Rapid head-pose detection for automated slice prescription of fetal-brain MRI

TL;DR

This paper introduces a fast, automated head-pose detection method for fetal-brain MRI that improves efficiency and accuracy, enabling automatic slice orientation and reducing motion-related delays during scans.

Contribution

The authors develop a robust, rapid head-pose detection algorithm using full-uterus scout scans, outperforming trained technologists in success rate and streamlining fetal MRI workflows.

Findings

Success rate exceeds 94% in the third trimester

Method outperforms trained technologists by up to 20%

Detection process takes approximately 2 seconds

Abstract

In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment. As motion limits acquisitions to thick slices that preclude retrospective resampling, technologists repeat ~55-second stack-of-slices scans (HASTE) with incrementally reoriented field of view numerous times, deducing the head pose from previous stacks. To address this inefficient workflow, we propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take ~5 seconds to acquire. Our ~2-second procedure automatically locates the fetal brain and eyes, which we derive from maximally stable extremal regions (MSERs). The success rate of the method exceeds 94% in the third trimester, outperforming a trained technologist by up to 20%. The pipeline may be used to automatically orient the anatomical sequence, removing the need to estimate the head pose from 2D views and reducing delays during which motion can occur.