Computing the scatter component of mammographic images

Simple method for computing scattered radiation in breast tomosynthesis

PURPOSE

Virtual clinical trials (VCT) are a powerful imaging tool that can be used to investigate digital breast tomosynthesis (DBT) technology. In this work, a fast and simple method is proposed to estimate the two-dimensional distribution of scattered radiation which is needed when simulating DBT geometries in VCTs.

METHODS

Monte Carlo simulations are used to precalculate scatter-to-primary ratio (SPR) for a range of low-resolution homogeneous phantoms. The resulting values can be used to estimate the two-dimensional (2D) distribution of scattered radiation arising from inhomogeneous anthropomorphic phantoms used in VCTs. The method has been validated by comparing the values of the scatter thus obtained against the results of direct Monte Carlo simulation for three different types of inhomogeneous anthropomorphic phantoms.

RESULTS

Differences between the proposed scatter field estimation method and the ground truth data for the OPTIMAM phantom had an average modulus and standard deviation of over the projected breast area of 2.4 ± 0.9% (minimum -17.0%, maximum 27.7%). The corresponding values for the University of Pennsylvania and Duke University breast phantoms were 1.8 ± 0.1% (minimum -8.7%, maximum 8.0%) and 5.1 ± 0.1% (minimum -16.2%, maximum 7.4%), respectively.

CONCLUSIONS

The proposed method, which has been validated using three of the most common breast models, is a useful tool for accurately estimating scattered radiation in VCT schemes used to study current designs of DBT system.

Breast composition measurements using retrospective standard mammogram form (SMF)

The standard mammogram form (SMF) representation of an x-ray mammogram is a standardized, quantitative representation of the breast from which the volume of non-fat tissue and breast density can be easily estimated, both of which are of significant interest in determining breast cancer risk. Previous theoretical analysis of SMF had suggested that a complete and substantial set of calibration data (such as mAs and kVp) would be needed to generate realistic breast composition measures and yet there are many interesting trials that have retrospectively collected images with no calibration data. The main contribution of this paper is to revisit our previous theoretical analysis of SMF with respect to errors in the calibration data and to show how and why that theoretical analysis did not match the results from the practical implementations of SMF. In particular, we show how by estimating breast thickness for every image we are, effectively, compensating for any errors in the calibration data. To illustrate our findings, the current implementation of SMF (version 2.2beta) was run over 4028 digitized film-screen mammograms taken from six sites over the years 1988-2002 with and without using the known calibration data. Results show that the SMF implementation running without any calibration data at all generates results which display a strong relationship with when running with a complete set of calibration data, and, most importantly, to an expert's visual assessment of breast composition using established techniques. SMF shows considerable promise in being of major use in large epidemiological studies related to breast cancer which require the automated analysis of large numbers of films from many years previously where little or no calibration data is available.

Mammogram synthesis using a 3D simulation. I. Breast tissue model and image acquisition simulation

A method is proposed for generating synthetic mammograms based upon simulations of breast tissue and the mammographic imaging process. A computer breast model has been designed with a realistic distribution of large and medium scale tissue structures. Parameters controlling the size and placement of simulated structures (adipose compartments and ducts) provide a method for consistently modeling images of the same simulated breast with modified position or acquisition parameters. The mammographic imaging process is simulated using a compression model and a model of the x-ray image acquisition process. The compression model estimates breast deformation using tissue elasticity parameters found in the literature and clinical force values. The synthetic mammograms were generated by a mammogram acquisition model using a monoenergetic parallel beam approximation applied to the synthetically compressed breast phantom.

Detecting film-screen artifacts in mammography using a model-based approach

Microcalcifications can be one of the earliest signs of breast cancer. Unfortunately, their appearance in mammograms can be mimicked by dust and dirt entering the imaging process and this has been shown previously to lead to false positives. We use a model of the imaging process and, in particular, the blurring functions inherent within it to detect the film-screen artifacts caused by dust and dirt and, thus, reduce false-positives. A crucial facet of the work is the choice of the correct image representation upon which to perform the image processing. After extensive testing, our algorithm has identified no microcalcifications as being artifacts and has an artifact detection rate of approaching 96%.

Estimation of compressed breast thickness during mammography

To estimate radiation dose during mammography the breast thickness must be known. We present a new method for estimating the thickness of a compressed breast using only the breast image as projected onto a mammogram, calibration data such as the mAs value and image processing techniques. The method proves to be of high accuracy (+/- 0.2 cm for craniocaudal mammograms) and has the advantage over other methods of allowing retrospective estimation of thickness.

The forms of knowledge mobilized in some machine vision systems

This paper describes a number of computer vision systems that we have constructed, and which are firmly based on knowledge of diverse sorts. However, that knowledge is often represented in a way that is only accessible to a limited set of processes, that make limited use of it, and though the knowledge is amenable to change, in practice it can only be changed in rather simple ways. The rest of the paper addresses the questions: (i) what knowledge is mobilized in the furtherance of a perceptual task?; (ii) how is that knowledge represented?; and (iii) how is that knowledge mobilized? First we review some cases of early visual processing where the mobilization of knowledge seems to be a key contributor to success yet where the knowledge is deliberately represented in a quite inflexible way. After considering the knowledge that is involved in overcoming the projective nature of images, we move the discussion to the knowledge that was required in programs to match, register, and recognize shapes in a range of applications. Finally, we discuss the current state of process architectures for knowledge mobilization.

Mammographic image analysis

We describe our recent progress aimed at computer analysis of mammograms. The overall aim is to provide the clinician with reliable quantitative information. We summarise a representation we have developed of the 'interesting' (non-adipose) tissue in a breast, then put the representation to work in three ways: (i) to propose a new quantitative measure to aid in diagnosing masses; (ii) to explore the possibility of reducing by half the radiation dose required for a mammogram; and (iii) recalling some of the results that can be provided by differential compression mammography, in which mammograms are taken at two slightly different compressions.

A representation for mammographic image processing

Mammographic image analysis is typically performed using standard, general-purpose algorithms. We note the dangers of this approach and show that an alternative physics-model-based approach can be developed to calibrate the mammographic imaging process. This enables us to obtain, at each pixel, a quantitative measure of the breast tissue. The measure we use is h(int) and this represents the thickness of 'interesting' (non-fat) tissue between the pixel and the X-ray source. The thicknesses over the image constitute what we term the h(int) representation, and it can most usefully be regarded as a surface that conveys information about the anatomy of the breast. The representation allows image enhancement through removing the effects of degrading factors, and also effective image normalization since all changes in the image due to variations in the imaging conditions have been removed. Furthermore, the h(int) representation gives us a basis upon which to build object models and to reason about breast anatomy. We use this ability to choose features that are robust to breast compression and variations in breast composition. In this paper we describe the h(int) representation, show how it can be computed, and then illustrate how it can be applied to a variety of mammographic image processing tasks. The breast thickness turns out to be a key parameter in the computation of h(int), but it is not normally recorded. We show how the breast thickness can be estimated from an image, and examine the sensitivity of h(int) to this estimate. We then show how we can simulate any projective X-ray examination and can simulate the appearance of anatomical structures within the breast. We follow this with a comparison between the h(int) representation and conventional representations with respect to invariance to imaging conditions and the surrounding tissue. Initial results indicate that image analysis is far more robust when specific consideration is taken of the imaging process and the h(int) representation is used.