Signal Aliasing Research Articles

Digital Mismatch Correction for Bandpass Sampling Four-Channel Time-Interleaved ADCs in Direct-RF Sampling Receivers

Time-interleaving analog-to-digital converters (ADCs) decrease the required sampling rate for one ADC to achieve giga samples per second (GS/s) rates. The gain and timing mismatches among the ADCs generate aliasing signals, degrading the spurious-free dynamic range of the time-interleaved ADC (TI-ADC). The conventional digital correction methods for TI-ADCs have not considered an application to direct-radio-frequency (RF) sampling receivers. We present a digital correction method for bandpass sampling four-channel TI-ADCs in the receivers. The proposed method, based on a correction method for two-channel TI-ADCs, reduces the in-band aliasing signals of the four-channel TI-ADCs by using in-phase/quadrature-phase (I/Q) downconversion mixers, cascaded integrator-comb (CIC) filters, and automatic gain control (AGC) in the receiver. This allows the correction circuit including mismatch estimation to have fewer building blocks than the conventional methods: seven adders, seven multipliers, and no finite-impulse-response filters. Simulations and measurements show that the proposed method reduces the aliasing signals of 1.2 GS/s 12-bit four-channel TI-ADCs to less than −80 dBFS.

Water Storage Variations in Tibet from GRACE, ICESat, and Hydrological Data

The monitoring of water storage variations is essential not only for the management of water resources, but also for a better understanding of the impact of climate change on hydrological cycle, particularly in Tibet. In this study, we estimated and analyzed changes of the total water budget on the Tibetan Plateau from the Gravity Recovery And Climate Experiment (GRACE) satellite mission over 15 years prior to 2017. To suppress overall leakage effect of GRACE monthly solutions in Tibet, we applied a forward modeling technique to reconstruct hydrological signals from GRACE data. The results reveal a considerable decrease in the total water budget at an average annual rate of −6.22 ± 1.74 Gt during the period from August 2002 to December 2016. In addition to the secular trend, seasonal variations controlled mainly by annual changes in precipitation were detected, with maxima in September and minima in December. A rising temperature on the plateau is likely a principal factor causing a continuous decline of the total water budget attributed to increase melting of mountain glaciers, permafrost, and snow cover. We also demonstrate that a substantial decrease in the total water budget due to melting of mountain glaciers was partially moderated by the increasing water storage of lakes. This is evident from results of ICESat data for selected major lakes and glaciers. The ICESat results confirm a substantial retreat of mountain glaciers and an increasing trend of major lakes. An increasing volume of lakes is mainly due to an inflow of the meltwater from glaciers and precipitation. Our estimates of the total water budget on the Tibetan Plateau are affected by a hydrological signal from neighboring regions. Probably the most significant are aliasing signals due to ground water depletion in Northwest India and decreasing precipitation in the Eastern Himalayas. Nevertheless, an integral downtrend in the total water budget on the Tibetan Plateau caused by melting of glaciers prevails over the investigated period.

Signal aliasing in Gaussian random fields for experiments with qualitative factors

Signal aliasing is an inevitable consequence of using fractional factorial designs. Unlike linear models with fixed factorial effects, for Gaussian random field models advocated in some Bayesian design and computer experiment literature, the issue of signal aliasing has not received comparable attention. In the present article, this issue is tackled for experiments with qualitative factors. The signals in a Gaussian random field can be characterized by the random effects identified from the covariance function. The aliasing severity of the signals is determined by two key elements: (i) the aliasing pattern, which depends only on the chosen design, and (ii) the effect priority, which is related to the variances of the random effects and depends on the model parameters. We first apply this framework to study the signal-aliasing problem for regular fractional factorial designs. For general factorial designs including nonregular ones, we propose an aliasing severity index to quantify the severity of signal aliasing. We also observe that the aliasing severity index is highly correlated with the prediction variance.

