Assessment of mass detection performance in contrast enhanced digital mammography

We address the detectability of contrast-agent enhancing masses for contrast-agent enhanced spectral mammography (CESM), a dual-energy technique providing functional projection images of breast tissue perfusion and vascularity using simulated CESM images. First, the realism of simulated CESM images from anthropomorphic breast software phantoms generated with a software X-ray imaging platform was validated. Breast texture was characterized by power-law coefficients calculated in data sets of real clinical and simulated images. We also performed a 2-alternative forced choice (2-AFC) psychophysical experiment whereby simulated and real images were presented side-by-side to an experienced radiologist to test if real images could be distinguished from the simulated images. It was found that texture in our simulated CESM images has a fairly realistic appearance. Next, the relative performance of human readers and previously developed mathematical observers was assessed for the detection of iodine-enhancing mass lesions containing different contrast agent concentrations. A four alternative-forced-choice (4 AFC) task was designed; the task for the model and human observer was to detect which one of the four simulated DE recombined images contained an iodineenhancing mass. Our results showed that the NPW and NPWE models largely outperform human performance. After introduction of an internal noise component, both observers approached human performance. The CHO observer performs slightly worse than the average human observer. There is still work to be done in improving model observers as predictors of human-observer performance. Larger trials could also improve our test statistics. We hope that in the future, this framework of software breast phantoms, virtual image acquisition and processing, and mathematical observers can be beneficial to optimize CESM imaging techniques.

[1]  W. Paul Segars,et al.  Population of 100 realistic, patient-based computerized breast phantoms for multi-modality imaging research , 2014, Medical Imaging.

[2]  Pablo Milioni de Carvalho.  Low-Dose 3D Quantitative Vascular X-ray Imaging of the Breast , 2014 .

[3]  John M Boone,et al.  Methodology for generating a 3D computerized breast phantom from empirical data. , 2009, Medical physics.

[4]  Hilde Bosmans,et al.  Two complementary model observers to evaluate reconstructions of simulated micro-calcifications in digital breast tomosynthesis , 2013, Medical Imaging.

[5]  B. Efron.  The jackknife, the bootstrap, and other resampling plans , 1987 .

[6]  M P Eckstein,et al.  Mass detection on mammograms: influence of signal shape uncertainty on human and model observers. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  Ann-Katherine Carton,et al.  A Virtual Human Breast Phantom Using Surface Meshes and Geometric Internal Structures , 2014, Digital Mammography / IWDM.

[8]  Serge Muller,et al.  Dual-energy contrast enhanced digital mammography using a new approach for breast tissue canceling , 2007, SPIE Medical Imaging.

[9]  Ann-Katherine Carton,et al.  Anatomical noise in contrast-enhanced digital mammography. Part II. Dual-energy imaging. , 2013, Medical physics.

[10]  M. Krohn,et al.  Contrast-enhanced spectral mammography versus MRI: Initial results in the detection of breast cancer and assessment of tumour size , 2013, European Radiology.

[11]  Miguel P. Eckstein,et al.  Evaluation of JPEG 2000 encoder options: human and model observer detection of variable signals in X-ray coronary angiograms , 2004, IEEE Transactions on Medical Imaging.

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

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

[14]  H H Barrett,et al.  Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[15]  Craig K. Abbey,et al.  A Practical Guide to Model Observers for Visual Detection in Synthetic and Natural Noisy Images , 2000 .

[16]  Ann-Katherine Carton,et al.  Optimization of contrast-enhanced spectral mammography depending on clinical indication , 2014, Journal of medical imaging.

[17]  S Suryanarayanan,et al.  Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: application for mammography. , 2010, Medical physics.

[18]  James G. Mainprize,et al.  An Observer Model for Lesion Detectability in Contrast-Enhanced Digital Mammography , 2010, Digital Mammography / IWDM.