Improved Ultrafast Power Doppler Imaging by Using Spatiotemporal Non-Local Means Filtering

Bedside cerebral microvascular imaging of patients with disorders of consciousness: a feasibility study

Background

Efficient bedside neurofunctional monitoring is crucial for managing disorders of consciousness (DoC). Ultrafast Power Doppler Imaging (uPDI) outperforms traditional Ultrasound in bedside for assessing the microcirculatory system. However, intracranial blood flow imaging traditionally faces limitations due to the skull's impedance. This constraint is circumvented in common post-craniectomy DoC patients, who present a unique observational window for uPDI.

Methods

We conducted uPDI scans on five DoC patients of different ages and consciousness levels who had undergone decompressive craniectomy. We compared the imaging results from uPDI with traditional PDI and identified the physiological and pathological conditions with uPDI.

Results

Detailed microvascular images of both cortical and subcortical areas were obtained using uPDI through the craniectomy window. Notably, uPDI demonstrates high sensitivity and imaging depth, revealing microvessels as small as 320 μm in diameter at 4 cm depth, and detecting blood flow signals up to 6 cm beneath the scalp.

Conclusion

Through the decompressive cranial windows of DoC patients, we obtained cerebral microvascular images with significantly higher sensitivity without the need for contrast agents.

Significance

Our research provides a novel bedside cerebral microcirculation imaging method for patients with DoC, offering convenient neurofunctional assessment to improve the clinical management of DoC patients.

Deep Power-Aware Tunable Weighting for Ultrasound Microvascular Imaging

Ultrasound microvascular imaging (UMI), including ultrafast power Doppler imaging (uPDI) and ultrasound localization microscopy (ULM), obtains blood flow information through plane wave (PW) transmissions at high frame rates. However, low signal-to-noise ratio (SNR) of PWs causes low image quality. Adaptive beamformers have been proposed to suppress noise energy to achieve higher image quality accompanied by increasing computational complexity. Deep learning (DL) leverages powerful hardware capabilities to enable rapid implementation of noise suppression at the cost of flexibility. To enhance the applicability of DL-based methods, in this work, we propose a deep power-aware tunable (DPT) weighting (i.e., postfilter) for delay-and-sum (DAS) beamforming to improve UMI by enhancing PW images. The model, called Yformer, is a hybrid structure combining convolution and Transformer. With the DAS beamformed and compounded envelope image as input, Yformer can estimate both noise power and signal power. Furthermore, we utilize the obtained powers to compute pixel-wise weights by introducing a tunable noise control factor (NCF), which is tailored for improving the quality of different UMI applications. In vivo experiments on the rat brain demonstrate that Yformer can accurately estimate the powers of noise and signal with the structural similarity index measure (SSIM) higher than 0.95. The performance of the DPT weighting is comparable to that of superior adaptive beamformer in uPDI with low computational cost. The DPT weighting was then applied to four different datasets of ULM, including public simulation, public rat brain, private rat brain, and private rat liver datasets, showing excellent generalizability using the model trained by the private rat brain dataset only. In particular, our method indirectly improves the resolution of liver ULM from 25.24 to m by highlighting small vessels. In addition, the DPT weighting exhibits more details of blood vessels with faster processing, which has the potential to facilitate the clinical applications of high-quality UMI.

VoxelMorph-Based Deep Learning Motion Correction for Ultrasound Localization Microscopy of Spinal Cord

Accurate assessment of spinal cord vasculature is important for the urgent diagnosis of injury and subsequent treatment. Ultrasound localization microscopy (ULM) offers super-resolution imaging of microvasculature by localizing and tracking individual microbubbles (MBs) across multiple frames. However, a long data acquisition often involves significant motion artifacts caused by breathing and heartbeat, which further impairs the resolution of ULM. This effect is particularly pronounced in spinal cord imaging due to respiratory movement. We propose a VoxelMorph-based deep learning (DL) motion correction method to enhance the ULM performance in spinal cord imaging. Simulations were conducted to demonstrate the motion estimation accuracy of the proposed method, achieving a mean absolute error of m. Results from in vivo experiments show that the proposed method efficiently compensates for rigid and nonrigid motion, providing improved resolution with smaller vascular diameters and enhanced microvessel reconstruction after motion correction. Nonrigid deformation fields with varying displacement magnitudes were applied to in vivo data for assessing the robustness of the algorithm.

High-Resolution Power Doppler Using Null Subtraction Imaging

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of [Formula: see text] was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.

