KEYWORDS: Signal to noise ratio, Phased arrays, Image quality, In vivo imaging, Chromium, Speckle, Histograms, Point spread functions, Autocorrelation, Analytics
PurposeEarly image quality metrics were often designed with clinicians in mind, and ideal metrics would correlate with the subjective opinion of practitioners. Over time, adaptive beamformers and other post-processing methods have become more common, and these newer methods often violate assumptions of earlier image quality metrics, invalidating the meaning of those metrics. The result is that beamformers may “manipulate” metrics without producing more clinical information.ApproachIn this work, Smith et al.’s signal-to-noise ratio (SNR) metric for lesion detectability is considered, and a more robust version, here called generalized SNR (gSNR), is proposed that uses generalized contrast-to-noise ratio (gCNR) as a core. It is analytically shown that for Rayleigh distributed data, gCNR is a function of Smith et al.’s Cψ (and therefore can be used as a substitution). More robust methods for estimating the resolution cell size are considered. Simulated lesions are included to verify the equations and demonstrate behavior, and it is shown to apply equally well to in vivo data.ResultsgSNR is shown to be equivalent to SNR for delay-and-sum (DAS) beamformed data, as intended. However, it is shown to be more robust against transformations and report lesion detectability more accurately for non-Rayleigh distributed data. In the simulation included, the SNR of DAS was 4.4±0.8, and minimum variance (MV) was 6.4±1.9, but the gSNR of DAS was 4.5±0.9, and MV was 3.0±0.9, which agrees with the subjective assessment of the image. Likewise, the DAS2 transformation (which is clinically identical to DAS) had an incorrect SNR of 9.4±1.0 and a correct gSNR of 4.4±0.9. Similar results are shown in vivo.ConclusionsUsing gCNR as a component to estimate gSNR creates a robust measure of lesion detectability. Like SNR, gSNR can be compared with the Rose criterion and may better correlate with clinical assessments of image quality for modern beamformers.
KEYWORDS: Image segmentation, Medical imaging, Kidney, Ultrasonography, Monte Carlo methods, Performance modeling, Image enhancement, Data modeling, Reliability, Uncertainty analysis
The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM’s performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM’s performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code has been released at: https://github.com/MedICL-VU/FNPC-SAM
We previously showed that domain adaptive deep neural networks (DNNs) can outperform delay-and-sum (DAS) beamforming in the context of abdominal imaging. We hypothesize the ability of our domain adaptive DNN framework to be applied to transthoracic echocardiography (TTE). We also propose architectural improvements, such as leveraging an encoder-decoder structure and skip connections, to further improve ultrasound image quality for echocardiography tasks such as the detection of thrombi in the left atrial appendage (LAA). DNN training data utilized simulated and in vivo cardiac data. Simulated anechoic and hypoechoic cysts with various amounts of clutter were generated through Field II and in vivo data was collected by scanning patients at Vanderbilt University Medical Center. Fundamental frequency TTE data from five separate cases were processed with DAS, ADMIRE, the baseline model, and multiple models with modified architectures. We found that even when varying the amount of training data, the DNNs consistently achieved higher generalized contrast-to-noise (gCNR) and contrast ratio (CR) but lower contrast-to-noise ratio when compared to DAS. The best-performing beamformer was one DNN with our architectural improvements, achieving higher average gCNR and CR values of .907 and 48.30 dB compared to the baseline DNN values of .788 and 39.45dB, and DAS values of .717 and 14.08dB. Our results demonstrate that our domain adaptive DNN can effectively be applied in the context of transthoracic cardiology, and an encoder-decoder architecture with skip connections can result in even more improvements. Further advancements may improve image quality even more.
