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24 featured work(s)

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preprint2018arXiv

Joint T1 and T2 Mapping with Tiny Dictionaries and Subspace-Constrained Reconstruction

Purpose: To develop a method that adaptively generates tiny dictionaries for joint T1-T2 mapping. Theory: This work breaks the bond between dictionary size and representation accuracy (i) by approximating the Bloch-response manifold by piece-wise linear functions and (ii) by adaptively refining the sampling grid depending on the locally-linear approximation error. Methods: Data acquisition was accomplished with use of an 2D radially sampled Inversion-Recovery Hybrid-State Free Precession sequence. Adaptive dictionaries are generated with different error tolerances and compared to a heuristically designed dictionary. Based on simulation results, tiny dictionaries were used for T1-T2 mapping in phantom and in vivo studies. Reconstruction and parameter mapping were performed entirely in subspace. Results: All experiments demonstrated excellent agreement between the proposed mapping technique and template matching using heuristic dictionaries. Conclusion: Adaptive dictionaries in combination with manifold projection allow to reduce the necessary dictionary sizes by one to two orders of magnitude.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

A Wearable IoT Aldehyde Sensor for Pediatric Asthma Research and Management

Mechanistic studies of pediatric asthma require objective measures of environmental exposure metrics correlated with physiological responses. Here we report a cloud-based wearable IoT sensor system which can measure an asthma patient's exposure to aldehydes, a known class of airway irritants, in real-life settings. The wrist-watch form sensor measures formaldehyde levels in air using fuel cell technology, and continuously operate over 7 days without recharging. Sensor data can be retrieved via Bluetooth Low Energy (BLE) communication. A smartphone app was developed as a gateway to transmit data to an informatics system deployed on Amazon Web Services (AWS) for data storage, management and analytics. Potential applications of this IoT sensor system include epidemiological studies of asthma development and exacerbation, personalized asthma management and environmental monitoring.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries

Computational fluid dynamics (CFD) models are emerging as tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation has made subject-specific modelling of the cardiovascular system a feasible task, which is particularly important in the case of pulmonary hypertension (PH), which requires a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation can easily propagate to CFD model predictions, making uncertainty quantification crucial for subject-specific models. This study quantifies the variability of one-dimensional (1D) CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow predictions. We analyse multiple segmentations of an image of an excised mouse lung using different pre-segmentation parameters. A custom algorithm extracts vessel length, vessel radii, and network connectivity for each segmented pulmonary network. We quantify uncertainty in geometric features by constructing probability densities for vessel radius and length, and then sample from these distributions and propagate uncertainties of haemodynamic predictions using a 1D CFD model. Results show that variation in network connectivity is a larger contributor to haemodynamic uncertainty than vessel radius and length.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Full-count PET Recovery from Low-count Image Using a Dilated Convolutional Neural Network

Positron Emission Tomography (PET) is an essential technique in many clinical applications that allows for quantitative imaging at the molecular level. This study aims to develop a denoising method using novel dilated convolutional neural network to recover full-count images from low-count images. We adopted similar hierarchal structure from the conventional uNet and incorporated dilated kernels in each convolution to allow the network to observe larger, and perhaps, more robust, features within the image. Our dNet were trained alongside a uNet for comparison. Our 2.5D model used a training set (N=30) and testing set (N=5) that were obtained from an ongoing 18F-FDG study. Low-count PET data (10% count) were generated through Poisson thinning from the full listmode file. Both low-count PET and full-count PET were reconstructed with the OSEM algorithm. Objective imaging metrics including mean absolute percent error (MAPE), peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) were used to analyze the denoising methods. Both the uNet and our proposed dNet were successfully trained to synthesize full-count PET images from low-count PET images. Compared to low-count PET, both the uNet and dNet methods significantly improved MAPE, PSNR and SSIM. Our dNet also systematically outperformed uNet on all three metrics and across all testing subjects. This study proposed a novel approach of using dilated convolutions for recovering full-count PET images from low-count PET images. Our dNet significantly outperformed the well-established uNet and demonstrates great potential for denoising low-count PET images.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Deep Model with Siamese Network for Viability and Necrosis Tumor Assessment in Osteosarcoma

