Human Observer Sensitivity to Temporal Noise During B-Mode Ultrasound Scanning: Characterization and Imaging Implications.

This work measures temporal signal-to-noise ratio (SNR) thresholds that indicate when random noise during ultrasound scanning becomes imperceptible to expert human observers. Visible noise compromises image quality and can potentially lead to non-diagnostic scans. Noise can arise from both stable acoustic sources (clutter) or randomly varying electronic sources (temporal noise). Extensive engineering effort has focused on decreasing noise in both of these categories. In this work, an observer study with five practicing sonographers was performed to assess sonographer sensitivity to temporal noise in ultrasound cine clips. Understanding the conditions where temporal noise is no longer visible during ultrasound imaging can inform engineering efforts seeking to minimize the impact this noise has on image quality. The sonographers were presented with paired temporal noise-free and noise-added simulated speckle cine clips and asked to select the noise-added clips. The degree of motion in the imaging target was found to have a significant effect on the SNR levels where noise was perceived, while changing imaging frequency had little impact. At realistic in vivo motion levels, temporal noise was not perceived in cine clips at and above 28 dB SNR. In a case study presented here, the potential of adaptive intensity adjustment based on this noise perception threshold is validated in a fetal imaging scenario. This study demonstrates how noise perception thresholds can be applied to help design or tune ultrasound systems for different imaging tasks and noise conditions.

Adaptive High-Resolution Imaging Method Based on Compressive Sensing.

Compressive sensing (CS) is a signal sampling theory that originated about 16 years ago. It replaces expensive and complex receiving devices with well-designed signal recovery algorithms, thus simplifying the imaging system. Based on the application of CS theory, a single-pixel camera with an array-detection imaging system is established for high-pixel detection. Each detector of the detector array is coupled with a bundle of fibers formed by fusion of four bundles of fibers of different lengths, so that the target area corresponding to one detector is split into four groups of target information arriving at different times. By comparing the total amount of information received by the detector with the threshold set in advance, it can be determined whether the four groups of information are calculated separately. The simulation results show that this new system can not only reduce the number of measurements required to reconstruct high quality images but can also handle situations wherever the target may appear in the field of view without necessitating an increase in the number of detectors.

System requirements to improve adaptive 4-dimensional computed tomography (4D CT) imaging.

Four-Dimensional Computed Tomography (4D CT) is of increasing importance in stereotactic body radiotherapy (SBRT) treatments affected by respiratory motion. However, 4D CT images are commonly impacted by irregular breathing, causing image artifacts that can propagate through to treatment, negatively effecting local control. REspiratory Adaptive CT (REACT) is a real-time gating method demonstrated to reduce motion artifacts by avoiding imaging during irregular respiration. Despite artifact reduction seen throughin silicoand clinical phantom-based studies, REACT has not been able to remove all artifacts. Here, we explore several hardware and software latencies (gantry rotation time, couch shifts, acquisition delays and phase calculation method) inherently linked to REACT and 4D CT in general and investigate their contribution to artifacts beyond those caused by irregular breathing. Imaging was simulated using the digital extended cardiac-torso (XCAT) phantom for fifty patient-measured respiratory traces. Imaging protocols included conventional cine 4D CT and five REACT scans with systematically varied parameters to test the effect of different latencies on artifacts. Artifacts were quantified by comparing the image normalized cross correlation across couch transition points and determining the volume error compared to a static phantom ground truth both as a total error and individually across pixel rows in the main plane of motion. Artifacts were determined for each lung, the whole heart and lung tumour and were compared back to conventional 4D CT and REACT with standard clinical scanning parameters. The gantry rotation time and acquisition delay were found to have the largest impact on reducing image artifacts and should be the focus of future development. The phase calculation method was also found to influence motion artifacts and should potentially be assessed on a patient-to-patient basis. Finally, the correlation between an increase in artifacts and baseline drift suggests that longer scan times allowing drift to occur may impact image quality.

Advances in ultrasonography: image formation and quality assessment.

