Search

The aim of this study is the development of a methodology for manufacturing 3D printed anthropomorphic structures, which mimic the X-ray properties of the human bone tissue.A mixing approach of two different materials is proposed for the fabrication of a radiologically equivalent hip bone for an anthropomorphic abdominal phantom. The materials employed for the phantom were polylactic acid (PLA) and Stonefil, while a custom-made dual motor filament extrusion setup and a custom-made software associating medical images directly with the 3D printing process were employed.Three phantoms representing the hip bone were 3D printed utilizing two filaments under three different printing scenarios. The phantoms are based on a patient's abdominal CT scan images. Histograms of CT scans of the printed hip bone phantoms were calculated and compared to the original patient's hip bone histogram, demonstrating that a constant mixing composition of 30% Stonefil and 70% PLA with 0.0375 extrusion rate per voxel (93.75% flow for fulfilling a single voxel) for the cancellous bone, and using 100% Stonefil with 0.04 extrusion rate per voxel (100% flow) for the cortical bone results in a realistic anatomy replication of the hip bone. Reproduced HU varied between 700 and 800, which are close to those of the hip bone.The study demonstrated that it is possible to mix two different filaments in real-time during the printing process to obtain phantoms with realistic and radiographically bone tissue equivalent attenuation. The results will be explored for manufacturing a CT-compatible abdominal phantom.

Purpose: To design, fabricate and characterize 3D printed, anatomically realistic, compressed breast phantoms for digital mammography (DM) and digital breast tomosynthesis (DBT) x-ray imaging. Materials: We realized 3D printed phantoms simulating healthy breasts, via fused deposition modeling (FDM), with a layer resolution of 0.1 mm and 100% infill density, using a dual extruder printer. The digital models were derived from a public dataset of segmented clinical breast computed tomography scans. Three physical phantoms were printed in polyethylene terephthalate (PET), acrylonitrile–butadiene–styrene (ABS), or in polylactic-acid (PLA) materials, using ABS as a substitute for adipose tissue, and PLA or PET filaments for replicating glandular and skin tissues. 3D printed phantoms were imaged at three clinical centers with DM and DBT scanners, using typical spectra. Anatomical noise of the manufactured phantoms was evaluated via the estimates of the β parameter both in DM images and in images acquired via a clinical computed tomography (CT) scanner. Results: DM and DBT phantom images showed an inner texture qualitatively similar to the images of a clinical DM or DBT exam, suitably reproducing the glandular structure of their computational phantoms. β parameters evaluated in DM images of the manufactured phantoms ranged between 2.84 and 3.79; a lower β was calculated from the CT scan. Conclusions: FDM 3D printed compressed breast phantoms have been fabricated using ABS, PLA and PET filaments. DM and DBT images with clinical x-ray spectra showed realistic textures. These phantoms appear promising for clinical applications in quality assurance, image quality and dosimetry assessments.

In recent years, there has clearly been a trend towards widespread dissemination of scientific results and achievements by sharing them in the form of educational materials. The application of the results of a research project in the education and knowledge development of students, young researchers and scientists, brings an added value to the outcomes of the scientific project. This work aims at contributing to the global process of free access to scientific materials by sharing the experience of a research group from the Medical University of Varna (MUV) in systematizing the results of three research projects, their transformation into educational resources and their application in innovative courses. The consolidated and systematized results of a research project are valuable assets for development of innovative educational materials. These resources, uploaded in a shared online repository, contribute to strengthen the international scientific community networking.

The aim of this work is to outline the management and risk management activities related to the successful implementation of scientific projects, which have been severely affected by the pandemic situation. The methodology includes a comparative analysis between planned and implemented activities on running scientific projects. Time, budget and scope are the main three constraints of the project used in the assessment. Five main groups of activities are analysed: Management, Scientific, Educational, Dissemination and Production. While published studies present qualitative and quantitative risk analysis due to the pandemic, this work goes a step forward by suggesting risk response planning, management procedures and activities to reduce threats to the project’s objectives. To the best of authors’ knowledge, there is no research systemized by other research groups. © 2021, Czestochowa University of Technology. All rights reserved.

