Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury

✍ Scribed by Nirmalya Ghosh; Rebecca Recker; Amul Shah; Bir Bhanu; Stephen Ashwal; Andre Obenaus

• Publisher: John Wiley and Sons
• Year: 2011
• Tongue: English
• Weight: 565 KB
• Volume: 33
• Category: Article
• ISSN: 1053-1807
• DOI: 10.1002/jmri.22488

✦ Synopsis

Abstract

Purpose:

To develop and compare an automated detection system for ischemic lesions in a neonatal model of bilateral carotid artery occlusion with hypoxia (BCAO‐H) from T2 weighted MRI (T2WI) to the currently used “gold standard” of manual segmentation.

Materials and Methods:

Forty‐three P10 BCAO‐H rat pups and 8 controls underwent T2WI at 1 day and 28 days. A computational imaging method, Hierarchical Region Splitting (HRS), was developed to automatically and rapidly detect and quantify 3D lesion and normal appearing brain matter (NABM) volumes.

Results:

HRS quantified lesion and NABM volumes within 15 s in comparison to 3 h for its manual counterpart, with a high correlation for injury (r^2^ = 0. 95; P = 8.6 × 10^−7^) and NABM (r^2^ = 0. 92; P = 1.4 × 10^−22^). Average lesion volumes for mild, moderate, and severe injuries were 3.85%, 28.85%, and 52.98% for HRS and 0.51%, 24.22%, and 48.74% for manual detection. Lesion volumes and locations were similar for both methods (sensitivity: 0.82, specificity: 0.86, and similarity: 1.47).

Conclusion:

HRS is an accurate, objective, and rapid method to quantify injury evolution in neonatal hypoxic ischemic injury models. J. Magn. Reson. Imaging 2011;33:772–781. © 2011 Wiley‐Liss, Inc.