Unwrapping phase contrast MRI by iterative graph cuts.

PURPOSE: To develop and evaluate a phase unwrapping method for cine phase contrast MRI based on graph cuts. METHODS: A proposed Iterative Graph Cuts method was evaluated in 10 cardiac patients with two-dimensional flow quantification which was repeated at low venc settings to provoke wrapping. The images were also unwrapped by a path-following method (ROMEO), and a Laplacian-based method (LP). Net flow was quantified using semi-automatic vessel segmentation. High venc images were also wrapped retrospectively to asses the residual amount of wrapped voxels. RESULTS: The absolute net flow error after unwrapping at venc = 100 cm/s was 1.8 mL, which was 0.83 mL smaller than for LP. The repeatability error at high venc without unwrapping was 2.5 mL. The error at venc = 50 cm/s was 7.5 mL, which was 8.2 mL smaller than for ROMEO and 5.7 mL smaller than for LP. For retrospectively wrapped images with synthetic venc of 100/50/25 cm/s, the residual amount of wrapped voxels was 0.00/0.12/0.79%, which was 0.09/0.26/8.0 percentage points smaller than for LP. With synthetic venc of 25 cm/s, omitting magnitude information resulted in 3.2 percentage points more wrapped voxels, and only spatial/temporal unwrapping resulted in 4.6/21 percentage points more wrapped voxels compared to spatiotemporal unwrapping. CONCLUSION: Iterative Graph Cuts enables unwrapping of cine phase contrast MRI with very small errors, except for at extreme blood velocities, with equal or better performance compared to ROMEO and LP. The use of magnitude information and spatiotemporal unwrapping is recommended.

Phase Noise Compensation Algorithm for Space-Borne Azimuth Multi-Channel SAR.

Azimuth multi-channel synthetic aperture radar (SAR) has always been an important technical means to achieve high-resolution wide-swath (HRWS) SAR imaging. However, in the space-borne azimuth multi-channel SAR system, random phase noise will be produced during the operation of each channel receiver. The phase noise of each channel is superimposed on the SAR echo signal of the corresponding channel, which will cause the phase imbalance between the channels and lead to the generation of false targets. In view of the above problems, this paper proposes a random phase noise compensation method for space-borne azimuth multi-channel SAR. This method performs feature decomposition by calculating the covariance matrix of the echo signal and converts the random phase noise estimation into the optimal solution of the cost function. Considering that the phase noise in the receiver has frequency-dependent and time-varying characteristics, this method calculates the phase noise estimation value corresponding to each range-frequency point in the range direction and obtains the phase noise estimation value by expectation in the azimuth direction. The proposed random phase noise compensation method can suppress false targets well and make the radar present a well-focused SAR image. Finally, the usefulness of the suggested method is verified by simulation experiments.

Understanding aliasing effects and their removal in SPEN MRI: A k-space perspective.

PURPOSE: To characterize the mechanism of formation and the removal of aliasing artifacts and edge ghosts in spatiotemporally encoded (SPEN) MRI within a k-space theoretical framework. METHODS: SPEN's quadratic phase modulation can be described in k-space by a convolution matrix whose coefficients derive from Fourier relations. This k-space model allows us to pose SPEN's reconstruction as a deconvolution process from which aliasing and edge ghost artifacts can be quantified by estimating the difference between a full sampling and reconstructions resulting from undersampled SPEN data. RESULTS: Aliasing artifacts in SPEN MRI reconstructions can be traced to image contributions corresponding to high-frequency k-space signals. The k-space picture provides the spatial displacements, phase offsets, and linear amplitude modulations associated to these artifacts, as well as routes to removing these from the reconstruction results. These new ways to estimate the artifact priors were applied to reduce SPEN reconstruction artifacts on simulated, phantom, and human brain MRI data. CONCLUSION: A k-space description of SPEN's reconstruction helps to better understand the signal characteristics of this MRI technique, and to improve the quality of its resulting images.

Why don't horseflies land on zebras?