Osteosarcoma is the most common primary malignant bone tumor, which has high mortality due to easy lung metastasis. Osteosarcoma is a highly anaplastic, pleomorphic tumor with a variety of tumor cell morphology, including fusiform, oval, epithelial, lymphocyte like, small round, transparent cells, etc. Due to the multiple patterns of osteosarcoma cell morphology, pathologists have differences in the classification (viable tumor, necrotic tumor, non-tumor) of osteosarcoma. Therefore, automatic and accurate recognition algorithms can help pathologists greatly reduce time and improve diagnostic accuracy. In recent years, deep learning technology has made great progress in the field of natural images and medical images, and has achieved excellent results beyond human performance in classification. In this paper, we propose a Deep Model with Siamese Network (DS-Net) for automatic classification in Hematoxylin and Eosin (H&E) stained osteosarcoma histology images.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

A Discreet Wearable IoT Sensor for Continuous Transdermal Alcohol Monitoring -- Challenges and Opportunities

Non-invasive continuous alcohol monitoring has potential applications in both population research and in clinical management of acute alcohol intoxication or chronic alcoholism. Current wearable monitors based on transdermal alcohol content (TAC) sensing are relatively bulky and have limited quantification accuracy. Here we describe the development of a discreet wearable transdermal alcohol (TAC) sensor in the form of a wristband or armband. This novel sensor can detect vapor-phase alcohol in perspiration from 0.09 ppm (equivalent to 0.09 mg/dL sweat alcohol concentration at 25 °C under Henry's Law equilibrium) to over 500 ppm at one-minute time resolution. The TAC sensor is powered by a 110 mAh lithium battery that lasts for over 7 days. In addition, the sensor can function as a medical "internet-of-things" (IoT) device by connecting to an Android smartphone gateway via Bluetooth Low Energy (BLE) and upload data to a cloud informatics system. Such wearable IoT sensors may enable large-scale alcohol-related research and personalized management. We also present evidence suggesting a hypothesis that perspiration rate is the dominant factor leading to TAC measurement variabilities, which may inform more reproducible and accurate TAC sensor designs in the future.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Dosimetric performance of a multi-point plastic scintillator dosimeter as a tool for real-time source tracking in high dose rate brachytherapy

Purpose: To present the performance of a multi-point plastic scintillation detector (mPSD) as a tool in vivo dosimetry in brachytherapy. Methods: A previously optimized three-point sensor system was used for in vivo HDR brachytherapy measurements (using the scintillators BCF-60, BCF-12, and BCF-10). The light detection system of the mPSD consisted of compactly assembled photomultiplier tubes (PMTs) and dichroic mirrors and filters to achieve a highly sensitive scintillation light collection. The PMT signals were recorded using a NI-DAQ board at a rate of 100 kHz. Dose measurements covering a range of 0.5 to 10 cm from the 192Ir source were carried out according to TG-43 U1 recommendations in order to: (1) characterize the system's response in terms of angular dependence; (2) obtain the relative contribution of positioning and measurement uncertainties to the total system uncertainty; (3) assess the system's temporal resolution; and (4) track the source position in real time. Results: The positioning uncertainty dominated close to the source, whereas the measurement uncertainty dominated at larger distances. A maximum measurement uncertainty of 17 % was observed for the BCF-60 scintillator at 10 cm from the source. The average best compromise between positioning and measurement uncertainties were reached at 17.4 mm. The detector further exhibited no angular dependence. The system provided an average location with a standard deviation under 1.7 mm. The maximum observed differences between measured and expected source location was 1.82 mm. Dose deviations remained below 5% in all the explored measurement conditions. With regard to dwell time measurement accuracy, the maximum deviation observed at all distances was 0.56 s. Conclusions: The performance of the system demonstrated that it could be used for real-time dose, position and dwell time measurements during HDR brachytherapy.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Physics-Guided Modular Deep-Learning Based Automated Framework for Tumor Segmentation in PET Images

