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Magnetic resonance elastography (MRE) is a non invasive imaging modality, which holds the promise of absolute quantification of the mechanical properties of human tissues in vivo. MRE reconstruction with algebraic inversion of the Helmholtz equation upon the curl of the shear displacement field may theoretically be flawless. However, its performances are challenged by multiple experimental parameters, especially the frequency and the amplitude of the mechanical wave, the voxel size and the signal-to-noise ratio of the MRE acquisition. A point source excitation was simulated and realistic displacement fields were analytically computed to simulate MRE data sets in an isotropic, homogeneous, linearly-elastic, and half-space infinite medium. Acquisition and reconstruction methods were challenged and the joint influence of the aforementioned parameters was studied. For a given signal-to-noise ratio, the conditions on the number of voxels per wavelength were determined for optimizing voxel-wise accuracy and precision in MRE. It was shown that, once data are acquired, the reconstruction quality could even be improved by effective interpolation or decimation so data could eventually fulfill favorable conditions for mechanical characterization of the tissue. Finally, the overall outcome, which is usually computed from the three acquired motion-encoded directions, may further be improved by appropriate averaging strategies that are based on adapted curl of shear displacement field quality-weighting.

Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (K trans and v e) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then K trans and v e for the ROI was extracted. The simulations were run 100 times for each ROI with new K trans and v e generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n = 6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (

Objective. Application of the backprojection image reconstruction algorithm in magnetic induction tomography for imaging of low contrast inclusions in conductive background is investigated. Approach. Both FDTD numerical simulation and experiments with the 16-channel measuring system and saline phantom are used. Main results. It is demonstrated that spatial redistribution of the sensitivity of measurements related with background conductivity does not affect the projection lines and keeps possibility of backprojection image reconstruction, which is important in biomedical applications of MIT. Significance. The applicability of backprojection in biomedical MIT has been proved. The reconstruction algorithm is explained in detail.

The anisotropic mechanical properties (mechanical anisotropy) and view-dependent ultrasonic backscattering (acoustic anisotropy) of striated muscle due to the underlying myofiber arrangement have been well documented, but whether they impact on ultrasound strain imaging (USI) techniques remains unclear. The aim of this study was therefore to investigate the performance of a cross-correlation-based two-dimensional (2D) USI method in anisotropic media under controlled quasi-static compression in silico and in vitro. First, synthetic pre- and post-deformed 2D radiofrequency images of anisotropic phantoms were simulated in two scenarios to examine the individual effect of the mechanical and acoustic anisotropies on strain estimation. In the first scenario, the phantom was defined to be transversely isotropic with the scatterer amplitudes following a zero-mean Gaussian distribution, while in the second scenario, the phantom was defined to be mechanically isotropic with Gaussian distributed scatterer amplitudes correlated along the principal directions of pre-defined fibers. These two anisotropies were then jointly incorporated into the ultrasound image simulation model with additional depth-dependent attenuation. Three imaging planes-the fiber plane with the fiber direction perpendicular to the ultrasound beam (TISperp_fb), the fiber plane with the fiber direction parallel to the beam (TISpara), and the transverse fiber plane (TISperp_cfb)-were studied. The absolute relative error (ARE) of the lateral strain estimates in TISperp_fb (20.99 ± 15.65%) was much higher than that in TISperp_cfb (4.14 ± 3.17%). The ARE in TISpara was unavailable owing to the large spatial extent of false peaks. The effect of tissue anisotropy on the performance of the 2D USI was further confirmed in an in vitro porcine skeletal muscle phantom. The best in-plane strain quality was again shown in TISperp_cfb (elastographic signal-to-noise ratio, or SNRe: >25 dB), whereas the most unreliable strain estimates were found as expected in TISpara (SNRe