Stripes deter horseflies (tabanids) from landing on zebras and, while several mechanisms have been proposed, these hypotheses have yet to be tested satisfactorily. Here, we investigated three possible visual mechanisms that could impede successful tabanid landings (aliasing, contrast and polarization) but additionally explored pattern element size employing video footage of horseflies around differently patterned coats placed on domestic horses. We found that horseflies are averse to landing on highly but not on lightly contrasting stripes printed on horse coats. We could find no evidence for horseflies being attracted to coats that better reflected polarized light. Horseflies were somewhat less attracted to regular than to irregular check patterns, but this effect was not large enough to support the hypothesis of disrupting optic flow through aliasing. More likely it is due to attraction towards larger dark patches present in the irregular check patterns, an idea bolstered by comparing landings to the size of dark patterns present on the different coats. Our working hypothesis for the principal anti-parasite features of zebra pelage are that their stripes are sharply outlined and thin because these features specifically eliminate the occurrence of large monochrome dark patches that are highly attractive to horseflies at close distances.

Revisiting aliasing noise to build more robust sparsity in nonuniform sampling 2D-NMR.

A continuing priority is to better understand and resolve the barriers to using nonuniform sampling (NUS) in challenging small molecule 2D NMR with subsampling of the Nyquist grid (a.k.a. coverage) below 50%. Possible causes for artifacts, often termed sampling noise, in 1D-NUS of 2D-NMR are revisited here, where weak aliasing artifacts are a growing concern as NUS becomes sparser. As NUS schedules become sparser, repeat sequences are shown to occur in the dense sampling regions early in the sampling schedule, causing aliasing artifacts in resulting spectra. An intuitive screening approach that detects patterns in sampling schedules based on a convolutional filter was implemented. Sampling schedules that have low proportions of repeat sequences show significantly reduced artifacts. Another route to remediate early repeat sequences is a short period of uniform sampling at the beginning of the schedule, which also leads to a significant suppression of unwanted sampling noise. Combining the repeat sequence filter with a survey of HSQC and LR-HSQMBC experiments, it is shown that very short initial uniform regions of about 2%-4% of the sampling space can ameliorate repeat sequences in sparser NUS and lead to robust spectral reconstructions by iterative soft thresholding (IST), even when the point spread function is unchanged. Using the principles developed here, a suite of 'one-click' schedules was developed for broader use.

Nyquist Sampling Conditions of Some Diffraction Algorithms with Adjustable Magnification.

Diffraction algorithms with adjustable magnification are dominant in holographic projection and imaging. However, the algorithms are limited by the Nyquist sampling conditions, and simulation results with inappropriate parameters sometimes appear with aliasing. At present, many diffraction algorithms have been proposed and improved, but there is a need for an overall analysis of their sampling conditions. In this paper, some classical diffraction algorithms with adjustable magnification are summarized, and their sampling conditions in the case of plane wave or spherical wave illumination are analyzed and compared, which helps to select the appropriate diffraction algorithm according to the specific parameter conditions of the simulation to avoid aliasing.

Usefulness of aliasing phenomenon for diagnosing venous valve stenosis of arteriovenous fistula in a hemodialysis patient.

We present venous valve stenosis, which is an uncommon cause of arteriovenous fistula (AVF) dysfunction. Owing to the thin structure in echography, venous valves are challenging to observe; however, we have found that the aliasing phenomenon is useful for diagnosing venous valve stenosis.

A Methodological Checklist for Studies of Pleasure and Enjoyment Responses to High-Intensity Interval Training: Part II. Intensity, Timing of Assessments, Data Modeling, and Interpretation.

Recent studies have concluded that high-intensity interval training should be seen as a "viable alternative" to, and may be more enjoyable than, moderate-intensity continuous exercise. If true, these claims have the potential to revolutionize the science and practice of exercise, establishing high-intensity interval training as not only a physiologically effective exercise modality but also a potentially sustainable one. However, these claims stand in contrast to voluminous evidence according to which high levels of exercise intensity are typically experienced as less pleasant than moderate levels. To help researchers, peer reviewers, editors, and critical readers appreciate possible reasons for the apparently conflicting results, we present a checklist that identifies crucial methodological elements in studies investigating the effects of high-intensity interval training on affect and enjoyment. This second installment covers how "high-intensity" and "moderate-intensity" experimental conditions are defined, the timing of assessments of affect, the modeling of affective responses, and data interpretation.