PURPOSE To present a dataset of computational digital breast phantoms derived from high-resolution three-dimensional (3D) clinical breast images for the use in virtual clinical trials in two-dimensional (2D) and 3D x-ray breast imaging. ACQUISITION AND VALIDATION METHODS Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi-automatic software was developed. The semi-automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation of the breast, using the tissue's elastic coefficient. This process was evaluated in terms of glandular fraction modification introduced by the compression procedure. The generated cohort of 150 uncompressed computational breast phantoms presented a mean value of the glandular fraction by mass of 12.3%; the average diameter of the breast evaluated at the center of mass was 105 mm. Despite the slight differences between the two manual segmentations, the resulting glandular tissue segmentation did not consistently differ from that obtained via the semi-automatic classification. The difference between the glandular fraction by mass before and after the compression was 2.1% on average. The 60 compressed phantoms presented an average glandular fraction by mass of 12.1% and an average compressed thickness of 61 mm. DATA FORMAT AND ACCESS The generated digital breast phantoms are stored in DICOM files. Image voxels can present one out of four values representing the different classified materials: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue, and 3 for the skin tissue. The generated computational phantoms datasets were stored in the Zenodo public repository for research purposes (http://doi.org/10.5281/zenodo.4529852, http://doi.org/10.5281/zenodo.4515360). POTENTIAL APPLICATIONS The dataset developed within the INFN AGATA project will be used for developing a platform for virtual clinical trials in x-ray breast imaging and dosimetry. In addition, they will represent a valid support for introducing new breast models for dose estimates in 2D and 3D x-ray breast imaging and as models for manufacturing anthropomorphic physical phantoms.

Purpose: To evaluate the bias to the mean glandular dose (MGD) estimates introduced by the homogeneous breast models in digital breast tomosynthesis (DBT) and to have an insight into the glandular dose distributions in 2D (digital mammography, DM) and 3D (DBT and breast dedicated CT, BCT) x-ray breast imaging by employing breast models with realistic glandular tissue distribution and organ silhouette. Methods: A Monte Carlo software for DM, DBT and BCT simulations was adopted for the evaluation of glandular dose distribution in 60 computational anthropomorphic phantoms. These computational phantoms were derived from 3D breast images acquired via a clinical BCT scanner. Results: g·c·s·T conversion coefficients based on homogeneous breast model led to a MGD overestimate of 18% in DBT when compared to MGD estimated via anthropomorphic phantoms; this overestimate increased up to 21% for recently computed DgNDBT conversion coefficients. The standard deviation of the glandular dose distribution in BCT resulted 60% lower than in DM and 55% lower than in DBT. The glandular dose peak – evaluated as the average value over the 5% of the gland receiving the highest dose – is 2.8 times the MGD in DM, this factor reducing to 2.6 and 1.6 in DBT and BCT, respectively. Conclusions: Conventional conversion coefficients for MGD estimates based on homogeneous breast models overestimate MGD by 18%, when compared to MGD estimated via anthropomorphic phantoms. The ratio between the peak glandular dose and the MGD is 2.8 in DM. This ratio is 8% and 75% higher than in DBT and BCT, respectively.

Objective. In this study, a novel voxel-by-voxel mixing method is presented, according to which two filaments of different material are combined during the three dimensional (3D) printing process. Approach. In our approach, two types of filaments were used for the replication of soft-tissues, a polylactic acid (PLA) filament and a polypropylene (PP) filament. A custom-made software was used, while a series of breast patient CT scan images were directly associated to the 3D printing process. Each phantom´s layer was printed twice, once with the PLA filament and a second time with the PP filament. For each material, the filament extrusion rate was controlled voxel-by-voxel and was based on the Hounsfield units (HU) of the imported CT images. The phantom was scanned at clinical CT, breast tomosynthesis and micro CT facilities, as the major processing was performed on data from the CT. A side by side comparison between patient´s and phantom´s CT slices by means of profile and histogram comparison was accomplished. Further, in case of profile comparison, the Pearson´s coefficients were calculated. Main results. The visual assessment of the distribution of the glandular tissue in the CT slices of the printed breast anatomy showed high degree of radiological similarity to the corresponding patient´s glandular distribution. The profile plots´ comparison showed that the HU of the replicated and original patient soft tissues match adequately. In overall, the Pearson´s coefficients were above 0.91, suggesting a close match of the CT images of the phantom with those of the patient. The overall HU were close in terms of HU ranges. The HU mean, median and standard deviation of the original and the phantom CT slices were −149, −167, ±65 and −121, −130, ±91, respectively. Significance. The results suggest that the proposed methodology is appropriate for manufacturing of anthropomorphic soft tissue phantoms for x-ray imaging and dosimetry purposes, since it may offer an accurate replication of these tissues.