Delay-and-sum (DAS) beamforming is widely used for generation of B-mode images from echo signals obtained with an array probe composed of transducer elements. However, the resolution and contrast achieved with DAS beamforming are determined by the physical specifications of the array, e.g., size and pitch of elements. To overcome this limitation, adaptive imaging methods have recently been explored extensively thanks to the dissemination of digital and programmable ultrasound systems. On the other hand, it is also important to evaluate the performance of such adaptive imaging methods quantitatively to validate whether the modification of the image characteristics resulting from the developed method is appropriate. Since many adaptive imaging methods have been developed and they often alter image characteristics, attempts have also been made to update the methods for quantitative assessment of image quality. This article provides a review of recent developments in adaptive imaging and image quality assessment.

Cerebral SPECT imaging with different acquisition schemes using varying levels of multiplexing versus sensitivity in an adaptive multi-pinhole brain-dedicated scanner.

Application of multi-pinhole collimator in pinhole-based SPECT increases detection sensitivity. The presence of multiplexing in projection images due to the usage of multiple pinholes can further improve the sensitivity at the cost of adding data ambiguity. We are developing a next-generation adaptive brain-dedicated SPECT system -AdaptiSPECT-C. The AdaptiSPECT-C can adapt the multiplexing level and system sensitivity using adaptable pinhole modules. In this study, we investigated the performance of 4 data acquisition schemes with different multiplexing levels and sensitivities on cerebral SPECT imaging. Schemes #1, #2, and #3 have <1%, 67%, and 31% overall multiplexing, respectively, while the 4th scheme without multiplexing is considered as ground truth. The ground-truth and schemes #1-3 have 1.0, 1.7, 5.1, and 4.0 times higher sensitivity, respectively, compared to a dual-headed parallel-hole SPECT system at matched spatial resolution. A customized XCAT brain perfusion digital phantom emulating the distribution of I-123 N-isopropyl iodoamphetamine (IMP) in a 99th percentile size male was used for simulations. Data acquisition for each scheme was performed at two count levels (low-count and high-count relative to the recommended clinical count level). The normalized root-mean-square error (NRMSE) for schemes #1, #2, and #3 with the low-count (high-count) scenario showed 11%, 4%, and 5% (10%, 5%, and 6%) deviation, respectively, from that of the multiplex-free ground truth. For both the low-count and high-count scenarios, scheme #1 resulted in the least accurate activity ratio (AR) for almost all the analyzed gray-matter brain regions. Further schemes #2 or #3 led to the most accurate AR values with both low-count and high-count scenarios for all the analyzed gray-matter regions. It was thus observed that even with this large head size which leads to significant multiplexing levels, the higher sensitivity from multiplexing could to some extent mitigate the data ambiguity and be translated into reconstructed images of higher quality.

Multitrack Compressed Sensing for Faster Hyperspectral Imaging.

Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ∼10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times.

Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT): Developing the next generation of cardiac cone beam CT imaging.

PURPOSE: An important factor when considering the use of interventional cone beam computed tomography (CBCT) imaging during cardiac procedures is the trade-off between imaging dose and image quality. Accordingly, Adaptive CaRdiac cOne BEAm computed Tomography (ACROBEAT) presents an alternative acquisition method, adapting the gantry velocity and projection rate of CBCT imaging systems in accordance with a patient's electrocardiogram (ECG) signal in real-time. The aim of this study was to experimentally investigate that ACROBEAT acquisitions deliver improved image quality compared to conventional cardiac CBCT imaging protocols with fewer projections acquired. METHODS: The Siemens ARTIS pheno (Siemens Healthcare, GmbH, Germany), a robotic CBCT C-arm system, was used to compare ACROBEAT with a commercially available conventional cardiac imaging protocol that utilizes multisweep retrospective ECG-gated acquisition. For ACROBEAT, real-time control of the gantry position was enabled through the Siemens Test Automation Control system. ACROBEAT and conventional image acquisitions of the CIRS Dynamic Cardiac Phantom were performed, using five patient-measured ECG traces. The traces had average heart rates of 56, 64, 76, 86, and 100 bpm. The total number of acquired projections was compared between the ACROBEAT and conventional acquisition methods. The image quality was assessed via the contrast-to-noise ratio (CNR), structural similarity index (SSIM), and root-mean square error (RMSE). RESULTS: Compared to the conventional protocol, ACROBEAT reduced the total number of projections acquired by 90%. The visual image quality provided by the ACROBEAT acquisitions, across all traces, matched or improved compared to conventional acquisition and was independent of the patient's heart rate. Across all traces, ACROBEAT averaged 1.4 times increase in the CNR, a 23% increase in the SSIM and a 29% decrease in the RMSE compared to conventional and was independent of the patient's heart rate. CONCLUSION: Adaptive patient imaging is feasible on a clinical robotic CBCT system, delivering higher quality images while reducing the number of projections acquired by 90% compared to conventional cardiac imaging protocols.

