Christopher E. Tromans

Evaluating the Effectiveness of Using Standard Mammogram Form to Predict Breast Cancer Risk: Case-Control Study

Breast density is a well-known breast cancer risk factor. Most current methods of measuring breast density are area based and subjective. Standard mammogram form (SMF) is a computer program using a volumetric approach to estimate the percent density in the breast. The aim of this study is to evaluate the current implementation of SMF as a predictor of breast cancer risk by comparing it with other widely used density measurement methods. The case-control study comprised 634 cancers with 1,880 age-matched controls combined from the Cambridge and Norwich Breast Screening Programs. Data collection involved assessing the films based both on Wolfes parenchymal patterns and on visual estimation of percent density and then digitizing the films for computer analysis (interactive threshold technique and SMF). Logistic regression was used to produce odds ratios associated with increasing categories of breast density. Density measures from all four methods were strongly associated with breast cancer risk in the overall population. The stepwise rises in risk associated with increasing density as measured by the threshold method were 1.37 [95% confidence interval (95% CI), 1.03-1.82], 1.80 (95% CI, 1.36-2.37), and 2.45 (95% CI, 1.86-3.23). For each increasing quartile of SMF density measures, the risks were 1.11 (95% CI, 0.85-1.46), 1.31 (95% CI, 1.00-1.71), and 1.92 (95% CI, 1.47-2.51). After the model was adjusted for SMF results, the threshold readings maintained the same strong stepwise increase in density-risk relationship. On the contrary, once the model was adjusted for threshold readings, SMF outcome was no longer related to cancer risk. The available implementation of SMF is not a better cancer risk predictor compared with the thresholding method. (Cancer Epidemiol Biomarkers Prev 2008;17(5):1074–81)

The standard attenuation rate for quantitative mammography

We introduce the Standard Attenuation Rate (SAR), a quantitative, and normalised measure of radiodensity per unit distance traversed by the primary beam incident on each pixel of an x-ray mammogram is presented We sketch an algorithm to compute the SAR The calculation utilises a physics model of image formation, including consideration of photon production in the x-ray tube, photon detection within the image receptor, and photon scattering occurring within the tissues of the breast Using the model, the difference in the flux incident upon, and exiting from, the breast is quantified relative to a reference material Experimental validation of the SAR representation is presented, based on a tissue equivalent phantom designed and manufactured specifically for the purpose The observed performance across the clinical range of acquisition parameters is very promising, supporting the suitability of this approach to form the basis of a next generation of diagnostic techniques based on quantitative tissue measurement.

Patient Specific Dose Calculation Using Volumetric Breast Density for Mammography and Tomosynthesis

Minimising the mean glandular dose (MGD) received by the patient whilst maximising image contrast during mammographic imaging is of paramount importance due to the widespread use of the modality for screening, where subjects are for the most part healthy. The advent of digital mammography brought about a general reduction in MGD, however the introduction of tomosynthesis, particularly when used in combination with conventional projection mammography has the potential for unwanted and often unnecessary MGD increases. We describe a method to calculate the patient-specific MGD using a representation of the patient’s volumetric breast density to derive the breast glandularity. This personalises the MGD to the individual woman, rather than assuming a constant value, or one that depends solely on compressed breast thickness. The calculated patient specific MGDs are compared to those reported by the manufacturer for a database of 2D mammograms. Though agreement is generally good for dense breasts, we have found that the MGD is underestimated in fatty breasts. A separate database of 2D mammogram and 3D tomosynthesis acquisitions acquired in “combo” is also analysed. In general, the MGDs are approximately equal for dense (VDG 3 and 4) breasts, but fatty (VDG 1 and 2) breasts exhibited significant differences with tomosynthesis MGDs being higher than mammogram MGDs for these cases.

A scatter model for use in measuring volumetric mammographic breast density

In order that accurate measurements of volumetric breast density may be made, a model of the scattered radiation present within an image is required: such a model is presented here. The model has the advantageous property of utilising a model of photon scattering, allowing cross sections to be calculated, and thus allowing scatter to be modelled for any object. An analysis is presented which uses the model to quantify the effect of varying small angle scattering properties of breast tissues; and the effect of the height within the breast at which tissues are present. Since the details of the anatomical structure of the breast under measurement are unknown, their precise effect on scatter cannot be calculated, but this model is used here to establish error bounds on the scatter estimate, which is a significant contribution to the error in breast density measurement.