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. ispartof: PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS vol:89 pages:114-128 ispartof: location:Italy status: published

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.

Anthropomorphic phantoms are models of real or virtual parts of the body, organ or tissue, represented by tissue-equivalent materials that aim to provide a realistic and accurate representation of their anatomy and properties. The aim of this study is to evaluate experimentally the suitability of 3D printed materials in the production of both, physical breast phantoms and abnormalities, to be used in optimization tasks in breast imaging. For this purpose, we designed three computational breast models, composed of skin, duct tree, adipose compartments and lesions. Subsequently, they were printed by using two 3D printing technologies and different printing materials, which were previously studied in details. The physical phantoms were scanned at a mammography machine, which allows 2D and 3D mammography (tomosynthesis) modes. The images were evaluated from an experienced radiologist. The results showed that tomosynthesis images are characterized with better realism compared to 2D mammography images. Next step is improvement in the printing quality of tumour formations as well as quantitative evaluation of the obtained results.

In this work, a newly integrated, windows-based prototype system is proposed for the needs of registering data of patients diagnosed with strokes in Varna region, Bulgaria. The implemented system architecture is based on a star model, consisting of a central core module, corresponding to the main stroke data record sheet and additional peripheral units. The main features of this system include: basic demographic patient information, disease history related to previous strokes, results from patient’s exams and tests initial and from scheduled follow ups, data for risk factors and death report. The current system is intended to be used exclusively by the project team members to study the disease epidemiology and management effectiveness in Varna region. This will create solid record volume in order epidemiologist from the team to have prerequisites for providing adequate timely care for patients with these diseases.

Anthropomorphic breast phantoms are both, computational and physical, anatomically and radiologically realistic models of the human female breast, and are considered an excellent tool for development, comparing, characterizing and optimizing of novel and existing breast screening and diagnostic modalities. Their role in the objective assessment of diagnostic task performance of breast imaging systems without conducting high cost and time-consuming clinical trials, as well as in developing and optimizing clinical protocols and image reconstruction algorithms is tangible increasing. The aim of this paper is to present a historical journey from the advent of the breast anthropomorphic models used with x-ray modalities, their further development and realistic improvement, to current achievements. Anthropomorphic breast models are both computational and physical. Computational phantoms are mainly based on mathematical formulations and patient medical images. The mathematical breast models consist of mathematical description of the breast structures, while the patient-based are created from segmented clinical datasets acquired by dedicated breast Computed Tomography. The later approach guarantees a very high degree of realism. Molding techniques, 3D printing and low cost paper-based approaches are the main methods for creation of physical breast anthropomorphic models. Among them, 3D printing technologies provide an excellent opportunity to create realistic models of the breast by using a number of printing materials with x-ray characteristics similar to these of breast tissues. The anatomical and radiological realism of the anthropomorphic breast phantoms together with the possibility for unlimited exposure during important diagnostic tasks is transforming them into a key instrument of every Radiology Department.

A complete software platform based on anthropomorphic breast models used with both planar and three-dimensional phase contrast breast imaging is presented and subjectively validated. For the development of the platform, tests with three anthropomorphic breast phantoms, available both in computational and physical form, were designed and implemented. The models are characterized with different complexity: two phantoms are with spheres and one anthropomorphic. Further on, two of the physical breast models were created with the use of 3D printing techniques. These phantoms with thickness of 40 mm and 31 mm, respectively, were based on digital phantoms created with in-house developed software tools. The third physical breast phantom is the L1 phantom developed at Katholieke Universiteit Leuven with 58 mm thickness. Based on this physical phantom, a computational one was created. The three physical breast phantoms were imaged at ID17 biomedical imaging line at ESRF. Two acquisition setups were used: planar and limited angle tomography modes. Simulated and experimental planar and three-dimensional images were compared in terms of visual reproducibility. Results showed that phantoms characterized with more simple structure produce subjectively similar experimental and simulation appearance in terms of object reproduction and similar edge effects. The thicker phantom demonstrated lower visual coincidence between the two types of planar images, due to higher thickness and higher energy incident beam. The results of this study will be used in the design of new experimental study, to be conducted at lower incident beam energy as well as improving the modelling of phase contrast imaging by using Monte Carlo techniques.

