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

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 preoperative 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, except for moderately larger ventriculomegaly progression in myeloschisis patients. 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.

Emerging magnetic resonance imaging techniques in open spina bifida in utero

“…Robust statistics are used to identify mis-registered or heavily corrupted data [ 52 ]. These HR volumes can facilitate early diagnosis of various congenital diseases and also allow for quantitative investigation of foetal development in vivo in spinal dysraphism [ 53 ].…”

An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset

“…Recently, convolutional neural networks have also been used to segment the fetal brain into different tissue types, but only for low-resolution scans in the coronal direction 22 , which has limited use. Other work has shown it to be possible to segment a single class within high-resolution fetal brain volumes, but multi-class semantic segmentation is not addressed 23 . The major bottleneck of developing segmentation algorithms for medical imaging is the lack of data, either availability of atlases for atlas-based segmentation, or training data for supervised machine learning methods.…”

Analysis of the Anatomical Variability of Fetal Brains with Corpus Callosum Agenesis

“…Longitudinal analyses have explored the anatomical characteristics of fetal brains with spina bifida. (Payette et al, 2019) investigated the change of ventricle shape in fetuses that underwent prenatal surgery. Using deformation-based morphometry, post-operation ventricles were registered to the pre-operation ones, and statistical analysis of the deformation maps was performed.…”

“…Such analyses rely heavily on brain segmentation and on prior hypotheses about which structures demonstrate relevant dynamics or impairments. Noteworthy exceptions include tensor-based morphometry (Rajagopalan et al, 2011(Rajagopalan et al, , 2012Pontabry et al, 2015;Payette et al, 2019), which considers the whole brain but only involves local shape changes, and longitudinal atlases that have so far focused on healthy brain growth, apart from (Fidon et al, 2021b). Quantitative studies of abnormal fetal brains are scarce and limited in the same way, to which CCA-specific studies are no exception: all prenatal studies have focused either on the hippocampus, cortical plate, or white matter fibers.…”