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arXiv2018

A GPU-enabled finite volume solver for large shallow water simulations

This paper presents the implementation of a HLLC finite volume solver using GPU technology for the solution of shallow water problems in two dimensions. It compares both CPU and GPU approaches for implementing all the solver's steps. The technology of graphics and central processors is highlighted with a particular emphasis on the CUDA architecture of NVIDIA. The simple and well-documented Application Programming Interface (CUDA API) facilitates the use of the display card workstation as an additional computer unit to the central processor. Four professional solutions of the NVIDIA Quadro line are tested. Comparison tests between CPU and GPU are carried out on unstructured grids of small sizes (up to 10,000 elements), medium and large sizes (up to 10,000,000 elements). For all test cases, the accuracy of results is of the same order of magnitude for both approaches. Furthermore, the obtained speed gains with the GPU strongly depend on the model of the graphics card, the size of the problem and the simulation time.

cs.CE↗

arXiv2018

Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network

Recently deep neural networks have been widely and successfully applied in computer vision tasks and attracted growing interests in medical imaging. One barrier for the application of deep neural networks to medical imaging is the need of large amounts of prior training pairs, which is not always feasible in clinical practice. In this work we propose a personalized representation learning framework where no prior training pairs are needed, but only the patient's own prior images. The representation is expressed using a deep neural network with the patient's prior images as network input. We then applied this novel image representation to inverse problems in medical imaging in which the original inverse problem was formulated as a constraint optimization problem and solved using the alternating direction method of multipliers (ADMM) algorithm. Anatomically guided brain positron emission tomography (PET) image reconstruction and image denoising were employed as examples to demonstrate the effectiveness of the proposed framework. Quantification results based on simulation and real datasets show that the proposed personalized representation framework outperform other widely adopted methods.

cs.CV↗

arXiv2018

Deconvolution-Based Backproject-Filter (BPF) Computed Tomography Image Reconstruction Method Using Deep Learning Technique

For conventional computed tomography (CT) image reconstruction tasks, the most popular method is the so-called filtered-back-projection (FBP) algorithm. In it, the acquired Radon projections are usually filtered first by a ramp kernel before back-projected to generate CT images. In this work, as a contrary, we realized the idea of image-domain backproject-filter (BPF) CT image reconstruction using the deep learning techniques for the first time. With a properly designed convolutional neural network (CNN), preliminary results demonstrate that it is feasible to reconstruct CT images with maintained high spatial resolution and accurate pixel values from the highly blurred back-projection image, i.e., laminogram. In addition, experimental results also show that this deconvolution-based CT image reconstruction network has the potential to reduce CT image noise (up to 20%), indicating that patient radiation dose may be reduced. Due to these advantages, this proposed CNN-based image-domain BPF type CT image reconstruction scheme provides promising prospects in generating high spatial resolution, low-noise CT images for future clinical applications.

physics.med-ph↗

arXiv2018

Bioadhesive Graft-Antenna for Stimulation and Repair of Peripheral Nerves

Peripheral nerve injuries are difficult to treat due to limited axon regeneration; brief electrical stimulation of injured nerves is an emerging therapy that can relieve pain and enhance regeneration. We report an original wireless stimulator based on a metal loop (diameter ~1 mm) that is powered by a transcranial magnetic stimulator (TMS). The loop can be integrated in a chitosan scaffold that functions as a graft when applied onto transected nerves (graft-antenna). The graft-antenna was bonded to rat sciatic nerves by a laser without sutures; it did not migrate after implantation and was able to trigger steady compound muscle action potentials for 12 weeks (CMAP ~1.3 mV). Eight weeks post-operatively, axon regeneration was facilitated in transected nerves that were repaired with the graft-antenna and stimulated by the TMS for 1 hour/week. The graft-antenna is an innovative and minimally-invasive device that functions concurrently as a wireless stimulator and adhesive scaffold for nerve repair.

physics.med-ph↗

arXiv2018

Evaluation of Forward Fall on the Outstretched Hand Using MADYMO Human Body Model

