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arXiv2018

Wideband impedance measurement in the human ear canal; In vivo study on 32 subjects

The human ear canal couples the external sound field to the eardrum and the solid parts of the middle ear. Therefore, knowledge of the acoustic impedance of the human ear is widely used in the industry to develop audio devices such as smartphones, headsets, and hearing aids. In this study acoustic impedance measurements in the human ear canal of 32 adult subjects is presented. Wideband measurement techniques developed specifically for this purpose enable impedance measurement to be obtained in the full audio band up to 20kHz. Full ear canal geometries of all subjects are also available from the first of its kind in vivo based magnetic resonance imaging study of the human outer ear. These ear canal geometries are used to obtain individual ear moulds of all subjects and to process the data. By utilizing a theoretical Webster's horn description, the measured impedance is propagated in each ear canal to a common theoretical reference plane across all subjects. At this plane the mean human impedance and standard deviation of the population is found. The results are further demographically divided by gender and age and compared to a widely used ear simulator (the IEC711 coupler).

physics.med-ph↗

arXiv2018

Unsupervised Learnable Sinogram Inpainting Network (SIN) for Limited Angle CT reconstruction

In this paper, we propose a sinogram inpainting network (SIN) to solve limited-angle CT reconstruction problem, which is a very challenging ill-posed issue and of great interest for several clinical applications. A common approach to the problem is an iterative reconstruction algorithm with regularization term, which can suppress artifacts and improve image quality, but requires high computational cost. The starting point of this paper is the proof of inpainting function for limited-angle sinogram is continuous, which can be approached by neural networks in a data-driven method, granted by the universal approximation theorem. Based on this, we propose SIN as the fitting function -- a convolutional neural network trained to generate missing sinogram data conditioned on scanned data. Besides CNN module, we design two differentiable and rapid modules, Radon and Inverse Radon Transformer network, to encapsulate the physical model in the training procedure. They enable new joint loss functions to optimize both sinogram and reconstructed image in sync, which improved the image quality significantly. To tackle the labeled data bottleneck in clinical research, we form a sinogram-image-sinogram closed loop, and the difference between sinograms can be used as training loss. In this way, the proposed network can be self-trained, with only limited-angle data for unsupervised domain adaptation. We demonstrate the performance of our proposed network on parallel beam X-ray CT in lung CT datasets from Data Science Bowl 2017 and the ability of unsupervised transfer learning in Zubal's phantom. The proposed method performs better than state-of-art method SART-TV in PSNR and SSIM metrics, with noticeable visual improvements in reconstructions.

physics.med-ph↗

arXiv2018

Unsupervised organization of cervical cells using high resolution digital holographic microscopy

We report results on unsupervised organization of cervical cells using microscopy of Pap-smear samples in brightfield (3-channel colour) as well as high resolution quantitative phase imaging modalities. A number of morphological parameters are measured for each of the 1450 cell nuclei (from $10$ woman subjects) imaged in this study. The Principal Component Analysis (PCA) methodology applied to this data shows that the cell image clustering performance improves significantly when brightfield as well as phase information is utilized for PCA as compared to when brightfield-only information is used. The results point to the feasibility of an image-based tool that will be able to mark suspicious cells for further examination by the pathologist. More importantly our results suggest that the information in quantitative phase images of cells that is typically not used in clinical practice is valuable for automated cell classification applications in general.

eess.IV↗

arXiv2018

Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography

Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data. Our algorithm, Shape Adaptive Nonlocal Artifact Removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity. SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR's performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain. This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies.

physics.med-ph↗

arXiv2018

Simulation of breast compression using a new biomechanical model

Mammography is currently the primary imaging modality for breast cancer screening and plays an important role in cancer diagnostics. A standard mammographic image acquisition always includes the compression of the breast prior x-ray exposure. The breast is compressed between two plates (the image receptor and the compression paddle) until a nearly uniform breast thickness is obtained. The breast flattening improves diagnostic image quality 1 and reduces the absorbed dose 2. However, this technique can also be a source of discomfort and might deter some women from attending breast screening by mammography 3,4. Therefore, the characterization of the pain perceived during breast compression is of potential interest to compare different compression approaches. The aim of this work is to develop simulation tools enabling the characterization of existing breast compression techniques in terms of patient comfort, dose delivered to the patient and resulting image quality. A 3D biomechanical model of the breast was developed providing physics-based predictions of tissue motion and internal stress and strain intensity. The internal stress and strain intensity are assumed to be directly correlated with the patient discomfort. The resulting compressed breast model is integrated in an image simulation framework to assess both image quality and average glandular dose. We present the results of compression simulations on two breast geometries, under different compression paddles (flex or rigid).

