23 research outputs found
Recursive quantum repeater networks
Internet-scale quantum repeater networks will be heterogeneous in physical
technology, repeater functionality, and management. The classical control
necessary to use the network will therefore face similar issues as Internet
data transmission. Many scalability and management problems that arose during
the development of the Internet might have been solved in a more uniform
fashion, improving flexibility and reducing redundant engineering effort.
Quantum repeater network development is currently at the stage where we risk
similar duplication when separate systems are combined. We propose a unifying
framework that can be used with all existing repeater designs. We introduce the
notion of a Quantum Recursive Network Architecture, developed from the emerging
classical concept of 'recursive networks', extending recursive mechanisms from
a focus on data forwarding to a more general distributed computing request
framework. Recursion abstracts independent transit networks as single relay
nodes, unifies software layering, and virtualizes the addresses of resources to
improve information hiding and resource management. Our architecture is useful
for building arbitrary distributed states, including fundamental distributed
states such as Bell pairs and GHZ, W, and cluster states.Comment: 14 page
Imaging the boundaries—innovative tools for microscopy of living cells and real-time imaging
Recently, light microscopy moved back into the spotlight, which is mainly due to the development of revolutionary technologies for imaging real-time events in living cells. It is truly fascinating to see enzymes “at work” and optically acquired images certainly help us to understand biological processes better than any abstract measurements. This review aims to point out elegant examples of recent cell-biological imaging applications that have been developed with a chemical approach. The discussed technologies include nanoscale fluorescence microscopy, imaging of model membranes, automated high-throughput microscopy control and analysis, and fluorescent probes with a special focus on visualizing enzyme activity, free radicals, and protein–protein interaction designed for use in living cells
Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response
Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future
Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response
Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future
Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals
Abstract Model-driven analysis of biophysical phenomena is gaining increased attention and utility for medical imaging applications. In magnetic resonance imaging (MRI), the availability of well-established models for describing the relations between the nuclear magnetization, tissue properties, and the externally applied magnetic fields has enabled the prediction of image contrast and served as a powerful tool for designing the imaging protocols that are now routinely used in the clinic. Recently, various advanced imaging techniques have relied on these models for image reconstruction, quantitative tissue parameter extraction, and automatic optimization of acquisition protocols. In molecular MRI, however, the increased complexity of the imaging scenario, where the signals from various chemical compounds and multiple proton pools must be accounted for, results in exceedingly long model simulation times, severely hindering the progress of this approach and its dissemination for various clinical applications. Here, we show that a deep-learning-based system can capture the nonlinear relations embedded in the molecular MRI Bloch–McConnell model, enabling a rapid and accurate generation of biologically realistic synthetic data. The applicability of this simulated data for in-silico, in-vitro, and in-vivo imaging applications is then demonstrated for chemical exchange saturation transfer (CEST) and semisolid macromolecule magnetization transfer (MT) analysis and quantification. The proposed approach yielded 63–99% acceleration in data synthesis time while retaining excellent agreement with the ground truth (Pearson’s r > 0.99, p < 0.0001, normalized root mean square error < 3%)
Target visualisation and microwave hyperthermia monitoring using nanoparticle-enhanced transmission ultrasound (NETUS)
Editors’ Choice 2023
The Editorial Board and Editorial Team are delighted to present a selection of short Research Highlights describing some of our favourite Communications Engineering publications of 2023