The objective of this study was to develop a PET tumor-segmentation framework that addresses the challenges of limited spatial resolution, high image noise, and lack of clinical training data with ground-truth tumor boundaries in PET imaging. We propose a three-module PET-segmentation framework in the context of segmenting primary tumors in 3D FDG-PET images of patients with lung cancer on a per-slice basis. The first module generates PET images containing highly realistic tumors with known ground-truth using a new stochastic and physics-based approach, addressing lack of training data. The second module trains a modified U-net using these images, helping it learn the tumor-segmentation task. The third module fine-tunes this network using a small-sized clinical dataset with radiologist-defined delineations as surrogate ground-truth, helping the framework learn features potentially missed in simulated tumors. The framework's accuracy, generalizability to different scanners, sensitivity to partial volume effects (PVEs) and efficacy in reducing the number of training images were quantitatively evaluated using Dice similarity coefficient (DSC) and several other metrics. The framework yielded reliable performance in both simulated (DSC: 0.87 (95% CI: 0.86, 0.88)) and patient images (DSC: 0.73 (95% CI: 0.71, 0.76)), outperformed several widely used semi-automated approaches, accurately segmented relatively small tumors (smallest segmented cross-section was 1.83 cm2), generalized across five PET scanners (DSC: 0.74), was relatively unaffected by PVEs, and required low training data (training with data from even 30 patients yielded DSC of 0.70). In conclusion, the proposed framework demonstrated the ability for reliable automated tumor delineation in FDG-PET images of patients with lung cancer.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Tumor ablation due to inhomogeneous -- anisotropic diffusion in generic 3-dimensional topologies

We derive a full 3-dimensional (3-D) model of inhomogeneous -- anisotropic diffusion in a tumor region coupled to a binary population model. The diffusion tensors are acquired using Diffusion Tensor Magnetic Resonance Imaging (DTI) from a patient diagnosed with glioblastoma multiform (GBM). Then we numerically simulate the full model with Finite Element Method (FEM) and produce drug concentration heat maps, apoptosis regions, and dose-response curves. Finally, predictions are made about optimal injection locations and volumes, which are presented in a form that can be employed by doctors and oncologists.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Ultra-fast prompt gamma detection in single proton counting regime for range monitoring in particle therapy

In order to fully exploit the ballistic potential of particle therapy, we propose an online range monitoring concept based on high-resolution Time-Of-Flight (TOF)-resolved Prompt Gamma (PG) detection in a single proton counting regime. In a proof of principle experiment, different types of monolithic scintillating gamma detectors are read in time coincidence with a diamond-based beam hodoscope, in order to build TOF spectra of PG generated in a heterogeneous target presenting an air cavity of variable thickness. Since the measurement was carried out at low beam currents ($<$ 1 proton/bunch) it was possible to reach excellent coincidence time resolutions, of the order of 100 ps ($σ$). Our goal is to detect possible deviations of the proton range with respect to treatment planning within a few intense irradiation spots at the beginning of the session and then carry on the treatment at standard beam currents. The measurements were limited to 10 mm proton range shift. A Monte Carlo simulation study reproducing the experiment has shown that a 3 mm shift can be detected at 2$σ$ by a single detector of $\sim 1.4 \times 10^{-3}$ absolute detection efficiency within a single irradiation spot ($\sim$10$^{8}$ protons) and an optimised experimental set-up.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Superiorized method for metal artifact reduction

Metal artifact reduction (MAR) is a challenging problem in computed tomography (CT) imaging. A popular class of MAR methods replace sinogram measurements that are corrupted by metal with artificial data. While these ``projection completion&#39;&#39; approaches are successful in eliminating severe artifacts, secondary artifacts may be introduced by the artificial data. In this paper, we propose an approach which uses projection completion to generate a prior image, which is then incorporated into an iterative reconstruction algorithm based on the superiorization framework. The prior image is reconstructed using normalized metal artifact reduction (NMAR), a popular projection completion approach. The iterative algorithm is a modified version of the simultaneous algebraic reconstruction technique (SART), which reduces artifacts by incorporating a polyenergetic forward model, least-squares weighting, and superiorization. The penalty function used for superiorization is a weighted average between a total variation (TV) term and a term promoting similarity with the prior image, similar to penalty functions used in prior image constrained compressive sensing. Because the prior is largely free of severe metal artifacts, these artifacts are discouraged from arising during iterative reconstruction; additionally, because the iterative approach uses the original projection data, it is able to recover information that is lost during the NMAR process. We perform numerical experiments modeling a simple geometric object, as well as several more realistic scenarios such as metal pins, bilateral hip implants, and dental fillings placed within an anatomical phantom. The proposed iterative algorithm is largely successful at eliminating severe metal artifacts as well as secondary artifacts introduced by the NMAR process, especially lost edges of bone structures in the neighborhood of the metal regions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