OBJECTIVE: The process of grouping neuronal spikes in an extracellular recording according to their neuronal sources, is generally referred to as spike sorting. Currently, the use of spike sorting is mainly limited to an offline usage, where spikes are sorted after the data acquisition has been completed. In this paper, we propose a discriminative template matching algorithm for threshold-based spike sorting on high-density extracellular data. Such threshold-based spike sorting has a low and deterministic algorithmic delay, allowing for fast online spike sorting. APPROACH: At its core, threshold-based spike sorting is driven by linear filters. The proposed discriminative template matching filter design algorithm optimizes the output signal-to-peak-interference ratio in a data-driven fashion, assuming the template of the target spike is available. The latter allows the filter to suppress the spikes of interfering neurons and to resolve spike overlap. The data-driven filter design algorithm requires only templates of the target neurons of interest, which can be retrieved, e.g. through a prior clustering on an initial recording. MAIN RESULTS: The proposed discriminative template matching filters are validated on in vivo ground truth data and are shown to provide single-unit activity with good accuracy using a simple thresholding operation on the filter outputs. SIGNIFICANCE: The low algorithmic complexity allows for computationally cheap and fast spike sorting. Also the proposed filters are guaranteed to be stable and have a deterministic delay. These characteristics make the proposed filter design method a valuable building block for online spike sorting, thereby enabling unit activity-based real-time and closed-loop experiments for high-density neural recordings. ispartof: Journal of Neural Engineering vol:15 issue:5 ispartof: location:England status: published

The aim of this study is to develop a signal intensity (S(t)) form of the standard Tofts pharmacokinetic model that avoids the need to calculate tissue contrast agent concentration (C(t)) as function of time (t). We refer to this as ‘SI-Tofts’ model. Physiological parameters (K trans and v e) calculated using the SI-Tofts and standard Tofts models were compared by using simulations and human prostate dynamic contrast enhanced (DCE) MRI data. This approach was also applied to the Patlak model to compare K trans values calculated from C(t) and S(t). Simulations were performed on DCE-MRI data from the quantitative imaging biomarkers alliance to validate SI-Tofts model. In addition, ultrafast DCE-MRI data were acquired from 18 prostate cancer patients on a Philips Achieva 3T-TX scanner. Regions-of-interest (ROIs) for prostate cancer, normal tissue, gluteal muscle, and iliac artery were manually traced. The C(t) was calculated for each ROI using the standard model with measured pre-contrast tissue T 1 values. Both the simulation and clinical results showed strong correlation (r = 0.87–0.99, p < 0.001) for K trans and v e calculated from the SI-Tofts and standard Tofts models. The SI-Tofts model with a correction factor using the T 1 ratio of blood to tissue significantly improved the K trans estimates. The correlation of K trans obtained from the Patlak model with C(t) vs S(t) was also strong (r = 0.95–0.99, p < 0.001). These preliminary results suggest that physiological parameters from DCE-MRI can be reliably estimated from the SI-Tofts model without contrast agent concentration calculation.

Beams-eye-view imaging applications such as real-time soft-tissue motion estimation are hindered by the inherently low image contrast of electronic portal imaging devices (EPID) currently available for clinical use. We introduce and characterize a novel EPID design that provides substantially increased detective quantum efficiency (DQE), contrast-to-noise ratio (CNR) and sensitivity without degradation in spatial resolution. The prototype design features a stack of four conventional EPID layers combined with low noise integrated readout electronics. Each layer consists of a copper plate, a scintillator ([Formula: see text]) and a photodiode/TFT-switch (aSi:H). We characterize the prototype's signal response to a 6 MV photon beam in terms of modulation transfer function (MTF), DQE and CNR. The presampled MTF is estimated using a slanted slit technique, the DQE is calculated from measured normalized noise power spectra (nNPS) and the MTF and CNR is estimated using a Las Vegas contrast phantom. The prototype has been designed and built to be interchangeable with the current clinical EPID on the Varian TrueBeam platform (AS-1200) in terms of size and data output specifications. Performance evaluation is conducted in absolute values as well as in relative terms using the Varian AS-1200 EPID as a reference detector. A fivefold increase of DQE(0) to about 6.7% was observed by using the four-layered design versus the AS-1200 reference detector. No substantial differences are observed between each layer's individual MTF and the one for all four layers operating combined indicating that defocusing due to beam divergence is negligible. Also, using four layers instead of one increases the signal to noise ratio by a factor of 1.7.

