Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study

A simple and accurate measurement of breast density is crucial for the understanding of its impact in breast cancer risk models. The feasibility to quantify volumetric breast density with a photon-counting spectral mammography system has been investigated using both computer simulations and physical phantom studies. A computer simulation model involved polyenergetic spectra from a tungsten anode x-ray tube and a Si-based photon-counting detector has been evaluated for breast density quantification. The figure-of-merit (FOM), which was defined as the signal-to-noise ratio of the dual energy image with respect to the square root of mean glandular dose, was chosen to optimize the imaging protocols, in terms of tube voltage and splitting energy. A scanning multi-slit photon-counting spectral mammography system has been employed in the experimental study to quantitatively measure breast density using dual energy decomposition with glandular and adipose equivalent phantoms of uniform thickness. Four different phantom studies were designed to evaluate the accuracy of the technique, each of which addressed one specific variable in the phantom configurations, including thickness, density, area and shape. In addition to the standard calibration fitting function used for dual energy decomposition, a modified fitting function has been proposed, which brought the tube voltages used in the imaging tasks as the third variable in dual energy decomposition. For an average sized 4.5 cm thick breast, the FOM was maximized with a tube voltage of 46 kVp and a splitting energy of 24 keV. To be consistent with the tube voltage used in current clinical screening exam (∼32 kVp), the optimal splitting energy was proposed to be 22 keV, which offered a FOM greater than 90% of the optimal value. In the experimental investigation, the root-mean-square (RMS) error in breast density quantification for all four phantom studies was estimated to be approximately 1.54% using standard calibration function. The results from the modified fitting function, which integrated the tube voltage as a variable in the calibration, indicated a RMS error of approximately 1.35% for all four studies. The results of the current study suggest that photon-counting spectral mammography systems may potentially be implemented for an accurate quantification of volumetric breast density, with an RMS error of less than 2%, using the proposed dual energy imaging technique.

[1]  J. Heine,et al.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 2. Serial breast tissue change and related temporal influences. , 2002, Academic radiology.

[2]  J. Boone,et al.  An accurate method for computer-generating tungsten anode x-ray spectra from 30 to 140 kV. , 1997, Medical physics.

[3]  Björn Cederström,et al.  Scatter rejection in multislit digital mammography. , 2006, Medical physics.

[4]  Björn Cederström,et al.  Physical characterization of a scanning photon counting digital mammography system based on Si-strip detectors. , 2007, Medical physics.

[5]  Sabee Molloi,et al.  Breast composition measurement with a cadmium-zinc-telluride based spectral computed tomography system. , 2012, Medical physics.

[6]  M. Danielsson,et al.  Photon-counting spectral computed tomography using silicon strip detectors: a feasibility study , 2010, Physics in medicine and biology.

[7]  P. Stomper,et al.  Mammographic changes associated with postmenopausal hormone replacement therapy: a longitudinal study. , 1990, Radiology.

[8]  Sabee Molloi,et al.  Quantification of breast density with dual energy mammography: an experimental feasibility study. , 2010, Medical physics.

[9]  Jb Sheffield,et al.  ImageJ, A Useful Tool for Biological Image Processing and Analysis , 2007, Microscopy and Microanalysis.

[10]  C. Byrne,et al.  Studying mammographic density: implications for understanding breast cancer. , 1997, Journal of the National Cancer Institute.

[11]  A. Macovski,et al.  Energy-selective reconstructions in X-ray computerised tomography , 1976, Physics in medicine and biology.

[12]  V. McCormack,et al.  Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis , 2006, Cancer Epidemiology Biomarkers & Prevention.

[13]  K. Oh,et al.  Significance of follow-up mammography in estimating the effect of tamoxifen in breast cancer patients who have undergone surgery. , 1999, AJR. American journal of roentgenology.

[14]  Ralph Highnam,et al.  Comparison of a New and Existing Method of Mammographic Density Measurement: Intramethod Reliability and Associations with Known Risk Factors , 2007, Cancer Epidemiology Biomarkers & Prevention.

[15]  J. Wolfe,et al.  Mammographic features and breast cancer risk: effects with time, age, and menopause status. , 1995, Journal of the National Cancer Institute.

[16]  Michael Brady,et al.  Evaluating the Effectiveness of Using Standard Mammogram Form to Predict Breast Cancer Risk: Case-Control Study , 2008, Cancer Epidemiology Biomarkers & Prevention.

[17]  Gyula Faigel,et al.  X-Ray Holography , 1999 .

[18]  Sabee Molloi,et al.  Quantification of breast density with dual energy mammography: a simulation study. , 2008, Medical physics.

[19]  Navin Parekh,et al.  Effects of mammographic density and benign breast disease on breast cancer risk (United States) , 2001, Cancer Causes & Control.

[20]  P. Shikhaliev,et al.  Photon counting spectral CT versus conventional CT: comparative evaluation for breast imaging application , 2011, Physics in medicine and biology.

[21]  J. Wolfe Breast patterns as an index of risk for developing breast cancer. , 1976, AJR. American journal of roentgenology.

[22]  M. Szklo,et al.  Mammographic parenchymal patterns and breast cancer risk. , 1987, Epidemiologic reviews.

[23]  M. Brady,et al.  Comparing measurements of breast density , 2007, Physics in medicine and biology.