First Principle Simulation of Coated Hydroxychloroquine on Ag, Au and Pt Nanoparticle as a Potential Candidate for Treatment of SARS-CoV-2 (COVID-19)

The {\it{in vitro}} antiviral activity of Hydroxychloroquine (HCQ) and chloroquine (CQ) against SARS-CoV-2 from the first month of pandemic proposed these drugs as the appropriate therapeutic candidate, although their side effect directed the clinical test toward optimizing the safe utilization strategies. The noble metal nanoparticles (NP) as promising materials with antiviral and antibacterial properties can deliver the drug to the target agent and decrease the side effect. In this work, we have applied quantum mechanical and classical atomistic molecular dynamics computational approaches to demonstrate the adsorption properties of HCQ on Ag, Au, AgAu, and Pt nanoparticles. The adsorption energies (less than -30 kcal/mole) were established for HCQ, and the (non)perturbative effects of this drug on the plasmonic absorption spectra of AgNP and AuNP have characterized with time-dependent density functional theory. The effect of size and compositions of nanoparticle on the coating with HCQ and CQ have obtained and proposed the appropriate candidate for drug delivery. This kind of modeling could help the experimental groups to find the efficient and safe therapies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Update of the CLRP TG-43 parameter database for low-energy brachytherapy sources

PURPOSE: To update the Carleton Laboratory for Radiotherapy Physics (CLRP) TG-43 dosimetry database for low-energy (< 50 keV) photon-emitting low-dose rate (LDR) brachytherapy sources utilizing the open-source EGSnrc application egs_brachy rather than the BrachyDose application used previously for 27 LDR sources in the 2008 CLRP version (CLRPv1). CLRPv2 covers 40 sources (Pd-103, I-125, Cs-131). A comprehensive set of TG-43 parameters is calculated, including dose-rate constants, radial dose functions with functional fitting parameters, 1D and 2D anisotropy functions, along-away dose-rate tables, Primary-Scatter separation dose tables (for some sources), and mean photon energies. The database also documents the source models which will become part of the egs_brachy distribution. METHODS: Datasets are calculated after a systematic recoding of the source geometries using the egs++ geometry package and its egs_brachy extensions. Full scatter water phantoms with varying voxel resolutions in cylindrical coordinates are used for dose calculations. New statistical uncertainties of source volume corrections for phantom voxels which overlap with brachytherapy sources are implemented in egs_brachy, and all CLRPv2 data include these uncertainties. For validation, data are compared to CLRPv1 and other data in the literature. DATA ACCESS/FORMAT: Data are available at https://physics.carleton.ca/ clrp/egs_brachy/seed_database_v2. As well as being presented graphically in comparisons to previous calculations, data are available in Excel (.xlsx) spreadsheets for each source. APPLICATIONS: The database has applications in research, dosimetry, and brachytherapy planning. This comprehensive update provides the medical physics community with more accurate TG-43 dose evaluation parameters, as well as fully-benchmarked source models distributed with egs_brachy.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A novel 3D multi-path DenseNet for improving automatic segmentation of glioblastoma on pre-operative multi-modal MR images