Extraction of Disturbance Signals Based on the Inertial Sensor with a Blind Source Separation Method

The inertial sensor is a key component in the photoelectric tracking system mounted on the moving platform, and its disturbance information extraction is a prerequisite for decoupling the system disturbance. In this paper, to get the specific signal of fs Hz, firstly, source signal of 2xfs Hz from the inertial sensor is needed. Then, single channel signal with frequency 2xfs is constructed into 2 aliased signals with the frequency of fs by down-sampling. Finally, a BSS (Blind Source Separation, BSS) algorithm is used to extract the disturbance signal in the aliased signals. Simulation results of ship borne data show that the algorithm can accurately extract the high-frequency disturbance signal of about 33 Hz in the aliased signals. The experimental results of the platform prove that the spectrum of the disturbance signal (10 Hz) and the spectrum of the target signal (0.1 Hz) do not overlap each other, and the correlation coefficient between the perturbation signal and the perturbation source signal is as high as 0.9976, which means the extraction of disturbance signal in the inertial sensor signals is realized.

Extended Coherence Length and Depth Ranging Using a Fourier-Domain Mode-Locked Frequency Comb and Circular Interferometric Ranging.

Fourier domain mode-locking (FDML) has been a popular laser design for high speed optical frequency domain imaging (OFDI) but the achievable coherence length, and therefore imaging range, has been limited. The narrow instantaneous linewidth of a frequency comb (FC) FDML laser could provide an attractive platform for high speed as well as long range OFDI. Unfortunately, aliasing artifacts arising from signals beyond the principle measurement depth of the free spectral range have prohibited the use of a FC FDML for imaging so far. To make the enhanced coherence length of FC FDML laser available, methods to manage such artifacts are required. Recently, coherent circular ranging has been demonstrated that uses frequency combs for imaging in much reduced RF bandwidths. Here, we revisit circular ranging as a tool of making the long coherence length of an FDML frequency comb laser as well as its use for tissue imaging accessible. Using an acousto-optic frequency shifter (AOFS), we describe an active method to mitigate signal aliasing that is both stable and wavelength independent. We show that an FC FDML laser offers an order of magnitude improved coherence length compared to traditional FDML laser designs without requiring precise dispersion engineering. We discuss design parameters of a frequency stepping laser resonator as well as aliasing from a frequency comb and AOFS in OFDI with numerical simulations. The use of circular ranging additionally reduced acquisition bandwidths 15-fold compared with traditional OFDI methods. The FC FDML/AOFS design offers a convenient platform for long range and high speed imaging as well as exploring signal and image processing methods in circular ranging.

Assessing the effects of sampling frequency on behavioural classification of accelerometer data

Understanding the behaviours of free-ranging animals over biologically meaningful time scales (e.g., diel, tidal, lunar, seasonal, annual) gives unique insight into their ecology. Bio-logging tools such as accelerometers allow the remote study of elusive or inaccessible animals by recording high resolution movement data. Machine learning (ML) is becoming a common tool for automatic classification of behaviours from these types of large data sets. These classifiers often perform best using high sampling frequencies; however, these frequencies also limit archival device recording duration through elevated battery and memory use. In this study we assess the effect of sampling frequency on a ML algorithm's ability to correctly classify behaviours from accelerometer data and present a framework for programming bio-logging devices that maintains classifier performance while optimizing data collection duration. Accelerometer data (30 Hz) were collected from juvenile lemon sharks (Negaprion brevirostris) during semi-captive trials at Bimini, Bahamas, and were ground-truthed to a discrete catalogue of behaviours through direct observation of sharks during trials. The ground-truthed data were re-sampled to a range of sampling frequencies (30, 15, 10, 5, 3 and 1 Hz) and behaviours (swim, rest, burst, chafe, headshake) were classified using a random forest ML algorithm. We demonstrate that as sampling frequency decreases, classifier performance decreases. Best overall classification was achieved at 30 Hz (F-score > 0.790), although 5 Hz was appropriate for classification of swim and rest (F-score > 0.964). For fine-scale behaviours characterised by faster kinematics (headshake, burst and chafe), classification performance was lower across the entire range of sampling frequencies (0.535–0.846, 1–30 Hz), though did not decrease significantly until sampling frequency was <5 Hz. We discuss the effects of signal aliasing and recommend that for best classification of fine-scale behaviours, frequencies >5 Hz are required. However, when seeking to maximise the available device memory and battery capacity and therefore extend deployment duration, 5 Hz is an appropriate sampling frequency for classifying behaviours in similar-sized animals.

Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI.

Resting-state functional magnetic resonance imaging (rs-fMRI) combined with optogenetics and electrophysiological/calcium recordings in animal models is becoming a popular platform to investigate brain dynamics under specific neurological states. Physiological noise originating from the cardiac and respiration signal is the dominant interference in human rs-fMRI and extensive efforts have been made to reduce these artifacts from the human data. In animal fMRI studies, physiological noise sources including the respiratory and cardiorespiratory artifacts to the rs-fMRI signal fluctuation have typically been less investigated. In this article, we demonstrate evidence of aliasing effects into the low-frequency rs-fMRI signal fluctuation mainly due to respiration-induced B0 offsets in anesthetized rats. This aliased signal was examined by systematically altering the fMRI sampling rate, i.e., the time of repetition (TR), in free-breathing conditions and by adjusting the rate of ventilation. Anesthetized rats under ventilation showed a significantly narrower frequency bandwidth of the aliasing effect than free-breathing animals. It was found that the aliasing effect could be further reduced in ventilated animals with a muscle relaxant. This work elucidates the respiration-related aliasing effects on the rs-fMRI signal fluctuation from anesthetized rats, indicating non-negligible physiological noise needed to be taken care of in both awake and anesthetized animal rs-fMRI studies.

Biomedical Photoacoustic Imaging Optimization with Deconvolution and EMD Reconstruction

A photoacoustic (PA) signal of an ideal optical absorbing particle is a single N-shape wave. PA signals are a combination of several individual N-shape waves. However, the N-shape wave basis leads to aliasing between adjacent micro-structures, which deteriorates the quality of final PA images. In this paper, we propose an image optimization method by processing raw PA signals with deconvolution and empirical mode decomposition (EMD). During the deconvolution procedure, the raw PA signals are de-convolved with a system dependent deconvolution kernel, which is measured in advance. EMD is subsequently adopted to further process the PA signals adaptively with two restrictive conditions: positive polarity and spectrum consistency. With this method, signal aliasing is alleviated, and the micro-structures and detail information, previously buried in the reconstructing images, can now be revealed. To validate our proposed method, numerical simulations and phantom studies are implemented, and reconstructed images are used for illustration.

Impact of Groundtrack Pattern of a Single Pair Mission on the Gravity Recovery Quality

For future gravity satellite missions, aliasing of high frequency geophysical signals into the lower frequencies is one of the most challenging obstacles to recovering true gravity signals, i.e., to recover the truth. Several studies have investigated the impact of satellite groundtrack pattern on the quality of gravity recovery. Among those works, the concept of sub-cycle has been discussed as well. However, most of that research has focused on the impact of sampling patterns on global solutions up to a fixed maximum spherical harmonic coefficient, rather than the associated coefficient defined by the Colombo-Nyquist and modified Colombo-Nyquist rules. This work tries to look more closely into the influence of sampling patterns on the gravity recovery quality for global and regional studies when the Colombo-Nyquist and modified Colombo-Nyquist rules apply. For the regional study, the impact of groundtrack patterns of different satellite constellation scenarios are investigated for a hydrological basin in central Africa. The quality of the gravity products are assessed by different metrics, e.g., by spatial covariance representation. The potential meaning of the sub-cycle concept in terms of global and local impacts is also investigated by different repeat-orbit scenarios with even and odd parities. Different solution scenarios in terms of the original and modified Colombo-Nyquist rules will be discussed. The results of our study emphasize the impact of maximum harmonics of the recovery, the influence of sub-cycle on the local gravity recovery and the mission formation impact on the recovery error.

