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Breast tissue deformation has recently gained interest in various medical applications. The recovery of large deformations caused by gravity or compression loads and image registration is a non-trivial task. The most effective tool for breast cancer visualisation is Magnetic Resonance Imaging (MRI). However, for MRI scans the patient is in a prone position with the breast placed in signal enhancement coils, while other procedures, i.e. surgery, PET-CT (Positron Emission Tomography fused with Computer Tomography) are performed with the patient in a supine position. The need therefore arises to estimate the large breast deformations caused by natural body movement during examinations or surgery. There is no doubt that a patient's breast in both positions has a different shape and that this influences relationships between intra-breast structures. In this work, we present the fundamentals of a method for transformation of breast images based on Finite Element Methods (FEMs). This 2D model uses the simplest constitutive tissue description, which makes it easily applicable and fast. According to the Jaccard Index, the average accuracy obtained is 95%, the lowest is 87%, and the highest is 99%. The model parameter set is proposed for six different breast size classes, covering the whole population. The algorithm provides reliable breast images in a supine position in a few simple steps.

Nowadays, the dynamic development of technology allows for the design of systems based on various information sources and their integration into hybrid expert systems. One of the areas of research where such systems are especially helpful is emotion analysis. The sympathetic nervous system controls emotions, while its function is directly reflected by the electrodermal activity (EDA) signal. The presented study aimed to develop a tool and propose a physiological data set to complement the psychological data. The study group consisted of 41 students aged from 19 to 26 years. The presented research protocol was based on the acquisition of the electrodermal activity signal using the Empatica E4 device during three exercises performed in a prototype Disc4Spine system and using the psychological research methods. Different methods (hierarchical and non-hierarchical) of subsequent data clustering and optimisation in the context of emotions experienced were analysed. The best results were obtained for the k-means classifier during Exercise 3 (80.49%) and for the combination of the EDA signal with negative emotions (80.48%). A comparison of accuracy of the k-means classification with the independent division made by a psychologist revealed again the best results for negative emotions (78.05%).

As a result of the constantly growing number of people in the World, disabilities became one of the most important aspects related to human health. According to the World Health Organization, a disabled person is “anyone who affects a reduction in performance or fitness in the construction or functioning of the body in terms of psychological, physical or anatomical.” On a global scale, the main cause of acquired motor disability is ischemic stroke. Therefore, it is essential to look for solutions that will help people with disabilities to rejoice as much independence and health as possible. For this purpose, the library databases of universities in Poland, units and scientific institutes from Poland, as well as the most popular databases—PubMed and Google Scholar were searched. As a result of the queries, 1057 records were obtained, which, according to the adopted criteria, only 24 were selected for further analysis. The main focus of the presented study was on issues related to supporting both diagnostics and therapy. Methods such as the use of basic devices for drainage and uprightization, walking trainers and multimodal platforms, biomechanical devices—exoskeletons and the use of virtual reality in the rehabilitation process were addressed.

One of the most common methods in breast cancer radiotherapy planning is Magnetic Resonance Imaging (MRI). It is also used for patient evaluation during treatment because of its sensitivity and lack of ionizing radiation. During each imaging session a patient position can be different and inaccuracies can occur. In this case it is very difficult to compare two image sets originating from different patient examination. The main goals of this work were to implement an algorithm, based on affine transformation with Mutual Information as the quality factor of images match and the method based on the Navier-Lame equation for elastic image co-registration. The rigid transformation is used for the preliminary processing, and the non-rigid transformation allows for successful co-registration of both image sets. Our results were evaluated visually, and the MI indices were calculated. These algorithms allowed for image co-registration in different imaging sessions during the course of treatment.

The exact delineation of tumour boundaries is of utmost importance in the planning of cancer therapy, either surgery or pre- or post-operative radiation treatment. In the case of breast cancer one of the most advanced modalities is magnetic resonance imaging (MRI). Although MRI scans provide wealth of information about the structure of a tumour and the surrounding tissues, the data obtained represent the patient in a prone position, with breast, in a coil while surgery is performed in a supine position, on lying breast. There is no doubt that a patient's breast in both positions has a different shape and that this influences the intra-breast relations. Our present preliminary study introduces a simple breast model developed from prone images. The model should be built rapidly and by a simple procedure, based only on essential structures, and the goal is to prove its usefulness in treatment planning.

According to recent studies, breast cancer is the most frequent women's cancer around the world. Fast and precise diagnosis and personalized treatment procedures are crucial in the face of the growing number of patients. One of the most informative and at the same time non-evasive diagnostic techniques is based on Magnetic Resonance Imaging (MRI). This examination is performed basically in prone position due to better visualisation of breast tissues as well as the proper magnetic coil force this position. On the other hand supplementary imaging techniques (e.g. Computer Tomography (CT) or Positron Emission Tomography (PET)) and, what is the most important, surgery are performed in supine position. Since changing position from prone to supine leads to breast deformation, we propose an algorithm based on finite element method (FEM) to model it. We compare our results with model created in ANSYS.

Increasing number of people are suffering from cancer these days. The highest incidence among women is breast cancer. One of the main sources of diagnostic information is Magnetic Resonance Imaging (MRI). Another one is Positron Emission Tomography with Computer Thomography (PET-CT). Both examinations take place in different patient positions (prone and supine respectively). The only way to obtain complete diagnostic information is to bring MRI images into PET-CT space and compare both. Our preliminary studies focus on creating a simple, deformable finite element model of the breast. Proposed algorithm allows to obtain breast model in supine position, created from MRI images obtained in prone position.

Segmentation and delineation of tumour boundaries are important and difficult step in emission tomography imaging, where acquired and reconstructed images presents large noise and a blurring level. Several methods have been previously proposed and can be used in single photon emission tomography (SPECT) or positron emission tomography (PET) imaging. Some of them relies on the standard uptake value (SUV) used in PET imaging. Presented approach can be used in both (SPECT and PET) modalities and it is based on support vector machines (SVM) classification system. System has been tested on standard phantom, widely used for testing the emission tomography devices. Results are presented for two classifiers SVM and DLDA.

Recent studies indicates that cellular cancerogenesis is connected with microRNA (miRNA) expression levels. In particular, different miRNAs can serve as classification features for distinguishing different cancer types. This paper provides classification attempt using miRNA isoforms with 3’-end modification as classification features. microRNA samples was obtained using next generation sequencing method. Data was preprocessed using authors algorithm developed in R. Support Vector Mashines and Partial Least Square methods were used to classify two types of miRNA samples: Follicular Adenoma and Follicular Thyroid Cancer. It was observed that only several miRNA modified isoforms were identified as the most differentiating for analyzed samples. Obtained results indicate that miRNA 3’-end modifications can be used as cancer tissue classification features.