Spatiotemporal Singular Value Decomposition Research Articles

Vector-flow imaging of slowly moving ex vivo blood with photoacoustics and pulse-echo ultrasound

We present a technique called photoacoustic vector-flow (PAVF) to quantify the speed and direction of flowing optical absorbers at each pixel from acoustic-resolution PA images. By varying the receiving angle at each pixel in post-processing, we obtain multiple estimates of the phase difference between consecutive frames. These are used to solve the overdetermined photoacoustic Doppler equation with a least-squares approach to estimate a velocity vector at each pixel. This technique is tested in bench-top experiments and compared to simultaneous pulse-echo ultrasound vector-flow (USVF) on whole rat blood at speeds on the order of 1 mm/s. Unlike USVF, PAVF can detect flow without stationary clutter filtering in this experiment, although the velocity estimates are highly underestimated. When applying spatio-temporal singular value decomposition clutter filtering, the flow speed can be accurately estimated with an error of 16.8% for USVF and −8.9% for PAVF for an average flow speed of 2.5 mm/s.

Endoscopic optical coherence tomography angiography using an externally driving catheter.

Endoscopic optical coherence tomography (OCT) is an imaging modality that enables cross-sectional subsurface imaging of tubular organs and cavities. Recently, endoscopic OCT angiography (OCTA) was successfully achieved in distal scanning systems using an internal-motor-driving catheter. In conventional OCT systems using externally driving catheters, the mechanical instability in the proximal actuation causes difficulties for differentiating capillaries in tissues. In this study, OCTA in an endoscopic OCT system using an external-motor-driving catheter was proposed. Blood vessels were visualized by using a high-stability inter-A-scan scheme and the spatiotemporal singular value decomposition algorithm. It is not limited by nonuniform rotation distortion caused by the catheter and physiological motion artifacts. Results show that microvasculature in a custom-made microfluidic phantom and the submucosal capillaries in the mouse rectum are successfully visualized. Furthermore, OCTA using a catheter with a small size (outer diameter less than 1 mm) makes it possible for early diagnosis of narrow lumens, such as pancreatic and bile duct cancers.

Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with a low-energy excitation light source.

Photoacoustic (PA) imaging is an emerging hybrid imaging modality that combines rich optical spectroscopic contrast and high ultrasonic resolution, and thus holds tremendous promise for a wide range of pre-clinical and clinical applications. Compact and affordable light sources such as light-emitting diodes (LEDs) and laser diodes (LDs) are promising alternatives to bulky and expensive solid-state laser systems that are commonly used as PA light sources. These could accelerate the clinical translation of PA technology. However, PA signals generated with these light sources are readily degraded by noise due to the low optical fluence, leading to decreased signal-to-noise ratio (SNR) in PA images. In this work, a spatiotemporal singular value decomposition (SVD) based PA denoising method was investigated for these light sources that usually have low fluence and high repetition rates. The proposed method leverages both spatial and temporal correlations between radiofrequency (RF) data frames. Validation was performed on simulations and in vivo PA data acquired from human fingers (2D) and forearm (3D) using a LED-based system. Spatiotemporal SVD greatly enhanced the PA signals of blood vessels corrupted by noise while preserving a high temporal resolution to slow motions, improving the SNR of in vivo PA images by 90.3%, 56.0%, and 187.4% compared to single frame-based wavelet denoising, averaging across 200 frames, and single frame without denoising, respectively. With a fast processing time of SVD (∼50 µs per frame), the proposed method is well suited to PA imaging systems with low-energy excitation light sources for real-time in vivo applications.

Endoscopic Ultrasound Localization Microscopy for the Evaluation of the Microvasculature of Gastrointestinal Tract Tumors in Rabbits.

The morphological and hemodynamic characterization of the microvascular network around the gastrointestinal (GI) tract can be of significant clinical value for the early diagnosis and treatment of GI tract cancer. Ultrasound localization microscopy (ULM) imaging has been demonstrated to be capable of resolving the microvascular network. However, the endoscopic application of ULM imaging techniques is still unknown. In this study, an endoscopic ultrasound localization microscopy (e-ULM) imaging technique was developed to evaluate the changes of microvasculature during GI tract tumor growth. A customized circular array transducer (center frequency: 6.8 MHz) and the coherent diverging wave compounding method were used to generate B-mode images. Spatiotemporal singular value decomposition processing was used to eliminate the background signals before signal localizations. The centroids of spatially isolated signals were localized and summed to generate the final super-resolution image. The final microvasculature map of a rabbit GI tract tumor reveals that e-ULM can be used to surpass the diffraction limit in traditional endoscopic ultrasound (EUS) imaging. Furthermore, it is observed that data from different stages of tumor growth exhibit significant differences in microvascular pattern and density. Our study demonstrated the implementation and application of an in vivo e-ULM imaging technique for the evaluation of the microvasculature of GI tumors. The efficient e-ULM imaging technique shows potential for use in the detection of GI tract tumor microcirculation changes and subsequent diagnosis of GI tract cancer.