Evaluation of Band-Selective HSQC and HMBC: Methodological Validation on the Cyclosporin Cyclic Peptide and Application for Poly(3-hydroxyalkanoate)s Stereoregularity Determination.

Band-selective (bs) HSQC, improving spectral resolution by restriction of the heteronuclear dimension without inducing spectral folding, has been recently used for polymer tacticity determination. Herein is reported an evaluation of various bs-HSQC and bs-HMBC sequences, first from a methodological point of view (selectivity, dependence to INEPT interpulse delay or relaxation delay), using the cyclic peptide cyclosporin selected as a model compound, and then from an applicative approach, comparing tacticity determined from bs-HSQC and bs-HMBC experiments to the one obtained from 1D 13C{1H} on poly(3-hydroxyalkanoate)s samples. For HSQC sequences, the 13C selectivity scheme consisting in substituting a 13C broadband refocalization by a selective one revealed itself problematic, with unwanted aliased signals, whereas the insertion of double pulsed field gradients spin-echo (DPFGSE) or the use of opposite sign gradients bracketing a selective refocalization gave satisfactory results. Determination of the probability of syndiotactic enchainments, Ps, by bs-HSQC is fully consistent and no precision loss was observed when decreasing acquisition time (37 min vs. 106 min for 1D 13C{1H}). Bs-HMBC, although not straightforwardly applicable for tacticity determination, could provide (after a calibration step) an alternative for compounds of which only 13C carbonyl signals are resolved enough for discriminating between syndiotactic and isotactic configurations.

Multistandard Receiver Design for Telemedicine Monitoring System

In short-distance wireless communications for telemedicine monitoring, different medical data measurement equipment has different wireless transmission modes. A multistandard receiver is designed that can adapt to different medical data measuring equipment. Using a second-order bandpass sampling for the design of antialiasing filters, two aliasing signals can be separated. Simultaneously, constraint conditions for sampling frequency are not as critical. The design is useful for a multistandard receiver in a telemedicine monitoring system and has the advantages such as saving spectrum resources and facilitating spectrum planning.

Acquisition of a full-resolution image and aliasing reduction for a spatially modulated imaging polarimeter with two snapshots.

A snapshot imaging polarimeter using spatial modulation can encode four Stokes parameters allowing instantaneous polarization measurement from a single interferogram. However, the reconstructed polarization images could suffer a severe aliasing signal if the high-frequency component of the intensity image is prominent and occurs in the polarization channels, and the reconstructed intensity image also suffers reduction of spatial resolution due to low-pass filtering. In this work, a method using two anti-phase snapshots is proposed to address the two problems simultaneously. The full-resolution target image and the pure interference fringes can be obtained from the sum and the difference of the two anti-phase interferograms, respectively. The polarization information reconstructed from the pure interference fringes does not contain the aliasing signal from the high-frequency component of the object intensity image. The principles of the method are derived and its feasibility is tested by both computer simulation and a verification experiment. This work provides a novel method for spatially modulated imaging polarization technology with two snapshots to simultaneously reconstruct a full-resolution object intensity image and high-quality polarization components.

Estimation of Spatiotemporal Sensitivity Using Band-limited Signals with No Additional Acquisitions for k-t Parallel Imaging.

