Anthropomorphic software breast phantoms are generated by simulating breast anatomy. Virtual Clinical Trial (VCT) tools are developed for evaluating novel imaging modalities, based on anthropomorphic breast phantoms. Simulation of breast anatomical structures requires informed selection of parameters, which is crucial for the simulation realism. Our goal is to optimize the parameter selection based upon the analysis of clinical images.
Adipose compartments defined by Cooper’s ligaments significantly contribute to breast image texture (parenchymal pattern) which affects image interpretation and lesion detection. We have investigated the distribution and orientation of compartments segmented from CT images of a mastectomy specimen. Ellipsoidal fitting was applied to 205 segmented compartments, by matching the moments of inertia. The goodness-of-fit was measured by calculating Dice coefficients. Compartment size, shape, and orientation were characterized by estimating the volume, axis ratio, and Euler’s angles of fitted ellipsoids. Potential correlations between estimated parameters were tested.
We found that the adipose compartments are well approximated with ellipsoids (average Dice coefficient of 0.79). The compartment size is correlated with the barycenter-chest wall distance (r=0.235, p-value<0.001). The goodness-of-fit to ellipsoids is correlated to the compartment shape (r=0.344, p-value<0.001). The shape is also correlated with barycenter coordinates. The compartment orientation is correlated to their size (Euler angle α: r=0.188, p-value=0.007; angle β: r=0.156, p-value=0.025) and the barycenter-chest wall distance (r=0.159, p-value=0.023). These results from the characterization of adipose compartments and the observed correlations could help improve the realism of simulated breast anatomy.
Virtual clinical trials (VCTs) were introduced as a preclinical alternative to clinical imaging trials, and for the evaluation of breast imaging systems. Realism in computer models of breast anatomy (software phantoms), critical for VCT performance, can be improved by optimizing simulation parameters based on the analysis of clinical images. We optimized the simulation to improve the realism of simulated tissue compartments, defined by the breast Cooper’s ligaments. We utilized the anonymized, previously acquired CT images of a mastectomy specimen to manually segment 205 adipose compartments. We generated 1,440 anthropomorphic breast phantoms based on octree recursive partitioning. These phantoms included variations of simulation parameters—voxel size, number of compartments, percentage of dense tissue, and shape and orientation of the compartments. We compared distributions of the compartment volumes in segmented CT images and phantoms using Kolmogrov-Smirnov (KS) distance, Kullback-Leibler (KL) divergence and a novel distance metric (based on weighted sum of distribution descriptors differences). We identified phantoms with the size distributions closest to CT images. For example, KS resulted in the phantom with 1000 compartments, ligament thickness of 0.4 mm and skin thickness of 12 mm. We applied multilevel analysis of variance (ANOVAN) to these distance measures to identify parameters that most significantly influence the simulated compartment size distribution. We have demonstrated an efficient method for the optimization of phantom parameters to achieve realistic distribution of adipose compartment size. The proposed methodology could be extended to other phantom parameters (e.g., ligaments and skin thicknesses), to further improve realism of the simulation and VCTs.
Anthropomorphic breast phantoms are important tools for a wide range of tasks including pre-clinical validation of novel imaging techniques. In order to improve the realism in the phantoms, assessment of simulated anatomical structures is crucial. Thickness of simulated Cooper’s ligaments influences the percentage of dense tissue, as well as qualitative and quantitative properties of simulated images.
We introduce three methods (2-dimensional watershed, 3-dimensional watershed, and facet counting) to assess the thickness of the simulated Cooper’s ligaments in the breast phantoms. For the validation of simulated phantoms, the thickness of ligaments has been measured and compared with the input thickness values. These included a total of 64 phantoms with nominal ligament thicknesses of 200, 400, 600, and 800 μm.
The 2-dimensional and 3-dimensional watershed transformations were performed to obtain the median skeleton of the ligaments. In the 2-dimensional watershed, the median skeleton was found cross-section by cross-section, while the skeleton was found for the entire 3-dimensional space in the 3-dimensional watershed. The thickness was calculated by taking the ratio of the total volume of ligaments and the volume of median skeleton. In the facet counting method, the ligament thickness was estimated as a ratio between estimated ligaments’ volume and average ligaments’ surface area.
We demonstrated that the 2-dimensional watershed technique overestimates the ligament thickness. Good agreement was found between the facet counting technique and the 3-dimensional watershed for assessing thickness. The proposed techniques are applicable for ligaments’ thickness estimation on clinical breast images, provided segmentation of Cooper’s ligaments has been performed.
Anthropomorphic software breast phantoms have been utilized for preclinical quantitative validation of breast imaging
systems. Efficacy of the simulation-based validation depends on the realism of phantom images. Anatomical
measurements of the breast tissue, such as the size and distribution of adipose compartments or the thickness of Cooper’s
ligaments, are essential for the realistic simulation of breast anatomy. Such measurements are, however, not readily
available in the literature. In this study, we assessed the statistics of adipose compartments as visualized in CT images of
a total mastectomy specimen. The specimen was preserved in formalin, and imaged using a standard body CT protocol
and high X-ray dose. A human operator manually segmented adipose compartments in reconstructed CT images using
ITK-SNAP software, and calculated the volume of each compartment. In addition, the time needed for the manual
segmentation and the operator’s confidence were recorded. The average volume, standard deviation, and the probability
distribution of compartment volumes were estimated from 205 segmented adipose compartments. We also estimated the
potential correlation between the segmentation time, operator’s confidence, and compartment volume. The statistical
tests indicated that the estimated compartment volumes do not follow the normal distribution. The compartment volumes
are found to be correlated with the segmentation time; no significant correlation between the volume and the operator
confidence. The performed study is limited by the mastectomy specimen position. The analysis of compartment volumes
will better inform development of more realistic breast anatomy simulation.