Bench testing performance of deep learning-based CT image denoising methods: influence of object background on image sharpness and noise texture

Rongping Zeng; Prabhat Kc; Brandon Nelson

doi:10.1117/12.2653635

7 April 2023 Bench testing performance of deep learning-based CT image denoising methods: influence of object background on image sharpness and noise texture

Rongping Zeng, Prabhat Kc, Brandon Nelson

Author Affiliations +

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124631O (2023) https://doi.org/10.1117/12.2653635
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

In CT imaging, a standard set of bench testing performances, including modulation transfer function (MTF) and noise power spectrum (NPS), is considered essential to ensure that patient images from CT systems have sufficient image quality. These performances are measured using CT quality assurance (QA) phantoms commonly of a uniform background. However, for deep learning (DL)-based CT image denoising models that are often overwhelmingly trained with patient CT images, it is unclear whether bench testing performances measured with a uniform background reflect the performance in anatomic patient backgrounds. In this work, we insert test objects into a uniform phantom and a patient slice and simulate their CT images to facilitate the measurement of contrast-dependent MTF (cdMTF) and NPS, which are indicators for image sharpness and noise properties. We compare the cdMTF and NPS behaviors of three DL denoising models (REDCNN, REDCNN-tv and DnCNN) measured with images of uniform and patient backgrounds. We observed that cdMTF performance appears consistent between backgrounds, but noise reduction and texture performances can vary substantially. We suggest that caution should be taken when conclusions regarding noise-related performances of a DL-based CT image denoiser are made purely based on measurements from a uniform phantom.

Conference Presentation

Citation Download Citation

Rongping Zeng, Prabhat Kc, and Brandon Nelson "Bench testing performance of deep learning-based CT image denoising methods: influence of object background on image sharpness and noise texture", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124631O (7 April 2023); https://doi.org/10.1117/12.2653635

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