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.