Implementation of ISO/IEEE 11073 PHD SpO2 and ECG Device Specializations over Bluetooth HDP following Health Care Profile for Smart Living.

Current m-Health scenarios in the smart living era, as the interpretation of the smart city at each person's level, present several challenges associated with interoperability between different clinical devices and applications. The Continua Health Alliance establishes design guidelines to standardize application communication to guarantee interoperability among medical devices. In this paper, we describe the implementation of two IEEE agents for oxygen saturation level (SpO2) measurements and electrocardiogram (ECG) data acquisition, respectively, and a smartphone IEEE manager for validation. We developed both IEEE agents over the Bluetooth Health Device Profile following the Continua guidelines and they are part of a telemonitoring system. This system was evaluated in a sample composed of 10 volunteers (mean age 29.8 ± 7.1 y/o; 5 females) under supervision of an expert cardiologist. The evaluation consisted of measuring the SpO2 and ECG signal sitting and at rest, before and after exercising for 15 min. Physiological measurements were assessed and compared against commercial devices, and our expert physician did not find any relevant differences in the ECG signal. Additionally, the system was assessed when acquiring and processing different heart rate data to prove that warnings were generated when the heart rate was under/above the thresholds for bradycardia and tachycardia, respectively.

Time efficiency analysis for undersampled quantitative MRI acquisitions.

To realize Quantitative MRI (QMRI) with clinically acceptable scan time, acceleration factors achieved by conventional parallel imaging techniques are often inadequate. Further acceleration is possible using model-based reconstruction. We propose a theoretical metric called TEUSQA: Time Efficiency for UnderSampled QMRI Acquisitions to inform sequence design and sample pattern optimisation. TEUSQA is designed for a particular class of reconstruction techniques that directly estimate tissue parameters, possibly using prior information to regularize the estimation. TEUSQA can be used to evaluate undersampling patterns for multi-contrast QMRI sequences targeting any tissue parameter. To verify the time efficiency predicted by TEUSQA, we performed Monte Carlo simulations and an accelerated parameter mapping with two sequences (Inversion prepared fast spin echo for T1 and T2 mapping and 3D GRASE for T2 and B0 inhomogeneity mapping). Using TEUSQA, we assessed several ways to generate undersampling patterns in silico, providing insight into the relation between sample distribution and time efficiency for different acceleration factors. The time efficiency predicted by TEUSQA was within 15% of that observed in the Monte Carlo simulations and the prospective acquisition experiment. The assessment of undersampling patterns showed that a class of good patterns could be obtained by low-discrepancy sampling. We believe that TEUSQA offers a valuable instrument for developers of novel QMRI sequences pushing the boundaries of acceleration to achieve clinically feasible protocols. Finally, we applied a time-efficient undersampling pattern selected using TEUSQA for a 32-fold accelerated scan to map T1 & T2 mapping of a healthy volunteer.

APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging.

PURPOSE: To improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC). METHODS: APIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment. RESULTS: Compared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm3 isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results. CONCLUSION: Compared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging.

Autocalibrated parallel imaging reconstruction with sampling pattern optimization for GRASE: APIR4GRASE.

PURPOSE: To reduce artifacts and scan time of GRASE imaging by selecting an optimal sampling pattern and jointly reconstructing gradient echo and spin echo images. METHODS: We jointly reconstruct images for the different echo types by considering these as additional virtual coil channels in the novel Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE) method. Besides image reconstruction, we identify optimal sampling patterns for the acquisition. The selected optimal patterns were validated on phantom and in-vivo acquisitions. Comparison to the conventional GRASE without acceleration, and to the GRAPPA reconstruction with a single echo type was also performed. RESULTS: Using identified optimal sampling patterns, APIR4GRASE eliminated modulation artifacts in both phantom and in-vivo experiments; mean square error (MSE) was reduced by 78% and 94%, respectively, compared to the conventional GRASE with similar scan time. Both artifacts and g-factor were reduced compared to the GRAPPA reconstruction with a single echo type. CONCLUSION: APIR4GRASE substantially improves the speed and quality of GRASE imaging over the state-of-the-art, and is able to reconstruct both spin echo and gradient echo images.