[24]  E. Frey,et al.  MicroCT with energy-resolved photon-counting detectors , 2011, Physics in medicine and biology.

[25]  S. Molloi,et al.  Scatter correction in digital mammography based on image deconvolution , 2010, Physics in medicine and biology.

[26]  John M Boone,et al.  Classification of breast computed tomography data. , 2008, Medical physics.

[27]  J. Wolfe Risk for breast cancer development determined by mammographic parenchymal pattern , 1976, Cancer.

[28]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[29]  Biao Chen,et al.  Cone-beam volume CT breast imaging: feasibility study. , 2002, Medical physics.

[30]  Mats Danielsson,et al.  Single-shot dual-energy subtraction mammography with electronic spectrum splitting: feasibility. , 2006, European journal of radiology.

[31]  John M Boone,et al.  Technique factors and their relationship to radiation dose in pendant geometry breast CT. , 2005, Medical physics.

[32]  N. Boyd,et al.  Mammographic density and the risk and detection of breast cancer. , 2007, The New England journal of medicine.

[33]  Reyer Zwiggelaar,et al.  Texture Based Segmentation , 2006, Digital Mammography / IWDM.

[34]  Sabee Molloi,et al.  Radiation dose reduction using a CdZnTe-based computed tomography system: comparison to flat-panel detectors. , 2010, Medical physics.

[35]  Jennifer A Harvey,et al.  Quantitative assessment of mammographic breast density: relationship with breast cancer risk. , 2004, Radiology.

[36]  Sabee Molloi,et al.  Least squares parameter estimation methods for material decomposition with energy discriminating detectors. , 2010, Medical physics.

[37]  J. Heine,et al.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors. , 2002, Academic radiology.

[38]  N. Boyd,et al.  The quantitative analysis of mammographic densities. , 1994, Physics in medicine and biology.

[39]  D. Venzon,et al.  Effect of tamoxifen on mammographic density. , 2000, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[40]  Dan Rico,et al.  A volumetric method for estimation of breast density on digitized screen-film mammograms. , 2003, Medical physics.

[41]  Hans Bornefalk,et al.  Contrast-enhanced dual-energy mammography using a scanned multislit system: evaluation of a differential beam filtering technique , 2007, J. Electronic Imaging.

[42]  N. Boyd,et al.  Effects at two years of a low-fat, high-carbohydrate diet on radiologic features of the breast: results from a randomized trial. Canadian Diet and Breast Cancer Prevention Study Group. , 1997, Journal of the National Cancer Institute.

[43]  Y. Furukawa,et al.  Si and CdTe pixel detector developments at SPring-8 , 2011 .

[44]  N. Boyd,et al.  The risk of breast cancer associated with mammographic parenchymal patterns: a meta-analysis of the published literature to examine the effect of method of classification. , 1992, Cancer detection and prevention.

[45]  E. Fishell,et al.  Radio-free America: what to do with the waste. , 1994, Environmental health perspectives.

[46]  M Aslund,et al.  Detectors for the future of X-ray imaging. , 2010, Radiation protection dosimetry.

[47]  N F Boyd,et al.  Mammographic parenchymal pattern and breast cancer risk: a critical appraisal of the evidence. , 1988, American journal of epidemiology.

[48]  P. Shikhaliev Computed tomography with energy-resolved detection: a feasibility study , 2008, Physics in medicine and biology.

[49]  A Fenster,et al.  An accurate method for direct dual-energy calibration and decomposition. , 1990, Medical physics.

[50]  N. Obuchowski,et al.  Automatic segmentation of mammographic density. , 2001, Academic radiology.

[51]  S. Kaufhold,et al.  Comparison of methods for the quantification of montmorillonite in bentonites , 2002 .

[52]  A. Miller,et al.  Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. , 1995, Journal of the National Cancer Institute.

[53]  S. Molloi,et al.  Segmentation and quantification of materials with energy discriminating computed tomography: a phantom study. , 2010, Medical physics.

[54]  J. Boone,et al.  Dedicated breast CT: initial clinical experience. , 2008, Radiology.

[55]  J. Boone Normalized glandular dose (DgN) coefficients for arbitrary X-ray spectra in mammography: computer-fit values of Monte Carlo derived data. , 2002, Medical physics.

[56]  Tuneyoshi Kamae,et al.  Application of CdTe for the NeXT Mission , 2005 .

[57]  Ruola Ning,et al.  A quantitative analysis of breast densities using cone beam CT images , 2009, Medical Imaging.

[58]  Paola Coan,et al.  X-ray phase-contrast imaging: from pre-clinical applications towards clinics , 2013, Physics in medicine and biology.

[59]  Olivier Alonzo-Proulx,et al.  Effect of Tissue Thickness Variation in Volumetric Breast Density Estimation , 2008, Digital Mammography / IWDM.

[60]  A. Oza,et al.  Mammographic parenchymal patterns: a marker of breast cancer risk. , 1993, Epidemiologic reviews.

[61]  Erik Fredenberg,et al.  Contrast-enhanced spectral mammography with a photon-counting detector. , 2010, Medical physics.

[62]  J. Boone,et al.  Glandular breast dose for monoenergetic and high-energy X-ray beams: Monte Carlo assessment. , 1999, Radiology.

[63]  Erik Fredenberg,et al.  Energy resolution of a photon-counting silicon strip detector , 2010, 2101.07789.