The results obtained from this study can be employed to enhance the extraction of clinically valuable information such as systolic time intervals.
Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and timeconsuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three wellestablished NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrastenhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B 1 inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%). Conclusions: The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters.
New sensor technologies open possibilities for measuring traditional biosignals in new innovative ways. This, together with the development of signal processing systems and their computing power, can sometimes give new life to old measurement techniques. Ballistocardiogram is one such technique, originally promising but quickly replaced by the now very popular electrocardiogram. A ballistocardiograph chair, designed to look like a normal office chair, was built and fitted with pressure sensitive EMFi-films. The films are connected via a charge amplifier to a medical bioamplifier. The system was accepted for medical use in Tampere University Hospital and patient measurements have been performed. The system is presented and it's performance evaluated. A wireless version of the system is needed to hide the cabling from the user. This will make the chair indistinguishable from a normal office chair. Overview of first wireless prototype is given. To analyze recorded BCG, individual BCG cycles must be extracted from the signal containing respiration and movement artifacts. A method for this and results of it's application are presented. The developed system can be used for BCG measurements and it is able to automatically extract individual BCG cycles, but it has some limitations which are presented in the paper.
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