In nature, the lateral line system (LLS) is a critical sensor organ of fish for rheotaxis in complex environments. Inspired by the LLS, numbers of artificial lateral line systems (ALLSs) have been designed to the fish-like robots for flow field perception, assisting the robots to be stable in the face of flow disturbances. However, almost all pressure sensor based ALLSs face the challenge of the low signal to noise ratio (SNR), resulting in inaccurate perception information. To solve this problem, this paper describes a dual-sensor fusion method by integrating the ALLSs with the inertial measurement unit (IMU), and shows the excellent performance by a higher precision and lower latency attitude holding of robotic fish. First, low-pass filtering is performed on ALLS data with low-SNR. Second, the ALLS data is mapped to the angle of attack based on an artificial neural network. Finally, a fusion perception method is established based on the time correlation between ALLS and IMU. To demonstrate the efficacy of our proposed method, we compare the result of attitude holding by three methods (dual-sensor fusion method, IMU based method, and ALLS based method). Furthermore, dual-sensor fusion method is tested at varied flow velocities and varied desired angles of attack, indicating that the algorithm can enable the robotic fish to perform dynamic movements in the incoming flow. This work provides a method for the attitude control of autonomous underwater vehicles (AUVs) by fusing the sensory data of ALLS and IMU, which is also applicable to other flow sensors and IMU.

Iterative reconstruction (IR) methods for x-ray CT is a promising approach to improve image quality or reduce radiation dose to patients. The goal of this work was to use task based image quality measures and the channelized Hotelling observer (CHO) to evaluate both analytic and IR methods for clinical x-ray CT applications. We performed realistic computer simulations at five radiation dose levels, from a clinical reference low dose D0 to 25% D0. A fixed size and contrast lesion was inserted at different locations into the liver of the XCAT phantom to simulate a weak signal. The simulated data were reconstructed on a commercial CT scanner (SOMATOM Definition Flash; Siemens, Forchheim, Germany) using the vendor-provided analytic (WFBP) and IR (SAFIRE) methods. The reconstructed images were analyzed by CHOs with both rotationally symmetric (RS) and rotationally oriented (RO) channels, and with different numbers of lesion locations (5, 10, and 20) in a signal known exactly (SKE), background known exactly but variable (BKEV) detection task. The area under the receiver operating characteristic curve (AUC) was used as a summary measure to compare the IR and analytic methods; the AUC was also used as the equal performance criterion to derive the potential dose reduction factor of IR. In general, there was a good agreement in the relative AUC values of different reconstruction methods using CHOs with RS and RO channels, although the CHO with RO channels achieved higher AUCs than RS channels. The improvement of IR over analytic methods depends on the dose level. The reference dose level D0 was based on a clinical low dose protocol, lower than the standard dose due to the use of IR methods. At 75% D0, the performance improvement was statistically significant (p < 0.05). The potential dose reduction factor also depended on the detection task. For the SKE/BKEV task involving 10 lesion locations, a dose reduction of at least 25% from D0 was achieved.

Compton Cameras emerged as an alternative for real-time dose monitoring techniques for Particle Therapy (PT), based on the detection of prompt-gammas. As a consequence of the Compton scattering process, the gamma origin point can be restricted onto the surface of a cone (Compton cone). Through image reconstruction techniques, the distribution of the gamma emitters can be estimated, using cone-surfaces backprojections of the Compton cones through the image space, along with more sophisticated statistical methods to improve the image quality. To calculate the Compton cone required for image reconstruction, either two interactions, the last being photoelectric absorption, or three scatter interactions are needed. Because of the high energy of the photons in PT the first option might not be adequate, as the photon is not absorbed in general. However, the second option is less efficient. That is the reason to resort to spectral reconstructions, where the incoming γ energy is considered as a variable in the reconstruction inverse problem. Jointly with prompt gamma, secondary neutrons and scattered photons, not strongly correlated with the dose map, can also reach the imaging detector and produce false events. These events deteriorate the image quality. Also, high intensity beams can produce particle accumulation in the camera, which lead to an increase of random coincidences, meaning events which gather measurements from different incoming particles. The noise scenario is expected to be different if double or triple events are used, and consequently, the reconstructed images can be affected differently by spurious data. The aim of the present work is to study the effect of false events in the reconstructed image, evaluating their impact in the determination of the beam particle ranges. A simulation study that includes misidentified events (neutrons and random coincidences) in the final image of a Compton Telescope for PT monitoring is presented. The complete chain of detection, from the beam particle entering a phantom to the event classification, is simulated using FLUKA. The range determination is later estimated from the reconstructed image obtained from a two and three-event algorithm based on Maximum Likelihood Expectation Maximization. The neutron background and random coincidences due to a therapeutic-like time structure are analyzed for mono-energetic proton beams. The time structure of the beam is included in the simulations, which will affect the rate of particles entering the detector.