Falls and their related injuries pose a significant risk to human health. One of the most common falls, the forward fall, frequently occurs among adults and the elderly. In this study, we propose using a human body model, developed using the MAthematical DYnamic MOdel (MADYMO) software, in place of human subjects, to investigate forward fall-related injuries. The MADYMO human body model is capable of simulating items that cannot be assessed on human subjects, such as human kinematics, human dynamics, and the possibility of injuries. In order to achieve our goal, a set of experiments was conducted to measure the impact force during a worst-case forward fall scenario (the outstretched hand position) for two short fall heights. Similar to the experimental design used on the human subjects, we generated a MADYMO human model. After performing the simulations, the results of the experiment on the human subjects and the MADYMO simulation model were compared. We demonstrated a significant correlation between the MADYMO simulation and the human subject experiments with respect to the magnitude and timing of the impact forces. Consequently, we validated the MADYMO human body model as a means to accurately assess forward fall-related injuries. Additionally, we compared the predicted results of a mathematical model with the MADYMO human body model. The MADYMO model is reliable and can demonstrate an accurate impact time. Therefore, we conclude that the MADYMO human model can be utilized as a reliable model to investigate forward fall-related injuries from a typical standing position.

physics.med-ph↗

arXiv2018

NIRS Based Bladder Volume Sensing for Patients Suffering with Neurogenic Bladder Dysfunction

Neurogenic Bladder Dysfunction has detrimental effects on day-to-day life of millions of people. Some of the most common symptoms faced by these patients include urinary incontinence, urgency and retention. Since elevated bladder pressure due to prolonged urine storage inside bladder may have adverse impacts on patient's renal health, urologists recommend clean-intermittent catheterization (CIC) every 2 to 4 hours throughout the day to relieve bladder pressure. However, since urine production by kidneys is an intermittent process and most of these patients have limited mobility, such frequent trips to washroom can prove to be challenging. Sometimes, bladder fills to capacity before the recommended CIC time is reached causing embarrassing situation due to leakage. Hence, time-based CIC strategy is difficult to implement and has high chances of failure. As such, continence is the primary concern for most of these patients but sadly there are no practical solutions available in the market that address this concern. A real-time notification system that could give feedback to patients on when "bladder is almost-full" could help these patients to better plan their bathroom trips. This work explores the feasibility of using a near infrared-light based wearable, non-invasive spectroscopy technique that can sense amount of urine present inside the bladder and give details on developing a bladder state estimation device. We present preliminary results by testing our device on optical phantoms and performing ex vivo measurements on porcine bladder and intestines. We later explored the possibility of using the device on human subjects, after study was approved by the UC Davis Institution Review Board (IRB).

eess.SP↗

arXiv2018

Robust Heartbeat Detection from Multimodal Data via CNN-based Generalizable Information Fusion

Objective: Heartbeat detection remains central to cardiac disease diagnosis and management, and is traditionally performed based on electrocardiogram (ECG). To improve robustness and accuracy of detection, especially, in certain critical-care scenarios, the use of additional physiological signals such as arterial blood pressure (BP) has recently been suggested. There, estimation of heartbeat location requires information fusion from multiple signals. However, reported efforts in this direction often obtain multimodal estimates somewhat indirectly, by voting among separately obtained signal-specific intermediate estimates. In contrast, we propose to directly fuse information from multiple signals without requiring intermediate estimates, and thence estimate heartbeat location in a robust manner. Method: We propose as a heartbeat detector, a convolutional neural network (CNN) that learns fused features from multiple physiological signals. This method eliminates the need for hand-picked signal-specific features and ad hoc fusion schemes. Further, being data-driven, the same algorithm learns suitable features from arbitrary set of signals. Results: Using ECG and BP signals of PhysioNet 2014 Challenge database, we obtained a score of 94%. Further, using two ECG channels of MIT-BIH arrhythmia database, we scored 99.92\%. Both those scores compare favourably with previously reported database-specific results. Also, our detector achieved high accuracy in a variety of clinical conditions. Conclusion: The proposed CNN-based information fusion (CIF) algorithm is generalizable, robust and efficient in detecting heartbeat location from multiple signals. Significance: In medical signal monitoring systems, our technique would accurately estimate heartbeat locations even when only a subset of channels are reliable.

eess.SP↗

arXiv2018

CD16: a MATLAB-based application for estimating radiation doses from x-ray guided cardiac catheterizations in children

