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Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging

The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses betwee...

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Autores principales: Hong, Jinwoo, Yun, Hyuk Jin, Park, Gilsoon, Kim, Seonggyu, Ou, Yangming, Vasung, Lana, Rollins, Caitlin K., Ortinau, Cynthia M., Takeoka, Emiko, Akiyama, Shizuko, Tarui, Tomo, Estroff, Judy A., Grant, Patricia Ellen, Lee, Jong-Min, Im, Kiho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8542770/
https://www.ncbi.nlm.nih.gov/pubmed/34707474
http://dx.doi.org/10.3389/fnins.2021.714252
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author Hong, Jinwoo
Yun, Hyuk Jin
Park, Gilsoon
Kim, Seonggyu
Ou, Yangming
Vasung, Lana
Rollins, Caitlin K.
Ortinau, Cynthia M.
Takeoka, Emiko
Akiyama, Shizuko
Tarui, Tomo
Estroff, Judy A.
Grant, Patricia Ellen
Lee, Jong-Min
Im, Kiho
author_facet Hong, Jinwoo
Yun, Hyuk Jin
Park, Gilsoon
Kim, Seonggyu
Ou, Yangming
Vasung, Lana
Rollins, Caitlin K.
Ortinau, Cynthia M.
Takeoka, Emiko
Akiyama, Shizuko
Tarui, Tomo
Estroff, Judy A.
Grant, Patricia Ellen
Lee, Jong-Min
Im, Kiho
author_sort Hong, Jinwoo
collection PubMed
description The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979, p < 0.001) and 3D (MAE = 1.114, p < 0.001) CNNs. The saliency maps from our method indicated that the anatomical information describing the cortex and ventricles was the primary contributor to brain age prediction. With the application of the proposed method to external MRIs from 21 healthy fetuses, we obtained an MAE of 0.508 weeks. Based on the external MRIs, we found that the stack-wise MAE of the 2D single-channel CNN (0.743 weeks) was significantly lower than those of the 2D multi-channel (1.466 weeks, p < 0.001) and 3D (1.241 weeks, p < 0.001) CNNs. These results demonstrate that our method with multiplanar slices accurately predicts fetal brain age without the need for increased dimensionality or complex MRI preprocessing steps.
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spelling pubmed-85427702021-10-26 Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging Hong, Jinwoo Yun, Hyuk Jin Park, Gilsoon Kim, Seonggyu Ou, Yangming Vasung, Lana Rollins, Caitlin K. Ortinau, Cynthia M. Takeoka, Emiko Akiyama, Shizuko Tarui, Tomo Estroff, Judy A. Grant, Patricia Ellen Lee, Jong-Min Im, Kiho Front Neurosci Neuroscience The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979, p < 0.001) and 3D (MAE = 1.114, p < 0.001) CNNs. The saliency maps from our method indicated that the anatomical information describing the cortex and ventricles was the primary contributor to brain age prediction. With the application of the proposed method to external MRIs from 21 healthy fetuses, we obtained an MAE of 0.508 weeks. Based on the external MRIs, we found that the stack-wise MAE of the 2D single-channel CNN (0.743 weeks) was significantly lower than those of the 2D multi-channel (1.466 weeks, p < 0.001) and 3D (1.241 weeks, p < 0.001) CNNs. These results demonstrate that our method with multiplanar slices accurately predicts fetal brain age without the need for increased dimensionality or complex MRI preprocessing steps. Frontiers Media S.A. 2021-10-11 /pmc/articles/PMC8542770/ /pubmed/34707474 http://dx.doi.org/10.3389/fnins.2021.714252 Text en Copyright © 2021 Hong, Yun, Park, Kim, Ou, Vasung, Rollins, Ortinau, Takeoka, Akiyama, Tarui, Estroff, Grant, Lee and Im. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Hong, Jinwoo
Yun, Hyuk Jin
Park, Gilsoon
Kim, Seonggyu
Ou, Yangming
Vasung, Lana
Rollins, Caitlin K.
Ortinau, Cynthia M.
Takeoka, Emiko
Akiyama, Shizuko
Tarui, Tomo
Estroff, Judy A.
Grant, Patricia Ellen
Lee, Jong-Min
Im, Kiho
Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging
title Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging
title_full Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging
title_fullStr Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging
title_full_unstemmed Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging
title_short Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging
title_sort optimal method for fetal brain age prediction using multiplanar slices from structural magnetic resonance imaging
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8542770/
https://www.ncbi.nlm.nih.gov/pubmed/34707474
http://dx.doi.org/10.3389/fnins.2021.714252
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