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Longitudinal Analysis of Fetal MRI in Patients with Prenatal Spina Bifida Repair

Payette, Kelly; Moehrlen, Ueli; Mazzone, Luca; Ochsenbein-Kölble, Nicole; Tuura, Ruth; Kottke, Raimund; Meuli, Martin; Jakab, András (2019). Longitudinal Analysis of Fetal MRI in Patients with Prenatal Spina Bifida Repair. Lecture Notes in Computer Science:161-170.

Abstract

Open spina bifida (SB) is one of the most common congenital defects and can lead to impaired brain development. Emerging fetal surgery methods have shown considerable success in the treatment of patients with this severe anomaly. Afterwards, alterations in the brain development of these fetuses have been observed. Currently no longitudinal studies exist to show the effect of fetal surgery on brain development. In this work, we present a fetal MRI neuroimaging analysis pipeline for fetuses with SB, including automated fetal ventricle segmentation and deformation-based morphometry, and demonstrate its applicability with an analysis of ventricle enlargement in fetuses with SB. Using a robust super-resolution algorithm, we reconstructed fetal brains at both pre-operative and post-operative time points and trained a U-Net CNN in order to automatically segment the ventricles. We investigated the change of ventricle shape post-operatively, and the impacts of lesion size, type, and GA at operation on the change in ventricle shape. No impact was found. Prenatal ventricle volume growth was also investigated. Our method allows for the quantification of longitudinal morphological changes to fully quantify the impact of prenatal SB repair and could be applied to predict postnatal outcomes.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Children's Hospital Zurich > Medical Clinic
04 Faculty of Medicine > University Children's Hospital Zurich > Clinic for Surgery
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Physical Sciences > Theoretical Computer Science
Physical Sciences > General Computer Science
Language:English
Date:17 October 2019
Deposited On:11 Dec 2019 15:01
Last Modified:03 Sep 2024 03:37
Publisher:Springer
ISSN:0302-9743
OA Status:Closed
Publisher DOI:https://doi.org/10.1007/978-3-030-32875-7_18
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