Early image quality metrics were often designed with clinicians in mind, and ideally better metrics would correlate with the subjective opinion of clinically better images. Over time, adaptive beamformers and other post-processing methods have become more common, and these newer methods often violate assumptions of earlier image quality metrics, making the meaning of these metrics inaccurate at best. The result is the possibility of beamformers that can “manipulate” metrics to be better, while not producing clinically better images. In this work, Smith et al.’s SNR metric for lesion detectability1 is considered, and a more robust version, here called generalized SNR (gSNR), is proposed that uses gCNR2, 3 as a core, and therefore is more robust to transformations and manipulations. SNR differs from gCNR in that it uses lesion size and spatial resolution as components of it’s calculation, which gCNR does not. It is analytically shown that for Rayleigh distributed data, gCNR can be written in terms of Smith et al.’s Cψ (and therefore can be used as a substitution), and more robust methods for estimating the resolution cell size are considered. This allows for a robust estimate of lesion detectability based on estimated gCNR that may correlate with clinical assessments of image quality.
Ultrasound is a commonly used modality for medical imaging. While this modality has great advantages in terms of safety and cost relative to other imaging modalities, it also has several limitations. Signal-to-noise ratio varies greatly depending on the acoustic properties of the tissue being imaged and the depth of the target structures. In this work, we evaluate the use of deep learning based methods to reconstruct 3D surfaces of general objects imaged with ultrasound. We evaluate three variants of the 3D U-Net with different training scenarios. We were able to train networks to reconstruct three distinct categories of objects relatively well when trained on limited data from each category. However, the performance of the networks did not generalize well when testing on categories of objects not included in the training. We also investigated the effects of employing dual-task autoencoding on generalizability. These results provide a baseline for exploring modifications to the U-Net framework to improve generalizability. A generalizable method could improve visualization for a number of ultrasound imaging tasks.
Immediate postoperative assessment of trans-arterial chemoembolization (TACE) using gold standard modalities, MRI and CT, is unreliable due to confounding interactions with lipiodol and post-embolization inflammatory changes. We previously demonstrated that recent advancements in power Doppler ultrasound processing enables changes in slow blood flow to be detected immediately following TACE. Recently, we have developed a filtering method that employs a higherorder singular value decomposition (HOSVD) applied to aperture data to mitigate thermal noise and acoustic clutter. Here, we investigate HOSVD as a tool to improve non-contrast ultrasound evaluation of TACE. Preliminary feasibility is demonstrated in a small pilot study. Treatment-induced changes in perfusion are visualized most readily using the HOSVD filter in comparison to conventional filtering methods. The HOSVD filter produced the greatest change in contrast between pre-TACE and post-TACE power Doppler images.
KEYWORDS: Signal to noise ratio, In vivo imaging, Ultrasonography, Doppler effect, Blood circulation, Skull, Signal processing, Signal attenuation, Scanners, Safety
Ultrasound power Doppler imaging is a useful clinical tool for measuring perfusion. Sensitivity to slow moving blood flow is important for many clinical applications, but thick abdominal walls or the presence of bone such as ribs or the skull cause significant attenuation and thereby reduce the signal-to-noise ratio (SNR) and flow sensitivity. One way to improve SNR is to inject microbubble contrast agents into the vascular system, but this is impractical for many applications. An alternative approach is to use coded excitation, a signal processing technique that can drastically increase SNR within FDA safety limits without contrast agents. This work encompasses a method to design long coded pulses that are simple to implement along with a pulse compression technique to completely suppress range lobes, thereby recovering axial resolution, maintaining contrast, and improving SNR by as much as a factor of 10log10(code length). In simulations we show that this approach reliably improves the SNR of power Doppler imaging across a range of noise levels. As the noise level increases with respect to the blood, contrast and contrast-to-noise ratio are maintained with coded excitation whereas they drop precipitously without coded excitation. In vivo feasibility is also shown in transcranial and transthoracic cardiac B-Mode imaging. Both simulation and in vivo results match theoretical expectations of SNR gain. Finally, preliminary results showing in vivo power Doppler imaging in the liver are presented as well. Coded excitation is able to improve the blood vessel to background CNR and CR as compared to a standard approach.