Convolutional neural networks have achieved excellent results in automatic medical image segmentation. In this study, we proposed a novel 3D multi-path DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four multi-modal pre-operative MR images. We hypothesized that the multi-path architecture could achieve more accurate segmentation than a single-path architecture. 258 GBM patients were included in this study. Each patient had four MR images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR) and the manually segmented tumor contour. We built a 3D multi-path DenseNet that could be trained to generate the corresponding GBM tumor contour from the four MR images. A 3D single-path DenseNet was also built for comparison. Both DenseNets were based on the encoder-decoder architecture. All four images were concatenated and fed into a single encoder path in the single-path DenseNet, while each input image had its own encoder path in the multi-path DenseNet. The patient cohort was randomly split into a training set of 180 patients, a validation set of 39 patients, and a testing set of 39 patients. Model performance was evaluated using the Dice similarity coefficient (DSC), average surface distance (ASD), and 95% Hausdorff distance (HD95%). Wilcoxon signed-rank tests were conducted to examine the model differences. The single-path DenseNet achieved a DSC of 0.911$\pm$0.060, ASD of 1.3$\pm$0.7 mm, and HD95% of 5.2$\pm$7.1 mm, while the multi-path DenseNet achieved a DSC of 0.922$\pm$0.041, ASD of 1.1$\pm$0.5 mm, and HD95% of 3.9$\pm$3.3 mm. The p-values of all Wilcoxon signed-rank tests were less than 0.05. Both 3D DenseNets generated GBM tumor contours in good agreement with the manually segmented contours from multi-modal MR images. The multi-path DenseNet achieved more accurate tumor segmentation than the single-path DenseNet.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Using Deep Learning to Predict Beam-Tunable Pareto Optimal Dose Distribution for Intensity Modulated Radiation Therapy

We propose to develop deep learning models that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities. We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35,000 plans. We studied and compared two different models, Model I and Model II. Model I directly uses beam angles as a second input to the network as a binary vector. Model II converts the beam angles into beam doses that are conformal to the PTV. Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions. Quantitatively, Model I prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), 1.10% (D2) was lower than that of Model II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50), 7.10% (D95), 6.50% (D98), 8.40% (D50), 6.30% (D2). Treatment planners who use our models will be able to use deep learning to control the tradeoffs between the PTV and OAR weights, as well as the beam number and configurations in real time. Our dose prediction methods provide a stepping stone to building automatic IMRT treatment planning.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Data-driven dose calculation algorithm based on deep learning

In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two dimensional (2D) fluence map was first converted into a three dimensional (3D) volume using ray traversal algorithm. A 3D U-Net like deep residual network was then established to learn a mapping between this converted 3D volume, CT and 3D dose distribution. Therefore an indirect relationship was built between a fluence map and its corresponding 3D dose distribution without using significantly complex neural networks. 200 patients, including nasopharyngeal, lung, rectum and breast cancer cases, were collected and applied to train the proposed network. Additional 47 patients were randomly selected to evaluate the accuracy of the proposed method through comparing dose distributions, dose volume histograms (DVH) and clinical indices with the results from a treatment planning system (TPS), which was used as the ground truth in this study. Results: The proposed deep learning based dose calculation algorithm achieved good predictive performance. For 47 tested patients, the average per-voxel bias of the deep learning calculated value and standard deviation (normalized to the prescription), relative to the TPS calculation, is 0.17%. The average deep learning calculated values and standard deviations for relevant clinical indices were compared with the TPS calculated results and the t-test p-values demonstrated the consistency between them. Conclusions: In this study we developed a new deep learning based dose calculation method. This approach was evaluated by the clinical cases with different sites. Our results demonstrated its feasibility and reliability and indicated its great potential to improve the efficiency and accuracy of radiation dose calculation for different treatment modalities

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Review and experimental verification of X-ray darkfield signal interpretations with respect to quantitative isotropic and anisotropic darkfield computed tomography

Talbot(-Lau) interferometric X-ray darkfield imaging has, over the past decade, gained substantial interest for its ability to provide insights into a sample&#39;s microstructure below the imaging resolution by means of ultra small angle scattering effects. Quantitative interpretations of such images depend on models of the signal origination process that relate the observable image contrast to underlying physical processes. A review of such models is given here and their relation to the wave optical derivations by Yashiro et al. and Lynch et al. as well as to small angle X-ray scattering is discussed. Fresnel scaling is introduced to explain the characteristic distance dependence observed in cone beam geometries. Moreover, a model describing the anisotropic signals of fibrous objects is derived. The Yashiro-Lynch model is experimentally verified both in radiographic and tomographic imaging in a monochromatic synchrotron setting, considering both the effects of material and positional dependence of the resulting darkfield contrast. The effect of varying sample-detector distance on the darkfield signal is shown to be non-negligible for tomographic imaging, yet can be largely compensated for by symmetric acquisition trajectories. The derived orientation dependence of the darkfield contrast of fibrous materials both with respect to variations in autocorrelation width and scattering cross section is experimentally validated using carbon fiber reinforced rods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Internal-Illumination Photoacoustic Tomography Enhanced by a Graded-scattering Fiber Diffuser

