Notes

The particular prognostic worth of high-grade cancer of prostate design upon MRI-targeted biopsies: predictors for diminishing and also significance of concomitant thorough biopsies.
Fetal magnetocardiography (fMCG) is a noninvasive method of measuring magnetic signals generated by the depolarizing heart. fMCG has proved to have superior signal-to-noise ratio characteristics and enables precise detection of the R-R intervals for fetal heart rate variability (FHRV) analysis. FHRV is one of the most useful clinical indicators for investigating fetal neurodevelopment. Currently, fMCG recordings rely on superconducting quantum interference devices (SQUIDs) which require cryogenics leading to a high cost device. New cryogenic-free sensors called optical pump magnetometers (OPMs) have emerged as alternative to SQUIDs. To take advantage of the flexibility of the OPM sensors, we explored the ability of OPM sensors to measure the fMCG at different maternal positions and sensor locations. Data were collected with a 14-channel OPM array using different sitting positions (mother leaning forward, backward, and prone). Projection operator algorithm based on minimum norm (POMN) was applied to extracted fMCG. R peaks were obtained to perform standard FHRV analysis. We were able to configure a standalone array of the OPMs that conforms to the shape of the maternal abdomen to obtain signals with sufficient quality. We extracted and quantified FHRV parameters in three low-risk fetuses. Results showed that FHRV values are in the range of previous SQUID studies.This paper presents a proof-of-concept for contactless and nonintrusive estimation of electrodermal activity (EDA) correlates using a camera. RGB video of the palm under three different lighting conditions showed that for a suitably chosen illumination strategy the data from the camera is sufficient to estimate EDA correlates which agree with the measurements done using laboratory grade physiological sensors. Selleck U0126 The effects we see in the recorded video can be attributed to sweat gland activity, which inturn is known to be correlated with EDA. These effects are so pronounced that simple pixel statistics can be used to quantify them. Such a method benefits from advances in computer vision and graphics research and has the potential to be used in affective computing and psychophysiology research where contact based sensors may not be suitable.This contribution deals with the detection and imaging of magnetic modulated nanoparticles by means of ultra-wideband sensing. We performed phantom measurements in a practical measurement setup where the magnetic nanoparticles are modulated by a static and a low periodic changing external magnetic field. We investigated the influence of the modulation type of the polarizing magnetic field on both, detectability and imaging of magnetic nanoparticles. We can conclude that both modulations generate a sufficient contrast in order that the nanoparticles were detected at the correct position in a three-dimensional volume. The imaging results, including 32 channels, indicate that the two state (ON/OFF) modulation of the magnetic field under constant environmental conditions shows better results compared to a sinusoidal excitation of the magnetic field.Low dose computed tomography (LDCT) is the current gold-standard for lung cancer diagnosis. However, accuracy of diagnosis is limited by the radiologist's ability to discern cancerous from non-cancerous nodules. To assist with diagnoses, a 4D-CT lung elastography method is proposed to distinguish nodules based on tissue stiffness properties. The technique relies on a patient-specific inverse finite element (FE) model of the lung solved using an optimization algorithm. The FE model incorporates hyperelastic material properties for tumor and healthy regions and was deformed according to respiration physiology. The tumor hyperelastic parameters and trans-pulmonary pressure were estimated using an optimization algorithm that maximizes similarity between the actual and simulated tumor and lung image data. The proposed technique was evaluated using an in-silico study where the lung tumor elastic properties were assumed. Following that evaluation, the technique was applied to clinical 4D-CT data of two lung cancer patients. Results from the evaluation study show that the elastography technique recovered known tumor parameters with only 6% error. Tumor hyperelastic properties from the clinical data are also reported. Results from this proof of concept study demonstrate the ability to perform lung elastography with 4D-CT data alone. Advancements in the technique could lead to improved diagnoses and timely treatment of lung cancer.Medical Microwave Imaging (MWI) has been studied as a technique to aid breast cancer diagnosis. Several different prototypes have been proposed but most of them require the use of a coupling medium between the antennas and the breast, in order to reduce skin backscattering and avoid refraction effects. The use of dry setups has been addressed and recent publications show promising results. In this paper, we assess the importance of considering refraction effects in the image reconstruction algorithms. To this end, we consider a simplified homogeneous spherical model of the breast and analytically compute the propagating rays through the air-body interface. The comparison of results considering only direct ray propagation or refracted rays shows negligible impact on the accuracy of the images for moderately high permittivity media. Thus, we may avoid the computational burden of calculating the refracted rays in convex shapes.Children with cerebral palsy and complex communication needs face limitations in their access technology (AT) usage. Speech recognition software and conventional ATs (e.g., mechanical switches) can be insufficient for those with speech impairment and limited control of voluntary motion. Automatic recognition of head movements represents a promising pathway. Previous studies have shown the robustness of head pose estimation algorithms on adult participants, but further research is needed to use these methods with children. An algorithm for head movement recognition was implemented and evaluated on videos recorded in a naturalistic environment when children were playing a videogame. A face-tracking algorithm was used to detect the main facial landmarks. Head poses were then estimated using the Pose from Orthography and Scaling with Iterations (POSIT) algorithm and three head movements were classified through Hidden Markov Models (HMMs). Preliminary classification results obtained from the analysis of videos of five typically developing children showed an accuracy of up to 95.
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