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Purpose We present the development and the current state of the MaXIMA Breast Lesions Models Database, which is intended to provide researchers with both segmented and mathematical computer-based breast lesion models with realistic shape. Methods The database contains various 3D images of breast lesions of irregular shapes, collected from routine patient examinations or dedicated scientific experiments. It also contains images of simulated tumour models. In order to extract the 3D shapes of the breast cancers from patient images, an in-house segmentation algorithm was developed for the analysis of 50 tomosynthesis sets from patients diagnosed with malignant and benign lesions. In addition, computed tomography (CT) scans of three breast mastectomy cases were added, as well as five whole-body CT scans. The segmentation algorithm includes a series of image processing operations and region-growing techniques with minimal interaction from the user, with the purpose of finding and segmenting the areas of the lesion. Mathematically modelled computational breast lesions, also stored in the database, are based on the 3D random walk approach. Results The MaXIMA Imaging Database currently contains 50 breast cancer models obtained by segmentation of 3D patient breast tomosynthesis images; 8 models obtained by segmentation of whole body and breast cadavers CT images; and 80 models based on a mathematical algorithm. Each record in the database is supported with relevant information. Two applications of the database are highlighted: inserting the lesions into computationally generated breast phantoms and an approach in generating mammography images with variously shaped breast lesion models from the database for evaluation purposes. Both cases demonstrate the implementation of multiple scenarios and of an unlimited number of cases, which can be used for further software modelling and investigation of breast imaging techniques. The created database interface is web-based, user friendly and is intended to be made freely accessible through internet after the completion of the MaXIMA project. Conclusions The developed database will serve as an imaging data source for researchers, working on breast diagnostic imaging and on improving early breast cancer detection techniques, using existing or newly developed imaging modalities.

Introduction: In X-ray imaging, test objects reproducing breast anatomy characteristics are realized to optimize issues such as image processing or reconstruction, lesion detection performance, image quality and radiation induced detriment. Recently, a physical phantom with a structured background has been introduced for both 2D mammography and breast tomosynthesis. A software version of this phantom and a few related versions are now available and a comparison between these 3D software phantoms and the physical phantom will be presented. Methods: The software breast phantom simulates a semi-cylindrical container filled with spherical beads of different diameters. Four computational breast phantoms were generated with a dedicated software application and for two of these, physical phantoms are also available and they are used for the side by side comparison. Planar projections in mammography and tomosynthesis were simulated under identical incident air kerma conditions. Tomosynthesis slices were reconstructed with an in-house developed reconstruction software. In addition to a visual comparison, parameters like fractal dimension, power law exponent β and second order statistics (skewness, kurtosis) of planar projections and tomosynthesis reconstructed images were compared. Results: Visually, an excellent agreement between simulated and real planar and tomosynthesis images is observed. The comparison shows also an overall very good agreement between parameters evaluated from simulated and experimental images. Conclusion: The computational breast phantoms showed a close match with their physical versions. The detailed mathematical analysis of the images confirms the agreement between real and simulated 2D mammography and tomosynthesis images. The software phantom is ready for optimization purpose and extrapolation of the phantom to other breast imaging techniques.

A common approach in the development and improvement of diagnostic imaging techniques is the use of anthropomorphic phantoms. These phantoms can be physical or computational. In this study the creation of computational breast phantoms with included pathological formations is presented. The creation of the realistic phantoms is achieved by utilizing real patient data in the form of tomographic images. The 3D tumour models are generated by segmenting the regions containing tumour formations in the patient images. The segmentation is performed with a developed software tool based on a semi-automatic algorithm, which makes use of a series of image processing and region growing techniques. The software tool also provides the user an opportunity for corrections after the automated segmentation. Then the acquired flat images are stacked in a 3D voxel matrix. Creation of the computational healthy breast model as well as the compression procedure is achieved with a software tool called BreastSimulator. The healthy breast model and the segmented tumour formation are then interactively combined with a software tool called XRAYImagingSimulator. While the user can select a location for the tumour formation, also an automatic software processing is applied for integration between the two computational models. The simulation procedure for acquiring tomographic images from the created realistic breast phantom with included tumour formation is performed with the XRAYImagingSimulator software tool. Finally, the acquired simulation images are reconstructed with a software tool called FDKR. The combination of mathematical models of the breast and tumour models segmented from real patient data leads to the creation of realistic breast phantoms, which can be used in X-ray imaging simulation studies. The presented approach gives an opportunity for generation of multiple cases of breast cancer; thus allowing for further progress in already existing software models and techniques in diagnostic imaging. Acknowledgements This research is supported by the Bulgarian National Science Fund under grant agreement DN17/2. This project also has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 692097.