In silico reproductions of clinical exams represent an alternative strategy in the research and development of medical devices, which permit to avoid issues and costs related to clinical trials on patient population. In this work, we present a platform for virtual clinical trials in 2D and 3D x-ray breast imaging. The platform, developed by the medical physics team at University of Naples, Italy, permits to simulate digital mammography (DM), digital breast tomosynthesis (DBT) and CT dedicated to the breast (BCT) examinations. It relies on Monte Carlo simulations based on Geant4 toolkit and adopts digital models of patients derived from high-resolution 3D clinical breast images acquired at UC Davis, USA. Uncompressed digital breast models for BCT exam simulations were produced by means of a tissue classification algorithm; the compressed digital breast models for simulating DM and DBT are derived by the uncompressed ones via a simulated tissue compression. For a selected exam, specifications and digital patient, the platform computes breast image projections and glandular dose maps within the organ. Energy integrating a well as photon counting and spectral imaging detection scheme have been simulated. The current version of the software uses the Geant4 standard physics list Option4 and simulates and tracks

We are developing a platform for virtual (in silico) clinical trials in x-ray breast imaging and dosimetry fully based on Monte Carlo simulations and which adopts patient-like digital phantoms. We report the initial steps and first results of this project. Our collaboration was established for producing a “user-friendly” computational platform which reproduces both 2D (full-fields digital mammography) and 3D (digital breast tomosynthesis, CT dedicated to the breast) breast imaging examinations for technique optimization and for developing protocols and computed aided diagnostic applications. Dose optimization strategies and technique performance comparisons will be explored. A dataset of anatomically realistic, 2D and 3D digital breast phantoms have been produced by means of voxel classification and digital compression of clinical breast CT scans acquired at UC Davis. Monte Carlo simulations, based on the Geant4 toolkit, have been developed for insilico FFDM, DBT and BCT examinations.

The use of anthropomorphic phantoms finds an increased application in the last years. From their use in virtual studies to assessment of new imaging technologies and reconstruction algorithms. The current study proposes a resource saving approach, in terms of time and money for assessing the creation of an anthropomorphic model by means of 3D printing. A four component object consisting of segmentations of adipose, gland, skin and lesion tissues was created from an MRI image set. From the resulting object two computational breast models were created – one with the elemental composition of the real breast tissues and a second one with the elemental composition of the low cost 3D printing materials planned to be used for the phantom manufacturing. Then, an in-house developed software was used to generate mammography images, which were subjected to both visual assessment and profile comparison. In this study we used nylon and acrylonitrile butadiene styrene 3D printing materials to represent the x-ray properties of the different breast tissues. The results showed that these 3D materials well represent the x-ray absorption characteristics of both glandular, adipose and skin tissues, while further investigation on suitable materials for representing the lesion characteristics is needed.

Purpose: To compare, via Monte Carlo simulations, homogeneous and non-homogenous breast models adopted for mean glandular dose (MGD) estimates in mammography vs. patient specific digital breast phantoms. Methods: We developed a GEANT4 Monte Carlo code simulating four homogenous cylindrical breast models featured as follows: (1) semi-cylindrical section enveloped in a 5-mm adipose layer; (2) semi-elliptical section with a 4-mm thick skin; (3) semi-cylindrical section with a 1.45-mm skin layer; (4) semi-cylindrical section in a 1.45-mm skin layer and 2-mm subcutaneous adipose layer. Twenty patient specific digital breast phantoms produced from a dedicated CT scanner were assumed as reference in the comparison. We simulated two spectra produced from two anode/filter combinations. An additional digital breast phantom was produced via BreastSimulator software. Results: With reference to the results for patient-specific breast phantoms and for W/Al spectra, models #1 and #3 showed higher MGD values by about 1% (ranges [–33%; +28%] and [−31%; +30%], respectively), while for model #4 it was 2% lower (range [−34%; +26%]) and for model #2 –11% (range [−39%; +14%]), on average. On the other hand, for W/Rh spectra, models #1 and #4 showed lower MGD values by 2% and 1%, while for model #2 and #3 it was 14% and 8% lower, respectively (ranges [−43%; +13%] and [−41%; +21%]). The simulation with the digital breast phantom produced with BreastSimulator showed a MGD overestimation of +33%. Conclusions: The homogeneous breast models led to maximum MGD underestimation and overestimation of 43% and 28%, respectively, when compared to patient specific breast phantoms derived from clinical CT scans.