physics.med-ph↗

arXiv2018

Monte Carlo simulation of 192Ir radioactive source in a phantom designed for brachytherapy dosimetry and source position evaluation

In this report simulations of 192Ir source located inside a phantom designed for measuring the absorbed dose and radioactive source position are presented. Monte Carlo simulations were performed and results were compared with a theoretical model that enables to determine radioactive source position. A good fit from the simulated data to the theoretical model was obtained. Results show that Monte Carlo simulations allow to evaluate source position and absorbed dose with implications in treatment planning.

physics.med-ph↗

arXiv2018

Super resolution imaging through the human skull

High resolution transcranial ultrasound imaging in humans has been a persistent challenge for ultrasound due to the imaging degradation effects from aberration and reverberation. These mechanisms depend strongly on skull morphology and they have high variability across individuals. Here we demonstrate the feasibility of human transcranial super-resolution imaging using a geometrical focusing approach to concentrate energy at the region of interest, and a phase correction focusing approach that takes the skull morphology into account. It is shown that using the proposed focused method, we can image a 208$μ$m microtube behind a human skull phantom in both an out-of-plane and an in-plane configuration. Individual phase correction profiles for the temporal region of the human skull were calculated and applied to transmit-receive a custom-focused super-resolution imaging sequence through a human skull phantom, targeting the microtube, at 68.5mm in depth, at 2.5 MHz. Microbubble contrast agents were diluted to a concentration of 1.6$\times$10$^6$ bubbles/mL and perfused through the microtube. It is shown that by correcting for the skull aberration, the RF signal amplitude from the tube improved by a factor of 1.6 in the out-of-plane focused emission case. The lateral registration error of the tube's position, which in the uncorrected case was 990 $μ$m, was reduced to 50$μ$m in the corrected case as measured in the B-mode images. Sensitivity in microbubble detection for the phase corrected case increased by a factor of 1.5 in the out-of-plane imaging case, while in the in-plane case it improved by a factor of 1.3 while achieving an axial registration correction from an initial 1885$μ$m error for the uncorrected emission, to a 284$μ$m error for the corrected counterpart. These findings suggest that super-resolution may be used more generally as a clinical imaging modality in the brain.

physics.med-ph↗

arXiv2018

Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify perfusion and vascular permeability. In most cases a bolus arrival time (BAT) delay exists between the arterial input function (AIF) and the contrast agent arrival in the tissue of interest which needs to be estimated. Existing methods for BAT estimation are tailored to tissue concentration curves which have a fast upslope to the peak as frequently observed in patient data. However, they may give poor results for curves that do not have this characteristic shape such as tissue concentration curves of small animals. In this paper, we propose a novel method for BAT estimation of signals that do not have a fast upslope to their peak. The model is based on splines which are able to adapt to a large variety of concentration curves. Furthermore, the method estimates BATs on a continuous time scale. All relevant model parameters are automatically determined by generalized cross validation. We use simulated concentration curves of small animal and patient settings to assess the accuracy and robustness of our approach. The proposed method outperforms a state-of-the-art method for small animal data and it gives competitive results for patient data. Finally, it is tested on in vivo acquired rat data where accuracy of BAT estimation was also improved upon the state-of-the-art method. The results indicate that the proposed method is suitable for accurate BAT estimation of DCE-MRI data, especially for small animals.

physics.med-ph↗

arXiv2018

Circumventing the Curse of Dimensionality in Magnetic Resonance Fingerprinting through a Deep Learning Approach

MR fingerprinting (MRF) is a rapid growing approach for fast quantitave MRI. A typical drawback of dictionary-based MRF is its explosion in size as a function of the number of reconstructed parameters, according to the curse of dimensionality. Deep Neural Networks (NNs) have been proposed as a feasible alternative, but these approaches are still in their infancy. We tested different NN pipelines on simulated data: we studied optimal training procedures by including different strategies of noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF sequences were considered, two of them designed to be more specific for $B_1^+$ parameter encoding: IR-FISP, IR-FISP-$B_1$, bSSFP and IR-bSSFP-$B_1$. A comparison between NN and the dictionary approaches 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 greater or equal than dictionary-based approach in reconstructing MR parameter maps: the difference in performance increased with the number of estimated parameters, because the dictionary method suffers from the coarse resolution of the MR parameter space sampling. The NN approach resulted more efficient in terms of memory and computational burden, and thus has great potential in large-scale MRF problems.