Purpose:Dynamic MR techniques, such as cardiac cine imaging, benefit from shorter acquisition times. The goal of the present study was to develop a method that achieves short acquisition times, while maintaining a cost-effective reconstruction, for dynamic MRI. k – t sensitivity encoding (SENSE) was identified as the base method to be enhanced meeting these two requirements.Methods:The proposed method achieves a reduction in acquisition time by estimating the spatiotemporal (x – f) sensitivity without requiring the acquisition of the alias-free signals, typical of the k – t SENSE technique. The cost-effective reconstruction, in turn, is achieved by a computationally efficient estimation of the x – f sensitivity from the band-limited signals of the aliased inputs. Such band-limited signals are suitable for sensitivity estimation because the strongly aliased signals have been removed.Results:For the same reduction factor 4, the net reduction factor 4 for the proposed method was significantly higher than the factor 2.29 achieved by k – t SENSE. The processing time is reduced from 4.1 s for k – t SENSE to 1.7 s for the proposed method. The image quality obtained using the proposed method proved to be superior (mean squared error [MSE] ± standard deviation [SD] = 6.85 ± 2.73) compared to the k – t SENSE case (MSE ± SD = 12.73 ± 3.60) for the vertical long-axis (VLA) view, as well as other views.Conclusion:In the present study, k – t SENSE was identified as a suitable base method to be improved achieving both short acquisition times and a cost-effective reconstruction. To enhance these characteristics of base method, a novel implementation is proposed, estimating the x – f sensitivity without the need for an explicit scan of the reference signals. Experimental results showed that the acquisition, computational times and image quality for the proposed method were improved compared to the standard k – t SENSE method.

Correlation Lengths for Estimating the Large‐Scale Carbon and Heat Content of the Southern Ocean

AbstractThe spatial correlation scales of oceanic dissolved inorganic carbon, heat content, and carbon and heat exchanges with the atmosphere are estimated from a realistic numerical simulation of the Southern Ocean. Biases in the model are assessed by comparing the simulated sea surface height and temperature scales to those derived from optimally interpolated satellite measurements. While these products do not resolve all ocean scales, they are representative of the climate scale variability we aim to estimate. Results show that constraining the carbon and heat inventory between 35°S and 70°S on time‐scales longer than 90 days requires approximately 100 optimally spaced measurement platforms: approximately one platform every 20° longitude by 6° latitude. Carbon flux has slightly longer zonal scales, and requires a coverage of approximately 30° by 6°. Heat flux has much longer scales, and thus a platform distribution of approximately 90° by 10° would be sufficient. Fluxes, however, have significant subseasonal variability. For all fields, and especially fluxes, sustained measurements in time are required to prevent aliasing of the eddy signals into the longer climate scale signals. Our results imply a minimum of 100 biogeochemical‐Argo floats are required to monitor the Southern Ocean carbon and heat content and air‐sea exchanges on time‐scales longer than 90 days. However, an estimate of formal mapping error using the current Argo array implies that in practice even an array of 600 floats (a nominal float density of about 1 every 7° longitude by 3° latitude) will result in nonnegligible uncertainty in estimating climate signals.

SafeMP: Using Mechanical Wave Communication for Safer Payment

The popularity of campus information intelligence aims to provide better applications and services. However, we found that a phenomenon that has brought great inconvenience to students is queuing when pay in smart campus which needs to be resolved urgently. For example, buying meals in the school cafeteria at lunch or dinner time, paying school tuition, and so on. Also the privacy security is the primary prerequisite for smart campus’s mobile electronic payment while transaction information can be intercepted easily by Hacker or hidden HD camera. In this paper, we propose the SafeMP, a mobile security and multi-to-one payment system employing the mechanical-wave transmission to reduce queue time and improve efficiency. The biggest conundrum with implementing this system is how to separate the multiple aliasing signals. The SafeMP is designed with a key finding: although the multiple aliasing signals are irregular, coupling degree between different mechanical wave signals is highly stable. We divide our work into three parts. First, we present a novel compensation method addressing the attenuation with the transmission distance. In addition, we define the degree of coupling to discriminate the aliasing for mechanical wave in ever slot. Finally, we design the rules of orthogonal coding to further connect the data bitstream of each time slot and restore the complete information which transmitted by different user, then the information separated can be corresponded to correct user. Our evaluation results show that the transmission distance does not influence the performance of the SafeMP. The SafeMP achieves an average accuracy of 97%.