K-space trajectories in 3D-GRASE sequence for high resolution structural imaging.

PURPOSE: To propose and evaluate new k-space trajectories for 3D-GRASE to improve scan time over 3D-FSE/TSE for high resolution structural imaging. METHODS: Five different Cartesian k-space trajectories were developed and evaluated. They combine ideas of existing k-space trajectories for 3D-GRASE and 3D-FSE/TSE. T2 and T2* are linearly or radially modulated in k-space to achieve the desired contrast while including the autocalibration region needed for the parallel imaging reconstruction technique. Phase modulation among echoes was corrected in reconstruction to remove remaining artefacts. Simulation and in-vivo experiments on a 3T scanner were conducted to evaluate the performance of the different k-space trajectories. RESULTS: Two of the proposed k-space trajectories for high resolution structural imaging with 3D-GRASE obtained images comparable to 3D-FSE with lower specific absorption rate (PD/T2: 41%/75%) and shorter acquisition time (PD/T2: 27%/20%). CONCLUSIONS: 3D-GRASE image quality strongly depends on the k-space trajectory. With an optimal trajectory, 3D-GRASE may be preferable over 3D-FSE/TSE for structural high-resolution MRI.

The influence of the COMT genotype in the underlying functional brain activity of context processing in schizophrenia and in relatives.

UNLABELLED: Context processing deficits have been shown to be present in chronic and first episode schizophrenia patients and in their relatives. This cognitive process is linked to frontal functioning and is highly dependent on dopamine levels in the prefrontal cortex (PFC). The catechol-O-methyltransferase (COMT) enzyme plays a prominent role in regulating dopamine levels in PFC. Genotypic variations in the functional polymorphism Val(158)Met COMT appear to have an impact in dopamine signaling in the PFC of healthy subjects and schizophrenia patients. We aimed to explore the effect of the Val(158)Met COMT polymorphism on brain activation during the performance of a context processing task in healthy subjects, schizophrenia spectrum patients and their healthy relatives. METHODS: 56 participants performed the Dot Probe Expectancy task (DPX) during the fMRI session. Subjects were genotyped and only the Val and Met homozygotes participated in the study. RESULTS: Schizophrenia spectrum patients and their relatives showed worse performance on context processing measures than healthy control subjects. The Val allele was associated with more context processing errors in healthy controls and in relatives compared to patients. There was a greater recruitment of frontal areas (supplementary motor area/cingulate gyrus) during context processing in patients relative to healthy controls. Met homozygotes subjects activated more frontal areas than Val homozygotes subjects. CONCLUSIONS: The Val(158)Met COMT polymorphism influences context processing and on its underlying brain activation, showing less recruitment of frontal areas in the subjects with the genotype associated to lower dopamine availability in PFC.

Automated quantification of epicardial adipose tissue in cardiac magnetic resonance imaging.

Cardiovascular disease is one of the leading causes of death worldwide. Epicardial adipose tissue (EAT) has emerged as an independent predictor of high cardiometabolic risk. Cardiovascular MRI has proven to be a feasible and reproducible method to assess EAT quantitatively. We present a novel approach for the automated quantification of EAT using "a priori" anatomical information. We extracted a region of interest (ROI) in the end-diastolic heart phase followed by a GVF-snake algorithm to smooth it. For the EAT and endocardial boundary detection, a Law's texture filter is applied. Left and right ventricle are localized using spatial prior information. Then, thresholding is applied to quantify the cardiac muscle. For the EAT, it is differentiated from the paracardial fat by K-cosine curvature analysis. Results for 10 morbidly obese patients show no significant differences between manual and automatic quantification with a remarkable time and effort saving between them.