Denoising of mammograms subject to structural and spatially-correlated noise: a virtual clinical trial
Lucas Rodrigues Azevedo Pinheiro Borges, Marco Aurelio Corte Brochi, Marcelo A. C. Vieira, Paulo Mazzoncini de Azevedo‐Marques · 2022
Image quality directly influences the accuracy of lesion detection and characterization in x-ray mammograms. Thus, it is crucial that acceptable image quality is maintained while using as little ionizing radiation as possible. In this scenario, denoising plays an important role in recovering image quality while keeping constant radiation dose. Although most ‘off-the-shelf’ denoising algorithms assume signal-independent and frequency-independent (white) Gaussian noise, in x-ray generation and detection this assumption is seldom valid. In this work we leverage a recently published variance-stabilizing transform and a frequency-dependent denoising algorithm to address signal-dependent and frequency-dependent denoising of x-ray mammograms subject to structural and correlated noise. To illustrate the application of the proposed pipeline, we restored synthetic mammograms generated by a virtual clinical trial platform. The results showed that the denoising pipeline was able to recover the quality of mammograms acquired at lower radiation levels to achieve similar image quality of full-dose acquisitions, in terms of the QILV, residual variance and power spectrum metrics. The bias2 metric indicates that even though the pipeline is able to achieve very similar noise levels to a full-dose acquisition, there is a penalty to the signal, which becomes biased due to blur and smearing as the dose level is reduced.