Ms. Natalie Perri Profile

Natalie Perri

at DePaul Univ.

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Proceedings Article | 4 April 2022 Poster + Paper

Normalization of MRI signal intensity in polycystic kidney disease and the effect on radiomic features

Linnea Kremer, Natalie Perri, Eliza Sorber, Arlene Chapman, Samuel Armato

Proceedings Volume 12033, 120332Z (2022) https://doi.org/10.1117/12.2613164

KEYWORDS: Kidney, Magnetic resonance imaging, Reliability, Tissues, Liver, Feature extraction, Image segmentation, Image classification, Feature selection, Signal processing

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This study investigated the impact of reference-tissue normalization on radiomic texture features extracted from magnetic resonance images (MRI) of non-cystic kidney parenchyma in patients with autosomal dominant polycystic kidney disease (ADPKD). Image normalization has been shown to improve robustness of features and disease classification. Texture analysis is a promising technique to differentiate between PKD1 and PKD2 variants of ADPKD, which differ in progression and patient outcomes. Regions of interest (ROIs) were placed on the liver and psoas muscle, and Z-score image normalization was performed separately based on the two different ROI placements. This pilot study included 7 PKD1 and 8 PKD2 patients (29 kidney images in total). Right and left kidneys were manually segmented on the single coronal image for each individual kidney that contained the renal artery, and a thresholding tool was used to exclude cysts from the pixels used for feature extraction. This was performed using the open-source platform Pyradiomics on the original and two variants of normalized images. Intraclass correlation coefficients (ICCs) were calculated to compare the reliability of features across normalized images. A linear discriminant analysis (LDA) classifier was used to merge the top-three performing reliable texture features for PKD1 and PKD2 classification based on the receiver operating characteristic (ROC) analysis. Seventeen of the 93 features demonstrated good-to-excellent reliability between normalization approaches. Psoas muscle-normalized images yielded the highest area under the ROC curve (AUC) value of 0.74 (0.53-0.89). Image normalization impacts texture features and classification of PKD1 and PKD2 using MRI-based texture features and should be further explored.

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