Siemens (Spain)

BackgroundInnovative diffusion Magnetic Resonance Imaging (MRI) models enable the non-invasive measurement of cancer biological properties in vivo. However, while cancers frequently spread to the liver, models tailored for liver application and easy to deploy in the clinic are still sought. We fill this gap by delivering a practical, clinically-viable framework for liver tumour diffusion imaging, informing its design through histology.Methods:We compare MRI and histological data from mice and cancer patients, namely: MRI and hemaotxylin-eosin (HE) stains from N = 7 fixed mouse livers; MRI of N = 38 patients suffering from liver solid tumours, N = 18 of whom with HE biopsies. We study five diffusion models, ranking them according to a total MRI-histology correlation score. Afterwards, we test metrics from the top-ranking model on our cohort, assessing their sensitivity to cell proliferation (Ki-67 staining, N = 10), evaluating their association with tumour volume (N = 140 tumours), and comparing them across primary cancer types.Results:We select a dMRI signal model of restricted intra-cellular diffusion with negligible extra-cellular contributions, which maximises radiological-histological correlations (total score: 0.625). The model provides cell size and density estimates that i) correlate with histology (e.g., for cell size: r = 0.44, p = 0.029), ii) are associated to Ki-67 cell proliferation (for MRI cell density: r = 0.80, p = 0.006) and tumour volume (r = 0.40, p < 10–5 for tumour volume regression), and iii) that distinguish melanoma (N = 8) from colorectal cancer (N = 13) (p = 0.011 for intra-cellular fraction).Conclusions:Our biologically meaningful approach may complement standard-of-care radiology, and become a new tool for enhanced cancer characterisation in precision oncology.

ABSTRACTNeuromelanin (NM) magnetic resonance imaging has been used to evaluate the loss of melanized neurons in the substantia nigra, the main characteristic of Parkinson disease, typically by measuring the contrast ratio (CR) between the NM‐rich areas and the cerebral peduncles in the midbrain. Neuromelanin contrast can be generated by employing a 3D gradient‐echo (GRE) sequence with magnetization transfer (MT). In this work, we first evaluated the effect of the MT pulse frequency on the CR and contrast‐to‐noise ratio (CNR), analyzing a large frequency range from 500 to 100K Hz. Secondly, the impact of the 3D GRE sequence flip angle (FA) was evaluated using angles from 5° to 40°. Additionally, images were acquired both with and without MT for each FA, providing an opportunity to examine the effect of MT on tissue proton density, T1, and T2* values. Results showed that the highest CR and CNR were obtained for the lower MT frequencies (500–2000 Hz) and lower FAs (5°–10°). The lower FAs improved the visualization of the neuromelanin‐rich area in the substantia nigra and facilitated delineating its volume. In addition, this work showed that the MT pulse decreased the T1 and PD of the tissue. Furthermore, simulations supported the obtained in vivo results.

Purpose.To provide a novel and personalized method (FLIP, FLowand Irradiation Personalized) using patient-specific circulating blood flows and individualized time-dependent irradiation distributions, to quantify the dose delivered to blood in large vessels during proton or photon external beam radiotherapy.Methods.Patient-specific data were obtained from ten cancer patients undergoing radiotherapy, including the blood velocity field in large vessels and the temporal irradiation scheme using photons or protons. The large vessels and the corresponding blood flow velocities are obtained from phase-contrast MRI sequences. The blood dose is obtained discretizing the fluid into individual blood particles (BPs). A Lagrangian approach was applied to simulate the BPs trajectories along the vascular velocity field flowlines. Beam delivery dynamics was obtained from beam delivery machine measurements. The whole IS is split into a sequence of successive IEs, each one with its constant dose rate, as well as its corresponding initial and final time. Calculating the dose rate and knowing the spatiotemporal distribution of BPs, the dose is computed by accumulating the energy received by each BP as the time-dependent irradiation beams take place during the treatment.Results.Blood dose volume histograms from proton therapy and photon radiotherapy patients were assessed. The irradiation times distribution is obtained for BPs in both modalities. Two dosimetric parameters are presented: (i)D3%, representing the minimum dose received by the 3% of BPs receiving the highest doses, and (ii)V0.5 Gy, denoting the blood volume percentage that has received at least 0.5 Gy.Conclusion.A novel methodology is proposed for quantifying the circulating blood dose along large vessels. This methodology involves the use of patient-specific vasculature, blood flow velocity field, and dose delivery dynamics recovered from the irradiation machine. Relevant parameters that affect the dose received, as the distance between large vessels and CTV, are identified.