The relationship between frequency content and representational dynamics in the decoding of neurophysiological data.

Decoding of high temporal resolution, stimulus-evoked neurophysiological data is increasingly used to test theories about how the brain processes information. However, a fundamental relationship between the frequency spectra of the neural signal and the subsequent decoding accuracy timecourse is not widely recognised. We show that, in commonly used instantaneous signal decoding paradigms, each sinusoidal component of the evoked response is translated to double its original frequency in the subsequent decoding accuracy timecourses. We therefore recommend, where researchers use instantaneous signal decoding paradigms, that more aggressive low pass filtering is applied with a cut-off at one quarter of the sampling rate, to eliminate representational alias artefacts. However, this does not negate the accompanying interpretational challenges. We show that these can be resolved by decoding paradigms that utilise both a signal's instantaneous magnitude and its local gradient information as features for decoding. On a publicly available MEG dataset, this results in decoding accuracy metrics that are higher, more stable over time, and free of the technical and interpretational challenges previously characterised. We anticipate that a broader awareness of these fundamental relationships will enable stronger interpretations of decoding results by linking them more clearly to the underlying signal characteristics that drive them.

Aliasing-free reduced field-of-view parallel imaging.

PURPOSE: To reconstruct aliasing-free full field-of-view (FOV) images for reduced FOV (rFOV) parallel imaging (PI) with Cartesian and Wave sampling, which suffers from aliasing artifacts using existing PI methods. THEORY AND METHODS: The sensitivity encoding method (SENSE) was extended to the Soft-SENSE models supporting multiple-set coil sensitivity maps (CSM) and point spread functions (PSF) for Cartesian and Wave sampled rFOV PI, respectively. The multiple-set CSM and PSF were created from full FOV CSM and PSF according to the image folding process induced by rFOV sampling. The Soft-SENSE reconstructions could be solved by the same algorithms for the conventional full FOV SENSE reconstruction. RESULTS: Soft-SENSE using multiple-set full FOV CSM and PSF successfully reconstruct aliasing-free full FOV image from rFOV PI data with Cartesian and Wave sampling. The proposed rFOV PI enables flexible control of the aliasing and achieves comparable geometry factors as the standard full FOV PI with the same net acceleration factor. Reduced FOV PI improves the computational efficiency of iterative compressed sensing (CS) and PI reconstruction, especially for high-resolution volumetric imaging, thanks to the reduced fast Fourier transform (FFT) size. Moreover, rFOV PI reconstruction provides a potential alternative to the phase oversampling for the FOV aliasing problem. CONCLUSION: The proposed Soft-SENSE using full FOV CSM and PSF could reconstruct aliasing-free full FOV image for rFOV PI, and make it a viable solution enabling more flexible PI acceleration and effectively improving the computational efficiency of iterative CSPI reconstruction.

Clinical validation of Wave-CAIPI susceptibility-weighted imaging for routine brain MRI at 1.5 T.