Ultrasonic array imaging of highly attenuative materials with spatio-temporal singular value decomposition

With the increasing use of high density polyethylene (HDPE) pipes in nuclear industry which puts safety at the forefront, ultrasonic array imaging methods play a vital role in the structural integrity of HDPE pipe materials. However, the viscoelastic attenuation of HDPE pipe materials significantly decreases the level of signals, leading to a low signal-to-noise ratio caused by electronic noise. In this work, a domain-adapted spatio-temporal singular value decomposition (STSVD) processing algorithm combined with the total focusing method is proposed to improve the ultrasonic array image quality. First, the real-valued radio frequency (RF) data or A-scan signals are demodulated into the complex analytic signals containing in-phase and quadrature (I/Q) components. Then, the STSVD processing algorithm is used to filter the I/Q data, and the filtered I/Q data is converted into RF signals. Finally, the total focusing method is applied to the processed RF signals to produce the image of the region under detection as a stage of post-processing. Experiments are carried out with an ultrasonic linear phased array in contact with the HDPE pipe materials containing multiple side-drilled holes and through-wall notches. Results show that the proposed method can produce images with high quality to provide good inspection and characterization of defects in highly attenuative materials, especially the deeper defects.

Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition.

.Significance: Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity.Aim: Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI.Approach: In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations.Results: Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance.Conclusions: Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.

Joint Blind Deconvolution and Robust Principal Component Analysis for Blood Flow Estimation in Medical Ultrasound Imaging

This article addresses the problem of high-resolution Doppler blood flow estimation from an ultrafast sequence of ultrasound images. Formulating the separation of clutter and blood components as an inverse problem has been shown in the literature to be a good alternative to spatio-temporal singular value decomposition (SVD)-based clutter filtering. In particular, a deconvolution step has recently been embedded in such a problem to mitigate the influence of the point spread function (PSF) of the imaging system. Deconvolution was shown in this context to improve the accuracy of the blood flow reconstruction. However, the PSF needs to be measured experimentally, and measuring it requires nontrivial experimental setups. To overcome this limitation, we propose herein a blind deconvolution method able to estimate both the blood component and the PSF from Doppler data. Numerical experiments conducted on simulated and in vivo data demonstrate qualitatively and quantitatively the effectiveness of the proposed approach in comparison with the previous method based on experimentally measured PSF and two other state-of-the-art approaches.

Independent Component-Based Spatiotemporal Clutter Filtering for Slow Flow Ultrasound.

Effective tissue clutter filtering is critical for non-contrast ultrasound imaging of slow blood flow in small vessels. Independent component analysis (ICA) has been considered by other groups for ultrasound clutter filtering in the past and was shown to be superior to principal component analysis (PCA)-based methods. However, it has not been considered specifically for slow flow applications or revisited since the onset of other slow flow-focused advancements in beamforming and tissue filtering, namely angled plane wave beamforming and full spatiotemporal singular value decomposition (SVD) (i.e., PCA-based) tissue filtering. In this work, we aim to develop a full spatiotemporal ICA-based tissue filtering technique facilitated by plane wave applications and compare it to SVD filtering. We compare ICA and SVD filtering in terms of optimal image quality in simulations and phantoms as well as in terms of optimal correlation to ground truth blood signal in simulations. Additionally, we propose an adaptive blood independent component sorting and selection method. We show that optimal and adaptive ICA can consistently separate blood from tissue better than principal component analysis (PCA)-based methods using simulations and phantoms. Additionally we demonstrate initial in vivo feasibility in ultrasound data of a liver tumor.

2D and 3D real-time passive cavitation imaging of pulsed cavitation ultrasound therapy in moving tissues

Pulsed cavitation ultrasound therapy (PCUT) is an effective non-invasive therapeutic approach in various medical indications that relies on the mechanical effects generated by cavitation bubbles. Even though limited by the poor contrast, conventional ultrasound B-Mode imaging has been widely used for the guidance and monitoring of the therapeutic procedure, allowing the visualization of the cavitation bubble cloud. However, the visualization of the bubble cloud is often limited in deep organs such as the liver and the heart and remains moreover completely subjective for the operator. Our goal is to develop a new imaging mode to better identify the cavitation cloud.Active and passive cavitation imaging methods have been developed but none of them has been able to locate the cavitation bubble created by PCUT in real-time and in moving organs. In this paper we propose a passive ultrasound imaging approach combined with a spatiotemporal singular value decomposition filter to detect and map the bubble cloud with high sensitivity and high contrast.In moving applications at a maximal motion speed of 10 mm s−1, the contrast-to-noise ratio for passive cavitation imaging is up to 10 times higher than for active cavitation imaging, with a temporal resolution of about 100 ms. The mapping of the bubble cloud can be overlaid in real-time to the conventional B-Mode, which permits to locate the cavitation phenomena in relation to the anatomic image. Finally, we extend the technique to volumetric imaging and show its feasibility on moving phantoms.

