Impact of deep-learning CT image denoising on the accuracy of radiomics parameter estimation

Pontus Pandurevic; Alex Back; Dennis Hein; Mats Persson

doi:10.1117/12.3006732

1 April 2024 Impact of deep-learning CT image denoising on the accuracy of radiomics parameter estimation

Pontus Pandurevic, Alex Back, Dennis Hein, Mats Persson

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129252C (2024) https://doi.org/10.1117/12.3006732
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In CT radiomics, numerical parameters extracted from CT images are analyzed to find biomarkers. Since these numerical parameters can vary with imaging parameters, there is a need to optimize acquisition protocols for radiomics. In this work, we investigate the effect of deep-learning-based image reconstruction on the accuracy of radiomic parameters of tumors. We image a 3D printed lung phantom containing four tumors (ellipsoidal, lobulated, spherical, and spiculated), using the CAD model as ground truth. The phantom was 3D printed using fused deposition modeling with a PLA filament and 80% fill rate with a gyroidal pattern to mimic soft tissue. CT images of the 3D printed phantom and tumors were acquired with a GE revolution scanner with 120kVp and 213mAs. We reconstructed images using FBP and a vendor-supplied deep learning image reconstruction (DLIR) method (TrueFidelity, GE HealthCare). We also applied 24 custom convolutional neural network denoisers with a U-net architecture, trained on the AAPM-Mayo Clinic Low Dose CT dataset. After segmentation, 14 radiomic features were extracted using SlicerRadiomics. Results show that the vendor-supplied DLIR gave a smaller relative error than FBP for 87% of radiomic features. 8 out of 24 custom denoisers yielded a smaller error than FBP in 50% or more of the radiomic measurements. One denoiser, (VGG16+L1 loss, 32 features, batch size 16), outperformed FBP in 84% of measurements and outperformed the vendor-supplied DLIR in 63% of the measurements. In conclusion, our results demonstrate that deep-learning-based denoising has the potential to improve the accuracy of CT radiomics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pontus Pandurevic, Alex Back, Dennis Hein, and Mats Persson "Impact of deep-learning CT image denoising on the accuracy of radiomics parameter estimation", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129252C (1 April 2024); https://doi.org/10.1117/12.3006732

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