Due to the limited number of views and limited angular span in digital breast tomosynthesis (DBT), the acquisition geometry design is an important factor that affects the image quality. Therefore, intensive studies have been conducted regarding the optimization of the acquisition geometry. However, different reconstruction algorithms were used in most of the reported studies. Because each type of reconstruction algorithm can provide images with its own image resolution, noise properties and artifact appearance, it is unclear whether the optimal geometries concluded for the DBT system in one study can be generalized to the DBT systems with a reconstruction algorithm different to the one applied in that study. Hence, we investigated the effect of the reconstruction algorithm on the optimization of acquisition geometry parameters through carefully designed simulation studies. Our results show that using various reconstruction algorithms, including the filtered back-projection, the simultaneous algebraic reconstruction technique, the maximum-likelihood method and the total-variation regularized least-square method, gave similar performance trends for the acquisition parameters for detecting lesions. The consistency of system ranking indicates that the choice of the reconstruction algorithm may not be critical for DBT system geometry optimization.

Accurate and robust reconstruction of the radioactivity concentration is of great importance in positron emission tomography (PET) imaging. Given the Poisson nature of photo-counting measurements, we present a reconstruction framework that integrates sparsity penalty on a dictionary into a maximum likelihood estimator. Patch-sparsity on a dictionary provides the regularization for our effort, and iterative procedures are used to solve the maximum likelihood function formulated on Poisson statistics. Specifically, in our formulation, a dictionary could be trained on CT images, to provide intrinsic anatomical structures for the reconstructed images, or adaptively learned from the noisy measurements of PET. Accuracy of the strategy with very promising application results from Monte-Carlo simulations, and real data are demonstrated.

In an optical coherence tomography (OCT) scan from a living tissue, red blood cells (RBCs) are the major source of backscattering signal from moving particles within microcirculatory system. Measuring the concentration and velocity of RBC particles allows assessment of RBC flux and flow, respectively, to assess tissue perfusion and oxygen/nutrition exchange rates within micro-structures. In this paper, we propose utilizing spectral estimation techniques to simultaneously quantify bi-directional particle flow and relative flux by spectral estimation of the received OCT signal from moving particles within capillary tubes embedded in tissue mimicking phantoms. The proposed method can be directly utilized for in vivo quantification of capillaries and microvessels. Compared to the existing methods in the literature that can either quantify flow direction or power, our proposed method allows simultaneous flow (velocity) direction and relative flux (power) estimation.

The noise power spectrum (NPS) of a digital x-ray imaging device is usually estimated from the average of periodograms of regions of interest (ROIs) in images obtained with uniform radiation fields. In order to mitigate low frequency trends, present in the images and not arising from stochastic processes, detrending methods are applied to the images before being Fourier transformed. The most common of these methods subtracts a second-order polynomial fit from the image. In this work, it is shown that the characteristics of low frequency trends can deviate from the quadratic dependence on spatial coordinates. This results in large residual trends that give rise to important correlations in the detrended images and produce an inaccurate rise of the NPS calculations at low frequencies. A new detrending method of uniform images is presented. The method operates in the subbands of a wavelet transform, removing the low frequency contents of the uniform image. To do this, the approximation subband of the highest level of the wavelet transform is cancelled. The effect on the NPS calculations for three digital detectors is shown and the importance of the parameters of the wavelet transform is discussed. The main result states that the performance of the new method improves those of two polynomial detrending methods commonly used and is close to the performance of the subtraction of uniform exposure images method. Finally, guidelines for the implementation of the procedure, like the number of levels in the wavelet decomposition, are provided. As the number of levels in the wavelet transform increases, the removal of trends is restricted to lower frequencies. The selection of the number of levels should be guided by the shape of the autocorrelation function of the detrended image, which has to resemble the shape expected from the propagation of noise through the imaging chain.

