Mercadoshah6677

Ultra-high field 7T magnetic resonance imaging (MRI) scanners produce images with exceptional anatomical details, which can facilitate diagnosis and prognosis. However, 7T MRI scanners are often cost prohibitive and hence inaccessible. In this paper, we propose a novel wavelet-based semi-supervised adversarial learning framework to synthesize 7T MR images from their 3T counterparts. Unlike most learning methods that rely on supervision requiring a significant amount of 3T-7T paired data, our method applies a semi-supervised learning mechanism to leverage unpaired 3T and 7T MR images to learn the 3T-to-7T mapping when 3T-7T paired data are scarce. This is achieved via a cycle generative adversarial network that operates in the joint spatial-wavelet domain for the synthesis of multi-frequency details. Extensive experimental results show that our method achieves better performance than state-of-the-art methods trained using fully paired data.The human brain exhibits dynamic interactions among brain regions when responding to stimuli and executing tasks, which can be recorded using functional magnetic resonance imaging (fMRI). Functional MRI signals collected in response to specific tasks consist of a combination of task-related and spontaneous (task-independent) activity. By exploiting the highly structured spatiotemporal patterns of resting state networks, this paper presents a matched-filter approach to decomposing fMRI signals into task and resting-state components. To perform the decomposition, we first use a temporal alignment procedure that is a windowed version of the brainsync transform to synchronize a resting template to the brain's response to tasks. The resulting 'matched filter' removes the components of the fMRI signal that can be described by resting connectivity, leaving the portion of brain activity directly related to tasks. CB1954 We present a closed-form expression for the windowed synchronization transform that is used by the matched filter. We demonstrate performance of this procedure in application to motor task and language task fMRI data. We show qualitatively and quantitatively that by removing the resting activity, we are able to identify task activated regions in the brain more clearly. Additionally, we show improved prediction accuracy in multivariate pattern analysis when using the matched filtered fMRI data.Diffusion MRI (dMRI), while powerful for the characterization of tissue microstructure, suffers from long acquisition times. In this paper, we propose a super-resolution (SR) reconstruction method based on orthogonal slice-undersampling for accelerated dMRI acquisition. Instead of scanning full diffusion-weighted (DW) image volumes, only a subsample of equally-spaced slices need to be acquired. We show that complementary information from DW volumes corresponding to different diffusion wave-vectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. We demonstrate that our SR reconstruction method outperforms typical interpolation methods and mitigates partial volume effects. Experimental results indicate that acceleration up to a factor of 5 can be achieved with minimal information loss.Magnetic resonance fingerprinting (MRF) is a relatively new imaging framework which allows rapid and simultaneous quantification of multiple tissue properties, such as T1 and T2 relaxation times, in one acquisition. To accelerate the data sampling in MRF, a variety of methods have been proposed to extract tissue properties from highly accelerated MRF signals. While these methods have demonstrated promising results, further improvement in the accuracy, especially for T2 quantification, is needed. In this paper, we present a novel deep learning approach, namely residual channel attention U-Net (RCA-U-Net), to perform the tissue quantification task in MRF. The RCA-U-Net combines the U-Net structure with residual channel attention blocks, to make the network focus on more informative features and produce better quantification results. In addition, we improved the preprocessing of MRF data by masking out the noisy signals in the background for improved quantification at tissue boundaries. Our experimental results on two in vivo brain datasets with different spatial resolutions demonstrate that the proposed method improves the accuracy of T2 quantification with MRF under high acceleration rates (i.e., 8 and 16) as compared to the state-of-the-art methods.Health Technology Assessment (HTA) is a systematic evaluation of a health technology, designed to appraise the direct or intended effects and indirect or unintended consequences of the technology with an overall goal of supporting informed decision making regarding the use of these health technologies in the healthcare system. In this paper, we present fundamental HTA concepts and provide a conceptual framework that embraces the processes and outcomes required for integrated healthcare decision-making. The "HTA Metro Map" was designed to guide the user through the different areas on where to use, what and whom to involve within the decision process. The map reflects the complexity and inter-connectedness of the different kind of healthcare services that need to work together to be able to efficiently deliver coordinated decisions at local, regional, national, and international levels. This tool may also serve as base for facilitating developments and improvements of the HTA structure worldwide. The paper disc is designed as a flexible model for easy adaptability and in accurately capturing the complexity inherent in any healthcare system. It is hoped that the map will assist different stakeholders to build network capacity, pool existing resources, and develop a more holistic vision that will result in a sustainable, efficient and collaborative decision-making process. Copyright © 2019 Chiumente et al.Lots of research efforts have been devoted to increase the transmission capacity in optical communications using orbital angular momentum (OAM) multiplexing. To enable long-haul OAM mode transmission, an in-line OAM fiber amplifier is desired. A ring-core fiber (RCF) is considered to be a preferable design for stable OAM mode propagation in the fiber. Here, we demonstrate an OAM fiber amplifier based on a fabricated ring-core erbium-doped fiber (RC-EDF). We characterize the performance of the RC-EDF-assisted OAM fiber amplifier and demonstrate its use in OAM multiplexing communications with OAM modes carrying quadrature phase-shift keying (QPSK) and quadrature amplitude modulation (QAM) signals. The amplification of two OAM modes over four wavelengths is demonstrated in a data-carrying OAM-division multiplexing and wavelength-division multiplexing system. The obtained results show favorable performance of the RC-EDF-assisted OAM fiber amplifier. These demonstrations may open up new perspectives for long-haul transmission in capacity scaling fiber-optic communications employing OAM modes.