Physical phantoms are a basic tool for the assessment and verification of performance standards in daily clinical practice of X-ray imaging modalities. Most of the physical phantoms have a homogeneous background with inserted test objects. For research purposes, many of the existing phantoms should be developed to a next degree of sophistication in order to mimic the real objects more closely in terms of radiographic and anatomical properties. The development of 3D printing technologies opens potentially new possibilities for phantom manufacturing. The aim of this study is to explore the absorption properties of common 3D printing materials such as resins, PLA, ABS, etc. and to estimate their potential for production of the anthropomorphic phantoms. To achieve this goal, step-wedge phantoms were computationally modeled and then manufactured using two popular 3D printing technologies: stereolithographic and fused-deposition modeling. X-ray images of the phantoms were acquired, using monochromatic beam at ID17, ESRF, Grenoble for three energies – 30 keV, 45 keV and 60 keV. Experimental data were further processed to obtain the linear attenuation coefficients of these materials. Comparison with theoretical data for the linear attenuation coefficients for breast tissues was performed. Based on the results, several breast anthropomorphic phantoms were manufactured. Finally, a practical approach for printing anthropomorphic phantoms has been established and verified. From the studied materials, most of the resins, Hybrid, PET-G show absorption properties close to the glandular tissue, while ABS shows absorption characteristics close to these of the adipose tissue. It allows the production of complex shapes, which are very advantageous for the case of breast phantoms. Acknowledgements This research is supported by the Bulgarian National Science Fund under grant agreement DN17/2. This project also has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 692097.

Digital tomosynthesis is a method of limited angle reconstruction of tomographic images produced at variable heights, on the basis of a set of angular projections taken in an arc around human anatomy. Reconstructed tomograms from unprocessed original projection images, however, are invariably affected by tomographic noise such as blurred images of objects lying outside the plane of interest and superimposed on the focused image of the fulcrum plane. The present work investigates the performance of two approaches for generation of tomograms with a reduced noise: a generalised post-processing method, based on constructing a noise mask from all planes in the reconstructed volume, and its subsequent subtraction from the in-focus plane and a filtered Multiple Projection Algorithm. The comparison between the two algorithms shows that the first method provides reconstructions with very good quality in case of high contrast features, especially for those embedded into a heterogeneous background.

The mean radiation dose to the glandular tissue and its dependence on the irradiation geometry, beam quality, breast size and composition in digital breast tomosynthesis (DBT) exams have been studied extensively via Monte Carlo calculations [1] . On the other hand, there are few comprehensive studies on the dose distribution within the irradiated breast [2] . The distribution of glandular dose for breast irradiation from a plurality of angles, as occurs in DBT, may be of large interest in scanner optimization as well as for developing suitable models for the evaluation of the cancer risk related to the X-ray exposure for non–homogeneous irradiation. For this reason, it is of interest to evaluate the level of homogeneity of the dose spread, via the assessment of 3D dose maps in breast models during a DBT scan. This work aimed at evaluating, via Monte Carlo (MC) simulations and measurements using radiochromic films, the dose distribution within compressed layered breast phantoms during DBT scans. For this purpose, two phantoms were employed: a PMMA homogeneous phantom and a heterogeneous phantom simulating a 50% glandular breast. A series of pre-calibrated (vs free-in-air air kerma) film pieces were inserted between the phantom slices and the 3D dose maps were measured for a set of DBT scans on different commercial units, for different sample thicknesses, at various exposure technique factors. We developed an MC code based on GEANT4 toolkit ver. 10.00, simulating the clinical setup specifications, whose results have been compared to measurements. This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 692097 for the MaXIMA project: Three dimensional breast cancer models for X-ray imaging research.