A model of primary and scattered photon fluence for mammographic x-ray image quantification

We present an efficient method to calculate the primary and scattered x-ray photon fluence component of a mammographic image. This can be used for a range of clinically important purposes, including estimation of breast density, personalized image display, and quantitative mammogram analysis. The method is based on models of: the x-ray tube; the digital detector; and a novel ray tracer which models the diverging beam emanating from the focal spot. The tube model includes consideration of the anode heel effect, and empirical corrections for wear and manufacturing tolerances. The detector model is empirical, being based on a family of transfer functions that cover the range of beam qualities and compressed breast thicknesses which are encountered clinically. The scatter estimation utilizes optimal information sampling and interpolation (to yield a clinical usable computation time) of scatter calculated using fundamental physics relations. A scatter kernel arising around each primary ray is calculated, and these are summed by superposition to form the scatter image. Beam quality, spatial position in the field (in particular that arising at the air-boundary due to the depletion of scatter contribution from the surroundings), and the possible presence of a grid, are considered, as is tissue composition using an iterative refinement procedure. We present numerous validation results that use a purpose designed tissue equivalent step wedge phantom. The average differences between actual acquisitions and modelled pixel intensities observed across the adipose to fibroglandular attenuation range vary between 5% and 7%, depending on beam quality and, for a single beam quality are 2.09% and 3.36% respectively with and without a grid.

Quantification and normalization of x-ray mammograms

The analysis of (x-ray) mammograms remains qualitative, relying on the judgement of clinicians. We present a novel method to compute a quantitative, normalized measure of tissue radiodensity traversed by the primary beam incident on each pixel of a mammogram, a measure we term the standard attenuation rate (SAR). SAR enables: the estimation of breast density which is linked to cancer risk; direct comparison between images; the full potential of computer aided diagnosis to be utilized; and a basis for digital breast tomosynthesis reconstruction. It does this by removing the effects of the imaging conditions under which the mammogram is acquired. First, the x-ray spectrum incident upon the breast is calculated, and from this, the energy exiting the breast is calculated. The contribution of scattered radiation is calculated and subtracted. The SAR measure is the scaling factor that must be applied to the reference material in order to match the primary attenuation of the breast. Specifically, this is the scaled reference material attenuation which when traversed by an identical beam to that traversing the breast, and when subsequently detected, results in the primary component of the pixel intensity observed in the breast image. We present results using two tissue equivalent phantoms, as well as a sensitivity analysis to detector response changes over time and possible errors in compressed thickness measurement.

Investigating the replacement of the physical anti-scatter grid with digital image processing

Scattered photons degrade mammographic image quality, so, almost universally, a physical anti-scatter grid is used to limit their effect Physical grids are not completely effective in rejecting only scattered photons, so patient dose must be increased in order to maintain low levels of quantum noise The standard attenuation rate (SAR), a quantitative normalised representation of breast tissue for image analysis applications, incorporates a model of scatter, and a software correction of the image blurring arising from scatter within the image signal A tissue equivalent phantom is used to investigate the possibility, in terms of both image sharpness and noise, of replacing the physical grid with the software correction in the SAR Encouraging results are reported, software correction almost matching the performance of the grid, whilst maintaining a superior signal-to-noise ratio.

A hypothesis-test framework for quantitative lesion detection and diagnosis

A method is presented which quantifies the radiodensity of lesions in projection images, providing a diagnostic indicator to better inform the decisions of both human readers and computer algorithms. The models of image formation underlying the Standard Attenuation Rate (SAR) are used to facilitate the forward simulation of the appearance of a lesion in a breast. By forming hypotheses, informed from measurements on the acquired image, virtual 3D scenes are constructed which predict the size, position and radiodensity of a suspect lesion and the surrounding breast tissue. Comparisons between simulations of this scene, and the acquired image enable both the refinement of the hypothesis, and the assessment of the likelihood of the hypothesis being correct. In the event of a high likelihood of correctness, the hypothesised lesion informs diagnosis. The application of the method to a patient image containing a cyst shows it has an attenuation corresponding to water (SAR 1.246), and an invasive carcinoma which is considerably denser at SAR 2.27. Thus the technique yields a quantitative radiodensity measure for discrimination in diagnostic decision making.

A clustering method for the extraction of microcalcifications using epipolar curves in digital breast tomosynthesis

DBT provides significantly more information than mammography This offers new opportunities to improve existing microcalcification detection methods In a companion work in this volume, we showed that the use of epipolar curves can improve both the sensitivity and specificity of microcalcification detection In this paper, we develop a clustering algorithm to form epipolar curves from candidate microcalcifications (which may be noise points), obtained after applying a detection algorithm to each individual projection This enables the subsequent 3D analysis for the classification of microcalcification clusters.

Microcalcification detection in digital breast tomosynthesis using an epipolar curve approach

The detection of microcalcifications is a key task in the early detection of breast cancer Digital breast tomosynthesis (DBT) offers new opportunities to improve existing microcalcification detection methods By utilizing the multiple projections in DBT, and a model of the DBT acquisition system, we propose the use of epipolar curves to constrain the position of a microcalcification in the multiple DBT views We show how this can improve both the sensitivity and specification of microcalcification detection.