physics.med-ph↗

arXiv2018

Expected performance of the TT-PET scanner

The TT-PET collaboration is developing a small animal TOF-PET scanner based on silicon detectors featuring 30 ps RMS time resolution and intended to be inserted in an existing MRI scanner. The TT-PET scanner makes use of a stack of layers of high-Z photon-converter and 100 $\mathrm{μm}$ thick silicon sensors, to achieve a scanner with 0.5 $\mathrm{\times}$ 0.5 $\mathrm{\times}$ 0.2 $\mathrm{mm^{3}}$ granularity, with precise depth-of-interaction measurement. In this paper we present the results of the Monte Carlo studies for the expected data rate, time resolution on the TOF measurements, spatial resolution and image reconstruction with and without the use of the timing information. Most of the studies have been performed according to the international standards used to assess the performance of small-animal PET system.

physics.med-ph↗

arXiv2018

Photon Fluence and Dose Estimation in Computed Tomography using a Discrete Ordinates Boltzmann Solver

In this study, cone-beam single projection and axial CT scans are modeled with a software package - DOCTORS, which solves the linear Boltzmann equation using the discrete ordinates method. Phantoms include a uniform 35 cm diameter water cylinder and a non-uniform abdomen phantom. Series simulations were performed with different simulation parameters, including the number of quadrature angles, the order of Legendre polynomial expansions, and coarse and fine mesh grid. Monte Carlo simulations were also performed to benchmark DOCTORS simulations. A quantitative comparison was made between the simulation results obtained using DOCTORS and Monte Carlo methods. The deterministic simulation was in good agreement with the Monte Carlo simulation on dose estimation, with a root-mean-square-deviation (RMSD) difference of around 2.87 percent. It was found that the contribution of uncollided photon fluence directly from the source dominates the local absorbed dose in the diagnostic X-ray energy range. The uncollided photon fluence can be calculated accurately using a ray-tracing algorithm. The accuracy of collided photon fluence estimation is largely affected by the pre-calculated multigroup cross-sections. The primary benefit of DOCTORS lies in its rapid computation speed. Using DOCTORS, parallel computing using GPU enables the cone-beam CT dose estimation nearly in real-time.

physics.med-ph↗

arXiv2018

Dual Objective Approach Using A Convolutional Neural Network for Magnetic Resonance Elastography

Traditionally, nonlinear inversion, direct inversion, or wave estimation methods have been used for reconstructing images from MRE displacement data. In this work, we propose a convolutional neural network architecture that can map MRE displacement data directly into elastograms, circumventing the costly and computationally intensive classical approaches. In addition to the mean squared error reconstruction objective, we also introduce a secondary loss inspired by the MRE mechanical models for training the neural network. Our network is demonstrated to be effective for generating MRE images that compare well with equivalents from the nonlinear inversion method.

physics.med-ph↗

arXiv2018

Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability

Determining whether hypotensive patients in intensive care units (ICUs) should receive fluid bolus therapy (FBT) has been an extremely challenging task for intensive care physicians as the corresponding increase in blood pressure has been hard to predict. Our study utilized regression models and attention-based recurrent neural network (RNN) algorithms and a multi-clinical information system large-scale database to build models that can predict the successful response to FBT among hypotensive patients in ICUs. We investigated both time-aggregated modeling using logistic regression algorithms with regularization and time-series modeling using the long short term memory network (LSTM) and the gated recurrent units network (GRU) with the attention mechanism for clinical interpretability. Among all modeling strategies, the stacked LSTM with the attention mechanism yielded the most predictable model with the highest accuracy of 0.852 and area under the curve (AUC) value of 0.925. The study results may help identify hypotensive patients in ICUs who will have sufficient blood pressure recovery after FBT.

cs.LG↗

arXiv2018

Comprehensive procedure for personalized dosimetry in computed tomography

The purpose of this work is to develop viable procedures for verifying the applicability of personalized dosimetry in computed tomography (CT) using Monte Carlo-based simulations. Mobile equipment together with customized software was developed and used for rapid, non-invasive determination of equivalent source models of CT scanners under clinical conditions. Standard and anthropomorphic CT dose phantoms equipped with real-time CT dose probes at five representative positions were scanned. The accumulated dose was measured during the scan at the five positions. ImpactMC, a Monte Carlo-based CT dose software program, was used to simulate the scan. The necessary inputs were obtained from the scan parameters, from the equivalent source models and from the material-segmented CT images of the phantoms. Post-scan 3D dose distributions in the phantoms were simulated and the dose values calculated at the five positions inside the phantom were compared to measured dose values. Initial results were obtained by means of a General Electric Optima CT 660 and a Toshiba (Canon) Aquilion ONE. In general, the measured and calculated dose values were within relative uncertainties that had been estimated to be less than 10%. The procedures developed, which allow the post-CT scan dose to be measured and calculated at five points inside anthropomorphic phantoms, were found to be viable and rapid. The procedures are applicable to any scanner type under clinical conditions. Results show that the procedures are well suited for verifying the applicability of personalized CT dosimetry based on post-scan Monte Carlo calculations.