OBJECTIVES: Wave-CAIPI (Controlled Aliasing in Parallel Imaging) enables dramatic reduction in acquisition time of 3D MRI sequences such as 3D susceptibility-weighted imaging (SWI) but has not been clinically evaluated at 1.5 T. We sought to compare highly accelerated Wave-CAIPI SWI (Wave-SWI) with two alternative standard sequences, conventional three-dimensional SWI and two-dimensional T2*-weighted Gradient-Echo (T2*w-GRE), in patients undergoing routine brain MRI at 1.5 T. METHODS: In this study, 172 patients undergoing 1.5 T brain MRI were scanned with a more commonly used susceptibility sequence (standard SWI or T2*w-GRE) and a highly accelerated Wave-SWI sequence. Two radiologists blinded to the acquisition technique scored each sequence for visualization of pathology, motion and signal dropout artifacts, image noise, visualization of normal anatomy (vessels and basal ganglia mineralization), and overall diagnostic quality. Superiority testing was performed to compare Wave-SWI to T2*w-GRE, and non-inferiority testing with 15% margin was performed to compare Wave-SWI to standard SWI. RESULTS: Wave-SWI performed superior in terms of visualization of pathology, signal dropout artifacts, visualization of normal anatomy, and overall image quality when compared to T2*w-GRE (all p < 0.001). Wave-SWI was non-inferior to standard SWI for visualization of normal anatomy and pathology, signal dropout artifacts, and overall image quality (all p < 0.001). Wave-SWI was superior to standard SWI for motion artifact (p < 0.001), while both conventional susceptibility sequences were superior to Wave-SWI for image noise (p < 0.001). CONCLUSIONS: Wave-SWI can be performed in a 1.5 T clinical setting with robust performance and preservation of diagnostic quality. KEY POINTS: • Wave-SWI accelerated the acquisition of 3D high-resolution susceptibility images in 70% of the acquisition time of the conventional T2*GRE. • Wave-SWI performed superior to T2*w-GRE for visualization of pathology, signal dropout artifacts, and overall diagnostic image quality. • Wave-SWI was noninferior to standard SWI for visualization of normal anatomy and pathology, signal dropout artifacts, and overall diagnostic image quality.

The Debiased Spatial Whittle likelihood.

We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle likelihood, makes important corrections to the well-known Whittle likelihood to account for large sources of bias caused by boundary effects and aliasing. We generalize the approach to flexibly allow for significant volumes of missing data including those with lower-dimensional substructure, and for irregular sampling boundaries. We build a theoretical framework under relatively weak assumptions which ensures consistency and asymptotic normality in numerous practical settings including missing data and non-Gaussian processes. We also extend our consistency results to multivariate processes. We provide detailed implementation guidelines which ensure the estimation procedure can be conducted in O ( n log n ) operations, where n is the number of points of the encapsulating rectangular grid, thus keeping the computational scalability of Fourier and Whittle-based methods for large data sets. We validate our procedure over a range of simulated and realworld settings, and compare with state-of-the-art alternatives, demonstrating the enduring practical appeal of Fourier-based methods, provided they are corrected by the procedures developed in this paper.

A Novel Efficient Convolutional Neural Algorithm for Multi-Category Aliasing Hardware Recognition.

When performing robotic automatic sorting and assembly operations of multi-category hardware, there are some problems with the existing convolutional neural network visual recognition algorithms, such as large computing power consumption, low recognition efficiency, and a high rate of missed detection and false detection. A novel efficient convolutional neural algorithm for multi-category aliasing hardware recognition is proposed in this paper. On the basis of SSD, the novel algorithm uses Resnet-50 instead of VGG16 as the backbone feature extraction network, and it integrates ECA-Net and Improved Spatial Attention Block (ISAB): two attention mechanisms to improve the ability of learning and extract target features. Then, we pass the weighted features to extra feature layers to build an improved SSD algorithm. At last, in order to compare the performance difference between the novel algorithm and the existing algorithms, three kinds of hardware with different sizes are chosen to constitute an aliasing scene that can simulate an industrial site, and some comparative experiments have been completed finally. The experimental results show that the novel algorithm has an mAP of 98.20% and FPS of 78, which are better than Faster R-CNN, YOLOv4, YOLOXs, EfficientDet-D1, and original SSD in terms of comprehensive performance. The novel algorithm proposed in this paper can improve the efficiency of robotic sorting and assembly of multi-category hardware.

Direction-of-Arrival Estimation Based on Frequency Difference-Wavenumber Analysis for Sparse Vertical Array Configuration.

Frequency-wavenumber (f-k) analysis can estimate the direction of arrival (DOA) of broadband signals received on a vertical array. When the vertical array configuration is sparse, it results in an aliasing error due to spatial sampling; thus, several striation patterns can emerge in the f-k domain. This paper extends the f-k analysis to a sparse receiver-array, wherein a multitude of sidelobes prevent resolving the DOA estimates due to spatial aliasing. The frequency difference-wavenumber (Δf-k) analysis is developed by adopting the concept of frequency difference, and demonstrated its performance of DOA estimation to a sparse receiver array. Experimental results verify the robustness of the proposed Δf-k analysis in the estimation of the DOA of cracking sounds generated by the snapping shrimps, which were recorded by a sparse vertical array configuration during the shallow water experiment.

