Hemi-brain growth as a biomarker for whole brain growth.

OBJECTIVE

Accurate cerebrospinal fluid (CSF) and brain volume estimation are important components for evaluating hydrocephalus treatments, including shunts and endoscopic third ventriculostomy (ETV) procedures. While MRI-based segmentation typically provides precise measurements, metallic artifacts from implanted shunts in hydrocephalus patients can impede accurate volume determination. This study introduces a method for assessing brain growth in hydrocephalus patients using artifact-affected MRI scans and presents an efficient, automated artificial intelligence (AI)-based pipeline for hemi-brain segmentation and subsequent volume assessment.

METHODS

The study consists of 75 patients participating in the Endoscopic versus Shunt Treatment of Hydrocephalus in Infants (ESTHI) trial. Pre- and post-operative T2 MRI scans were collected. Hemi-brain growth curves for the artifact-free hemisphere are proposed to assess postoperative brain growth from MRI with metallic shunt artifacts. An AI-based hemi-brain volume estimation pipeline was developed, consisting of a brain/CSF segmentation model and a hemi-brain mask generator. Segmentation labels, including left/right hemi-brain masks and brain/CSF segmentation maps were created. The AI pipeline was trained and validated using a manually segmented data subset. The volumes of left and right brain hemispheres after surgeries were calculated and analyzed.

RESULTS

Postoperative hemisphere volume ratios approach the normal ratio and remained constant over time, confirming the feasibility for use of hemi-brain measurements as proxies for whole-brain volume assessment in the presence of metallic artifacts. Additionally, the AI-based pipeline demonstrated high accuracy in generating hemi-brain masks and segmenting brain/CSF, effectively automating the process of hemi-brain volume estimation.

CONCLUSIONS

The hemi-brain volume estimation of the unaffected hemisphere offers a feasible method for assessing brain growth over time. This process can be automated using a highly accurate AI pipeline, providing a valuable tool for monitoring brain growth in pediatric hydrocephalus patients with shunts.