The penetration depth of photoacoustic imaging in biological tissues has been fundamentally limited by the strong optical attenuation when light is delivered externally through the tissue surface. To address this issue, we previously reported internal-illumination photoacoustic imaging using a customized radial-emission optical fiber diffuser, which, however, has complex fabrication, high cost, and non-uniform light emission. To overcome these shortcomings, we have developed a new type of low-cost fiber diffusers based on a graded-scattering method in which the optical scattering of the fiber diffuser is gradually increased as the light travels. The graded scattering can compensate for the optical attenuation and provide relatively uniform light emission along the diffuser. We performed Monte Carlo numerical simulations to optimize several key design parameters, including the number of scattering segments, scattering anisotropy factor, divergence angle of the optical fiber, and reflective index of the surrounding medium. These optimized parameters collectively result in uniform light emission along the fiber diffuser and can be flexibly adjusted to accommodate different applications. We fabricated and characterized the prototype fiber diffuser made of agarose gel and intralipid. Equipped with the new fiber diffuser, we performed thorough proof-of-concept studies on ex vivo tissue phantoms and an in vivo swine model to demonstrate the deep-imaging capability (~10 cm achieved ex vivo) of photoacoustic tomography. We believe that the internal light delivery via the optimized fiber diffuser is an effective strategy to image deep targets (e.g., kidney) in large animals or humans.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Free-running SIMilarity-Based Angiography (SIMBA) for simplified anatomical MR imaging of the heart

Purpose: Whole-heart MRA techniques typically target pre-determined motion states and address cardiac and respiratory dynamics independently. We propose a novel fast reconstruction algorithm, applicable to ungated free-running sequences, that leverages inherent similarities in the acquired data to avoid such physiological constraints. Theory and Methods: The proposed SIMilarity-Based Angiography (SIMBA) method clusters the continuously acquired k-space data in order to find a motion-consistent subset that can be reconstructed into a motion-suppressed whole-heart MRA. Free-running 3D radial datasets from six ferumoxytol-enhanced scans of pediatric cardiac patients and twelve non-contrast scans of healthy volunteers were reconstructed with a non-motion-suppressed regridding of all the acquired data (All Data), our proposed SIMBA method, and a previously published free-running framework (FRF) that uses cardiac and respiratory self-gating and compressed sensing. Images were compared for blood-myocardium interface sharpness, contrast ratio, and visibility of coronary artery ostia. Results: Both the fast SIMBA reconstruction (~20s) and the FRF provided significantly higher blood-myocardium sharpness than All Data (P<0.001). No significant difference was observed among the former two. Significantly higher blood-myocardium contrast ratio was obtained with SIMBA compared to All Data and FRF (P<0.01). More coronary ostia could be visualized with both SIMBA and FRF than with All Data (All Data: 4/36, SIMBA: 30/36, FRF: 33/36, both P<0.001) but no significant difference was found between the first two. Conclusion: The combination of free-running sequences and the fast SIMBA reconstruction, which operates without a priori assumptions related to physiological motion, forms a simple workflow for obtaining whole-heart MRA with sharp anatomical structures.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Maximal Interaction of Electromagnetic Radiation with Corona-Virions