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.

Virtual clinical trials (VCT) are in-silico reproductions of medical examinations, which adopt digital models of patients and simulated devices. They are intended to produce clinically equivalent outcome data avoiding long execution times, ethical issues related to radiation induced risks and huge costs related to real clinical trials with a patient population. In this work, we present a platform for VCT in 2D and 3D X-ray breast imaging. The VCT platform uses Monte Carlo simulations based on the Geant4 toolkit and patient breast models derived from a cohort of high resolution dedicated breast CT (BCT) volume data sets. Projection images of the breast and three-dimensional glandular dose maps are generated for a given breast model, by simulating both 2D full-field digital mammography (DM) and 3D BCT examinations. Uncompressed voxelized breast models were derived from segmented patient images. Compressed versions of the digital breast phantoms for DM were generated using a previously published digital compression algorithm. The Monte Carlo simulation framework has the capability of generating and tracking ~105 photons/s using a server equipped with 16-cores and 3.0 GHz clock speed. The VCT platform will provide a framework for scanner design optimization, comparison between different scanner designs and between different modalities or protocols on computational breast models, without the need for scanning actual patients as in conventional clinical trials.

Virtual clinical trials (VCTs) have been proposed to overcome the limitations of clinical trials using a patient population. VCTs are in-silico reproductions of medical examinations using digital models of the patients and simulated imaging devices. In this work, we present a VCT framework for imaging and dosimetry in breast computed tomography (BCT), digital breast tomosynthesis (DBT) and 2D digital mammography (DM), realized by Univ. Napoli Federico II in collaboration with the medical physics teams at Univ. California Davis and the Medical University of Varna, Bulgaria. Computational phantoms of the uncompressed (pendant) breast were generated by clinical BCT scans acquired at UC Davis. A dataset of digital breast phantoms was produced by means of voxel classification of the uncompressed breast CT images. The voxels were classified as air, skin, adipose and glandular tissue using a semi-automatic algorithm. A software compression algorithm (developed at U. Varna) applied to the 3D phantoms produces compressed breast digital phantoms for virtual DM and DBT investigations using a clinical scanners' technical specifications and geometry as inputs. Monte Carlo simulations, based on Geant4, were used to provide in-silico reproductions of real scans of a given patient breast model. The software permits the estimation of mean glandular dose (MGD) in 2D and 3D imaging as well as the 3D dose distribution. The platform produces breast projection images which are then reconstructed using analytical or iterative algorithms. Patient-specific MGD estimations, as well as simulated BCT volume data sets were compared with the clinical BCT scans. The VCT platform reported herein will be used for scanner optimization and for virtual trials comparing BCT against mammography and DBT, in terms of image quality and glandular dose distributions. In addition to in-silico evaluation, 3D printing methods were used to produce compressed and uncompressed anthropomorphic breast phantoms from the patient image-derived digital breast phantoms for the purpose of experimental validation.

This paper presents a web-based platform dedicated to evaluation studies in x-ray breast imaging. The backend development is based on ASP.NET and C#, while the frontend is implemented under HTML, CSS and JavaScript. The platform is designed to own simple and intuitive graphical user-friendly interface. Questions to be evaluated are entered by the users and stored in the application database. In the same database, two other features are kept as well: the results of the evaluation and the regions of interest taken from patient and simulated mammography images. The content of the database is subject to modification, as well as further addition of data. The designed web-based platform was tested in a particular evaluation study, which concerned the realism of the generated simulated mammograms from computational breast models. The new approach in evaluating images was estimated to be more convenient and faster compared to our previous experience. The system will be further updated and will be exploited in the routine research work of the laboratory as well as will be shared with other research groups.