Aliasing layers for processing parallel imaging and EPI ghost artifacts efficiently in convolutional neural networks.

PURPOSE: The purposes of this work are to develop a method for efficiently processing MR-specific artifacts using a convolutional neural network (CNN), and to present its applications for the removal of the artifacts without suppressing actual signals. In MR images that are acquired using parallel imaging and/or EPI, the locations of aliasing artifacts and/or N-half ghost artifacts can be analytically calculated. However, existing methods using CNNs do not take the structures of the artifacts into account, and therefore need a large number of convolution layers for processing the artifacts. METHODS: For processing the artifacts, a new layer that is named the aliasing layer (AL) is proposed. Because a CNN stands on the assumption that an image has spatial locality, a convolution layer is formulated as a linear function of neighbor locations. For processing the artifacts, the AL preprocesses MR images by moving the calculated locations to the locations accessible through summations over all channels in a standard convolution layer. To evaluate the application of ALs for the removal of parallel imaging and EPI artifacts, CNNs with ALs were compared with those without ALs. RESULTS: The results showed that image-quality metrics of a six-layer CNN with ALs were better than those of a 12-layer CNN without ALs. The results also showed that CNNs with ALs suppressed the artifacts selectively. CONCLUSION: The aliasing layer is proposed for processing MR-specific artifacts efficiently. The experimental results demonstrated that the AL improved CNNs for removing artifacts from parallel imaging and EPI.

Confounding factors in breast magnetic resonance fingerprinting: B1+ , slice profile, and diffusion effects.

PURPOSE: Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B1+ , slice profile and diffusion effects on T1 and T2 estimation in the female breast at 1.5T. METHODS: Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE. RESULTS: Theoretic analysis predicts T1 under-/T2 overestimation if the nominal flip angles are underestimated and inversely, T1 under-/T2 overestimation if omitting slice profile correction, and T1 under-/T2 underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B1+ effects occurred in vivo, causing T2 left-right inhomogeneity between both breasts. Their correction decreased the T2 difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T2 most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions. CONCLUSION: Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.

OPERA: a novel method to reduce ghost and aliasing artifacts.

OBJECTIVE: A method for Orthogonal Phase Encoding Reduction of Artifact (OPERA) was developed and tested. MATERIALS AND METHODS: Because the position of ghosts and aliasing artifacts is predictable along columns or rows, OPERA combines the intensity values of two images acquired using the same parameters, but with swapped phase-encoding directions, to correct the artifacts. Simulations and phantom experiments were conducted to define the efficacy, robustness, and reproducibility. Clinical validation was performed on a total of 1003 images by comparing the OPERA-corrected images and the corresponding image standard in terms of Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR). The method efficacy was also rated using a Likert-type scale response by two experienced independent radiologists using a single-blinded procedure. RESULTS: Simulations and phantom experiments demonstrated the robustness and effectiveness of OPERA in reducing artifacts strength. OPERA application did not significantly change the SNR [+ 4.16%; inter-quartile range (IQR): 2.72-5.01%] and CNR (+ 4.30%; IQR: 2.86-6.04%) values. The two radiologists observed a total of 893 original images with artifacts (89.03% of the total images), a reduction in the perceived artifacts of 82.0% and 83.9% (p < 0.0001), and an improvement in the perceived SNR (82.8% and 88.5%; K = 0.714) and perceived CNR (86.9-88.9%; K = 0.722). DISCUSSION: The study demonstrated that OPERA reduces MR artifacts and improves the perceived image quality.

Computer monitor pixellation and Landolt C visual acuity.