CD16 is a MATLAB-based dosimetry system designed for rapid estimation of organ doses for x-ray guided cardiac catheterization procedures. CD16 is principally designed for epidemiological studies in which dose estimates are required for thousands of cohort members, often based on limited exposure information. The minimum data required for dose estimates are (1) total examination kerma area product (PKA), (2) patient size (age or mass), (3) x-ray spectrum represented by half value layer (HVL, in mm Al) and (4) examination type. These data can be inputted using an Excel spreadsheet. Organ dose estimates are outputted into the same spreadsheet. Doses are estimated using lookup tables of k-factors relating PKA to organ doses for a wide range of beam angles, six patient sizes and two field sizes. These K-factors were calculated using the Monte Carlo code PCXMC V2.0. CD16 initially calculates organ doses for a beam energy represented by a HVL of 4.8 mm Al, then adjusts to other beam energies if specified. Beam angles can be specified if known, otherwise CD16 assigns beam angles based on examination type. CD16 is the final version of this dosimetry system to utilise PCXMC. Future versions will use the general purpose Monte Carlo code MCNP and hybrid phantoms.

physics.med-ph↗

arXiv2018

Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks

Knowledge-based planning (KBP) is an automated approach to radiation therapy treatment planning that involves predicting desirable treatment plans before they are then corrected to deliverable ones. We propose a generative adversarial network (GAN) approach for predicting desirable 3D dose distributions that eschews the previous paradigms of site-specific feature engineering and predicting low-dimensional representations of the plan. Experiments on a dataset of oropharyngeal cancer patients show that our approach significantly outperforms previous methods on several clinical satisfaction criteria and similarity metrics.

cs.LG↗

arXiv2018

Comparing Respiratory Monitoring Performance of Commercial Wireless Devices

This paper addresses the performance of systems which use commercial wireless devices to make bistatic RF channel measurements for non-contact respiration sensing. Published research has typically presented results from short controlled experiments on one system. In this paper, we deploy an extensive real-world comparative human subject study. We observe twenty patients during their overnight sleep (a total of 160 hours), during which contact sensors record ground-truth breathing data, patient position is recorded, and four different RF breathing monitoring systems simultaneously record measurements. We evaluate published methods and algorithms. We find that WiFi channel state information measurements provide the most robust respiratory rate estimates of the four RF systems tested. However, all four RF systems have periods during which RF-based breathing estimates are not reliable.

eess.SP↗

arXiv2018

A novel filter for accurate estimation of fluid pressure and fluid velocity using poroelastography

Fluid pressure and fluid velocity carry important information for cancer diagnosis, prognosis and treatment. Recent work has demonstrated that estimation of these parameters is theoretically possible using ultrasound poroelastography. However, accurate estimation of these parameters requires high quality axial and lateral strain estimates from noisy ultrasound radio frequency (RF) data. In this paper, we propose a filtering technique combining two efficient filters for removal of noise from strain images, i.e., Kalman and nonlinear complex diffusion filters (NCDF). Our proposed filter is based on a novel noise model, which takes into consideration both additive and amplitude modulation noise in the estimated strains. Using finite element and ultrasound simulations, we demonstrate that the proposed filtering technique can significantly improve image quality of lateral strain elastograms along with fluid pressure and velocity elastograms. Technical feasibility of the proposed method on an in vivo set of data is also demonstrated. Our results show that the CNRe of the lateral strain, fluid pressure and fluid velocity as estimated using the proposed technique is higher by at least 10.9%, 51.3% and 334.4% when compared to the results obtained using a Kalman filter only, by at least 8.9%, 27.6% and 219.5% when compared to the results obtained using a NCDF only and by at least 152.3%, 1278% and 742% when compared to the results obtained using a median filter only for all SNRs considered in this study.

eess.IV↗

arXiv2018

Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.

physics.med-ph↗

arXiv2018

Requirements and Design Methodology for a Spatio-Angular Vitreoretinal Surgical Microscope

This paper presents the requirements and design methodology for a vitreoretinal surgical microscope based on plenoptic imaging. The design parameters of the imaging lens, micro-lens array, and sensor are specified. The proposed design provides an extended depth imaging range when compared to proof-of-concept systems based on a commercial plenoptic sensor, and serves as the guideline for the implementation of clinically relevant surgical microscopes.

physics.med-ph↗

arXiv2018

Multifactorial cancer treatment outcome prediction through multifaceted radiomics