In vivo real-time cavitation imaging in moving organs

The stochastic nature of cavitation implies visualization of the cavitation cloud in real-time and in a discriminative manner for the safe use of focused ultrasound therapy. This visualization is sometimes possible with standard echography, but it strongly depends on the quality of the scanner, and is hindered by difficulty in discriminating from highly reflecting tissue signals in different organs. A specific approach would then permit clear validation of the cavitation position and activity. Detecting signals from a specific source with high sensitivity is a major problem in ultrasound imaging. Based on plane or diverging wave sonications, ultrafast ultrasonic imaging dramatically increases temporal resolution, and the larger amount of acquired data permits increased sensitivity in Doppler imaging. Here, we investigate a spatiotemporal singular value decomposition of ultrafast radiofrequency data to discriminate bubble clouds from tissue based on their different spatiotemporal motion and echogenicity during histotripsy. We introduce an automation to determine the parameters of this filtering. This method clearly outperforms standard temporal filtering techniques with a bubble to tissue contrast of at least 20 dB in vitro in a moving phantom and in vivo in porcine liver.

Monitoring and guidance of histotripsy using ultrafast imaging

To use histotripsy safely, it is required to visualize the cavitation clouds and the lesion formation in real-time. Conventional ultrasound imaging (USI) is a good candidate for the real-time monitoring of the procedures. However, bubble clouds are not always clearly visible in vivo using USI because of flows, motion or high echogenicity of the target region. Ultrafast imaging, based on plane wave or diverging waves sonications, is able to record a larger amount of data, providing broad information about how signals originating from different sources vary in time. Here, we apply a spatiotemporal singular value decomposition of ultrafast acquisitions that enables the discrimination of cavitation bubbles from moving tissue in vivo. An automation to determine the filtering parameters will be introduced. Moreover, USI depicts the histotripsy lesion only at an advanced stage and can be hindered by the presence of dissolving gas in the tissue. As the tissue microstructure is greatly affected by the treatment, a...

Spatiotemporal Clutter Filtering of Ultrafast Ultrasound Data Highly Increases Doppler and fUltrasound Sensitivity.

Ultrafast ultrasonic imaging is a rapidly developing field based on the unfocused transmission of plane or diverging ultrasound waves. This recent approach to ultrasound imaging leads to a large increase in raw ultrasound data available per acquisition. Bigger synchronous ultrasound imaging datasets can be exploited in order to strongly improve the discrimination between tissue and blood motion in the field of Doppler imaging. Here we propose a spatiotemporal singular value decomposition clutter rejection of ultrasonic data acquired at ultrafast frame rate. The singular value decomposition (SVD) takes benefits of the different features of tissue and blood motion in terms of spatiotemporal coherence and strongly outperforms conventional clutter rejection filters based on high pass temporal filtering. Whereas classical clutter filters operate on the temporal dimension only, SVD clutter filtering provides up to a four-dimensional approach (3D in space and 1D in time). We demonstrate the performance of SVD clutter filtering with a flow phantom study that showed an increased performance compared to other classical filters (better contrast to noise ratio with tissue motion between 1 and 10mm/s and axial blood flow as low as 2.6 mm/s). SVD clutter filtering revealed previously undetected blood flows such as microvascular networks or blood flows corrupted by significant tissue or probe motion artifacts. We report in vivo applications including small animal fUltrasound brain imaging (blood flow detection limit of 0.5 mm/s) and several clinical imaging cases, such as neonate brain imaging, liver or kidney Doppler imaging.

Dynamic component analysis for multi-input/multi-output problems, with application to speech and neurophysiology

We explore a set of methods referred to collectively as dynamic component analysis (DCA) to derive dictionaries of dynamical patterns for speech (TIMIT database). The methods use spatio-temporal singular value decomposition (ST-SVD) and common spatio-temporal pattern (CSTP) matrix computations. When used on the speech spectrogram, these yield a transformation to a new set of time series representing extracted features at reduced bandwidth. The method is computationally efficient (closed-form solutions suitable for real-time) and robust to additive noise, with diverse applications in speech processing and general multi-input/multi-output (MI/MO) modeling. When used to predict a single neural output (MI/SO), this gives an efficient new method for deriving the spectro-temporal receptive field (STRF), which is shown in our human cortical data to yield improved predictions. We also use DCA to reconstruct speech from cortical activity, wherein dynamical dictionaries for electrocortical data are derived, with application to brain-computer interfaces (BCI).