There are two main areas in which the Internet of Ships (IoS) can help: firstly, the production stage, in all its phases, from material bids to manufacture, and secondly, the operation of the ship. Intelligent ship management requires a lot of information, as does the shipbuilding process. In these two phases of the ship’s life cycle, IoS acts as a key to the keyhole. IoS tools include sensors, process information and real-time decision-making, fog computing, or delegated processes in the cloud. The key point to address this challenge is the design phase. Getting the design process right will help in both areas, reducing costs and making agile use of technology to achieve a highly efficient and optimal outcome. But this raises a lot of new questions that need to be addressed: At what stage should we start adding control sensors? Which sensors are best suited to our solution? Is there anything that offers more than simple identification? As we begin the process of answering all these questions, we realize that a Computer Aided Design (CAD) tool, as well as Artificial Intelligence (AI), mixed in a single tool, could significantly help in all these processes. AI combined with specialized CAD tools can enhance the sensitization phases in the shipbuilding process to improve results throughout the ship’s life cycle. This is the base of the framework developed in this paper.

Whiplash injury (WHI) is characterised by a forced neck flexion/extension, which frequently occurs after motor vehicle collisions. Previous studies characterising differences in brain metabolite concentrations and correlations with neuropathic pain (NP) components with chronic whiplash-associated disorders (WAD) have been demonstrated in affective pain-processing areas such as the anterior cingulate cortex (ACC). However, the detection of a difference in metabolite concentrations within these cortical areas with chronic WAD pain has been elusive. In this study, single-voxel magnetic resonance spectroscopy (MRS), following the latest MRSinMRS consensus group guidelines, was performed in the anterior cingulate cortex (ACC), left dorsolateral prefrontal cortex (DLPFC), and occipital cortex (OCC) to quantify differences in metabolite concentrations in individuals with chronic WAD with or without neuropathic pain (NP) components. Healthy individuals (n = 29) and participants with chronic WAD (n = 29) were screened with the Douleur Neuropathique 4 Questionnaire (DN4) and divided into groups without (WAD-noNP, n = 15) or with NP components (WAD-NP, n = 14). Metabolites were quantified with LCModel following a single session in a 3 T MRI scanner within the ACC, DLPFC, and OCC. Participants with WAD-NP presented moderate pain intensity and interference compared with the WAD-noNP group. Single-voxel MRS analysis demonstrated a higher glutamate concentration in the ACC and lower total choline (tCho) in the DLPFC in the WAD-NP versus WAD-noNP group, with no intergroup metabolite difference detected in the OCC. Best fit and stepwise multiple regression revealed that the normalised ACC glutamate/total creatine (tCr) (p = 0.01), DLPFC n-acetyl-aspartate (NAA)/tCr (p = 0.001), and DLPFC tCho/tCr levels (p = 0.02) predicted NP components in the WAD-NP group (ACC r 2 = 0.26, α = 0.81; DLPFC r 2 = 0.62, α = 0.98). The normalised Glu/tCr concentration was higher in the healthy than the WAD-noNP group within the ACC (p < 0.05), but not in the DLPFC or OCC. Neither sex nor age affected key normalised metabolite concentrations related to WAD-NP components when compared to the WAD-noNP group. This study demonstrates that elevated glutamate concentrations within the ACC are related to chronic WAD-NP components, while higher NAA and lower tCho metabolite levels suggest a role for increased neuronal-glial signalling and cell membrane dysfunction in individuals with chronic WAD-NP components.