Grating-based Spectral CT using Small Angle X-ray Beam Deflections.

Interest in spectral CT for diagnostics and therapy evaluation has been growing. Acquisitions of data from distinct energy spectra provide, among other advantages, quantitative density estimations for multiple materials. We introduce a novel spectral CT concept that includes a fine-pitch grating for prefiltration of the x-ray beam. The attenuation behavior of this grating changes significantly if x-rays are slightly angled in relation to the grating structures. To apply such an angle (i.e. switch between the different filtrations) we propose a fast, controllable, and precise solution by moving the focal spot of the x-ray tube. In this work, we performed preliminary evaluations with a grating prototype on a CT test bench. Our results include x-ray spectrometer measurements that reveal diverse and controllable spectral shaping between 4° and 6° for a specific grating design. Additional experiments with a contrast agent phantom illustrated the capability to decompose clinically relevant iodine concentrations (5, 10, 20, and 50mg/mL) - demonstrating the feasibility of the grating-based approach. Ongoing and future studies will investigate the potential of this novel concept as a relatively simple upgrade to standard energy-integrating CT.

Route to Intelligent Imaging Reconstruction via Terahertz Nonlinear Ghost Imaging.

Terahertz (THz) imaging is a rapidly emerging field, thanks to many potential applications in diagnostics, manufacturing, medicine and material characterisation. However, the relatively coarse resolution stemming from the large wavelength limits the deployment of THz imaging in micro- and nano-technologies, keeping its potential benefits out-of-reach in many practical scenarios and devices. In this context, single-pixel techniques are a promising alternative to imaging arrays, in particular when targeting subwavelength resolutions. In this work, we discuss the key advantages and practical challenges in the implementation of time-resolved nonlinear ghost imaging (TIMING), an imaging technique combining nonlinear THz generation with time-resolved time-domain spectroscopy detection. We numerically demonstrate the high-resolution reconstruction of semi-transparent samples, and we show how the Walsh-Hadamard reconstruction scheme can be optimised to significantly reduce the reconstruction time. We also discuss how, in sharp contrast with traditional intensity-based ghost imaging, the field detection at the heart of TIMING enables high-fidelity image reconstruction via low numerical-aperture detection. Even more striking-and to the best of our knowledge, an issue never tackled before-the general concept of "resolution" of the imaging system as the "smallest feature discernible" appears to be not well suited to describing the fidelity limits of nonlinear ghost-imaging systems. Our results suggest that the drop in reconstruction accuracy stemming from non-ideal detection conditions is complex and not driven by the attenuation of high-frequency spatial components (i.e., blurring) as in standard imaging. On the technological side, we further show how achieving efficient optical-to-terahertz conversion in extremely short propagation lengths is crucial regarding imaging performance, and we propose low-bandgap semiconductors as a practical framework to obtain THz emission from quasi-2D structures, i.e., structure in which the interaction occurs on a deeply subwavelength scale. Our results establish a comprehensive theoretical and experimental framework for the development of a new generation of terahertz hyperspectral imaging devices.

Ultrasound Baseband Delay-Multiply-and-Sum (BB-DMAS) nonlinear beamforming.