physics.med-ph↗

arXiv2018

Inertial Migration of Aerosol Particles in Confined Microfluidic Channels

In recent years, manipulation of particles by inertial microfluidics has attracted significant attention. Most studies focused on inertial focusing of particles suspended within liquid phase, in which the ratio of the density of the particle to that of the medium is O(1). the investigation on manipulation of aerosol particles in an inertial microfluidics is very limited. In this study, we numerically investigate the aerosol particle motion in a 3D straight microchannel with rectangular cross section by fully resolved simulation of the particle-air flow based on the contiuum model. The air flow is modeled by the Navier-Stokes equations, and particle's motions, including transition and rotation, are governed, respectively, by the Newton's second law and the Euler equations without using any approximation models for the lift and drag forces. The coupled mathematical model is numerically solved by combining immersed boundary with lattice Boltzmann method (IB-LBM). We find that the Reynolds numer, the particle's initial position, particle's density, and particle's diameter are the influential parameters in this process. the equilibrium positions and their stabilities of aerosols are different form those suspended in liquid.

physics.flu-dyn↗

arXiv2018

Multi-materials beam hardening artifacts correction for computed tomography (CT) based on X-ray spectrum estimation

Due to the energy-dependent nature of the attenuation coefficient and the polychromaticity of the X-ray source, beam hardening effect occurs when X-ray photons penetrate through an object, causing a nonlinear projection data. When a linear reconstruction algorithm, such as filtered backprojection, is applied to reconstruct the projection data, beam hardening artifacts which show as cupping and streaks are present in the CT image. The aim of this study was to develop a fast and accurate beam hardening correction method which can deal with beam hardening artifacts induced by multi-materials objects. Based on spectrum estimation, the nonlinear attenuation process of the X-ray projection was modeled by reprojecting a template image with the estimated polychromatic spectrum. The template images were obtained by segmenting the uncorrected into different components using a simple segmentation algorithm. Numerical simulations, experimental phantom data and animal data which were acquired on a modern diagnostic CT scanner (Discovery CT750 HD, GE Healthcare, WI, USA) and a modern C-Arm CT scanner (Artis Zee, Siemens Healthcare, Forchheim, Germany), respectively, were used to evaluate the proposed method. The results show the proposed method significantly reduced both cupping and streak artifacts, and successfully recovered the Hounsfield Units (HU) accuracy.

physics.med-ph↗

arXiv2018

On the Connection Between Planets, Dark Matter and Cancer

In a recent paper, Zioutas and Valachovic (2018) claim that dark matter is responsible for a significant fraction of the melanoma skin cancer. This conclusion is drawn from their observation of a significant correlation between skin melanoma incidence in the US and the inner planets positions (especially those of Mercury and Earth). Here I present a number of objections to their interpretation. Some (but not all) of the counterarguments are based on the analysis of a larger dataset from the same source, considering more cancer types and separating by patient attributes, such as race. One of the counterarguments is that, if the melanoma fluctuations with periods similar to planetary orbits were produced by dark matter density enhancements on Earth, then we would have to conclude that the black population is somehow immune to dark matter, a conclusion that seems incompatible with the current WIMP paradigm. Interestingly, some periodicities are present in the data, including the ones reported by Zioutas and Valachovic, but I argue that they must have a societal rather than astronomical origin.

physics.med-ph↗

arXiv2018

Automatic Mapping of Atrial Fiber Orientations for Patient-Specific Modeling of Cardiac Electromechanics using Image-Registration

Knowledge of appropriate local fiber architecture is necessary to simulate patient-specific electromechanics in the human heart. However, it is not yet possible to reliably measure in-vivo fiber directions, especially in human atria. Thus, we present a method which defines the fiber architecture in arbitrarily shaped atria using image registration and reorientation methods based on atlas atria with fibers predefined from detailed histological observations. Thereby, it is possible to generate detailed fiber families in every new patient-specific geometry in an automated, time-efficient process. We demonstrate the good performance of the image registration and fiber definition on ten differently shaped human atria. Additionally, we show that characteristics of the electrophysiological activation pattern which appear in the atlas atria also appear in the patients' atria. We arrive at analogous conclusions for coupled electro-mechano-hemodynamical computations.

physics.med-ph↗