Purpose We present the development and the current state of the MaXIMA Breast Tumours Models’ Database, which is intended to provide researchers with both segmented and mathematically modelled realistic in shape computer-based breast tumours. Methods The database contains various 3D images of breast cancers of irregular shapes, collected from routine patient examinations or dedicated scientific experiments. It also contains images of simulated tumour models. To extract the 3D shapes of the breast cancers from patient images, an in–house developed and well evaluated segmentation algorithm was applied on 60 tomosynthesis sets from patients diagnosed with malignant and benign lesions. In addition, three breast mastectomy cases scanned at a CT system were added. This algorithm includes a series of image processing operations and region-growing techniques with minimal interaction from the user to find and segment the areas of the lesions. Modelled computational breast tumours are generated following different approaches and also stored in the database. Each record in the database is supported with relevant information, e.g. voxel size and resolution, matrix size, geometrical centre, etc. Results The MaXIMA Imaging Database currently contains 68 unique breast cancer models obtained by segmentation of 3D patient breast tomosynthesis images; 8 models obtained by segmentation of 3D micro CT images of biopsy specimens; and 20 models based on mathematical algorithm. An application of the tumour models is to insert them into computationally generated healthy breast phantom generated with dedicated software tools (e.g. the BreastSimulator tool). The resulted combined computational breast models are used to study the visibility of breast tumours in 3D breast imaging techniques. This approach allows implementation of multiple scenarios and unlimited number of cases, which can be used for further software modelling and investigation of breast imaging techniques. The database interface is web-based, i.e. is platform independent, user friendly and is indented to be made freely accessible through internet after the completion of the MaXIMA project. Conclusions The developed database serves as an imaging data source for researchers, working on breast imaging and early breast cancer detection with the help of existing or newly developed imaging modalities.

Purpose This paper presents a complete and validated computer-based system dedicated to X-ray phase contrast breast imaging research. The system comprises three main modules: (a) a module for generation of computational breast models, (b) a module for generation of phase-contrast X-ray images from computational phantoms in 2D and 3D acquisition setup, and (c) a module for image reconstruction. Methods The module for computational breast models is based on the BreastSimulator tool, used to generate breast models of various sizes and to compress them to a desired thickness. The module for the generation of phase-contrast images is used to model the image acquisition geometry and formation of X-ray images. The image reconstruction is done with a software tool, based on an in–house built reconstruction techniques class library, which is a dedicated object-oriented library for X-ray based applications. The capability of this system to correctly calculate phase-contrast tomographic images was tested by comparing simulated versus experimental images obtained from anthropomorphic breast phantoms. Three anthropomorphic computational breast phantoms were designed from white and grey resin by using the available stereolithography 3D printer. Experimental projection images were acquired and simulated images were generated, considering a breast tomosynthesis setup. Experiments were conducted at beamline ID17, ESRF. Simulations replicated the experimental setup. Images in a tomosynthesis mode were generated and tomograms were calculated by using the reconstruction module. Results Results show very good visual agreement between simulated and experimentally obtained tomosynthesis images. In addition, selected tomosynthesis images from the physical and computational breast phantoms were quantitatively evaluated for set of imaging parameters such as skewness, kurtosis, the power-law exponent, β , of the power spectrum and fractal dimension. Analysis and the comparison of these parameters between simulated and experimental images also show a very good coincidence. Conclusions The platform is currently used in the process of development of a dedicated breast phantom for phase contrast imaging techniques. The system will be a valuable tool in studying new X-ray imaging techniques based on phase contrast, such as phase contrast tomosynthesis and CT.

Introduction Anthropomorphic phantoms play a critical role in the contemporary development of Diagnostic Radiology. The Anthropomorphic Phantom educational module has been successfully developed to provide education and training of Medical Physics Experts in the field of Radiology. Purpose The aims were to familiarize the participants with the role of the physical and virtual anthropomorphic phantoms and the possibility of performing virtual clinical trials using existing and new Diagnostic and Interventional Radiology technologies. Method The Anthropomorphic Phantoms module is one of the 12 modules of the EUTEMPE-RX course. Teaching methodology includes e-learning and face-to-face approaches. The course is organized in a blended format that includes lectures, computer-based exercises, visits to hospital for experimental work and discussion sessions. The online part was developed on SEKOIA platform and included 10 chapters with state of the art reviews in the field, introduction to software used for X-ray imaging and examples. Results The face-to-face part started on September 7, 2015 at the Technical University of Varna, and lasted one week. Lectures and practical work were delivered to seventeen participants from 14 European countries. All lectures were led by worldwide recognized researchers in the field of anthropomorphic phantoms and their use in the research and clinical practice. The focus was on the practical work and the development of a work project. All participants passed successfully the exam. Conclusions The main aims of the module were successfully obtained. Selected project works are currently under detail development and will be submitted for publication.