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Comparison of Brain Normalization Software and Lesion Compensation Techniques in Chronic Perinatal Stroke Imaging
Journal article   Open access   Peer reviewed

Comparison of Brain Normalization Software and Lesion Compensation Techniques in Chronic Perinatal Stroke Imaging

Gillian N. Miller, Clara J. Steeby, Jorge Ortega-Márquez, Alberto Castro Palacin, Carrie Chui, Kenda Alhadid, Alyssa W. Sullivan, Aaron D. Boes, Patricia L. Musolino and Alexander L. Cohen
Imaging neuroscience (Cambridge, Mass.), Vol.3, IMAGa1048
2025
DOI: 10.1162/IMAG.a.1048
PMCID: PMC13288496
PMID: 42344990
url
https://doi.org/10.1162/IMAG.a.1048View
Published (Version of record) Open Access

Abstract

Neuroimaging research depends on registration, the alignment of patients’ brains to a template or standard space, to enable accurate comparisons across individuals. Decades of work have advanced our ability to accurately register typical brains, but registering atypical brains, such as those with injury or highly distorted anatomy, remains a challenge. In particular, registration of perinatal stroke imaging is often complicated by delayed injury identification, which results in imaging obtained during the chronic stage where secondary structural impacts are evident. While analyses in native space can be valuable for subject-specific investigations, group-level studies require registration to a common template space, which enables between-subject comparisons of lesion locations and their network correlates. Although many registration algorithms exist, as do various compensation techniques for focal lesions, it is unclear how effective they are when applied to the highly distorted anatomy often present in this chronic perinatal stroke imaging. Here, we quantitatively and qualitatively compared the performance of three registration algorithms (FNIRT, ANTs, EasyReg) in registering eleven variably distorted brains with perinatal stroke to a standard template using their default lesion-compensation techniques. We also assessed the impact of “brain grafting”, i.e., inserting a healthy tissue mask in place of the defined lesion area prior to registration. Our findings show that ANTs and EasyReg are significantly more accurate than FNIRT for chronic perinatal stroke imaging, although all three software packages have marked difficulty with large lesions. Notably, brain grafting significantly improved the lesion mask normalization performance of FNIRT. In light of these comparisons, the recently released EasyReg appears to be an appropriate starting point for registering cohorts with chronic perinatal strokes, but we still emphasize the necessity of consistent visual inspection of registered brains.
 Neuroimaging registration MRI perinatal stroke focal lesions spatial normalization

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