Development of an inkjet calibration phantom for x-ray imaging studies

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

Computational Breast Cancer Models Created from Patient Specific CT Images: Preliminary Results

Breast cancer remains the most common cause of death for women below seventy years of age. Although, screening nowadays is a common practice the standard tools for such procedure in some cases of breast cancers are not as efficient as desired. New approaches are constantly being developed to detect and diagnose the cancerous formations as earlier as possible. These new techniques require extensive optimization of parameters which is best performed with computer-based models. Our main objective is the creation of comprehensive breast cancer computer database for the purposes of developing, testing and optimizing new x-ray imaging techniques. This paper reports on a semi-automatic approach for segmentation of cancerous tissue extracted from patient specific CT datasets and the creation of solid breast cancer models

Models of breast lesions based on three-dimensional X-ray breast images

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

Analytical simulation and experimental comparison of the losses in resonant DC/DC converter with Si and SiC switches

High efficiency is among the most important targets in power electronic converters. A possible approach to obtain this goal is the usage of better switches. This paper is focused at the study and comparison of SiC and Si power MOSFETS in soft switching resonant DC/DC converter. The latter converter being a part of light battery charger used in ultra-light electric vehicles is realized and investigated. The losses in the power switches are compared mathematically, with simulations and experimental verification. Two types of transistors are considered. First traditional state of the art Si-technology using a FREDFET (fast recovery diode MOSFET) and it is compared with what the SiC technology offers, using a fast Schottky antiparallel diode. As an example a 1500 W series resonance converter with input voltage of 400 V and output voltage of 100 V is used. The latter voltage permitting the use of Schottky diodes at the secondary side. The losses in both converters are compared via calorimetric measurements to avoid accuracy problems as in loss measurements with an oscilloscope for example

SEGMENTATION OF CANCER FORMATIONS FROM BREAST TOMOSYNTHESIS

This paper presents an algorithm for segmentation of lesions in breast tomosynthesis slices. The implemented approach is based on finding specific values in a region of interest containing the tumor, followed by a set of morphology operations to remove any structures not belonging to the tumor structure, and automatic region growing algorithm to finally obtain the tumor image. The developed algorithm was applied on ten patient sets of breast tomosynthesis images to segment successfully the breast tumors

Experimental Evaluation of Physical Breast Phantoms for 2D and 3D Breast X-Ray Imaging Techniques

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

Models of breast lesions based on three-dimensional X-ray breast images

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.status: publishe

Development of breast lesions models database

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.status: publishe