Virtual assessment of stereoscopic viewing of digital breast tomosynthesis projection images

Abstract. Digital breast tomosynthesis (DBT) acquires a series of projection images from different angles as an x-ray source rotates around the breast. Such imaging geometry lends DBT naturally to stereoscopic viewing as two projection images with a reasonable separation angle can easily form a stereo pair. This simulation study assessed the efficacy of stereo viewing of DBT projection images. Three-dimensional computational breast phantoms with realistically shaped synthetic lesions were scanned by three simulated DBT systems. The projection images were combined into a sequence of stereo pairs and presented to a stereomatching-based model observer for deciding lesion presence. Signal-to-noise ratio was estimated, and the detection performance with stack viewing of reconstructed slices was the benchmark. We have shown that: (1) stereo viewing of projection images may underperform stack viewing of reconstructed slices for current DBT geometries; (2) DBT geometries may impact the efficacy of the two viewing modes differently: narrow-arc and wide-arc geometries may be better for stereo viewing and stack viewing, respectively; (3) the efficacy of stereo viewing may be more robust than stack viewing to reductions in dose. While in principle stereo viewing is potentially effective for visualizing DBT data, effective stereo viewing may require specifically optimized DBT image acquisition.

[1]  Subok Park,et al.  Model observer design for multi-signal detection in the presence of anatomical noise. , 2017, Physics in medicine and biology.

[2]  Federica Zanca,et al.  Two-view and single-view tomosynthesis versus full-field digital mammography: high-resolution X-ray imaging observer study. , 2012, Radiology.

[3]  J. Boone,et al.  Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities. , 2013, Physics in medicine and biology.

[4]  Alan C. Bovik,et al.  A Steerable, Multiscale Singularity Index , 2013, IEEE Signal Processing Letters.

[5]  Nooshin Kiarashi,et al.  Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study , 2016, Journal of medical imaging.

[6]  Kyle J Myers,et al.  Optimization of digital breast tomosynthesis (DBT) acquisition parameters for human observers: effect of reconstruction algorithms , 2017, Physics in medicine and biology.

[7]  Rongping Zeng,et al.  Evaluating the sensitivity of the optimization of acquisition geometry to the choice of reconstruction algorithm in digital breast tomosynthesis through a simulation study. , 2015, Physics in medicine and biology.

[8]  J. Iqbal,et al.  Are two-centimeter breast cancers large or small? , 2013, Current oncology.

[9]  Anne Marie Murphy,et al.  Beyond the mammography quality standards act: measuring the quality of breast cancer screening programs. , 2014, AJR. American journal of roentgenology.

[10]  Ehsan Samei,et al.  Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection. , 2011, Medical physics.

[11]  Mia K. Markey,et al.  Computational assessment of stereoscopic viewing a sequence of stereo pairs of breast tomosynthesis projection images , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Erik Fredenberg,et al.  The influence of anatomical noise on optimal beam quality in mammography. , 2014, Medical physics.

[13]  Alan C. Bovik,et al.  Stereoscopic versus monoscopic detection of masses on breast tomosynthesis projection images , 2012, Medical Imaging.

[14]  S. Glick,et al.  Evaluation of a variable dose acquisition technique for microcalcification and mass detection in digital breast tomosynthesis. , 2009, Medical physics.

[15]  Nooshin Kiarashi,et al.  Finite-element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation. , 2016, Medical physics.

[16]  Martin J Yaffe,et al.  The relationship between anatomic noise and volumetric breast density for digital mammography. , 2012, Medical physics.

[17]  Isabel dos Santos Silva,et al.  The spatial distribution of radiodense breast tissue: a longitudinal study , 2009, Breast Cancer Research.

[18]  A E Burgess Visual signal detection with two-component noise: low-pass spectrum effects. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[19]  Kate Northstone,et al.  Randot Preschool Stereoacuity Test: normative data and validity. , 2007, Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus.

[20]  Mia K. Markey,et al.  Model observer design for detecting multiple abnormalities in anatomical background images , 2016, SPIE Medical Imaging.

[21]  H. Hirschmüller Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information , 2005, CVPR.

[22]  Ehsan Samei,et al.  Population of 224 realistic human subject-based computational breast phantoms. , 2015, Medical physics.

[23]  I. Sechopoulos A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications. , 2013, Medical physics.

[24]  Matt A. King,et al.  Channelized hotelling and human observer correlation for lesion detection in hepatic SPECT imaging. , 2000, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[25]  B. Julesz Foundations of Cyclopean Perception , 1971 .

[26]  Aldo Badano,et al.  Computational observers and visualization methods for stereoscopic medical imaging. , 2014, Optics express.

[27]  Bo Zhao,et al.  Image artifacts in digital breast tomosynthesis: investigation of the effects of system geometry and reconstruction parameters using a linear system approach. , 2008, Medical physics.

[28]  Carrie M. Rochman,et al.  Digital Breast Tomosynthesis in the Diagnostic Setting: Indications and Clinical Applications. , 2015, Radiographics : a review publication of the Radiological Society of North America, Inc.

[29]  R M Nishikawa,et al.  Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. , 2010, Medical physics.

[30]  Alan C. Bovik,et al.  Disparity Estimation on Stereo Mammograms , 2015, IEEE Transactions on Image Processing.