Blind Source Separation based on Whale Optimization Algorithm

Aiming at the problem of linear instantaneous aliasing in blind source separation, a new method of blind signal separation using whale optimization algorithm is proposed in this paper, which provides a new research idea and method for blind signal separation. The new method adopts the method of independent component analysis, optimizes the objective function by using the whale optimization algorithm, realizes the blind separation of instantaneous aliasing signals, and effectively avoids the problem of complex parameters and slow convergence rate of the particle swarm optimization algorithm. The simulation results show that the performance of whale optimization algorithm is better than that of particle swarm optimization for blind source separation, and it is effective for blind signal separation.

Aliasing of the Schumann resonance background signal by sprite-associated Q-bursts

The Earth's naturally occurring Schumann resonances (SR) are composed of a quasi-continuous background component and a larger-amplitude, short-duration transient component, otherwise called ‘Q-burst’ (Ogawa et al., 1967). Sprites in the mesosphere are also known to accompany the energetic positive ground flashes that launch the Q-bursts (Boccippio et al., 1995). Spectra of the background Schumann Resonances (SR) require a natural stabilization period of ∼10–12 min for the three conspicuous modal parameters to be derived from Lorentzian fitting. Before the spectra are computed and the fitting process is initiated, the raw time series data need to be properly filtered for local cultural noise, narrow band interference as well as for large transients in the form of global Q-bursts. Mushtak and Williams (2009) describe an effective technique called Isolated Lorentzian (I-LOR), in which, the contributions from local cultural and various other noises are minimized to a great extent. An automated technique based on median filtering of time series data has been developed. These special lightning flashes are known to have greater contribution in the ELF range (below 1 kHz) compared to general negative CG strikes (Huang et al., 1999; Cummer et al., 2006). The global distributions of these Q-bursts have been studied by Huang et al. (1999) Rhode Island, USA by wave impedance methods from single station ELF measurements at Rhode Island, USA and from Japan Hobara et al. (2006). The present work aims to demonstrate the effect of Q-bursts on SR background spectra using GPS time-stamped observation of TLEs. It is observed that the Q-bursts selected for the present work do alias the background spectra over a 5-s period, though the amplitudes of these Q-bursts are far below the background threshold of 16 Core Standard Deviation (CSD) so that they do not strongly alias the background spectra of 10–12 min duration. The examination of one exceptional Q-burst shows that appreciable spectral aliasing can occur even when 12-min spectral integrations are considered. The statistical result shows that for a 12-min spectrum, events above 16 CSD are capable of producing significant frequency aliasing of the modal frequencies, although the intensity aliasing might have a negligible effect unless the events are exceptionally large (∼200 CSD). The spectral CSD methodology may be used to extract the time of arrival of the Q-burst transients. This methodology may be combined with a hyperbolic ranging, thus becoming an effective tool to detect TLEs globally with a modest number of networked observational stations.

MTF and DQE enhancement using an apodized-aperture x-ray detector design.

Acquisition of high-quality x-ray images using low patient exposures requires detectors with high detective quantum efficiency (DQE). We describe a novel apodized-aperture pixel (AAP) design that increases high-frequency modulation transfer function (MTF) and DQE values. The AAP design makes a separation of physical sensor elements from image pixels by using very small sensor elements (e.g., 0.010-0.025 mm) to synthesize desired larger image pixels (e.g., 0.1-0.2 mm). A cascaded systems model of signal and noise propagation is developed to describe the benefits of the AAP approach in terms of the MTF, Wiener noise power spectrum (NPS), and DQE. The theoretical model was validated experimentally using a CMOS/CsI detector with 0.05 mm sensor elements to synthesize 0.20 mm image pixels and a clinical Se detector with 0.07 mm sensor elements to synthesize 0.28 mm pixels. A Monte Carlo study and x-ray images of a star-pattern and rat leg are used to visually compare AAP images. When used with a high-resolution converter layer and sensor elements one quarter the size of image pixels, the MTF is increased by 53% and the DQE by a factor of 2.3× at the image sampling cut-off frequency. Both simulated and demonstration images show improved detectability of high-frequency content and removal of aliasing artifacts. Evidence of Gibbs ringing is sometimes seen near high-contrast edges. It is shown that the AAP approach preserves the MTF of the small sensor elements and attenuates frequencies above the image sampling cut-off frequency. This has the double benefit of improving the MTF while reducing both signal and noise aliasing, resulting in an increase of the DQE at high spatial frequencies. For optimal implementation, the converter layer must have very high spatial resolution and the detector must have low readout noise.

