The creation of a large set of realistic synthetic microcalcification clusters for simulation in (contrast-enhanced) mammography images

Characterization of microcalcification clusters in the breast and differentiation between benign and malignant structures on (contrast-enhanced) mammography (CEM) images is of great importance to determine cancerous lesions. Computer algorithms may help performing these tasks, but typically need large sets of data for model training. Therefore this paper develops a method to create synthetic microcalcification clusters that can later be used to overcome data sparsity problems. Starting from descriptors of the shape and size, both benign and malignant microcalcifications were created and then combined into 3-dimensional cluster models given realistic geometric properties. The distributions of the largest diameter and the number of microcalcifications per cluster in a set of 500 simulated clusters were set such that they agreed with those of real clusters. An existing simulation tool was then extended to insert the clusters into processed, low-energy CEM background images with appropriate contrast values. In a validation study comprised of 40 real and 40 synthetic cases, radiologists were asked to evaluate realism and malignancy. It was found that the shape and the structure of the individual microcalcifications as well as the complete clusters were realistic. Thus the descriptors were chosen correctly and enabled a good classification between benign and malignant cases. The realistic brightness and boundary smoothness proved the simulation tool can correctly insert the 3D clusters into real background images and is suitable of creating a large set of realistic microcalcification clusters simulated in existing (contrast-enhanced) mammography images. With improvements on the correspondence of insertion location in craniocaudal and mediolateral oblique view, which proved more challenging to simulate realistically, this promising method is expected to be applicable for modeling complete synthetic cases. Such a dataset can be used for data enrichment where data sources are limited and for development and training purposes

Radiomics software for breast imaging optimization and simulation studies

Background and Objective: The development, control and optimisation of new x-ray breast imaging modalities could benefit from a quantitative assessment of the resulting image textures. The aim of this work was to develop a software tool for routine radiomics applications in breast imaging, which will also be available upon request. Methods: The tool (developed in MATLAB) allows image reading, selection of Regions of Interest (ROI), analysis and comparison. Requirements towards the tool also included convenient handling of common medical and simulated images, building and providing a library of commonly applied algorithms and a friendly graphical user interface. Initial set of features and analyses have been selected after a literature search. Being open, the tool can be extended, if necessary. Results: The tool allows semi-automatic extracting of ROIs, calculating and processing a total of 23 different metrics or features in 2D images and/or in 3D image volumes. Computations of the features were verified against computations with other software packages performed with test images. Two case studies illustrate the applicability of the tool – (i) features on a series of 2D ‘left’ and ‘right’ CC mammograms acquired on a Siemens Inspiration system were computed and compared, and (ii) evaluation of the suitability of newly proposed and developed breast phantoms for x-ray-based imaging based on reference values from clinical mammography images. Obtained results could steer the further development of the physical breast phantoms. Conclusions: A new image analysis toolbox was realized and can now be used in a multitude of radiomics applications, on both clinical and test images

OPTIMAM Image Simulation Toolbox - Recent Developments and Ongoing Studies

Virtual clinical trials (VCTs) are increasingly being seen as a viable pre-clinical method for evaluation of imaging systems in breast cancer screening. The CR-UK funded OPTIMAM project is aimed at producing modelling tools for use in such VCTs. In the initial phase of the project, modelling tools were produced to simulate 2D-mammography and digital breast tomosynthesis (DBT) imaging systems. This paper elaborates on the new tools that have recently been developed for the current phase of the OPTIMAM project. These new additions to the framework include tools for simulating synthetic breast tissue, spiculated masses and variable-angle DBT systems. These tools are described in the paper along with the preliminary validation results. Four-alternative forced choice (4-AFC) type studies deploying these new tools are underway. The results of the ongoing 4AFC studies investigating minimum detectable contrast/size of masses/microcalcifications for different modalities and system designs are presented

Estimating cancer risk from dental cone-beam CT exposures based on skin dosimetry

The aim of this study was to measure entrance skin doses on patients undergoing cone-beam computed tomography (CBCT) examinations, to establish conversion factors between skin and organ doses, and to estimate cancer risk from CBCT exposures. 266 patients (age 8-83) were included, involving three imaging centres. CBCT scans were acquired using the SCANORA 3D (Soredex, Tuusula, Finland) and NewTom 9000 (QR, Verona, Italy). Eight thermoluminescent dosimeters were attached to the patient's skin at standardized locations. Using previously published organ dose estimations on various CBCTs with an anthropomorphic phantom, correlation factors to convert skin dose to organ doses were calculated and applied to estimate patient organ doses. The BEIR VII age- and gender-dependent dose-risk model was applied to estimate the lifetime attributable cancer risk. For the SCANORA 3D, average skin doses over the eight locations varied between 484 and 1788μGy. For the NewTom 9000 the range was between 821 and 1686μGy for Centre 1 and between 292 and 2325μGy for Centre 2. Entrance skin dose measurements demonstrated the combined effect of exposure and patient factors on the dose. The lifetime attributable cancer risk, expressed as the probability to develop a radiation-induced cancer, varied between 2.7 per million (age >60) and 9.8per million (age 8-11) with an average of 6.0 per million. On average, the risk for female patients was 40% higher. The estimated radiation risk was primarily influenced by the age at exposure and the gender, pointing out the continuing need for justification and optimization of CBCT exposures, with a specific focus on children. © 2014 Institute of Physics and Engineering in Medicine