Compared to conventional Delay-and-Sum (DAS) beamforming, Delay-Multiply-and-Sum (DMAS) imaging uses multiplicative coupling of channel pairs for spatial coherence of receiving aperture to improve image resolution and contrast. However, present DMAS imaging is based on the radio-frequency (RF) channel signals (RF-DMAS) and thus requires large oversampling to avoid aliasing and switching of band-pass filtering to isolate the corresponding spectral components for imaging. Baseband DMAS (BB-DMAS) beamforming in this study is based on the demodulated channel signals to provide similar results but with simplified signal processing. The BB-DMAS beamforming scales the magnitude of time-delayed channel signal by p-th root while maintaining the phase. After channel sum, the output dimensionality is restored by p-th power. The multiplicative coupling in BB-DMAS always renders baseband signal and thus the need for oversampling is eliminated. Besides, the BB-DMAS can use any rational p values to provide flexible image quality and an explicit relation between BB-DMAS beamforming and channel-domain phase coherence exists. Our results show that the image characteristics between BB-DMAS and RF-DMAS are similar. The suppression of lateral side lobe level, grating lobe level and uncorrelated random noises gradually increases with the rational p value in BB-DMAS beamforming. The image contrast improves from -24.8 dB in DAS to -34.3 dB, -43.0 dB and -51.4 dB in BB-DMAS, respectively with p value of 1.5, 2.0 and 2.5. In conclusion, BB-DMAS beamforming provides flexible manipulation of image quality by introducing baseband spatial coherence in the ultrasonic imaging.

Large field of view correction by using conjugate adaptive optics with multiple guide stars.

Adaptive optics has been widely used in the optical microscopy to recover high-resolution images deep into the sample. However, the corrected field of view (FOV) with a single correction is generally limited, which seriously restricts the imaging speed. In this article, we demonstrate a high-speed wavefront correction method by using the conjugate adaptive optical correction with multiple guide stars (CAOMG) based on the coherent optical adaptive technique. The results show that the CAOMG method can greatly improve the corrected FOV. For 120-µm-thick mouse brain tissue, the corrected FOV can be improved up to ~243 times of the conventional pupil adaptive optics (PAO) without additional time consumption. Therefore, this study shows the potential of high-speed imaging through scattering medium in biological science.

In Toto Imaging and Reconstruction of Post-Implantation Mouse Development at the Single-Cell Level.

The mouse embryo has long been central to the study of mammalian development; however, elucidating the cell behaviors governing gastrulation and the formation of tissues and organs remains a fundamental challenge. A major obstacle is the lack of live imaging and image analysis technologies capable of systematically following cellular dynamics across the developing embryo. We developed a light-sheet microscope that adapts itself to the dramatic changes in size, shape, and optical properties of the post-implantation mouse embryo and captures its development from gastrulation to early organogenesis at the cellular level. We furthermore developed a computational framework for reconstructing long-term cell tracks, cell divisions, dynamic fate maps, and maps of tissue morphogenesis across the entire embryo. By jointly analyzing cellular dynamics in multiple embryos registered in space and time, we built a dynamic atlas of post-implantation mouse development that, together with our microscopy and computational methods, is provided as a resource. VIDEO ABSTRACT.

Performance Evaluation of Adaptive Imaging Based on Multiphase Apodization with Cross-correlation: A Pilot Study in Abdominal Ultrasound.

Degradation of image contrast caused by phase aberration, off-axis clutter, and reverberation clutter remains one of the most important problems in abdominal ultrasound imaging. Multiphase apodization with cross-correlation (MPAX) is a novel beamforming technique that enhances ultrasound image contrast by adaptively suppressing unwanted acoustic clutter. MPAX employs multiple pairs of complementary sinusoidal phase apodizations to intentionally introduce grating lobes that can be used to derive a weighting matrix, which mostly preserves the on-axis signals from tissue but reduces acoustic clutter contributions when multiplied with the beamformed radio-frequency (RF) signals. In this paper, in vivo performance of the MPAX technique was evaluated in abdominal ultrasound using data sets obtained from 10 human subjects referred for abdominal ultrasound at the USC Keck School of Medicine. Improvement in image contrast was quantified, first, by the contrast-to-noise ratio (CNR) and, second, by the rating of two experienced radiologists. The MPAX technique was evaluated for longitudinal and transverse views of the abdominal aorta, the inferior vena cava, the gallbladder, and the portal vein. Our in vivo results and analyses demonstrate the feasibility of the MPAX technique in enhancing image contrast in abdominal ultrasound and show potential for creating high contrast ultrasound images with improved target detectability and diagnostic confidence.

Data-driven optimal binning for respiratory motion management in PET.

