis suggests that the regional destiny of cortical neurons and the relative size of the cortical area are genetically determined early during embryonic development. The protocortex hypothesis suggests that the regional growth rate is predominantly shaped by external influences. In order to determine regional volumes of cortical compartments (cortical plate (CP) or subplate (SP)) and estimate their growth rates, we acquired T2-weighted in utero MRIs of 40 healthy fetuses and grouped them into early (<25.5 GW), mid- (25.5-31.6 GW), and late (>31.6 GW) prenatal periods. MRIs were segmented into CP and SP and further parcellated into 22 gyral regions. No significant difference was found between periods in regional volume fractions of the CP or SP. However, during the early and mid-prenatal periods, we found significant differences in relative growth rates (% increase per GW) between regions of cortical compartments. Thus, the relative size of these regions are most likely conserved and determined early during development whereas more subtle growth differences between regions are fine-tuned later, during periods of peak thalamocortical growth. This is in agreement with both the protomap and protocortex hypothesis.
Collapse
Affiliation(s)
- Lana Vasung
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Caitlin K Rollins
- Computational Radiology Laboratory, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Hyuk Jin Yun
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jennings Zhang
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
48
|
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.
Collapse
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
| |
Collapse
|
49
|
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.
Collapse
|
50
|
Afacan O, Estroff JA, Yang E, Barnewolt CE, Connolly SA, Parad RB, Mulkern RV, Warfield SK, Gholipour A. Fetal Echoplanar Imaging: Promises and Challenges. Top Magn Reson Imaging 2019; 28:245-254. [PMID: 31592991 PMCID: PMC6788763 DOI: 10.1097/rmr.0000000000000219] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Fetal magnetic resonance imaging (MRI) has been gaining increasing interest in both clinical radiology and research. Echoplanar imaging (EPI) offers a unique potential, as it can be used to acquire images very fast. It can be used to freeze motion, or to get multiple images with various contrast mechanisms that allow studying the microstructure and function of the fetal brain and body organs. In this article, we discuss the current clinical and research applications of fetal EPI. This includes T2*-weighted imaging to better identify blood products and vessels, using diffusion-weighted MRI to investigate connections of the developing brain and using functional MRI (fMRI) to identify the functional networks of the developing brain. EPI can also be used as an alternative structural sequence when banding or standing wave artifacts adversely affect the mainstream sequences used routinely in structural fetal MRI. We also discuss the challenges with EPI acquisitions, and potential solutions. As EPI acquisitions are inherently sensitive to susceptibility artifacts, geometric distortions limit the use of high-resolution EPI acquisitions. Also, interslice motion and transmit and receive field inhomogeneities may create significant artifacts in fetal EPI. We conclude by discussing promising research directions to overcome these challenges to improve the use of EPI in clinical and research applications.
Collapse
Affiliation(s)
- Onur Afacan
- Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Judy A. Estroff
- Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Advanced Fetal Care Center, Boston Children’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Advanced Fetal Care Center, Boston Children’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Carol E. Barnewolt
- Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Advanced Fetal Care Center, Boston Children’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Susan A. Connolly
- Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Advanced Fetal Care Center, Boston Children’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Richard B. Parad
- Advanced Fetal Care Center, Boston Children’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Department of Pediatric Newborn Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Robert V. Mulkern
- Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Simon K. Warfield
- Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| |
Collapse
|