Work

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

Year: 2021

Type: article

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.

Source: International Journal of Imaging Systems and Technology

DOI: 10.1002/ima.22563

Authors: Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski, Leah Morgan, Paul Wighton, Matthew Dylan Tisdall, Martin Reuter, Elfar Adalsteinsson, Patricia Ellen Grant, Lawrence L. Wald, André J. W. van der Kouwe

Institutions: Harvard University, Massachusetts General Hospital, Boston Children's Hospital, Massachusetts Institute of Technology, University of Pennsylvania, German Center for Neurodegenerative Diseases

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,750

Funders: National Institutes of Health, National Institute on Aging, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: G1540917792, G1649372110, High-Speed Motion-Corrected Pediatric Neuroimaging with MRI, Neonatal imaging as an early marker of neurodevelopment and predictor of cognitive performance in infants exposed to HIV and ART in utero and perinatally, G3030627447, Multichannel Coils and Motion Correction for Developing Brain, G4936591272, G5087358038, G6955302056, G7620899450, G8228211528, G845830115, Mapping the Human Connectome During Typical Aging, G9000011378

OpenAlex ID: W3134026069

FWCI: 1.058

Cites: 64

Cited by: 17

Related to: 10