Mammography is currently the primary imaging modality for breast cancer screening and plays an important role in cancer diagnostics. A standard mammographic image acquisition always includes the compression of the breast prior xray exposure. The breast is compressed between two plates (the image receptor and the compression paddle) until a nearly uniform breast thickness is obtained. The breast flattening improves diagnostic image quality<sup>1</sup> and reduces the absorbed dose<sup>2</sup> . However, this technique can also be a source of discomfort and might deter some women from attending breast screening by mammography<sup>3,4</sup>. Therefore, the characterization of the pain perceived during breast compression is of potential interest to compare different compression approaches. The aim of this work is to develop simulation tools enabling the characterization of existing breast compression techniques in terms of patient comfort, dose delivered to the patient and resulting image quality. A 3D biomechanical model of the breast was developed providing physics-based predictions of tissue motion and internal stress and strain intensity. The internal stress and strain intensity are assumed to be directly correlated with the patient discomfort. The resulting compressed breast model is integrated in an image simulation framework to assess both image quality and average glandular dose. We present the results of compression simulations on two breast geometries, under different compression paddles (flex or rigid).
Anthropomorphic breast phantoms are useful for development and characterization of breast x-ray imaging systems. Rapid prototyping (RP) opens a new way for generating complex shapes similar to real breast tissue patterns at reasonably high resolution and a high degree of reproducibility. Such a phantom should have x-ray attenuation properties similar to adipose and fibroglandular tissue across a broad x-ray energy range. However material selection is limited to those that are compatible with the printing system, which often requires adding non-organic dopants. Fortunately, there are some off-the-shelf materials that may be suitable for breast phantoms. Here a polyamide-12/water texture phantom is being investigated, which can be used for mammography, tomosynthesis and breast CT. Polyamide-12 (PA-12) is shown to have linear attenuation coefficients across an energy range of 15 – 40 keV matching adipose tissue to within 10% effective breast density. A selective laser sintering (SLS) printer is used for manufacturing the phantom. The phantom was imaged on the Senographe Pristina (GE Healthcare, Chicago, IL), while initial assessment of 3D fidelity with the original design was performed by acquiring volume images of the phantom on a micro-CT system. A root mean distance error of 0.22 mm was seen between the micro-CT volume and the original. The PA-12 structures appeared to be slightly smaller than in the original, possibly due to infiltration of the water into the PA-12 surfaces. Power spectra measurements for mammograms of the simulated and physical phantoms both demonstrated an inverse power-law spectrum shape with exponent β= 3.72 and 3.76, respectively.
The ultimate way to assess the performance of imaging systems is a clinical trial. Due to its limitation by cost and duration, several research groups are investigating the potential to replace clinical trials in part with virtual clinical trials (VCT) as a more efficient alternative. In this paper, we propose a VCT design to compare the microcalcification (μcalc) detection performance in full field digital mammography (FFDM) and digital breast tomosynthesis (DBT). Digital breast phantoms with uniform and breast-texture like backgrounds and digital μcalcs were created. The μcalcs had diameters ranging from 100μm to 600μm and their attenuation properties were varied to be equivalent to 20% to 60% of the attenuation of Aluminum at 22keV. FFDM and DBT image acquisitions according to the nominal topology of a commercial imaging system were simulated with a software x-ray imaging platform. Projection images were processed with commercial image processing and reconstruction algorithms. Microcalcification detection performance was estimated by an objective taskbased assessment using channelized Hotelling observers (CHO) with Laguerre-Gauss channels and by a human observer. For DBT, single-slice (CHO3ss) and a multi-slice CHO (CHO3msa) model observers were considered. Model and human observers performed a lesion-known-statistically and location-known exactly rating-scale detection task. The decision outcomes were used as input to a receiver operating characteristic analysis and the area under the curve was used as the figure-of-merit. Using our VCT set-up, the performance of the CHO and the human observer seems to be fairly well linearly correlated. There is a trend that µcalc detection performance in DBT is higher than in FFDM.
We address the detectability of contrast-agent enhancing masses for contrast-agent enhanced spectral mammography (CESM), a dual-energy technique providing functional projection images of breast tissue perfusion and vascularity using simulated CESM images. First, the realism of simulated CESM images from anthropomorphic breast software phantoms generated with a software X-ray imaging platform was validated. Breast texture was characterized by power-law coefficients calculated in data sets of real clinical and simulated images. We also performed a 2-alternative forced choice (2-AFC) psychophysical experiment whereby simulated and real images were presented side-by-side to an experienced radiologist to test if real images could be distinguished from the simulated images. It was found that texture in our simulated CESM images has a fairly realistic appearance. Next, the relative performance of human readers and previously developed mathematical observers was assessed for the detection of iodine-enhancing mass lesions containing different contrast agent concentrations. A four alternative-forced-choice (4 AFC) task was designed; the task for the model and human observer was to detect which one of the four simulated DE recombined images contained an iodineenhancing mass. Our results showed that the NPW and NPWE models largely outperform human performance. After introduction of an internal noise component, both observers approached human performance. The CHO observer performs slightly worse than the average human observer. There is still work to be done in improving model observers as predictors of human-observer performance. Larger trials could also improve our test statistics. We hope that in the future, this framework of software breast phantoms, virtual image acquisition and processing, and mathematical observers can be beneficial to optimize CESM imaging techniques.
In breast X-ray imaging, breast texture has been characterized by a radial noise power spectrum (NPS) that has an inverse power-law shape with exponent β. The technique to estimate the radial power-law coefficient β is typically based on averaging 2-dimensional noise power spectra (NPS), calculated from partly overlapping image regions each weighted by a suitable window function. The linear regression applied over a selected frequency range to the logarithm of the 1- dimensional NPS as a function of the logarithm of the radial frequencies, gives β. For each step in this process, several alternative techniques have been proposed. This paper investigates the effect of image region of interest (ROI) size, image data windowing and alternative ways to determine radial frequency in terms of bias, variance and root mean square error (RMSE) in the estimated β. The effects of these three factors were analytically derived and evaluated using synthetic images with known β varying from 1 to 4 to cover the range of textures encountered in 2D and 3D breast X-ray imaging. Our results indicate that the RMSE in estimated β is smallest when the ROIs are multiplied with an appropriate window function and either no radial averaging or radial averaging with small frequency bins is applied. The ROI size yielding the smallest RMSE depends on several factors and needs to be validated with numerical simulations. In clinical practice however, there might be a need to compromise in the choice of the ROI size to balance between the RMSE magnitudes inherent to the applied β estimation technique and encompass the breast texture range so as to obtain an accurate shape of the NPS. When using 2.56 cm x 2.56 cm ROI sizes, applying a 2D Hann window and no radial frequency averaging, the RMSE in the estimated β ranges from 0.04 to 0.1 for true β values equal to 1 and 4. While many subtleties in real images were not modeled to simplify the mathematics in deriving our results, this work is illustrative in demonstrating the limits of commonly used algorithm steps to estimate accurate β values.