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

Abstract

Corpus Callosum Agenesis (CCA), one of the most common congenital anomalies, has uncertain neurodevelopmental outcome, especially when the disease is isolated. To provide parents with informed counselling, it is crucial to identify anatomical markers linked to a predicted outcome early in pregnancy. Quantitative exploration of fetal brains with CCA is rare and has been mostly limited to the study of specific brain structures. Here, we propose a pipeline to analyse fetal brain Magnetic Resonance Imaging (MRI) that is based on diffeomorphic transformation. It consists in two steps: a semi-automatic fetal MRI preprocessing procedure and a pipeline to quantify anatomical deviations from normal development. Following MRI preprocessing, each volumetric fetal brain is compared to an age-matched healthy template brain at a global scale using registration. Deformations are parallel transported to the same space to correct age differences between fetuses. Deformation modes specific to CCA are identified using Principal Component Analysis and classification. The pipeline is tested on retrospectively selected MRIs from 38 healthy fetuses and 73 fetuses with CCA. In accordance with more local analyses, the most relevant deformation mode for classification combines well-known alterations of brains with CCA. This preliminary work is promising for the quantitative exploration of abnormal fetal brains and will be used in the future to identify anatomical features correlated to poor clinical outcome.

Wavelet-Based Multiscale Flow For Realistic Image Deformation in the Large Diffeomorphic Deformation Model Framework

“…These differences have a simple explanation: the original version simultaneously estimates the overall shape of the characters and details such as the location and orientation of the arms and legs, making it more dependent on the initial template image and leading to the selection of erroneous features, while the coarse-to-fine strategy first focuses on estimating the characters main features, which are then refined when the finer scales are optimized. This phenomenon is illustrated by movies showing template optimization across iterations, available at the first author's webpage 3 .…”

“…To evaluate the performance of our coarse-tofine approach on a dataset of clinical images, we use 30 fetal brain MRIs with agenesis of the corpus callosum acquired in Hopital Trousseau, France [3]. Gestational ages are comprised between 32 and 34 weeks of gestation (mean = 32.9 ± 0.6).…”

“…The template image can be used as reference to describe average anatomical structures or serve as a tool to automatically segment new subjects. Variations around the template may be used to characterize pathological deviations from normality [3] or to isolate subgroups in the population [4]. Atlases can also be defined in a spatio-temporal fashion to characterize normal or pathological changes, such as brain growth across gestation [5] or hippocampal reduction in Alzheimer's disease [4].…”

SynthStrip: Skull-Stripping for Any Brain Image

“…To enable full 3D views of the fetal brain, post-processing tools for super-resolution reconstruction have emerged, that aim to reconstruct a high-quality volume of isotropic resolution from a number of slice stacks acquired at different angles (Rousseau et al, 2006;Kainz et al, 2015;Ebner et al, 2020;). Yet, these methods hinge on successful brain extraction which is challenging due to frequent artifacts and because the relatively small brain first needs to be localized within a wide FOV encompassing the maternal anatomy (Gaudfernau et al, 2021). In addition, substantially fewer public fetal datasets are available for training in comparison to vast public adult brain datasets.…”

A multiscale algorithm for computing realistic image transformation: application to the modelling of fetal brain growth

“…Most often, spatio-temporal atlases establish a discrete representation of the brain across gestation 1 . However, continuous models have the potential to depict these anatomical changes more accurately 2,3 , while providing a straightforward way of comparing subjects of different gestational ages 4 . To build such a model, geodesic regression is an adequate tool that encodes in a single deformation the time-dependent changes of objects acquired at different time points 5 .…”