Foveated model observer to predict human search performance on virtual digital breast tomosynthesis phantoms

Model observers for image quality assessment have been extensively used in the field of medical imaging. The majority of model observer developments have involved signal detection tasks with a few number of signal locations and models that have not explicitly incorporated the varying resolution in visual processing across the visual field (foveated vision). Here, we evaluate search performance by human and model observers in 3D search and 2D single-slice search with DBT virtual phantoms images for a simulated single simulated macrocalcification. We compare the ability of a Channelized Hotelling Observer model (CHO) and a Foveated Channelized Hotelling model (FCHO) in predicting human performance across 2D and 3D search. Human performance detecting the macrocalcification signal was significantly higher in 2D than in 3D (proportion correct, PC = 0.89 vs 0.68). However, the CHO model predicted a lower performance in 2D than in 3D search (PC = 0.84 vs 0.93). The FCHO, that processes the visual field with lowering spatial detail as the distance increases from the point of fixation, executes eye movements, and scrolls across slices, correctly predicts the relative performance for the detection of the macrocalcification in 2D and 3D search (PC = 0.92 vs 0.59). These results suggest that foveation is a key component for model observers when predicting human performance detecting small signals in DBT search.

[1]  A E Burgess,et al.  Visual signal detectability with two noise components: anomalous masking effects. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[2]  M P Eckstein,et al.  Lesion detection in structured noise. , 1995, Academic radiology.

[3]  Sheng Zhang,et al.  Model observers for complex discrimination tasks: assessments of multiple coronary stent placements , 2010, Medical Imaging.

[4]  H.H. Barrett,et al.  Model observers for assessment of image quality , 1993, 2002 IEEE Nuclear Science Symposium Conference Record.

[5]  Craig K. Abbey,et al.  Interactions of lesion detectability and size across single-slice DBT and 3D DBT , 2018, Medical Imaging.

[6]  Miguel P. Eckstein,et al.  Foveal analysis and peripheral selection during active visual sampling , 2014, Proceedings of the National Academy of Sciences.

[7]  Hilde Bosmans,et al.  Design of a model observer to evaluate calcification detectability in breast tomosynthesis and application to smoothing prior optimization. , 2016, Medical physics.

[8]  Craig K. Abbey,et al.  Observer efficiency in free-localization tasks with correlated noise , 2014, Front. Psychol..

[9]  Kyle J Myers,et al.  Channelized-ideal observer using Laguerre-Gauss channels in detection tasks involving non-Gaussian distributed lumpy backgrounds and a Gaussian signal. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  Matthew A Kupinski,et al.  Correlation between a 2D channelized Hotelling observer and human observers in a low‐contrast detection task with multislice reading in CT , 2017, Medical physics.

[11]  Craig K. Abbey,et al.  The efficiency of reading around learned backgrounds , 2006, SPIE Medical Imaging.

[12]  A. Burgess,et al.  Human observer detection experiments with mammograms and power-law noise. , 2001, Medical physics.

[13]  Andrew D. A. Maidment,et al.  Realistic Simulation of Breast Tissue Microstructure in Software Anthropomorphic Phantoms , 2014, Digital Mammography / IWDM.

[14]  Miguel P. Eckstein,et al.  Automated optimization of JPEG 2000 encoder options based on model observer performance for detecting variable signals in X-ray coronary angiograms , 2004, IEEE Transactions on Medical Imaging.

[15]  R. F. Wagner,et al.  Efficiency of human visual signal discrimination. , 1981, Science.

[16]  Mini Das,et al.  Visual-search observers for assessing tomographic x-ray image quality. , 2016, Medical physics.

[17]  A. Burgess Statistically defined backgrounds: performance of a modified nonprewhitening observer model. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  Miguel P Eckstein,et al.  The role of extra-foveal processing in 3D imaging , 2017, Medical Imaging.

[19]  Miguel P. Eckstein,et al.  The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds , 2006, IEEE Transactions on Medical Imaging.

[20]  Ewout Vansteenkiste,et al.  Channelized Hotelling observers for the assessment of volumetric imaging data sets. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[21]  Craig K. Abbey,et al.  Evaluation of search strategies for microcalcifications and masses in 3D images , 2018, Medical Imaging.

[22]  Wilson S. Geisler,et al.  Optimal eye movement strategies in visual search , 2005, Nature.

[23]  H C Gifford Efficient visual-search model observers for PET. , 2014, The British journal of radiology.

[24]  N Karssemeijer,et al.  Can a channelized Hotelling observer assess image quality in acquired mammographic images of an anthropomorphic breast phantom including image processing? , 2019, Medical physics.

[25]  Miguel P Eckstein,et al.  Foveated model observers to predict human performance in 3D images , 2017, Medical Imaging.

[26]  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.

[27]  Howard C Gifford A visual-search model observer for multislice-multiview SPECT images. , 2013, Medical physics.

[28]  M P Eckstein,et al.  Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds. , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.

[29]  Krista A. Ehinger,et al.  Comparing search patterns in digital breast tomosynthesis and full-field digital mammography: an eye tracking study , 2017, Journal of medical imaging.

[30]  Craig K. Abbey,et al.  Stabilized estimates of Hotelling-observer detection performance in patient-structured noise , 1998, Medical Imaging.

[31]  Trafton Drew,et al.  Scanners and drillers: characterizing expert visual search through volumetric images. , 2013, Journal of vision.

[32]  Andrew D. A. Maidment,et al.  OpenVCT: a GPU-accelerated virtual clinical trial pipeline for mammography and digital breast tomosynthesis , 2018, Medical Imaging.

[33]  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.

[34]  Craig K. Abbey,et al.  A foveated channelized Hotelling search model predicts dissociations in human performance in 2D and 3D images , 2019, Medical Imaging.

[35]  H L Kundel,et al.  Peripheral vision, structured noise and film reader error. , 1973, Radiology.

[36]  Craig K. Abbey,et al.  Model observers for signal-known-statistically tasks (SKS) , 2001, SPIE Medical Imaging.

[37]  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.

[38]  Hilde Bosmans,et al.  Systematic approach to a channelized Hotelling model observer implementation for a physical phantom containing mass-like lesions: Application to digital breast tomosynthesis. , 2019, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.