Reverberation clutter is a difficult source of image degradation in patients, and minimum variance (MV) in particular is poorly equipped to handle such sources, as we demonstrate here. We propose that a pre-processing step such as ADMIRE should be implemented in cases with high reverberation clutter when we still want to be able to implement MV to realize improvements in lateral resolution. The ADMIRE, or aperture domain model image reconstruction, method is specifically designed to suppress or eliminate reverberant and off-axis sources of clutter while returning the decluttered channel data with its original dimensionality, allowing us to sequentially process the data with MV. We show that in simulated data this combined method results in clear improvements to image quality, contrast ratio, and target boundary thickness compared to DAS and MV alone. In in vivo cases, contrast ratio and general image quality are improved, and boundary thickness is generally on par with DAS and MV.
Non-contrast ultrasound blood flow imaging is difficult at slow blood flow rates. Singular value decomposition (SVD) and independent component analysis (ICA) are useful for separating tissue, blood, and noise sources for Doppler filtering. In addition, it has been shown that applying SVD and ICA in a block-wise manner further improves source separation; noise within a small block is theoretically more stationary, and thus easier to separate. Yet, there is much discussion on how to select independent components; several methods have been introduced with some success. We present a novel, adaptive hierarchical clustering approach for selecting appropriate independent components for blood flow image filtering that utilizes Kurtosis and Normalized Cross Correlation. Components are clustered based on the Kurtosis and NCC of each component; an optimal number of clusters is chosen using the Silhouette Method. Appropriate clusters are selected based on the Autocorrelation Function of each cluster. Our method was tested on 1 mm/s and 5 mm/s flowrate phantoms containing a 0.6 mm vessel and resulted in average SNR and CNR increases of 6.2 dB and 3.7 dB, respectively, for 1 mm/s blood flow velocities. We demonstrate that our method improves tissue and noise suppression throughout the field of view while maintaining blood flow information.
B-mode ultrasound displays hyperechoic and hypoechoic targets as larger and smaller, respectively, compared to the true structure. A method to correct for this distortion would enable B-mode to better represent the true structure. For this work, we investigated training DNN beamformers to reduce this B-mode sizing distortion. Aperture domain DNN beamformers were trained using training data generated from simulated anechoic cysts. The DNN beamformers were trained to suppress signals originating from inside the cyst and to preserve signals originating from outside the cyst. The results suggest that DNN beamformers can be trained to reduce B-mode sizing distortions.
Ultrasonic flow imaging remains susceptible to cluttered imaging environments, which often results in degraded image quality. Coherent Flow Power Doppler (CFPD)–a beamforming technique–has demonstrated efficacy in addressing sources of diffuse clutter. CFPD depicts the normalized spatial coherence of the backscattered echo, which is described by the van Cittert-Zernike theorem. However, the use of a normalized coherence metric in CFPD uncouples the image intensity from the magnitude of the underlying blood echo. As a result, CFPD is not a robust approach to study gradation in blood echo energy, which depicts the fractional moving blood volume. We have developed a modified beamforming scheme, termed power-preserving Coherent Flow Power Doppler (ppCFPD), which employs a measure of signal covariance across the aperture, rather than normalized coherence. As shown via Field II simulations, this approach retains the clutter suppression capability of CFPD, while preserving the underlying signal energy, similar to standard power Doppler (PD). Furthermore, we describe ongoing work, in which we have proposed a thresholding scheme derived from a statistical analysis of additive noise, to further improve perception of flow. Overall, this adaptive approach shows promise as an alternative technique to depict flow gradation in cluttered imaging environments.