Absorption and scattering of the impinging electromagnetic waves are the two fundamental operations describing the energy exchange of any, organic or inorganic, particle with its environment. In the case of virion cells, substantial extinction power, counting both absorbing and scattering effects, is a prerequisite for performing a variety of coupling actions against the viral particles and, thus, a highly sought-after feature. By considering realistic dispersion for the dielectric permittivity of proteins and a core-shell modeling allowing for rigorous formulation via Mie theory, we report optical extinction resonances for corona-virions at mid-infrared range that are not significantly perturbed by changes in the objects size or the background host. Our findings indicate the optimal regime for interaction of photonic radiation with viral particles and may assist towards the development of equipment for thermal damage, disintegration or neutralization of coronavirus cells.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Mathematical Modeling and Computer Simulation of Needle Insertion into Soft Tissue

In this study we present a kinematic approach to modeling needle insertion into soft tissues. The kinematic approach allows the presentation of the problem as Dirichlet-type (i.e. driven by enforced motion of boundaries) and therefore weakly sensitive to unknown properties of the tissues and needle-tissue interaction. The parameters used in the kinematic approach are straightforward to determine from images. Our method uses Meshless Total Lagrangian Explicit Dynamics (MTLED) framework to compute soft tissue deformations. The proposed scheme was validated against experiments of needle insertion into silicone gel samples. We also present a simulation of needle insertion into the brain demonstrating the method&#39;s insensitivity to assumed mechanical properties of tissue.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge

Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of &#34;Advances in sensitivity encoding with arbitrary k-space trajectories&#34; by Pruessmann et al. Methods: The task of the challenge was to reconstruct radially acquired multi-coil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion: While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, e.g., density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient meta-data accompanying open data sets. Defining reproducibility quantitatively turned out to be non-trivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Improving blood vessel tortuosity measurements via highly sampled numerical integration of the Frenet-Serret equations

Measures of vascular tortuosity--how curved and twisted a vessel is--are associated with a variety of vascular diseases. Consequently, measurements of vessel tortuosity that are accurate and comparable across modality, resolution, and size are greatly needed. Yet in practice, precise and consistent measurements are problematic--mismeasurements, inability to calculate, or contradictory and inconsistent measurements occur within and across studies. Here, we present a new method of measuring vessel tortuosity that ensures improved accuracy. Our method relies on numerical integration of the Frenet-Serret equations. By reconstructing the three-dimensional vessel coordinates from tortuosity measurements, we explain how to identify and use a minimally-sufficient sampling rate based on vessel radius while avoiding errors associated with oversampling and overfitting. Our work identifies a key failing in current practices of filtering asymptotic measurements and highlights inconsistencies and redundancies between existing tortuosity metrics. We demonstrate our method by applying it to manually constructed vessel phantoms with known measures of tortuousity, and 9,000 vessels from medical image data spanning human cerebral, coronary, and pulmonary vascular trees, and the carotid, abdominal, renal, and iliac arteries.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Vortex dynamics and transport phenomena in stenotic aortic models using Echo-PIV

In this work, we propose a novel approach which combines ultrasound with Eulerian and Lagrangian descriptors, to analyse blood flow dynamics and fluid transport in stenotic aortic models with morphology, mechanical and optical properties close to those of real arteries. To this end, vorticity, particle residence time (PRT), particle&#39;s final position (FP) and finite time Lyapunov&#39;s exponents (FTLE) were computed from the experimental fluid velocity fields acquired using ultrasonic particle imaging velocimetry (Echo-PIV). For the experiments, CT-images were used to create morphological realistic models of the descending aorta with 0%, 35% and 50% occlusion degree with same mechanical properties as real arteries. Each model was connected to a circuit with a pulsatile programmable pump which mimics physiological flow and pressure conditions. The pulsatile frequency was set to 0.9 Hz (55 bpm) and the upstream peak Reynolds number (Re) was changed from 1100 to 2000. Flow in the post-stenotic region was composed of two main structures: a high velocity jet over the stenosis throat and a recirculation region behind the stenosis where vortex form and shed. We characterized vortex kinematics showing that vortex propagation velocity increases with Re. Moreover, from the FTLE field we identified Lagrangian Coherent Structures (i.e. material barriers) that dictate transport behind the stenosis. The size and strength of those barriers increased with Re and the occlusion degree. Finally, from the PRT and FP, we showed that independently of Re, the same amount of fluid remains on the stenosis over more than a pulsatile period, which combined with large FTLE values may provide an alternative way to understand stenosis growth.

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