The Physics of Physik

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Artificial intelligence in clinical imaging: a health system approach.

The development and application of artificial intelligence (AI) to radiology requires an approach that encompasses a health system. The UK government and National Health Service (NHS) are creating an ecosystem to facilitate academic/industrial partnerships aimed at accelerating the creation of relevant and robust AI tools, which will improve the development and delivery of healthcare imaging. A series of recent initiatives are described, which will drive the development and adoption of AI in clinical imaging

Interdisciplinary approaches to metastasis

Interdisciplinary research is making a significant contribution to understanding metastasis - one of the grand challenges in cancer research. Examples drawn from apparently unconnected areas of physics, and described at a recent workshop on metastasis, illustrate the value of interdiscplinary thinking

A nuclear magnetic resonance study of water in aggrecan solutions

Aggrecan, a highly-charged macromolecule found in articular cartilage, was investigated in aqueous salt solutions with proton Nuclear Magnetic Resonance. The longitudinal and transverse relaxation rates were determined at two different field strengths, 9.4 T and 0.5 T, for a range of temperatures and aggrecan concentrations. The diffusion coefficients of the water molecules were also measured as a function of temperature and aggrecan concentration, using a pulsed field gradient technique at 9.4 T. Assuming an Arrhenius relationship, the activation energies for the various relaxation processes and the translational motion of the water molecules were determined from temperature dependencies as a function of aggrecan concentration in the range 0 – 5.3 % w/w. The longitudinal relaxation rate and inverse diffusion coefficient were approximately equally dependent on concentration and only increased by ≤ 20% from that of the salt solution. The transverse relaxation rate at high field demonstrated greatest concentration dependence, changing by an order of magnitude across the concentration range examined. We attribute this primarily to chemical exchange. Activation energies appeared to be approximately independent of aggrecan concentration, except for that of the low-field transverse relaxation rate, which decreased with concentration

Activation Energy Mapping in Articular Cartilage

Magnetic resonance imaging of articular cartilage has, in the past, had a primary focus on clinical, qualitative imaging or quantitative mapping purely using relaxation times (e.g. T1 and T2) and the development of phenomenological models to link these to physical properties, such as GAG content. Recent work in the field has looked at more advanced imaging methods, such as dGEMRIC, gagCEST and diffusion tensor imaging in order to attempt to develop a more direct link to physical and biochemical properties of the cartilage. This link is important to further understand the structure and function of cartilage, and here we investigate a novel method to probe the physical and biochemical properties of cartilage tissue. We present a novel method for visualising cartilage using magnetic resonance imaging techniques, which have been developed for probing the structure, mobility and hydration of soft-matter systems. This approach has been used to determine the dynamic activation energy (EA) of water within articular cartilage. Two related imaging methods have been explored: firstly quantitative mapping of the T1-relaxation time over a range of temperatures and secondly, quantitative mapping of the apparent diffusion coefficient over a range of temperatures. These are complementary techniques that probe the local tissue environment by extracting the rotational activation energy of the water within articular cartilage from the T1-relaxation time mapping, and the translational activation energy from the apparent diffusion coefficient mapping. These methods have been shown to provide different information from within the articular cartilage tissue to that seen with other imaging techniques. These quantitative maps can provide a link to biochemical contents or physical properties of articular cartilage tissue and can be interpreted in terms of the known structure and properties of cartilage from other methods

Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling

We designed a mathematical model to describe and quantify the mechanisms and dynamics of tumor growth, cell-kill and resistance as they affect durations of benefit after cancer treatment. Our aim was to explore how treatment efficacy may be related to primary tumor characteristics, with the potential to guide future trial design and appropriate selection of therapy. Assuming a log-normal distribution of both resistant disease and tumor doubling times generates disease-free survival (DFS) or invasive DFS curves with specific shapes. Using a multivariate mathematical model, both treatment and tumor characteristics are related to quantified resistant disease and tumor regrowth rates by allowing different mean values for the influence of different treatments or clinical subtypes on these two log-normal distributions. Application of the model to the CALGB 9741 adjuvant breast cancer trial showed that dose-dense therapy was estimated to achieve an extra 3/4 log of cell-kill compared to standard therapy, but only in patients with more rapidly growing ER-negative tumors. Application of the model to the AZURE trial of adjuvant bisphosphonate treatment suggested that the 5-year duration of zoledronic acid was adequate for ER-negative tumors, but may not be so for ER-positive cases, with increased recurrences after ceasing the intervention. Mathematical models can identify different effects of treatment by subgroup and may aid in treatment design, trial analysis, and appropriate selection of therapy. They may provide a more appropriate and insightful tool than the conventional Cox model for the statistical analysis of response durations

Interdisciplinary research: shaping the healthcare of the future

The hospitals of the future will be shaped by scientific and technical advances made across a wide range of disciplines because complex problems in healthcare cannot be addressed successfully by a single discipline. This paper considers how interdisciplinary research is being promoted and the prospects for developing stronger and deeper collaborations between medicine, health and other disciplines, drawing on case studies from mathematics, physics and engineering. The anticipated impact of greater interdisciplinarity on clinical training and the provision of care is also reviewed. While the role and training of clinicians in the provision of care will continue to evolve, they will remain leading members of a much broader and more diverse interdisciplinary team, alert to the value of deep and sustained interdisciplinary research