Rapid head‐pose detection for automated slice prescription of fetal‐brain MRI
Name
Int J Imaging Syst Tech - 2021 - Hoffmann - Rapid head%u2010pose detection for automated slice prescription of fetal%u2010brain MRI.pdf
Description
Published version
Size
2.7 MB
Format
Adobe PDF
Checksum (MD5)
a696481519d02abdf6df955fbbc66421
Author(s)
Hoffmann, Malte
Abaci Turk, Esra
Gagoski, Borjan
Morgan, Leah
Wighton, Paul
Tisdall, Matthew Dylan
Reuter, Martin
Adalsteinsson, Elfar
Grant, Patricia Ellen
Wald, Lawrence L
Date Issued
2021
Journal
International Journal of Imaging Systems and Technology
Publisher
Wiley
Citation
Hoffmann, Malte, Abaci Turk, Esra, Gagoski, Borjan, Morgan, Leah, Wighton, Paul et al. 2021. "Rapid head‐pose detection for automated slice prescription of fetal‐brain MRI." International Journal of Imaging Systems and Technology, 31 (3).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/IMA.22563
