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Technical Note: Noise models for virtual clinical trials of digital breast tomosynthesis

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)2683-2689
Number of pages7
JournalMedical Physics
Issue number6
Publication statusPublished - 1 Jun 2019
Publication typeA1 Journal article-refereed


Purpose: To investigate the use of an affine-variance noise model, with correlated quantum noise and spatially dependent quantum gain, for the simulation of noise in virtual clinical trials (VCT) of digital breast tomosynthesis (DBT). Methods: Two distinct technologies were considered: an amorphous-selenium (a-Se) detector with direct conversion and a thallium-doped cesium iodide (CsI(Tl)) detector with indirect conversion. A VCT framework was used to generate noise-free projections of a uniform three-dimensional simulated phantom, whose geometry and absorption match those of a polymethyl methacrylate (PMMA) uniform physical phantom. The noise model was then used to generate noisy observations from the simulated noise-free data, while two clinically available DBT units were used to acquire projections of the PMMA physical phantom. Real and simulated projections were then compared using the signal-to-noise ratio (SNR) and normalized noise power spectrum (NNPS). Results: Simulated images reported errors smaller than 4.4% and 7.0% in terms of SNR and NNPS, respectively. These errors are within the expected variation between two clinical units of the same model. The errors increase to 65.8% if uncorrelated models are adopted for the simulation of systems featuring indirect detection. The assumption of spatially independent quantum gain generates errors of 11.2%. Conclusions: The investigated noise model can be used to accurately reproduce the noise found in clinical DBT. The assumption of uncorrelated noise may be adopted if the system features a direct detector with minimal pixel crosstalk.


  • digital breast tomosynthesis, electronic noise, noise simulation, quantum noise, virtual clinical trials

Publication forum classification

Field of science, Statistics Finland