OBJECTIVE: Due to its high temporal resolution, electroencephalography (EEG) has become a promising tool for quantifying cortical dynamics and effective connectivity in a mobile setting. While many connectivity estimators are available, the efficacy of these measures has not been rigorously validated in real-world scenarios. The goal of this study was to quantify the accuracy of independent component analysis and multiple connectivity measures on ground-truth connections while exposed real-world volume conduction and head motion. APPROACH: We collected high-density EEG from a phantom head with embedded antennae, using neural mass models to generate transiently interconnected signals. The head was mounted upon a motion platform that mimicked recorded human head motion at various walking speeds. We used cross-correlation and signal to noise ratio to determine how well independent component analysis recovered the original antenna signals. For connectivity measures, we computed the average and standard deviation across frequency of each estimated connectivity peak. MAIN RESULTS: Independent component analysis recovered most antenna signals, as evidenced by cross-correlations primarily above 0.8, and maintained consistent signal to noise ratio values near 10 dB across walking speeds compared to scalp channel data, which had decreased signal to noise ratios of ~2 dB at fast walking speeds. The connectivity measures used were generally able to identify the true interconnections, but some measures were susceptible to spurious high-frequency connections inducing large standard deviations of ~10 Hz. SIGNIFICANCE: Our results indicate that independent component analysis and some connectivity measures can be effective at recovering underlying connections among brain areas. These results highlight the utility of validating EEG processing techniques with a combination of complex signals, phantom head use, and realistic head motion.

Objective Successful use of a prosthetic limb by an amputee is facilitated by haptic feedback-both a sense of touch and proprioception. Stimulating afferent fibers within peripheral nerves has been shown to provide somatosensation enabling amputees to modulate the control of prosthetic limbs. Peripheral nerve interfaces (PNIs) have also been used to decode patients' motor intentions. It seems ideal to use PNIs to record efferent fibers for motor control while stimulating afferent fibers to create concurrent sensory feedback. However, while many PNIs claim to be bi-directional, few can both stimulate and record at the same time due to stimulation artifacts which are orders of magnitude larger than the recorded motor signals. This study uses computational modelling to compare the stimulation artifact at threshold levels of stimulation for thin-film transverse intrafascicular multichannel electrodes (tfTIMEs) with micro-channel sieve electrodes. Approach Finite element models of micro-channel sieves and tfTIMESs were used to solve for electric fields generated during peripheral nerve stimulation. Electrophysiological responses were simulated using axon models. Stimulation artifacts were calculated for stimuli eliciting axonal action potentials. Simulations were carried out for multiple micro-channel geometries and electrode configurations. Main results Stimulation artifacts generated for threshold stimulation currents are lower for micro-channel devices compared to tfTIMEs. Consequently, stimulus artifacts at threshold currents were substantially higher for the tfTIME. Micro-channel width has a moderate impact on recruitment thresholds and stimulus artifacts. Using the micro-channel sieve in bipolar and tripolar stimulation configurations greatly decreases stimulation artifacts particularly for optimized contact placements (CPs). Electroneurogram (ENG) signals from the companion paper were incorporated showing a great improvement in signal-to-artifact ratio for the micro-channel electrode compared to tfTIMEs. Significance Stimulating regenerated nerve tissue using micro-channel sieve electrodes can decrease stimulation artifacts and elicit neuronal responses at low stimulation amplitudes. Further analysis provides clues to optimal implementations of micro-channel devices. Finally, stimulation artifacts for simulated tfTIME devices were 2-3 orders of magnitude larger than ENG levels. In contrast, for some micro-channel configurations stimulation artifacts were 3-4 orders of magnitude smaller than ENG levels.

Generating synthetic physiological signals using information extracted from real world physiological signals plays an important role in the field of medical device development and education. Most of the existing approaches are limited in the sense that they either focus on a particular physiological signal or lack flexibility in generating signals that mimic real world scenarios. In this paper, we present a cubic B-Spline interpolator-based flexible signal generator intended for simulating a variety of physiological signals. A simulated artifact generator (SAG) is also included in the proposed scheme to add artifacts to the physiological signals mimicking signal deviations associated with real world scenarios. In addition, the proposed method offers the ability to easily present a parametric representation to model a case-specific physiological signal. To demonstrate the ability of the proposed method, case studies on electromyogram (EMG), electro-oculogram (EOG), and electrocardiogram (ECG) during ventricular fibrillation are presented. Using a database of 20 ECG signals, the proposed approach was compared with an existing-model-based method and the results confirm the flexibility of our proposed approach as well as higher signal reproduction accuracy (a mean root mean square error improvement of 47.9% for waveform-based modeling and 4.3% for parametric-based modeling).

