Artifact quantification of venous stents in the MRI environment: Differences between braided and laser-cut designs

PurposeTo quantify B₀- and B₁-induced imaging artifacts of braided venous stents and to compare the artifacts to a set of laser-cut stents used in venous interventions.MethodsThree prototypes of braided venous stents with different geometries were tested in vitro. B₀ field distortion maps were measured via the frequency shift $\mathrm{\Delta }f$ using multi-echo imaging. B₁ distortions were quantified using the double angle method. The relative amplitudes $B_1^{\mathit{rel}}$ were calculated to compare the intraluminal alteration of B₁. Measurements were repeated with the stents in three different orientations: parallel, diagonal and orthogonal to B₀.ResultsAt 1.5 T, the braided stents induced a maximum frequency shift of $\left|\mathrm{\Delta }f\left(\mathit{x}\right)\right|<100\mathrm{H}\mathrm{z}$. Signal voids were limited to a distance of 2 mm to the stent walls at an echo time of 3 ms. No substantial difference in the B₀ field distortions was seen between laser-cut and braided venous stents. $B_1^{\mathit{rel}}$ maps showed strongly varying distortion patterns in the braided stents with the mean intraluminal $B_1^{\mathit{rel}}$ ranging from $\left(63\pm 18\right)\%$ in prototype 1 to $\left(98\pm 38\right)\%$ in prototype 2. Compared to laser-cut stents the braided stents showed a 5 to 9 times higher coefficient of variation of the intraluminal $B_1^{\mathit{rel}}$.ConclusionBraided venous stent prototypes allow for MR imaging of the intraluminal area without substantial signal voids due to B₀-induced artifacts. Whereas B₁ is attenuated homogeneously in laser-cut stents, the B₁ distortion in braided stents is more inhomogeneous and shows areas with enhanced amplitude. This could potentially be used in braided stent designs for intraluminal signal amplification.     

A New Compartmental Epidemiological Model for COVID-19 with a Case Study of Portugal

We propose a compartmental mathematical model for the spread of the COVID-19 disease, showing its usefulness with respect to the pandemic in Portugal, from the first recorded case in the country till the end of the three states of emergency. New results include the compartmental model, described by a system of seven ordinary differential equations; proof of positivity and boundedness of solutions; investigation of equilibrium points and their stability analysis; computation of the basic reproduction number; and numerical simulations with official real data from the Portuguese health authorities. Besides completely new, the proposed model allows to describe quite well the spread of COVID-19 in Portugal, fitting simultaneously not only the number of active infected individuals but also the number of hospitalized individuals, respectively with a $L^2$ error of $9.2152e-04$ and $1.6136e-04$ with respect to the initial population. Such results are very important, from a practical point of view, and far from trivial from a mathematical perspective. Moreover, the obtained value for the basic reproduction number is in agreement with the one given by the Portuguese authorities at the end of the three emergency states.     

Measuring breathing induced oesophageal motion and its dosimetric impact

Purpose: Stereotactic body radiation therapy allows for a precise dose delivery. Organ motion bears the risk of undetected high dose healthy tissue exposure. An organ very susceptible to high dose is the oesophagus. Its low contrast on CT and the oblong shape render motion estimation difficult. We tackle this issue by modern algorithms to measure oesophageal motion voxel-wise and estimate motion related dosimetric impacts.Methods: Oesophageal motion was measured using deformable image registration and 4DCT of 11 internal and 5 public datasets. Current clinical practice of contouring the organ on 3DCT was compared to timely resolved 4DCT contours. Dosimetric impacts of the motion were estimated by analysing the trajectory of each voxel in the 4D dose distribution. Finally an organ motion model for patient-wise comparisons was built.Results: Motion analysis showed mean absolute maximal motion amplitudes of 4.55 $\pm$ 1.81 mm left-right, 5.29 $\pm$ 2.67 mm anterior-posterior and 10.78 $\pm$ 5.30 mm superior-inferior. Motion between cohorts differed significantly. In around 50% of the cases the dosimetric passing criteria was violated. Contours created on 3DCT did not cover 14% of the organ for 50% of the respiratory cycle and were around 38% smaller than the union of all 4D contours. The motion model revealed that the maximal motion is not limited to the lower part of the organ. Our results showed motion amplitudes higher than most reported values in the literature and that motion is very heterogeneous across patients.Conclusions: Individual motion information should be considered in contouring and planning.     

Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human Proteins

Infectious diseases in humans appear to be one of the most primary public health issues. Identification of novel disease-associated proteins will furnish an efficient recognition of the novel therapeutic targets. Here, we develop a Graph Convolutional Network (GCN)-based model called PINDeL to identify the disease-associated host proteins by integrating the human Protein Locality Graph and its corresponding topological features. Because of the amalgamation of GCN with the protein interaction network, PINDeL achieves the highest accuracy of $83.45\%$ while AUROC and AUPRC values are $0.90$ and $0.88$, respectively. With high accuracy, recall, F1-score, specificity, AUROC, and AUPRC, PINDeL outperforms other existing machine-learning and deep-learning techniques for disease gene/protein identification in humans. Application of PINDeL on an independent dataset of 24320 proteins, which are not used for training, validation, or testing purposes, predicts 6448 new disease-protein associations of which we verify 3196 disease-proteins through experimental evidence like disease ontology, Gene Ontology, and KEGG pathway enrichment analyses. Our investigation informs that experimentally-verified 748 proteins are indeed responsible for pathogen-host protein interactions of which 22 disease-proteins share their association with multiple diseases such as cancer, aging, chem-dependency, pharmacogenomics, normal variation, infection, and immune-related diseases. This unique Graph Convolution Network-based prediction model is of utmost use in large-scale disease-protein association prediction and hence, will provide crucial insights on disease pathogenesis and will further aid in developing novel therapeutics.     

A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations.

MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimensionality, which determines an explosion of resource requirements. In this work, we describe a deep learning approach for MRF parameter map reconstruction using a fully connected architecture. Employing simulations, we have investigated how the performance of the Neural Networks (NN) approach scales with the number of parameters to be retrieved, compared to the standard dictionary approach. We have also studied optimal training procedures by comparing different strategies for noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-$B_1$, and IR-bSSFP-$B_1$. A comparison between NN and the dictionary approaches in reconstructing parameter maps as a function of the number of parameters to be retrieved was performed using a numerical brain phantom. Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance. NN performance was at least as good as the dictionary-based approach in reconstructing parameter maps using Gaussian noise as a source of artifacts: the difference in performance increased with the number of estimated parameters because the dictionary method suffers from the coarse resolution of the parameter space sampling. The NN proved to be more efficient in memory usage and computational burden, and has great potential for solving large-scale MRF problems.