Kidney stones are often poorly visualized with ultrasound despite the fact that they have a large impedance mismatch. In previous kidney stone studies conducted by our group, we demonstrated that the Mid-Lag Spatial Coherence (MLSC) beamforming method suppresses the incoherent background speckle while enhancing coherent scatterers. This allowed kidney stones to be highlighted. To study this approach in more detail Field-II simulations and in-house phantoms containing kidney stones were used to test the effectiveness of MLSC with different parameters. The number of lags used during beamforming and the brightness of the point target were altered. Then, the CNR, SNR, CR, and PSNR of the phantoms and simulations were compared. The CNR experienced little change between lag ranges, but the SNR and PSNR increased with the start lag. SNR increased by 12.9% ± 2.9% between the lowest and highest lag range while PSNR increased by 27.9% ± 4.6% between the lowest and highest lag range. CR did not change in a regular pattern but remained consistently higher than delay and sum beamforming. We also compare MLSC against short-lag spatial coherence (SLSC) and show that we also see improvements over this method including an increase of MLSC over SLSC ranging between 250% and 401% for PSNR and between 414% and 879% for CR.
Non-contrast perfusion ultrasound imaging is difficult, mainly because of tissue clutter interference with blood. We previously developed an adaptive tissue clutter demodulation technique to overcome this problem and showed that power Doppler image quality can be improved when combining adaptive demodulation with improvements in beamforming and tissue filtering, namely angled plane wave beamforming and singular value decomposition filtering. In this work we aim to evaluate an independent component analysis-based filtering method using angled plane wave beamforming and compare it to singular value decomposition filtering with and without adaptive demodulation using single vessel simulations and phantoms. We show that with optimal filter cutoffs, independent component analysis-based filtering consistently improves signal and contrast-to-noise ratios, and it resulted in an 8.4dB average increase in optimal signal-to-noise ratio compared to singular value decomposition filtering in phantoms with 1mm/s flow and a 700ms ensemble.
We investigated using deep neural networks (DNNs) to beamform ultrasound images with high dynamic range targets. The DNNs operated on frequency domain data, the inputs consisted of the separated in-phase and quadrature components observed across the aperture of the array, and the outputs of the DNNs had the same structure as the inputs. We compared several methods for generating training data, including training with hypoechoic and anechoic cysts. All training data was generated using a linear ultrasound simulation tool. The results demonstrate the potential for using DNN beamformers to extend the dynamic range of ultrasound beamforming.
We are interested in examining how our model-based beamforming algorithm, referred to as aperture-domain model image reconstruction (ADMIRE), performs on plane wave sequences in conjunction with synthetic aperture beamforming. We also aim to identify the impact of ADMIRE applied before and after synthetic focusing. We employed simulated phantoms using Field II and tissue-mimicking phantoms to evaluate ADMIRE as applied to synthetic sequencing. We generated plane wave images with and without synthetic aperture focusing (SAF) and measured contrast and contrast-to-noise ratio (CNR). For simulated cyst images formed from single plane waves, the contrast for delay-and-sum (DAS) and ADMIRE are 15.64 and 28.34 dB, respectively, whereas the CNR are 1.76 and 3.90 dB, respectively. We also applied ADMIRE to simulated resolution phantoms having a point target at 3 cm depth on-axis. We simulated the point spread functions from data obtained from 1 plane wave and 75 steered plane waves, along with linear scans with 3 and 4 cm- focal depths. We then compared the outcome of applying ADMIRE before and after SAF using 3 and 11 steered plane waves. Finally, we applied this to an in vivo carotid artery. Based on the findings in this study, ADMIRE can be adapted to full field insonification sequences to improve image quality in plane wave imaging. Additionally, we investigated how robustly ADMIRE performs in the presence of random noise. We then address identified limitations using a conventional envelope detection method with decluttered signals.
