Constrained, localized warping reduced registration errors due to lesions in functional neuroimages

摘要

The constrained, localized warping (CLW) algorithm was developed to minimize the registration errors caused by hypoperfusion lesions. SPECT brain perfusion images from 21 Alzheimer patients and 35 controls were analyzed. CLW automatically determines homologous landmarks on patient and template images. CLW was constrained by anatomy and where lesions were probable. CLW was compared with 3rd-degree, polynomial warping (AIR 3.0). Accuracy was assessed by correlation, overlap, and variance. 16 lesion types were simulated, repeated with 5 images. The errors in defect volume and intensity after registration were estimated by comparing the images resulting from warping transforms calculated when the defects were or were not present. Registration accuracy of normal studies was very similar between CLW and polynomial warping methods, and showed marked improvement over linear registration. The lesions had minimal effect on the CLW algorithm accuracy, with small errors in volume (> -4%) and intensity (< %). The accuracy improvement compared with not warping was nearly constant regardless of defect: .5% overlap and .001 correlation. Polynomial warping caused larger errors in defect volume (< -10%) and intensity (> .5%) for most defects. CLW is recommended because it caused small errors in defect estimation and improved the registration accuracy in all cases.