Attenuation of noise and simultaneous source interference using wavelet denoising

High-resolution seismic imaging requires noise attenuation to achieve signal-to-noise ratio (S/N) improvements without compromising data bandwidth. Amplitude versus offset analysis requires good amplitude fidelity in premigration processes. Any nonreflected wavefield energy in the data will degrade the seismic image quality. Despite significant progress over the years, preserving low-frequency signals without compromising the S/N and avoiding the smearing of aliased signal are still a challenge for conventional methods. This problem is compounded when additional interference noise is added with simultaneous source acquisition. Because noise characteristics vary from shot to shot and receiver to receiver, we need a method that is robust and effective. In addition, we also want the method to be efficient and easy to use from a practical perspective. We have recently developed an approach using a wavelet transform to deterministically separate the primary signal from the noise, including simultaneous source interference. The goals are (1) improving the S/N without compromising bandwidth, (2) preserving the low-frequency and near-offset primaries without compromising the S/N, and (3) preserving the local primary wavefield while attenuating noise. For distance-separated simultaneous source acquisition, the goal is preserving long-offset primaries while removing interference. This wavelet denoising flow consists of a linear transformation and filtering using the complex wavelet transform (CWT). For reflection signals, normal moveout (NMO) is used. NMO transforms the low-velocity surface waves and the interference noise to where it is easily identified and rejected with a dip filter in the multidimensional CWT domain. Land field data examples have demonstrated significantly improved S/Ns and low-frequency signal preservation in migrated images after wavelet denoising. Since the numerical implementation of the CWT is as fast as a fast Fourier transform, this flow is able to suppress noise and interference simultaneously on the 3D land data much faster than the other inversion methods.

Comparison of High Resolution X-Ray detectors with Conventional FPDs using Experimental MTFs and Apodized Aperture Pixel Design for Reduced Aliasing.

Apodized Aperture Pixel (AAP) design, proposed by Ismailova et. al, is an alternative to the conventional pixel design1. The advantages of AAP processing with a sinc filter in comparison with using other filters include non-degradation of MTF values and elimination of signal and noise aliasing, resulting in an increased performance at higher frequencies, approaching the Nyquist frequency3. If high resolution small field-of-view (FOV) detectors with small pixels used during critical stages of Endovascular Image Guided Interventions (EIGIs) could also be extended to cover a full field-of-view typical of flat panel detectors (FPDs) and made to have larger effective pixels, then methods must be used to preserve the MTF over the frequency range up to the Nyquist frequency of the FPD while minimizing aliasing. In this work, we convolve the experimentally measured MTFs of an Microangiographic Fluoroscope (MAF) detector, (the MAF-CCD with 35μm pixels) and a High Resolution Fluoroscope (HRF) detector (HRF-CMOS50 with 49.5μm pixels) with the AAP filter and show the superiority of the results compared to MTFs resulting from moving average pixel binning and to the MTF of a standard FPD. The effect of using AAP is also shown in the spatial domain, when used to image an infinitely small point object. For detectors in neurovascular interventions, where high resolution is the priority during critical parts of the intervention, but full FOV with larger pixels are needed during less critical parts, AAP design provides an alternative to simple pixel binning while effectively eliminating signal and noise aliasing yet allowing the small FOV high resolution imaging to be maintained during critical parts of the EIGI.