This paper presents a simple methodology for creation of anthropomorphic physical breast phantoms dedicated for x-ray breast imaging. The use of physical phantoms in diagnostic radiology is a well-established approach for patient dose estimation, quality control of developed diagnostic systems and development of new breast imaging techniques. An overview of the design process used for the creation of the presented phantom and evaluation approach is given. A combination of materials (Clear resin, Gray resin and PLA) and methods (fused deposition modelling and stereolithography) are used for the manufacturing of the breast phantom. The phantom was subjected to an evaluation aiming at its suitability for studies with digital mammography technique. In particular, the created phantom was evaluated by using two sets of phantom images, taken using 22 kVp and 28 kVp, which are compared with real mammograms. The comparison of the images is based on extracted statistical parameters – namely skewness, kurtosis, fractals, PSA, GLCMContrast and GLCMEnergy.

We propose a novel approach for the fabrication of realistic in shape, size as well as in x-ray absorption properties 3D physical breast models. The approach eliminates the need for segmentation of breast tissues by directly mimicking the radiodensity of each voxel in a computed tomography (CT) image. This is done through a recently published study that enables the use of variable filament extrusion rate when creating 3D phantoms with fused deposition modeling printers. We use the CT image of a real patient’s breast to produce a model through the suggested approach. We then validate the fabricated physical breast phantom by obtaining a CT scan of it and comparing the latter to the original CT image of the source patient’s breast.

Phase-contrast imaging is an imaging technique, which provides an advantage over the traditional x-ray imaging by improving the contours of the tissues, thus providing higher contrast between tissues. To advance in this filed, the researchers from the Laboratory of Computer Simulations in Medicine are building a custom optical setup to generate diffraction pattern corresponding to the phase contrast data, but in the optical range. This paper reports on the current state of the optical system prototype and its initial validation and use.

The main goal of this paper is to create a methodology for generation of synthetic 3D model from a digitalized x-ray photo. Three-dimensional perspective gives more detailed and sophisticated in depth for analyzing different irregular lesions. In this research, the application is for detection and analysis in mammography realism.

The development of new techniques for detection of breast cancer often relies on virtual clinical trials. These trials however require vast number of x-ray images of the breast containing realistically looking pathologies. For this purpose, as many as possible and as realistic as possible physical models of breast lesions are needed. The goal of this study is to create a methodology for manufacturing various breast abnormalities, with a detail description of the stages needed for their development. These models are to be used for development of new breast cancer detection technologies.

3D breast modelling for 2D and 3D breast x-ray imaging would benefit from the availability of digital and physical phantoms that reproduce accurately the complexity of the breast anatomy. While a number of groups have produced digital phantoms with increasing level of complexity, physical phantoms reproducing that software approach have been scarcely developed. One possibility is offered by 3D printing technology. This implies the assessment of the energy dependent absorption index β of 3D printing materials for absorption based imaging, as well as the assessment of the refractive index decrement, δ, of the printing material, for phase contrast imaging studies, at the energies of interest for breast imaging. In this work we set-up a procedure and performed a series of measurements (at 30, 45 and 60 keV, at the European Synchrotron Radiation Facility) for assessing the relative value of δ with respect to that of breast tissues, for twelve 3D printing materials. The method included propagation based phase contrast 2D imaging and retrieval of the estimated phase shift map, using the Paganin's algorithm. Breast glandular, adipose and skin tissues were used as reference materials of known ratio δ/β. A percentage difference Δδ was introduced to assess the suitability of the printing materials as tissue substitutes. The accuracy of the method (about 4%) was assessed based on the properties of PMMA and Nylon, acting as gold standard. Results show that, for the above photon energies, ABS is a good substitute for adipose tissue, Hybrid as a substitute of the glandular tissue and PET-G for simulating the skin. We plan to realize a breast phantom manufactured by fused deposition modelling (FDM) technology using ABS, Hybrid and PET-G as substitutes of the glandular and skin tissue and a second phantom by stereolithography (SLA) technology with the resins Flex, Tough and Black.