International audience; A new method for the estimation of the light scattering phase function of particles is presented. The method allows us to measure the light scattering phase function of particles of any shape in the full angular range (0° - 180°) and is based on analysis of laser Doppler power density spectra. The theoretical background of the method and results of its validation using data from Monte Carlo simulations will be presented. For estimation of the scattering phase function, a phantom measurement setup is proposed containing a laser Doppler measurement system and a simple model in which a liquid sample flows through a glass tube fixed in an optically turbid material. The scattering phase function estimation error was thoroughly investigated in relation to the light scattering anisotropy factor g. The error of g estimation is lower than 10% for anisotropy factors larger than 0.5 and decreases with increase of the anisotropy factor (e.g. for g = 0.98, the error of estimation is 0.01%). The analysis of influence of the noise in the measured laser Doppler spectrum showed that the g estimation error is lower than 1% for signal to noise ratio higher than 50 dB.

Monte Carlo simulations of emission tomography have proven useful to assist detector design and optimize acquisition and processing protocols. The more realistic the simulations, the more straightforward the extrapolation of conclusions to clinical situations. In emission tomography, accurate numerical models of tomographs have been described and well validated under specific operating conditions (collimator, radionuclide, acquisition parameters, count rates, etc). When using these models under these operating conditions, the realism of simulations mostly depends on the activity distribution used as an input for the simulations. It has been proposed to derive the input activity distribution directly from reconstructed clinical images, so as to properly model the heterogeneity of the activity distribution between and within organs. However, reconstructed patient images include noise and have limited spatial resolution. In this study, we analyse the properties of the simulated images as a function of the properties of the reconstructed images used to define the input activity distributions in (18)F-FDG PET and (131)I SPECT simulations. The propagation through the simulation/reconstruction process of the noise and spatial resolution in the input activity distribution was studied using simulations. We found that the noise properties of the images reconstructed from the simulated data were almost independent of the noise in the input activity distribution. The spatial resolution in the images reconstructed from the simulations was slightly poorer than that in the input activity distribution. However, using high-noise but high-resolution patient images as an input activity distribution yielded reconstructed images that could not be distinguished from clinical images. These findings were confirmed by simulated highly realistic (131)I SPECT and (18)F-FDG PET images from patient data. In conclusion, we demonstrated that (131)I SPECT and (18)F-FDG PET images indistinguishable from real scans can be simulated using activity maps with spatial resolution higher than that used in routine clinical applications.

Statistical iterative reconstruction (SIR) algorithms have shown potential to substantially improve low-dose cone-beam CT (CBCT) image quality. The penalty term plays an important role in determining the performance of SIR algorithms. In this work, we quantitatively evaluate the impact of the penalties on the performance of a statistics-based penalized weighted least-squares (PWLS) iterative reconstruction algorithm for improving the image quality of low-dose CBCT. Three different edge-preserving penalty terms, exponential form anisotropic quadratic (AQ) penalty (PWLS-Exp), inverse square form AQ penalty (PWLS-InverseSqr) and total variation penalty (PWLS-TV), were compared against the conventional isotropic quadratic form penalty (PWLS-Iso) using both computer simulation and experimental studies. Noise in low-dose CBCT can be substantially suppressed by the PWLS reconstruction algorithm and edges are well preserved by both AQ- and TV-based penalty terms. The noise-resolution tradeoff measurement shows that the PWLS-Exp exhibits the best spatial resolution of all the three anisotropic penalty terms at matched noise level for reconstructing high-contrast objects. For the reconstruction of low-contrast objects, the TV-based penalty outperforms the AQ-based one with better resolution preservation at matched noise levels. Different penalty terms may be used for better edge preservation at different targeted contrast levels.

In this study, the performance and implementation of magneto-acousto-electrical tomography (MAET) is investigated using a linear phased array (LPA) transducer. The goal of MAET is to image the conductivity distribution in biological bodies. It uses the interaction between ultrasound and a static magnetic field to generate velocity current density distribution inside the body. The resultant voltage due to velocity current density is sensed by surface electrodes attached on the body. In this study, the theory of MAET is reviewed. A 16-element LPA transducer with 1 MHz excitation frequency is used to provide beam directivity and steerability of acoustic waves. Different two-dimensional numerical models of breast and tumour are formed to analyze the multiphysics problem coupled with acoustics and electromagnetic fields. In these models, velocity current density distributions are obtained for pulse type ultrasound excitations. The static magnetic field is assumed as 1 T. To sense the resultant voltage caused by the velocity current density, it is assumed that two electrodes are attached on the surface of the body. The performance of MAET is shown through sensitivity matrix analysis. The sensitivity matrix is obtained for two transducer positions with 13 steering angles between [Formula: see text] to [Formula: see text] with [Formula: see text] angular intervals. For the reconstruction of the images, truncated singular value decomposition method is used with different signal-to-noise ratio (SNR) values (20 dB, 40 dB, 60 dB and 80 dB). The resultant images show that the perturbation (5 mm × 5 mm) placed 35 mm depth can be detected even if the SNR is 20 dB.