Accurately predicting the treatment outcome plays a greatly important role in tailoring and adapting a treatment planning in cancer therapy. Although the development of different modalities and personalized medicine can greatly improve the accuracy of outcome prediction, they also bring the three mainly simultaneous challenges including multi-modality, multi-classifier and multi-criteria, which are summarized as multifactorial outcome prediction (MFOP) in this paper. Compared with traditional outcome prediction, MFOP is a more generalized problem. To handle this novel problem, based on the recent proposed radiomics, we propose a new unified framework termed as multifaceted radiomics (M-radiomics). M-radiomics trains multiple modality-specific classifiers first and then optimally combines the output from the outputs of different classifiers which are trained according to multiple different criteria such as sensitivity and specificity. It considers multi-modality, multi-classifier and multi-criteria into a unified framework, which makes the prediction more accurate. Furthermore, to obtain the more reliable predictive performance which is to maximize the similarity between predicted output and labelled vector, a new validation set based reliable fusion (VRF) strategy and reliable optimization models as well as a new recursive two stage hybrid optimization algorithm (RTSH) were also developed. Two clinical problems for predicting distant metastasis and locoregional recurrence in head & neck cancer were investigated to validate the performance and reliability of the proposed M-radiomics. By using the proposed RF strategy and RTSH optimization algorithm, the experimental results demonstrated that M-radiomics performed better than current radiomic models that rely on a single objective, modality or classifier.

physics.med-ph↗

arXiv2018

Noise Reduction in Diffusion MRI Using Non-Local Self-Similar Information in Joint x-q Space

Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated acquisition, and real data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.

physics.med-ph↗

arXiv2018

High-Permittivity Pad Design Tool for 7T Neuroimaging and 3T Body Imaging

Purpose: High-permittivity materials in the form of flexible 'dielectric pads' have proved very useful for addressing RF inhomogeneities in high field MRI. Finding the optimal design of such pads is however a tedious task, reducing the impact of this technique. In this work we present an easy to use software tool which allows researchers and clinicians to design dielectric pads efficiently on standard computer systems, for 7T neuroimaging and 3T body imaging applications. Methods: The tool incorporates advanced computational methods based on field decomposition and model order reduction as a framework to efficiently evaluate the B1+ fields resulting from dielectric pads. The tool further incorporates an optimization routine to perform either straightforward design for one or two pads based on a target field approach, or a trade-off analysis between homogeneity and efficiency of the B1+ field in a specific region of interest. The 3T version further allows for shifting of the imaging landmark to enable different imaging targets to be centered in the body coil.Results: Example design results are shown for imaging the inner ear at 7T and for cardiac imaging at 3T. Computation times for all cases were under a couple of minutes. The change in homogeneity and efficiency with the pad's dimensions, location, and constitution are clearly listed. Conclusion: The developed tool can be easily used to design pads for any 7T neuroimaging and 3T body imaging application within minutes. This bridges the gap between the advanced design methods and the practical application by the MR community.

physics.med-ph↗

arXiv2018

The Optimality Principle for MR signal excitation and reception: New physical insights into ideal radiofrequency coil design

Purpose: Despite decades of collective experience, radiofrequency coil optimization for MR has remained a largely empirical process, with clear insight into what might constitute truly task-optimal, as opposed to merely 'good,' coil performance being difficult to come by. Here, a new principle, the Optimality Principle, is introduced, which allows one to predict, rapidly and intuitively, the form of optimal current patterns on any surface surrounding any arbitrary body. Theory: The Optimality Principle, in its simplest form, states that the surface current pattern associated with optimal transmit field or receive sensitivity at a point of interest (per unit current integrated over the surface) is a precise scaled replica of the tangential electric field pattern that would be generated on the surface by a precessing spin placed at that point. A more general perturbative formulation enables efficient calculation of the pattern modifications required to optimize signal-to-noise ratio in body-noise-dominated situations. Methods and Results: The unperturbed principle is validated numerically, and convergence of the perturbative formulation is explored in simple geometries. Current patterns and corresponding field patterns in a variety of concrete cases are then used to separate signal and noise effects in coil optimization, to understand the emergence of electric dipoles as strong performers at high frequency, and to highlight the importance of surface geometry in coil design. Conclusion: Like the Principle of Reciprocity from which it is derived, the Optimality Principle offers both a conceptual and a computational shortcut. In addition to providing quantitative targets for coil design, the Optimality Principle affords direct physical insight into the fundamental determinants of coil performance.

physics.ins-det↗

arXiv2018

A novel method of tactile assessment of arteries using computational approach

In this study, palpating and rolling arteries with different physical properties and tactile distinction of them from each other, using finite element method, are presented. Five models have been created: healthy artery containing blood, axisymmetric stenotic artery containing blood, nonaxisymmetric stenotic artery containing blood, artery empty of blood, and filled artery. After solving these models, it was observed that the stress graph of the models included a time-dependent stress peak except for the last two models. The value of graphs peak and difference between their maximum and minimum stresses are considered as the criteria of distinction of the models.

physics.med-ph↗