Deep CNN task-based image quality assessment: application to digital breast tomosynthesis reconstruction and denoising

Mingjie Gao; Mark A. Helvie; Ravi K. Samala; Lubomir M. Hadjiiski; Jeffrey A. Fessler; Heang-Ping Chan

doi:10.1117/12.2655419

7 April 2023 Deep CNN task-based image quality assessment: application to digital breast tomosynthesis reconstruction and denoising

Mingjie Gao, Mark A. Helvie, Ravi K. Samala, Lubomir M. Hadjiiski, Jeffrey A. Fessler, Heang-Ping Chan

Author Affiliations +

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 1246319 (2023) https://doi.org/10.1117/12.2655419
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Image noise in digital breast tomosynthesis (DBT) reduces the detectability of subtle signs of breast cancer such as microcalcifications (MC). This study investigated the potential of applying DNGAN, our previously developed deep convolutional neural network (CNN) DBT denoiser, to different reconstruction stages to improve the image quality, including projection views before reconstruction, intermediate images during iterative reconstruction, or final reconstructed images, and a combination of the different stages of denoising. We also proposed two CNNs as task-based image quality measures to compare different reconstructions: a CNN noise estimator (CNN-NE) trained to evaluate the noise level of a given DBT image, and a CNN MC classifier (CNN-MC) trained to estimate the detectability of MCs by classifying clustered MCs from MC-free backgrounds. The CNN-NE was trained with virtual DBTs reconstructed from projections generated by the VICTRE tool over a wide range of noise levels. The CNN-MC was trained with human subject DBTs. We adopted the training strategy of transfer learning to train CNN-NE and CNN-MC due to the limited training data. We found that the increase in AUC estimated by the CNN-MC classifier correlated well with the decrease in image noise by DNGAN estimated by CNN-NE on an independent human subject test set. A combination of DNGAN-regularized plug-and-play reconstruction and an additional DNGAN post-reconstruction denoising achieved the lowest noise level and the best MC detectability. The AUC and noise rankings from the CNNs matched our visual judgement that less noisy images had better MC conspicuity.

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Mingjie Gao, Mark A. Helvie, Ravi K. Samala, Lubomir M. Hadjiiski, Jeffrey A. Fessler, and Heang-Ping Chan "Deep CNN task-based image quality assessment: application to digital breast tomosynthesis reconstruction and denoising", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 1246319 (7 April 2023); https://doi.org/10.1117/12.2655419

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