PURPOSE: Respiratory gating has been used in PET imaging to reduce the amount of image blurring caused by patient motion. Optimal binning is an approach for using the motion-characterized data by binning it into a single, easy to understand/use, optimal bin. To date, optimal binning protocols have utilized externally driven motion characterization strategies that have been tuned with population-derived assumptions and parameters. In this work, we are proposing a new strategy with which to characterize motion directly from a patient's gated scan, and use that signal to create a patient/instance-specific optimal bin image. METHODS: Two hundred and nineteen phase-gated FDG PET scans, acquired using data-driven gating as described previously, were used as the input for this study. For each scan, a phase-amplitude motion characterization was generated and normalized using principle component analysis. A patient-specific "optimal bin" window was derived using this characterization, via methods that mirror traditional optimal window binning strategies. The resulting optimal bin images were validated by correlating quantitative and qualitative measurements in the population of PET scans. RESULTS: In 53% (n = 115) of the image population, the optimal bin was determined to include 100% of the image statistics. In the remaining images, the optimal binning windows averaged 60% of the statistics and ranged between 20% and 90%. Tuning the algorithm, through a single acceptance window parameter, allowed for adjustments of the algorithm's performance in the population toward conservation of motion or reduced noise-enabling users to incorporate their definition of optimal. In the population of images that were deemed appropriate for segregation, average lesion SUV max were 7.9, 8.5, and 9.0 for nongated images, optimal bin, and gated images, respectively. The Pearson correlation of FWHM measurements between optimal bin images and gated images were better than with nongated images, 0.89 and 0.85, respectively. Generally, optimal bin images had better resolution than the nongated images and better noise characteristics than the gated images. DISCUSSION: We extended the concept of optimal binning to a data-driven form, updating a traditionally one-size-fits-all approach to a conformal one that supports adaptive imaging. This automated strategy was implemented easily within a large population and encapsulated motion information in an easy to use 3D image. Its simplicity and practicality may make this, or similar approaches ideal for use in clinical settings.

Adaptive Microwave Staring Correlated Imaging for Targets Appearing in Discrete Clusters.

Microwave staring correlated imaging (MSCI) can achieve ultra-high resolution in real aperture staring radar imaging using the correlated imaging process (CIP) under all-weather and all-day circumstances. The CIP must combine the received echo signal with the temporal-spatial stochastic radiation field. However, a precondition of the CIP is that the continuous imaging region must be discretized to a fine grid, and the measurement matrix should be accurately computed, which makes the imaging process highly complex when the MSCI system observes a wide area. This paper proposes an adaptive imaging approach for the targets in discrete clusters to reduce the complexity of the CIP. The approach is divided into two main stages. First, as discrete clustered targets are distributed in different range strips in the imaging region, the transmitters of the MSCI emit narrow-pulse waveforms to separate the echoes of the targets in different strips in the time domain; using spectral entropy, a modified method robust against noise is put forward to detect the echoes of the discrete clustered targets, based on which the strips with targets can be adaptively located. Second, in a strip with targets, the matched filter reconstruction algorithm is used to locate the regions with targets, and only the regions of interest are discretized to a fine grid; sparse recovery is used, and the band exclusion is used to maintain the non-correlation of the dictionary. Simulation results are presented to demonstrate that the proposed approach can accurately and adaptively locate the regions with targets and obtain high-quality reconstructed images.

Computational optical coherence tomography [Invited].

Optical coherence tomography (OCT) has become an important imaging modality with numerous biomedical applications. Challenges in high-speed, high-resolution, volumetric OCT imaging include managing dispersion, the trade-off between transverse resolution and depth-of-field, and correcting optical aberrations that are present in both the system and sample. Physics-based computational imaging techniques have proven to provide solutions to these limitations. This review aims to outline these computational imaging techniques within a general mathematical framework, summarize the historical progress, highlight the state-of-the-art achievements, and discuss the present challenges.

Adaptive optimization of ultrasound beamforming sound velocity using sub-aperture differential phase gradient.