Interest in ultrasound perfusion imaging has grown with the development of more sensitive algorithms to detect slow blood flow. Unfortunately, there are not many phantoms that can be used to evaluate these techniques. Some have used small linear tubes, while others have adapted dialysis cartridges. Here we propose a technique using conventional gelatin cast around a sacrificial polymer network. Specifically, we form a gelatin phantom, doped with graphite scatterers to mimic the diffuse scattering in soft tissue, around a polymer resin. The resin structure can be dissolved leaving behind a network of small randomly oriented channels that are connected to a large channel which is connected to a pump to perfuse blood mimicking fluid through the phantom. The phantoms were qualitatively demonstrated to show perfusion through visual confirmation and the speckle SNR, and speed of sound were calculated.
We developed a method that uses deep neural networks (DNNs) to suppress off-axis scattering in ultrasound images. This approach operates in the frequency domain and networks were trained using the simulated responses from individual point targets. The network inputs consisted of the separated in-phase and quadrature components observed across the aperture of the array. The output had the same structure as the input and an inverse short- time Fourier transform was used to convert the processed data back to the time domain. In this work, we examined the noise handling characteristics of the DNN beamformer and also the relation between final image quality and the loss function for training networks.
KEYWORDS: Data modeling, Data acquisition, In vivo imaging, Ultrasonography, Image quality, Transducers, Fourier transforms, Tissues, Multichannel imaging systems, Scattering
Previous studies demonstrated that our aperture domain model image reconstruction (ADMIRE) beamforming algorithm mitigates some common ultrasound imaging artifacts, which may increase ultrasound's clinical utility and reliability. Specifically, ADMIRE can suppress clutter caused by reverberation, off-axis scattering and wavefront aberration. Along with this, we demonstrated that ADMIRE is robust to model-mismatch caused by gross sound speed deviation. These findings suggest that ADMIRE may be an effective tool to provide high quality images in real clinical applications. Many of our previous effort have occurred on research platforms, but it is thought that dedicated clinical systems have better front-end electronics and transducers compared to research oriented platforms. If this is true then it is important to perform in vivo evaluations using the highest quality data possible in order to appropriately characterize (and not overemphasize possible) algorithmic gains. To this end, we modified a Siemens ACUSON SC2000 ultrasound system to capture I/Q channel signals. We acquired channel data using a full synthetic receive sequence. We also acquired channel data in conjunction with pulse inversion sequencing to obtain harmonic images. In this study, we collected data from a tissue-mimicking phantom and a human subject's abdomen and liver. We reconstructed both fundamental and harmonic B-mode images before and after applying ADMIRE. We then measured contrast and contrast-to-noise ratio (CNR). When comparing in vivo images, ADMIRE using low and high degrees of freedom improves contrast by 12.2 ± 2.6 dB and 2.5 ± 0.5 dB, respectively, relative to fundamental delay-and-sum(DAS) B-mode, and boosts contrast by 8.7 ± 3.7 dB and 2.0 ± 0.7 dB, respectively, with harmonic B-mode images.
Tissue clutter caused by patient and sonographer hand motion makes perfusion ultrasound imaging difficult. We previously introduced an adaptive frequency and amplitude demodulation scheme to address this challenge. Our initial implementation used a conventional high-pass infinite impulse response (IIR) filter to attenuate the tissue signal after applying adaptive demodulation. However, other groups have shown that singular value decomposition (SVD) filtering is superior to conventional frequency domain filters. Here we evaluate the SVD filter both in comparison and in conjunction with our proposed adaptive demodulation technique. Blood-to-background SNRs were compared using power Doppler images made from single small vessel simulations with realistic tissue clutter. Additionally, filtering methods were qualitatively assessed using power Doppler images of a cut-in-half perfusion-mimicking phantom. Furthermore, in vivo power Doppler images were compared before and after muscle contraction. SVD filtering with adaptive demodulation resulted in a 7dB increase in simulated blood-to-background SNR compared to a conventional IIR filter and a 54.6% increase in power after in vivo muscle contraction compared to a 1.74% increase using a conventional IIR filter.
