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Abstract #4304

Radiomic feature-based assessment of deep learning-based compressed sensing reconstruction

Tomoki Miyasaka1, Satoshi Funayama2, Daiki Tamada2, Hiroyuki Morisaka2, Hiroshi Onishi2, and Yasuhiko Terada1
1Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan, 2Department of Radiology, University of Yamanashi, Chuo, Japan


Deep learning has been attracting attention as a new tool for image reconstruction. However, there is a lack of appropriate automatic evaluation metrics for reconstruction performance of small structures such as lesions, which poses a high hurdle for clinical application. Here, we explored the relationship between radiomic features of tumors and various DL reconstruction conditions, and proposed a new method based on radiomics to evaluate the reconstruction performance of DL against lesions. Based on the analysis using the concordance correlation coefficients for ground truth images, we explored several texture features that are sensitive to differences in reconstruction methods and conditions.

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