Ultrasound array imaging systems rely on a presumed beamforming sound velocity to calculate the time compensation of each element for receive focusing. The mismatch between the tissue sound velocity and the beamforming sound velocity can degrade the focusing quality due to loss of phase coherence. Since the tissue sound velocity cannot be known in prior, an adaptive optimization of beamforming sound velocity is required to improve the image quality. Differential phase gradient of channel data is proposed to estimate the optimal sound velocity for beamforming. The sound velocity optimization is achieved when the differential phase gradient between the left and the right sub-apertures approaches zero. Channel-domain autocorrelation is utilized for the estimation of phase gradient due to its high rejection to noise interference and low computational complexity. Results indicate that, compared to the conventional phase variance method, the proposed differential phase gradient reduces the standard deviation of sound velocity estimation from 0.5% to 0.2% while the accuracy remains comparable. The contrast ratio of the cyst region achieves the peak when the optimized sound velocity is utilized for beamforming. The lateral resolution of point target also improves by 14.3% after sound velocity optimization. The proposed method increases the robustness of sound velocity optimization. It is suggested to be implemented at transmit focal depth and without beam steering for better performance.

Adaptive foveated single-pixel imaging with dynamic supersampling.

In contrast to conventional multipixel cameras, single-pixel cameras capture images using a single detector that measures the correlations between the scene and a set of patterns. However, these systems typically exhibit low frame rates, because to fully sample a scene in this way requires at least the same number of correlation measurements as the number of pixels in the reconstructed image. To mitigate this, a range of compressive sensing techniques have been developed which use a priori knowledge to reconstruct images from an undersampled measurement set. Here, we take a different approach and adopt a strategy inspired by the foveated vision found in the animal kingdom-a framework that exploits the spatiotemporal redundancy of many dynamic scenes. In our system, a high-resolution foveal region tracks motion within the scene, yet unlike a simple zoom, every frame delivers new spatial information from across the entire field of view. This strategy rapidly records the detail of quickly changing features in the scene while simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This architecture provides video streams in which both the resolution and exposure time spatially vary and adapt dynamically in response to the evolution of the scene. The degree of local frame rate enhancement is scene-dependent, but here, we demonstrate a factor of 4, thereby helping to mitigate one of the main drawbacks of single-pixel imaging techniques. The methods described here complement existing compressive sensing approaches and may be applied to enhance computational imagers that rely on sequential correlation measurements.

Adaptive illumination reduces photobleaching in structured illumination microscopy.

Photobleaching is a major factor limiting the observation time in fluorescence microscopy. We achieve photobleaching reduction in structured illumination microscopy (SIM) by locally adjusting the illumination intensities according to the sample. Adaptive SIM is enabled by a digital micro-mirror device (DMD), which provides a projection of the grayscale illumination patterns. We demonstrate a reduction in photobleaching by a factor of three in adaptive SIM compared to the non-adaptive SIM based on a spot grid scanning approach. Our proof-of-principle experiments show great potential for DMD-based microscopes to become a more useful tool in live-cell SIM imaging.

Autocorrelation-based generalized coherence factor for low-complexity adaptive beamforming.

BACKGROUND: Generalized coherence factor (GCF) can be adaptively estimated from channel data to suppress sidelobe artifacts. Conventionally, Fast Fourier Transform (FFT) is utilized to calculate the full channel spectrum and suffers from high computation load. In this work, autocorrelation (AR)-based algorithm is utilized to provide the spectral parameters of channel data for GCF estimation with reduced complexity. METHODS: Autocorrelation relies on the phase difference among neighboring channel pairs to estimate the mean frequency and bandwidth of channel spectrum. Based on these two parameters, the spectral power within the defined range of main lobe direction can be analytically computed from a pseudo spectrum with the presumed shape as the GCF weighting value. A bandwidth factor Q can be further included in the formulation of pseudo channel spectrum to optimize the performance. RESULTS: While the GCF computation complexity of a N-channel system reduces from O(Nlog2N) with FFT to O(N) with AR, the lateral side-lobe level is effectively suppressed in the GCF-AR method. In B-mode speckle imaging, the GCF-AR method can provide a higher image contrast together with a relatively low speckle variation. The resultant Contrast-to-Noise Ratio (CNR) improves from 6.7 with GCF-FFT method to 9.0 with GCF-AR method. CONCLUSION: GCF-AR method reduces the computation complexity of adaptive imaging while providing superior image quality. GCF-AR method is more resistant to the speckle black-region artifacts near strong reflectors and thus improves the overall image contrast.