[31]  Thomas Boehm,et al.  Influence of breast lesion size and histologic findings on tumor detection rate of a computer-aided detection system. , 2003, Radiology.

[32]  Xin He,et al.  Model Observers in Medical Imaging Research , 2013, Theranostics.

[33]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  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.

[35]  Fabio Falcini,et al.  Multicentric/multifocal breast cancer: Overview, biology, and therapy , 2013 .

[36]  Jovan G. Brankov,et al.  Optimization of the internal noise models for channelized Hotelling observer , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[37]  Wei Zhao,et al.  Three-dimensional linear system analysis for breast tomosynthesis. , 2008, Medical physics.

[38]  Marcus Nyström,et al.  Investigation of viewing procedures for interpretation of breast tomosynthesis image volumes: a detection-task study with eye tracking , 2012, European Radiology.

[39]  Ehsan Samei,et al.  Optimized image acquisition for breast tomosynthesis in projection and reconstruction space. , 2009, Medical physics.

[40]  Mia K. Markey,et al.  A stereo matching model observer for stereoscopic viewing of 3D medical images , 2014, Medical Imaging.

[41]  Emily F Conant,et al.  Breast cancer screening using tomosynthesis in combination with digital mammography. , 2014, JAMA.

[42]  Alan C. Bovik,et al.  Stereoscopic Interpretation of Low-Dose Breast Tomosynthesis Projection Images , 2013, Journal of Digital Imaging.

[43]  Matthew A. Kupinski,et al.  Ideal-Observer Performance under Signal and Background Uncertainty , 2003, IPMI.

[44]  Robert M. Nishikawa,et al.  Stereoscopic digital mammography: improved specificity and reduced rate of recall in a prospective clinical trial. , 2013, Radiology.

[45]  P C Brennan,et al.  Digital tomosynthesis: a new future for breast imaging? , 2013, Clinical radiology.

[46]  Stephen J. Glick,et al.  Effect of postreconstruction filter strength on microcalcification detection at different imaging doses in digital breast tomosynthesis: human and model observer studies , 2012, Medical Imaging.

[47]  R. L. Birdwell Digital Breast Tomosynthesis: A Pilot Observer Study , 2009 .

[48]  Bo Zhao,et al.  Imaging performance of an amorphous selenium digital mammography detector in a breast tomosynthesis system. , 2008, Medical physics.

[49]  Mia K. Markey,et al.  Digital breast tomosynthesis for detecting multifocal and multicentric breast cancer: influence of acquisition geometry on model observer performance in breast phantom images , 2017, Medical Imaging.

[50]  Arthur E Burgess,et al.  Signal detection in power-law noise: effect of spectrum exponents. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[51]  Robert M Lewitt,et al.  Small nodule detectability evaluation using a generalized scan-statistic model , 2006, Physics in medicine and biology.

[52]  I. Sechopoulos A review of breast tomosynthesis. Part I. The image acquisition process. , 2013, Medical physics.

[53]  Kyle J. Myers,et al.  Investigating the feasibility of using partial least squares as a method of extracting salient information for the evaluation of digital breast tomosynthesis , 2013, Medical Imaging.

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

[55]  Carl J. D'Orsi,et al.  Stereoscopic digital mammography: improving detection and diagnosis of breast cancer , 2001, CARS.

[56]  Lubomir M. Hadjiiski,et al.  Image quality of microcalcifications in digital breast tomosynthesis: effects of projection-view distributions. , 2011, Medical physics.

[57]  Luis de Sisternes,et al.  A computational model to generate simulated three-dimensional breast masses. , 2015, Medical physics.

[58]  Miguel P Eckstein,et al.  Evaluation of internal noise methods for Hotelling observer models. , 2007, Medical physics.

[59]  Prathima Kanumuri,et al.  Characteristics of Multifocal and Multicentric Breast Cancers , 2015, Annals of Surgical Oncology.

[60]  Alessandro Neri,et al.  “Clinical significance of multifocal and multicentric breast cancers and choice of surgical treatment: a retrospective study on a series of 1158 cases” , 2015, BMC Surgery.

[61]  B. Rogers,et al.  Similarities between motion parallax and stereopsis in human depth perception , 1982, Vision Research.

[62]  S Park,et al.  WE-DE-207B-03: Influence of Local Anatomical Variations On Detection of Multifocal and Multicentric Breast Cancer. , 2016, Medical physics.

[63]  Kyle J. Myers,et al.  Comparison of Channel Methods and Observer Models for the Task-Based Assessment of Multi-Projection Imaging in the Presence of Structured Anatomical Noise , 2016, IEEE Transactions on Medical Imaging.

[64]  Joseph Y. Lo,et al.  Lesion detectability in stereoscopically viewed digital breast tomosynthesis projection images: a model observer study with anthropomorphic computational breast phantoms , 2017, Medical Imaging.

[65]  E. Birch,et al.  Assessment of a new Distance Randot stereoacuity test. , 2006, Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus.

[66]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[67]  Andrew Smith Fundamentals of Breast Tomosynthesis Improving the Performance of Mammography , 2008 .

[68]  P. Prorok,et al.  Breast-Cancer Tumor Size, Overdiagnosis, and Mammography Screening Effectiveness. , 2016, The New England journal of medicine.