The accuracy of calculations of both the degree and angle of polarization depend strongly on the noise in the measurements used. The noise in the measurements recorded by both camera based systems and spectrometers can lead to significant artefacts and incorrect conclusions about high degrees of polarization when in fact none exist. Three approaches are taken in this work: Firstly, the absolute error introduced as a function of the signal to noise ratio for polarization measurements is quantified in detail. An important finding here is the reason for why several studies incorrectly suggest that black (low reflectivity) objects are highly polarized. The high degree of polarization is only an artefact of the noise in the calculation. Secondly, several simplesteps to avoid such errors are suggested. Thirdly, if these points can’t be followed, two methods are presented for mitigating the effects of noise: a maximum likelihood estimation method and a new denoising algorithm to best calculate the degree of polarization of natural polarization information.

Three fundamental problems about the vortex flow sensor—bluff body design, signal detection method and signal processing method—have been mainly discussed by a number of researchers in recent years. However, another significant thing is that the position for vortex signal detection, the location of a piezoelectric pressure probe in a vortex flow sensor, is seldom investigated, which is the focus of this paper. It is known that the quality of vortex signals is directly related to the probe's location. Therefore, the low limit of measurement for a vortex flow sensor can be expanded by searching the optimum position for detection. Experiments were carried out in a water channel to analyze the pressure signals downstream of the bluff body. The vortex signal intensity, frequency, signal-to-noise ratio and uncertainty of the Strouhal number at each position were discussed. Finally, the optimum regions for vortex signal detection (probe location) for three bluff bodies were given: the distance between the bluff body and the position for detection is 50–75 mm when the width of the bluff body d = 14 mm, the distance is 75–125 mm when d = 22.5 mm and the distance is 75–150 mm when d = 28 mm. Meanwhile, numerical simulations on flow field were adopted to explain the experimental results.

In this paper, we address the problem of two-dimensional image reconstruction from fan-beam data acquired along a full 2pi scan. Conventional approaches that follow the filtered-backprojection (FBP) structure require a weighted backprojection with the weight depending on the point to be reconstructed and also on the source position; this weight appears only in the case of divergent beam geometries. Compared to reconstruction from parallel-beam data, the backprojection weight implies an increase in computational effort and is also thought to have some negative impacts on noise properties of the reconstructed images. We demonstrate here that direct FBP reconstruction from full-scan fan-beam data is possible with no backprojection weight. Using computer-simulated, realistic fan-beam data, we compared our novel FBP formula with no backprojection weight to the use of an FBP formula based on equal weighting of all data. Comparisons in terms of signal-to-noise ratio, spatial resolution and computational efficiency are presented. These studies show that the formula we suggest yields images with a reduced noise level, at almost identical spatial resolution. This effect increases quickly with the distance from the center of the field of view, from 0% at the center to 20% less noise at 20 cm, and to 40% less noise at 25 cm. Furthermore, the suggested method is computationally less demanding and reduces computation time with a gain that was found to vary between 12% and 43% on the computers used for evaluation.

Objective. Differential measurement of efferent and afferent peripheral nerve activity offers a promising means of improving the clinical utility of implantable neuroprostheses. The tripolar nerve cuff electrode has historically served as the gold standard for achieving high signal-to-noise ratios (SNRs) of the recordings. However, the symmetrical geometry of this electrode array (i.e. electrically-shorted side contacts) precludes it from measuring electrical signals that can be used to obtain directional information. In this study, we investigated the feasibility of using a bipolar nerve cuff electrode to achieve high-SNR of peripheral nerve activity. Approach. A finite element model was implemented to investigate the effects of electrode design parameters—electrode length, electrode edge length (EEL), and a conductive shielding layer (CSL)—on simulated single fiber action potentials (SFAP) and also artifact noise signals (ANS). Main results. Our model revealed that the EEL was particularly effective in increasing the peak-to-peak amplitude of the SFAP (319%) and reducing the common mode ANS (67%) of the bipolar cuff electrode. By adding a CSL to the bipolar cuff electrode, the SNR was found to be 65.2% greater than that of a conventional tripolar cuff electrode. In vivo experiments in anesthetized rats confirmed that a bipolar cuff electrode can achieve a SNR that is 38% greater than that achieved by a conventional tripolar cuff electrode (p < 0.05). Significance. The current study showed that bipolar nerve cuff electrodes can be designed to achieve SNR levels that are comparable to that of tripolar configuration. Further work is needed to confirm that these bipolar design parameters can be used to record bi-directional neural activity in a physiological setting.

