Predrag R. Bakic

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.

Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm.

PURPOSE We present a novel algorithm for computer simulation of breast anatomy for generation of anthropomorphic software breast phantoms. A realistic breast simulation is necessary for preclinical validation of volumetric imaging modalities. METHODS The anthropomorphic software breast phantom simulates the skin, regions of adipose and fibroglandular tissue, and the matrix of Coopers ligaments and adipose compartments. The adipose compartments are simulated using a seeded region-growing algorithm; compartments are grown from a set of seed points with specific orientation and growing speed. The resulting adipose compartments vary in shape and size similar to real breasts; the adipose region has a compact coverage by adipose compartments of various sizes, while the fibroglandular region has fewer, more widely separated adipose compartments. Simulation parameters can be selected to cover the breadth of variations in breast anatomy observed clinically. RESULTS When simulating breasts of the same glandularity with different numbers of adipose compartments, the average compartment volume was proportional to the phantom size and inversely proportional to the number of simulated compartments. The use of the software phantom in clinical image simulation is illustrated by synthetic digital breast tomosynthesis images of the phantom. The proposed phantom design was capable of simulating breasts of different size, glandularity, and adipose compartment distribution. The region-growing approach allowed us to simulate adipose compartments with various size and shape. Qualitatively, simulated x-ray projections of the phantoms, generated using the proposed algorithm, have a more realistic appearance compared to previous versions of the phantom. CONCLUSIONS A new algorithm for computer simulation of breast anatomy has been proposed that improved the realism of the anthropomorphic software breast phantom.

Mammogram synthesis using a 3D simulation. II. Evaluation of synthetic mammogram texture

We have evaluated a method for synthesizing mammograms by comparing the texture of clinical and synthetic mammograms. The synthesis algorithm is based upon simulations of breast tissue and the mammographic imaging process. Mammogram texture was synthesized by projections of simulated adipose tissue compartments. It was hypothesized that the synthetic and clinical texture have similar properties, assuming that the mammogram texture reflects the 3D tissue distribution. The size of the projected compartments was computed by mathematical morphology. The texture energy and fractal dimension were also computed and analyzed in terms of the distribution of texture features within four different tissue regions in clinical and synthetic mammograms. Comparison of the cumulative distributions of the mean features computed from 95 mammograms showed that the synthetic images simulate the mean features of the texture of clinical mammograms. Correlation of clinical and synthetic texture feature histograms, averaged over all images, showed that the synthetic images can simulate the range of features seen over a large group of mammograms. The best agreement with clinical texture was achieved for simulated compartments with radii of 4-13.3 mm in predominantly adipose tissue regions, and radii of 2.7-5.33 and 1.3-2.7 mm in retroareolar and dense fibroglandular tissue regions, respectively.

Optimized generation of high resolution breast anthropomorphic software phantoms

PURPOSE The authors present an efficient method for generating anthropomorphic software breast phantoms with high spatial resolution. Employing the same region growing principles as in their previous algorithm for breast anatomy simulation, the present method has been optimized for computational complexity to allow for fast generation of the large number of phantoms required in virtual clinical trials of breast imaging. METHODS The new breast anatomy simulation method performs a direct calculation of the Coopers ligaments (i.e., the borders between simulated adipose compartments). The calculation corresponds to quadratic decision boundaries of a maximum a posteriori classifier. The method is multiscale due to the use of octree-based recursive partitioning of the phantom volume. The method also provides user-control of the thickness of the simulated Coopers ligaments and skin. RESULTS Using the proposed method, the authors have generated phantoms with voxel size in the range of (25-1000 μm)(3)∕voxel. The power regression of the simulation time as a function of the reciprocal voxel size yielded a log-log slope of 1.95 (compared to a slope of 4.53 of our previous region growing algorithm). CONCLUSIONS A new algorithm for computer simulation of breast anatomy has been proposed that allows for fast generation of high resolution anthropomorphic software phantoms.

Breast percent density: estimation on digital mammograms and central tomosynthesis projections.

PURPOSE To evaluate inter- and intrareader agreement in breast percent density (PD) estimation on clinical digital mammograms and central digital breast tomosynthesis (DBT) projection images. MATERIALS AND METHODS This HIPAA-compliant study had institutional review board approval; all patients provided informed consent. Breast PD estimation was performed on the basis of anonymized digital mammograms and central DBT projections in 39 women (mean age, 51 years; range, 31-80 years). All women had recently detected abnormalities or biopsy-proved cancers. PD was estimated by three experienced readers on the mediolateral oblique views of the contralateral breasts by using software; each reader repeated the estimation after 2 months. Spearman correlations of inter- and intrareader and intermodality PD estimates, as well as kappa statistics between categoric PD estimates, were computed. RESULTS High correlation (rho = 0.91) was observed between PD estimates on digital mammograms and those on central DBT projections, averaged over all estimations; the corresponding kappa coefficient (0.79) indicated substantial agreement. Mean interreader agreement for PD estimation on central DBT projections (rho = 0.85 +/- 0.05 [standard deviation]) was significantly higher (P < .01) than that for PD estimation on digital mammograms (rho = 0.75 +/- 0.05); the corresponding kappa coefficients indicated substantial (kappa = 0.65 +/- 0.12) and moderate (kappa = 0.55 +/- 0.14) agreement for central DBT projections and digital mammograms, respectively. CONCLUSION High correlation between PD estimates on digital mammograms and those on central DBT projections suggests the latter could be used until a method for PD estimation based on three-dimensional reconstructed images is introduced. Moreover, clinical PD estimation is possible with reduced radiation dose, as each DBT projection was acquired by using about 22% of the dose for a single mammographic projection.

Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation: A Preliminary Study

RATIONALE AND OBJECTIVES Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superposition, offering superior parenchymal texture visualization compared to mammography. The aim of this study was to investigate the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation. MATERIALS AND METHODS DBT and digital mammographic (DM) images of 39 women were analyzed. Texture features, shown in previous studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. The relative performances of the DBT and DM texture features were compared in correlating with two measures of breast cancer risk: (1) the Gail and Claus risk estimates and (2) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk. RESULTS No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density than DM features (P < or = .05). When dividing the study population into groups of increasing breast percent density, the DBT texture features appeared to be more discriminative, having regression lines with overall lower P values, steeper slopes, and higher R(2) estimates. CONCLUSION Although preliminary, the results of this study suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation.

Mammogram synthesis using a three-dimensional simulation. III. Modeling and evaluation of the breast ductal network.

A method is proposed for realistic simulation of the breast ductal network as part of a computer three-dimensional (3-D) breast phantom. The ductal network is simulated using tree models. Synthetic trees are generated based upon a description of ductal branching by ramification matrices (R matrices), whose elements represent the probabilities of branching at various levels of a tree. We simulated the ductal network of the breast, consisting of multiple lobes, by random binary trees (RBT). Each lobe extends from the ampulla and consists of branching ductal segments of decreasing size, and the associated terminal ductal-lobular units. The lobes follow curved paths that project from the nipple toward the chest wall. We have evaluated the RBT model by comparing manually-traced ductal networks from 25 projections of ductal lobes in clinical galactograms and manually-traced networks from 23 projections of synthetic RBTs. A root-mean-square (rms) fractional error of 41%, between the R-matrix elements corresponding to clinical and synthetic images, was computed. This difference was influenced by projection and segmentation artifacts and by the limited number of available images. In addition, we analyzed 23 synthetic trees generated using R matrices computed from clinical images. A comparison of these synthetic and clinical images yielded a rms fractional error of 11%, suggesting the possibility that a more appropriate model of the ductal branching morphology may be developed. Rejection of the RBT model also suggests the existence of a relationship between ductal branching morphology and the state of mammary developmentand pathology.

A Representation and Classification Scheme for Tree-Like Structures in Medical Images: Analyzing the Branching Pattern of Ductal Trees in X-ray Galactograms

We propose a multistep approach for representing and classifying tree-like structures in medical images. Tree-like structures are frequently encountered in biomedical contexts; examples are the bronchial system, the vascular topology, and the breast ductal network. We use tree encoding techniques, such as the depth-first string encoding and the Prufer encoding, to obtain a symbolic string representation of the trees branching topology; the problem of classifying trees is then reduced to string classification. We use the tf-idf text mining technique to assign a weight of significance to each string term (i.e., tree node label). Similarity searches and k-nearest neighbor classification of the trees is performed using the tf-idf weight vectors and the cosine similarity metric. We applied our approach to characterize the ductal tree-like parenchymal structure in X-ray galactograms, in order to distinguish among different radiological findings. Experimental results demonstrate the effectiveness of the proposed approach with classification accuracy reaching up to 86%, and also indicate that our method can potentially aid in providing insight to the relationship between branching patterns and function or pathology.

Mammogram registration: a phantom-based evaluation of compressed Breast Thickness variation effects

The temporal comparison of mammograms is complex; a wide variety of factors can cause changes in image appearance. Mammogram registration is proposed as a method to reduce the effects of these changes and potentially to emphasize genuine alterations in breast tissue. Evaluation of such registration techniques is difficult since ground truth regarding breast deformations is not available in clinical mammograms. In this paper, we propose a systematic approach to evaluate sensitivity of registration methods to various types of changes in mammograms using synthetic breast images with known deformations. As a first step, images of the same simulated breasts with various amounts of simulated physical compression have been used to evaluate a previously described nonrigid mammogram registration technique. Registration performance is measured by calculating the average displacement error over a set of evaluation points identified in mammogram pairs. Applying appropriate thickness compensation and using a preferred order of the registered images, we obtained an average displacement error of 1.6 mm for mammograms with compression differences of 1-3 cm. The proposed methodology is applicable to analysis of other sources of mammogram differences and can be extended to the registration of multimodality breast data.

Development of an anthropomorphic breast software phantom based on region growing algorithm

Software breast phantoms offer greater flexibility in generating synthetic breast images compared to physical phantoms. The realism of such generated synthetic images depends on the method for simulating the three-dimensional breast anatomical structures. We present here a novel algorithm for computer simulation of breast anatomy. The algorithm simulates the skin, regions of predominantly adipose tissue and fibro-glandular tissue, and the matrix of adipose tissue compartments and Coopers ligaments. The simulation approach is based upon a region growing procedure; adipose compartments are grown from a selected set of seed points with different orientation and growth rate. The simulated adipose compartments vary in shape and size similarly to the anatomical breast variation, resulting in much improved phantom realism compared to our previous simulation based on geometric primitives. The proposed simulation also has an improved control over the breast size and glandularity. Our software breast phantom has been used in a number of applications, including breast tomosynthesis and texture analysis optimization.