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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 processing of inputs in the first two layers of the probabilistic neural network (PNN) is highly parallel which makes it quite appropriate for hardware implementations with FPGA. One of the main inconveniences however remains the implementation of the nonlinear activation function of the pattern layer neurons. In the present study, we investigate the applicability of three approximations of the exponential activation function with look-up tables of different precision and the effect this has on the training process and the classification accuracy. Furthermore, seeking for a highly-parallel hardware-friendly algorithm for the automated adjustment of the spread factor $$\sigma _i$$, we investigated the performance of fifteen PNN training setups, which are based on the differential evolution (DE) or unified particle swarm optimization (UPSO) methods. The experimental evaluation was performed following a common experimental protocol, which makes use of the Parkinson Speech Dataset, as this research aims to support the development of portable medical devices that are capable to detect episodes with exacerbation in patients with Parkinson’s disease. The performance of the most successful setups is discussed in terms of error rates and from the perspective of the resources required for an FPGA-based implementation.

In the present work, we investigate the behavior of the Locally Recurrent Probabilistic Neural Network (LRPNN) with different activation functions in the recurrent layer neurons. Specifically, we evaluate the performance of the modified activation function proposed here, which belongs to the family of Rectified Linear Units (ReLU), and compare it with other ReLU-based functions, the traditional sigmoid activation function, as well as with the Swish and E-Swish activation functions. Furthermore, we investigate the efficiency of a training procedure which simultaneously adjusts the spread factor sigma and the weights in the recurrent layer of the LRPNN. This training helps for coping with practical tasks, such as the recognition of Parkinson condition from speech signals, which operate under limited amount of training data.

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.

This paper presents a method for creation of computational models of breast lesions with irregular shapes from patient Digital Breast Tomosynthesis (DBT) images or breast cadavers and whole-body Computed Tomography (CT) images. The approach includes six basic steps: (a) normalization of the intensity of the tomographic images; (b) image noise reduction; (c) binarization of the lesion area, (d) application of morphological operations to further decrease the level of artefacts; (e) application of a region growing technique to segment the lesion; and (f) creation of a final 3D lesion model. The algorithm is semi-automatic as the initial selection of the region of the lesion and the seeds for the region growing are done interactively. A software tool, performing all of the required steps, was developed in MATLAB. The method was tested and evaluated by analysing anonymized sets of DBT patient images diagnosed with lesions. Experienced radiologists evaluated the segmentation of the tumours in the slices and the obtained 3D lesion shapes. They concluded for a quite satisfactory delineation of the lesions. In addition, for three DBT cases, a delineation of the tumours was performed independently by the radiologists. In all cases the abnormality volumes segmented by the proposed algorithm were smaller than those outlined by the experts. The calculated Dice similarity coefficients for algorithm-radiologist and radiologist-radiologist showed similar values. Another selected tumour case was introduced into a computational breast model to recursively assess the algorithm. The relative volume difference between the ground-truth tumour volume and the one obtained by applying the algorithm on the synthetic volume from the virtual DBT study is 5% which demonstrates the satisfactory performance of the proposed segmentation algorithm. The software tool we developed was used to create models of different breast abnormalities, which were then stored in a database for use by researchers working in this field. ispartof: PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS vol:57 pages:80-87 ispartof: location:Italy status: published

Introduction : 3D anthropomorphic models of human tissues have become a requirement for conducting realistic virtual studies. One of the current directions in the research of X-ray imaging is the development of physical models with 3D printing techniques using specific materials aiming to obtain replica of the human body tissues with similar radiological characteristics. Aim : The aim of this study is to create a calibration phantom for establishing the X-ray properties of different cartridge infills and their suitability to represent the X-ray properties of different breast types. Materials and Methods : A physical calibration model consisting of 22 objects was designed and printed by using an inkjet printer. A mixture was obtained from 5 mL printer ink and 3 g of potassium iodide (KI), which was used to fill the printer’s cartridge and to print the model on a set of plain office paper. Experimental X-ray images of the physical model were acquired on radiographic system SEDECAL X PLUS LP+. The obtained attenuation coefficient of the printing mixture was evaluated and compared to the breast tissue coefficients corresponding to the used X-ray energy. Results and Discussion : The physical model was printed on ten office sheets and stacked above one another. The obtained attenuation coefficient of the printing mixture was found very similar to that of the glandular tissue of the breast for the used X-ray energy. Conclusion: The obtained printer ink-KI mixture is suitable for representing the glandular part of breast tissue. The method has the potential to be used for creation of a realistic physical breast model.

The current contribution presents an approach for creation of complex computational breast phantoms, with included breast abnormalities. In-house developed and validated software tool, called BreastSimulator, is used to create different models of healthy breasts, by varying parameters like shape, size, duct tree features, Cooper ligaments, skin, etc. Separately, different 3D tumour models are constructed from segmented tumour regions on sets of real tomosynthesis images from real patients. A semi-automatic algorithm was developed to segment the abnormalities in the images. The algorithm applies a series of image processing operations and region-growing techniques with minimal interaction from the user to find the areas of the lesions. The flat tumour images are then correctly stacked and stored in a 3D voxel matrix, matching the desired resolution. Relevant information about the lesion (voxel size, matrix size, geometrical centre, etc.) is also saved. Selected tumour models are then inserted at various locations into the computational models of the healthy breasts. Examples of simulated mammographic and tomosynthesis images from such breast phantoms are presented to illustrate the potential of the proposed approach. Combining mathematical breast modelling with segmented real 3D tumour shapes results in the creation of realistic breast phantoms. The 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.

Goal of this study is to compare five statistical algorithms used for evaluation of software packages based on Monte Carlo techniques. These meth-ods are the following analysis: regression, correlation and Bland Altman as well as Wilcoxon rank sum and Kolmogorov-Smirnov tests. The methods were ap-plied for the case study of validating a dedicated computer code for calculation of scattered radiation reaching the eyes of the operator during interventional pro-cedures. The transport of x-rays and their interactions with the matter have been accomplished by the use of Monte Carlo techniques. Results were presented in the form of number of registered photons as a function of their energy and further compared to data from literature. From the five statistical methods for compari-son, the most suitable for our application turns out to be the Bland-Altman method. Further effort is related to the development of a specific software appli-cation for evaluation of data generated from general purpose x-ray simulations, as well as with analysis of more data obtained from different incident x-ray spec-tra.

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.