OBJECTIVE Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]). APPROACH Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law. MAIN RESULTS We found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder. SIGNIFICANCE The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.

Statistical methods for image reconstruction such as the maximum likelihood expectation maximization are more robust and flexible than analytical inversion methods and allow for accurate modelling of the counting statistics and photon transport during acquisition of projection data. Statistical reconstruction is prohibitively slow when applied to clinical x-ray CT due to the large data sets and the high number of iterations required for reconstructing high-resolution images. Recently, however, powerful methods for accelerating statistical reconstruction have been proposed which, instead of accessing all projections simultaneously for updating an image estimate, are based on accessing a subset of projections at the time during iterative reconstruction. In this paper we study images generated by the convex algorithm accelerated by the use of ordered subsets (the OS convex algorithm (OSC)) for data sets with sizes, noise levels and spatial resolution representative of x-ray CT imaging. It is only in the case of extremely high acceleration factors (higher than 50, corresponding to fewer than 20 projections per subset), that areas with incorrect grey values appear in the reconstructed images, and that image noise increases compared with the standard convex algorithm. These image degradations can be adequately corrected for by running the final iteration of OSC with a reduced number of subsets. Even by applying such a relatively slow final iteration, OSC produces almost an equal resolution and lesion contrast as the standard convex algorithm, but more than two orders of magnitude faster.

Hybrid reconstruction techniques have been introduced for the volume reconstruction of axially truncated cone-beam computed tomography projection data acquired along a circular source-detector trajectory. The introduction of weighted half-scan techniques into this framework is described in this paper. Due to the cone-beam geometry it is not possible to perform the weighting on the projections as is typically done in conventional single-line computed tomography. Hence, in this paper we present an efficient way to incorporate angular weighting functions, depending on the object point position, into the framework of hybrid cone-beam reconstruction. Four different angular weighting functions are introduced and discussed with respect to their cone-beam artefact behaviour and their influence on the signal-to-noise ratio. As a result, the most effective angular weighting function for hybrid circular cone-beam reconstruction is determined by means of a simulation study based on mathematical phantoms and clinical data sets. This distance-weighted angular weighting scheme yields the best results in terms of high image quality, low computational complexity and signal-to-noise variations in the reconstruction volume.

Objective Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

Results of principal component analysis depend on data scaling. Recently, based on theoretical considerations, several data transformation procedures have been suggested in order to improve the performance of principal component analysis of image data with respect to the optimum separation of signal and noise. The aim of this study was to test some of those suggestions, and to compare several procedures for data transformation in analysis of principal components experimentally. The experiment was performed with simulated data and the performance of individual procedures was compared using the non-parametric Friedman's test. The optimum scaling found was that which unifies the variance of noise in the observed images. In data with a Poisson distribution, the optimum scaling was the norm used in correspondence analysis. Scaling mainly affected the definition of the signal space. Once the dimension of the signal space was known, the differences in error of data and signal reproduction were small. The choice of data transformation depends on the amount of available prior knowledge (level of noise in individual images, number of components, etc), on the type of noise distribution (Gaussian, uniform, Poisson, other), and on the purpose of analysis (data compression, filtration, feature extraction).

A method for computing the slope or the derivative of time-activity curves containing statistical fluctuations is described. The algorithm presented employs digital differentiation as a digital filter. In designing an effective differentiating filter the Chebyshev-type min-max method and Remez exchange algorithm are used to minimise the weighted Chebyshev error. The validity of this method has been investigated using computer-based Monte Carlo simulation. Renogram curve analysis is presented as an example of an application of this method to radioisotope dynamic analysis. The ratio of the bilateral slopes of the renogram's second segments estimated using this method correlated well with the renal plasma flow ratio (r = 0.97, n = 16). The functional images of the slope of the renogram's second segments have been constructed and ascertained to be clinically useful. This method is considered to provide a powerful tool for extraction of quantitative information both in research and in routine nuclear medicine clinical work, and may be useful in various other fields.