In our previous studies, we demonstrated that our aperture domain model-based clutter suppression algorithm improved image quality of in vivo B-mode data obtained from focused transmit beam sequences. Our approach suppresses off-axis clutter and reverberation and tackles limitations of related algorithms because it preserves RF channel signals and speckle statistics. We call the algorithm aperture domain model image reconstruction (ADMIRE). We previously focused on reverberation suppression, but ADMIRE is also effective at suppressing off-axis clutter. We are interested in how ADMIRE performs on plane wave sequences and the impact of AD- MIRE applied before and after synthetic beamforming of steered plane wave sequences. We employed simulated phantoms using Field II and tissue-mimicking phantoms to evaluate ADMIRE applied to plane wave sequencing. We generated images acquired from plane waves with and without synthetic aperture synthesis and measured contrast and contrast-to-noise ratio (CNR). For simulated cyst images formed from single plane waves, the contrast for delay-and-sum (DAS) and ADMIRE are 15.64 dB and 28.34 dB, respectively, while the CNR are 1.76 dB and 3.90 dB, respectively. Based on these findings, ADMIRE improves plane wave image quality. We also applied ADMIRE to resolution phantoms having a point target at 3 cm depth on-axis, simulating the point spread functions from data obtained from 1 and 75 steered plane waves, along with linear scan at focus of 3 and 4 cm depth. We then examined the outcome of applying ADMIRE before and after synthetic aperture processing. Finally, we applied this to an in vivo carotid artery.
Multipath scattering, or reverberation, takes a substantial toll on image quality in many clinical exams. We have suggested a model-based solution to this problem, which we refer to as aperture domain model image reconstruction (ADMIRE). For ADMIRE to work well, it must be trained with precisely characterized data. To solve this specific problem and the general problem of efficiently simulating reverberation, we propose an approach to simulate reverberation with linear simulation tools. Our simulation method defines total propagation time, first scattering site, and a final scattering site. We use a linear simulation package, such as Field II, to simulate scattering from the final site and then shift the simulated wavefront later in time based on the total propagation time and the geometry of the first scattering site. We validate our simulations using theoretical descriptions of clutter in the literature and data acquired from ex vivo tissue. We found that ex vivo tissue clutter had a mean speckle SNR of 1.40±0.23, which we could simulate with about 2 scatterers per resolution cell. Axial clutter distributions drawn from an exponential distribution with a mean of 5 mm and at least 0.5 scatters per resolution cell resulted in clutter that was statistically indistinguishable from the van Cittert–Zernike behavior predicted by literature.
There is growing evidence that reverberation is a primary mechanism of clinical image degradation. This has led to a number of new approaches to suppress reverberation, including our recently proposed model-based algorithm. The algorithm can work well, but it must be trained to reject clutter, while preserving the signal of interest. One way to do this is to use simulated data, but current simulation methods that include multipath scattering are slow and do not readily allow separation of clutter and signal. Here, we propose a more convenient pseudo non-linear simulation method that utilizes existing linear simulation tools like Field II.
The approach functions by linearly simulating scattered wavefronts at shallow depths, and then time-shifting these wavefronts to deeper depths. The simulation only requires specification of the first and last scatterers encountered by a multiply reflected wave and a third point that establishes the arrival time of the reverberation. To maintain appropriate 2D correlation, this set of three points is fixed for the entire simulation and is shifted as with a normal linear simulation scattering field. We show example images, and we compute first order speckle statistics as a function of scatterer density. We perform ex vivo measures of reverberation where we find that the average speckle SNR is 1.73, which we can simulate with 2 reverberation scatterers per resolution cell. We also compare ex vivo lateral speckle statistics to those from linear and pseudo non-linear simulation data. Finally, the van Cittert-Zernike curve was shown to match empirical and theoretical observations.