PURPOSE: To investigate the effects of computer monitor pixel density on Landolt C visual acuity measurements and to provide appropriate pixel density recommendations for Landolt C vision chart design. METHODS: Participants were 10 healthy observers aged 18-31 years (mean 21.7 ± 3.6). Logarithmic progression charts were used, consisting of eight rows of five Landolt C optotypes ranging from -0.40 logMAR to 0.30 logMAR. Monitor pixel angular subtense varied from 0.10 min arc to 1.97 min arc, achieved by changing the chart test distance. Testing took place with two filtering conditions: unfiltered optotypes, (pixels rendered either black or white) and anti-aliased optotypes with pixel luminance averaged for a pixel square and rendered as grey levels. RESULTS: Each participant's acuity versus pixel size data sets were fitted by a nonlinear relationship with acuity equal to an asymptotic threshold visual acuity (VAas ) for small pixel sizes below a critical pixel size (Pcrit ). For pixel sizes larger than Pcrit  there was a linear relationship between acuity thresholds and pixel size. For anti-aliased Landolt Cs, mean Pcrit  was 1.23 min, and for unfiltered Landolt Cs average Pcrit was 0.65 min. For anti-aliased LandoltCs, Pcrit  was 2.01xVAas , and for unfiltered Landolt Cs Pcrit  was 1.05xVAas . CONCLUSION: These results are quantitatively very similar to previous research on pixellation and Sloan optotype acuity. We have demonstrated that spatially filtering Landolt C optotypes acts as anti-aliasing, to make them more robust to the degradation effects of pixellation. Previous recommendations for maximum pixel size on Sloan letter vision charts can be applied safely to Landolt C charts.

Comparison of ultrafast wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and standard MP-RAGE in non-sedated children: initial clinical experience.

BACKGROUND: Fast magnetic resonance imaging (MRI) sequences are advantageous in pediatric imaging as they can lessen child discomfort, decrease motion artifact and improve scanner availability. OBJECTIVE: To evaluate the feasibility of an ultrafast wave-CAIPI (controlled aliasing in parallel imaging) MP-RAGE (magnetization-prepared rapid gradient echo) sequence for brain imaging of awake pediatric patients. MATERIALS AND METHODS: Each MRI included a standard MP-RAGE sequence and an ultrafast wave-MP-RAGE sequence. Two neuroradiologists evaluated both sequences in terms of artifacts, noise, anatomical contrast and pathological contrast. A predefined 5-point scale was used by two independent pediatric neuroradiologists. A Wilcoxon signed-rank test was used to evaluate the difference between sequences for each variable. RESULTS: Twenty-four patients (14 males; mean age: 11.5±4.5 years, range: 1 month to 17.8 years) were included. Wave-CAIPI MP-RAGE provided a 77% reduction in scan time using a 32-channel coil and a 70% reduction using a 20-channel coil. Visualization of the pathology, artifacts and pathological enhancement (including parenchymal, leptomeningeal and dural enhancement) was not significantly different between standard MP-RAGE and wave-CAIPI MP-RAGE (all P>0.05). For central (P<0.001) and peripheral (P<0.001) noise, and the evaluation of the anatomical structures (P<0.001), the observers favored standard MP-RAGE over wave-CAIPI MP-RAGE. CONCLUSION: Ultrafast brain imaging with wave-CAIPI MP-RAGE is feasible in awake pediatric patients, providing a substantial reduction in scan time at a cost of subjectively increased image noise.

Bionic Birdlike Imaging Using a Multi-Hyperuniform LED Array.

Digital cameras obtain color information of the scene using a chromatic filter, usually a Bayer filter, overlaid on a pixelated detector. However, the periodic arrangement of both the filter array and the detector array introduces frequency aliasing in sampling and color misregistration during demosaicking process which causes degradation of image quality. Inspired by the biological structure of the avian retinas, we developed a chromatic LED array which has a geometric arrangement of multi-hyperuniformity, which exhibits an irregularity on small-length scales but a quasi-uniformity on large scales, to suppress frequency aliasing and color misregistration in full color image retrieval. Experiments were performed with a single-pixel imaging system using the multi-hyperuniform chromatic LED array to provide structured illumination, and 208 fps frame rate was achieved at 32 × 32 pixel resolution. By comparing the experimental results with the images captured with a conventional digital camera, it has been demonstrated that the proposed imaging system forms images with less chromatic moiré patterns and color misregistration artifacts. The concept proposed verified here could provide insights for the design and the manufacturing of future bionic imaging sensors.