Radiomic feature reliability of amide proton transfer‐weighted MR images acquired with compressed sensing at 3 T

Author:

Wu Jingpu12,Huang Qianqi13,Shen Yiqing14,Guo Pengfei14,Zhou Jinyuan1,Jiang Shanshan1ORCID

Affiliation:

1. Department of Radiology, School of Medicine Johns Hopkins University Baltimore Maryland USA

2. Department of Applied Mathematics and Statistics, Whiting School of Engineering Johns Hopkins University Baltimore Maryland USA

3. Department of Biomedical Engineering, Whiting School of Engineering Johns Hopkins University Baltimore Maryland USA

4. Department of Computer Science, Whiting School of Engineering Johns Hopkins University Baltimore Maryland USA

Abstract

AbstractCompressed sensing (CS) is a novel technique for MRI acceleration. The purpose of this paper was to assess the effects of CS on the radiomic features extracted from amide proton transfer‐weighted (APTw) images. Brain tumor MRI data of 40 scans were studied. Standard images using sensitivity encoding (SENSE) with an acceleration factor (AF) of 2 were used as the gold standard, and APTw images using SENSE with CS (CS‐SENSE) with an AF of 4 were assessed. Regions of interest (ROIs), including normal tissue, edema, liquefactive necrosis, and tumor, were manually drawn, and the effects of CS‐SENSE on radiomics were assessed for each ROI category. An intraclass correlation coefficient (ICC) was first calculated for each feature extracted from APTw images with SENSE and CS‐SENSE for all ROIs. Different filters were applied to the original images, and the effects of these filters on the ICCs were further compared between APTw images with SENSE and CS‐SENSE. Feature deviations were also provided for a more comprehensive evaluation of the effects of CS‐SENSE on radiomic features. The ROI‐based comparison showed that most radiomic features extracted from CS‐SENSE‐APTw images and SENSE‐APTw images had moderate or greater reliabilities (ICC ≥ 0.5) for all four ROIs and all eight image sets with different filters. Tumor showed significantly higher ICCs than normal tissue, edema, and liquefactive necrosis. Compared to the original images, filters (such as Exponential or Square) may improve the reliability of radiomic features extracted from CS‐SENSE‐APTw and SENSE‐APTw images.

Funder

Foundation for the National Institutes of Health

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

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