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

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.

[1]  Andrew D. A. Maidment,et al.  Optimized generation of high resolution breast anthropomorphic software phantoms. , 2012, Medical physics.

[2]  Frank W. Samuelson,et al.  Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial , 2018, JAMA network open.

[3]  Andrew D. A. Maidment,et al.  Restoration of low-dose digital breast tomosynthesis , 2018 .

[4]  Arthur Burgess.  On the noise variance of a digital mammography system. , 2004, Medical physics.

[5]  Ioannis Sechopoulos,et al.  Optimization of the acquisition geometry in digital tomosynthesis of the breast. , 2009, Medical physics.

[6]  Tao Wu,et al.  A comparison of reconstruction algorithms for breast tomosynthesis. , 2004, Medical physics.

[7]  Ehsan Samei,et al.  Does image quality matter? Impact of resolution and noise on mammographic task performance. , 2007, Medical physics.

[8]  Thomas Mertelmeier,et al.  Experimental validation of a three-dimensional linear system model for breast tomosynthesis. , 2008, Medical physics.

[9]  M J Yaffe,et al.  Screen-film and digital mammography. Image quality and radiation dose considerations. , 2000, Radiologic clinics of North America.

[10]  Wei Zhao,et al.  Imaging performance of amorphous selenium based flat-panel detectors for digital mammography: characterization of a small area prototype detector. , 2003, Medical physics.

[11]  Hilde Bosmans,et al.  Characterisation of noise and sharpness of images from four digital breast tomosynthesis systems for simulation of images for virtual clinical trials , 2017, Physics in medicine and biology.

[12]  W. Marsden.  I and J , 2012 .

[13]  I. Cunningham.  Applied Linear-Systems Theory , 2000 .

[14]  Andrew D. A. Maidment,et al.  Virtual clinical trial of lesion detection in digital mammography and digital breast tomosynthesis , 2018, Medical Imaging.

[15]  R L Siddon,et al.  Prism representation: a 3D ray-tracing algorithm for radiotherapy applications , 1985, Physics in medicine and biology.

[16]  Kyle J Myers,et al.  A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data. , 2013, Medical physics.

[17]  Ann-Katherine Carton,et al.  Development of a physical 3D anthropomorphic breast phantom. , 2011, Medical physics.

[18]  Andrew D. A. Maidment,et al.  Method for Simulating Dose Reduction in Digital Breast Tomosynthesis , 2017, IEEE Transactions on Medical Imaging.

[19]  F. Haight.  Handbook of the Poisson Distribution , 1967 .

[20]  Aruna A. Vedula,et al.  A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging. , 2006, Medical physics.

[21]  Raffaella Rossi,et al.  Physical characteristics of GE Senographe Essential and DS digital mammography detectors. , 2008, Medical physics.

[22]  James T. Dobbins.  Image Quality Metrics for Digital Systems , 2000 .

[23]  Walter Huda,et al.  Experimental investigation of the dose and image quality characteristics of a digital mammography imaging system. , 2003, Medical physics.