KEYWORDS: In vivo imaging, Doppler effect, Ultrasonography, Demodulation, Data acquisition, Blood circulation, Tissues, Digital filtering, Linear filtering, Image filtering
A Doppler ultrasound clutter filter that enables estimation of low velocity blood flow could considerably improve ultrasound as a tool for clinical diagnosis and monitoring, including for the evaluation of vascular diseases and tumor perfusion. Conventional Doppler ultrasound is currently used for visualizing and estimating blood flow. However, conventional Doppler is limited by frame rate and tissue clutter caused by involuntary movement of the patient or sonographer. Spectral broadening of the clutter due to tissue motion limits ultrasound’s ability to detect blood flow less than about 5mm/s at an 8MHz center frequency. We propose a clutter filtering technique that may increase the sensitivity of Doppler measurements to at least as low as 0.41mm/s. The proposed filter uses an adaptive demodulation scheme that decreases the bandwidth of the clutter. To test the performance of the adaptive demodulation method at removing sonographer hand motion, six volunteer subjects acquired data from a basic quality assurance phantom. Additionally, to test initial in vivo feasibility, an arterial occlusion reactive hyperemia study was performed to assess the efficiency of the proposed filter at preserving signals from blood velocities 2mm/s or greater. The hand motion study resulted in initial average bandwidths of 577Hz (28.5mm/s), which were decreased to 7.28Hz (0.36mm/s) at -60 dB at 3cm using our approach. The in vivo power Doppler study resulted in 15.2dB and 0.15dB dynamic ranges between the lowest and highest blood flow time points for the proposed filter and conventional 50Hz high pass filter, respectively.
A new scattering model for ultrasound is proposed. The new model accounts for the dominant sources of tissue
scattering including reverberation. In addition to the proposed model a parameter estimation scheme for the model is presented. Using the decomposition scheme, the received ultrasound signal can be decomposed into the various scattering sources arriving concurrently. Sources that are within the expected region of the ballistic wave are kept and used to reconstruct a decluttered B-Mode image.
KEYWORDS: Speckle, In vivo imaging, Signal to noise ratio, Image quality, Point spread functions, Data acquisition, Transducers, Target acquisition, Tissues, Ultrasonography
Conventional wisdom in ultrasonic array design drives development towards larger arrays because of the inverse
relationship between aperture size and resolution. We propose a method using synthetic aperture beamforming
to study image quality as a function of aperture size in simulation, in a phantom and in vivo. A single data acquisition can be beamformed to produce matched images with a range of aperture sizes, even in the presence of target motion. In this framework we evaluate the reliability of typical image quality metrics – speckle signal-tonoise ratio, contrast and contrast-to-noise ratio – for use in in vivo studies. Phantom and simulation studies are in good agreement in that there exists a point of diminishing returns in image quality at larger aperture sizes. We demonstrate challenges in applying and interpreting these metrics in vivo, showing results in hypoechoic vasculature regions. We explore the use of speckle brightness to describe image quality in the presence of in vivo clutter and underlying tissue inhomogeneities.
Intravascular ultrasound (IVUS) currently has a limited ability to characterize endovascular anatomic properties. IVUS elastography enhances the ability to characterize the biomechanical properties of arterial walls. A mathematical phantom generator was developed based on the characteristics of 30MHz, 64 element IVUS catheter images from excised canine femoral arteries. The difference between high and low-pressure intra-arterial images was modeled using phase shifts. The increase in phase shift occurred randomly, generally at every three pixels in our images. Using mathematical phantoms, different methods for calculating elastograms were quantitatively analyzed. Specifically, the effect of standard cross correlation versus cross correlation of the integral of the inflection characteristics for a given set of data, and the effect of an algorithm utilizing a non-constant kernel, were assessed. The specific methods found to be most accurate on the mathematical phantom data were then applied to ex vivo canine data of a scarred and a healthy artery. The algorithm detected significant differences between these two sets of arterial data. It will be necessary to obtain and analyze several more sets of canine arterial data in order to determine the accuracy and reproducibility of the algorithm.
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