Since the introduction of scientific disciplines into the shipbuilding process, there has been a search for optimisation of human and material resources. The current environmental crisis is putting additional pressure on global resource management and special attention to materials sourcing and utilisation. This paper discusses the potential solution for the current lack of effectiveness at early design stages, which are still based on 2D drawings. The industry is demanding a new 3D approach, which implies, first, a change in the procedures, and second, having a suitable CAD/CAM tool for the early generation of a digital mock-up, from which the project is developed throughout all design and production phases, from conceptual design to operation. The proposed solution shown in this paper would improve the general arrangement definition, with the use of CAD for the 3D definition of the compartments and the main equipment positioning; improve the naval architecture calculation; and finally improve the basic/class design stage, with better reuse of data already developed in the general arrangement model and in the definition of a 3D model of structure, with the main equipment positioning, pipes, and electrical equipment already into the model. Intelligent P&amp;I diagrams and single-wire electric diagrams would be used at this stage and connected to the 3D model. Additionally, it will allow for early estimating of materials, weights, and associated processes. This will be further supported by the use of topology to consider design alternatives and produce early information on materials for procurement and production. The cost reduction associated with the definition of the early design stages in 3D has been estimated at around 15% of the overall design and production stages.

Monitoring renal allograft function after transplantation is key for the early detection of allograft impairment, which in turn can contribute to preventing the loss of the allograft. Multiparametric renal MRI (mpMRI) is a promising noninvasive technique to assess and characterize renal physiopathology; however, few studies have employed mpMRI in renal allografts with stable function (maintained function over a long time period). The purposes of the current study were to evaluate the reproducibility of mpMRI in transplant patients and to characterize normal values of the measured parameters, and to estimate the labeling efficiency of Pseudo‐Continuous Arterial Spin Labeling (PCASL) in the infrarenal aorta using numerical simulations considering experimental measurements of aortic blood flow profiles. The subjects were 20 transplant patients with stable kidney function, maintained over 1 year. The MRI protocol consisted of PCASL, intravoxel incoherent motion, and T1 inversion recovery. Phase contrast was used to measure aortic blood flow. Renal blood flow (RBF), diffusion coefficient (D), pseudo‐diffusion coefficient (D*), flowing fraction (f), and T1 maps were calculated and mean values were measured in the cortex and medulla. The labeling efficiency of PCASL was estimated from simulation of Bloch equations. Reproducibility was assessed with the within‐subject coefficient of variation, intraclass correlation coefficient, and Bland‐Altman analysis. Correlations were evaluated using the Pearson correlation coefficient. The significance level was p less than 0.05. Cortical reproducibility was very good for T1, D, and RBF, moderate for f, and low for D*, while medullary reproducibility was good for T1 and D. Significant correlations in the cortex between RBF and f (r = 0.66), RBF and eGFR (r = 0.64), and D* and eGFR (r = −0.57) were found. Normal values of the measured parameters employing the mpMRI protocol in kidney transplant patients with stable function were characterized and the results showed good reproducibility of the techniques.

Industrial gas turbines for power generation are advanced engines that require constant and detailed monitorization using internal and external sensors. These sensors generate a large flow of data in the form of multivariate time series that are amenable to analysis using pattern recognition methods with the objective of improving and optimizing turbine operation. One aspect this may take is visual analytics, where dimensionality reduction methods can be used to intuitively visualize the multivariate time series. This brief paper provides a proof of concept and some case scenarios of a visual turbine-monitorization tool, based on the UMAP method, combined with clustering using HDBSCAN and unsupervised Agnostic Feature Selection. It can be considered as a first step towards a data-centered approach to gas turbine management within the industry 4.0 framework, based on the mining of turbine sensor data.