Rapid head-pose detection for automated slice prescription of fetal-brain MRI
This addresses a specific workflow inefficiency in clinical fetal MRI, reducing delays and manual effort, but is incremental as it builds on existing imaging techniques.
The paper tackled the problem of inefficient head-pose detection in fetal-brain MRI, which requires repeated scans and manual estimation, by proposing an automated algorithm using full-uterus scout scans that achieves over 94% success rate in the third trimester, outperforming trained technologists by up to 20%.
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.