Competitive Swarm Optimized SVD Clutter Filtering for Ultrafast Power Doppler Imaging

Ultrafast power Doppler imaging (uPDI) can significantly increase the sensitivity of resolving small vascular paths in ultrasound. While clutter filtering is a fundamental and essential method to realize uPDI, it commonly uses singular value decomposition (SVD) to suppress clutter signals and noise. However, current SVD-based clutter filters using two cutoffs cannot ensure sufficient separation of tissue, blood, and noise in uPDI. This article proposes a new competitive swarm-optimized SVD clutter filter to improve the quality of uPDI. Specifically, without using two cutoffs, such a new filter introduces competitive swarm optimization (CSO) to search for the counterparts of blood signals in each singular value. We validate the CSO-SVD clutter filter on public in vivo datasets. The experimental results demonstrate that our method can achieve higher contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and blood-to-clutter ratio (BCR) than the state-of-the-art SVD-based clutter filters, showing a better balance between suppressing clutter signals and preserving blood signals. Particularly, our CSO-SVD clutter filter improves CNR by 0.99 ± 0.08 dB, SNR by 0.79 ± 0.08 dB, and BCR by 1.95 ± 0.03 dB when comparing a spatial-similarity-based SVD clutter filter in the in vivo dataset of rat brain bolus.

Optimization Method to Predict Optimal Noise Reduction Parameters for the Non-Local Means Algorithm Based on the Scintillator Thickness in Radiography

The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 μm), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future.

Ultrafast power Doppler imaging for ischemic encephalopathy: A case report

BACKGROUND

Severely elevated intracranial pressure due to various reasons, such as decreased cerebral perfusion, can lead to devastating neurological outcomes, such as brain herniation. Decompression craniectomy is a life-saving procedure that is commonly performed for such a critical situation, but the changes in cerebral microvessels after brain herniation and decompression are unclear. Ultrafast power Doppler imaging (uPDI) is a new microvascular imaging technology that utilizes high frame rate plane/diverging wave transmission and advanced clutter filters. uPDI significantly improves Doppler sensitivity and can detect microvessels, which are usually invisible using traditional ultrasound Doppler imaging.

CASE SUMMARY

In this report, uPDI was used for the first time to observe the brain blood flow of a hypoperfusion area in a 4-year-old girl who underwent decompression craniectomy due to refractory intracranial hypertension (ICP) after malignant brain tumor surgery. B-mode imaging was used to verify the increased densities of the cerebral cortex and basal ganglia that were observed by computed tomography.

CONCLUSION

uPDI showed the local blood supplies and anatomical structures of the patient after decompressive craniectomy. uPDI is potentially a more intuitive and noninvasive method for evaluating the effects of severe ICP on cerebral microvessels.

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.

Super-resolution Ultrasound Microvascular Angiography for Spinal Cord Penumbra Imaging

OBJECTIVE

After spinal cord injury (SCI) or ischemia, timely intervention in the penumbra, such as recanalization and tissue reperfusion, is essential for preservation of its function. However, limited by imaging resolution and micro-blood flow sensitivity, golden standard angiography modalities, including magnetic resonance angiography (MRA) and digital subtraction angiography (DSA), are still not applicable for spinal cord microvascular imaging. Regarding spinal cord penumbra, to the best of authors' knowledge, currently, there is no efficient in vivo imaging modality for its evaluation. With tens-of-micrometer resolution and deep penetration, advanced ultrasound localization microscopy (ULM) could potentially meet the needs of emergent diagnosis and long-term monitoring of spinal cord penumbra.

METHODS

ULM microvasculature imaging was performed on rats with all laminae removed to obtain the blood supply in major spinal cord segments (C5-L5). For adult rats with spinal cord penumbra induced by compression injury (1 s, 10 s and 15 s), ULM imaging was conducted. The corresponding angiography results are investigated in terms of microvessel saturation, morphology, and flow velocity. The Basso/Beattie/Bresnahan (BBB) locomotor rating scale and hematoxylin and eosin staining were utilized for model validation and comparison.

RESULTS

The feasibility of ULM enabling spinal cord penumbra imaging and development monitoring was demonstrated. The focal injury core and penumbra can be clearly identified using the proposed method. Significant difference of perfusion can be observed after 1 s, 10 s and 15 s compression. Quantitative results show a high correlation between in vivo ultrasonic angiography, BBB functional evaluation and ex vivo histology assessment under different compression duration.

CONCLUSION

It is demonstrated that the super-resolution ULM micro-vasculature imaging can be used to evaluate the penumbra in spinal cord at acute and early stage of chronic phase, providing an efficient modality for micro-hemodynamics monitoring of the spinal cord.

Case report: High-resolution, intra-operative µDoppler-imaging of spinal cord hemangioblastoma

Surgical resection of spinal cord hemangioblastomas remains a challenging endeavor: the neurosurgeon's aim to reach total tumor resections directly endangers their aim to minimize post-operative neurological deficits. The currently available tools to guide the neurosurgeon's intra-operative decision-making consist mostly of pre-operative imaging techniques such as MRI or MRA, which cannot cater to intra-operative changes in field of view. For a while now, spinal cord surgeons have adopted ultrasound and its submodalities such as Doppler and CEUS as intra-operative techniques, given their many benefits such as real-time feedback, mobility and ease of use. However, for highly vascularized lesions such as hemangioblastomas, which contain up to capillary-level microvasculature, having access to higher-resolution intra-operative vascular imaging could potentially be highly beneficial. µDoppler-imaging is a new imaging modality especially fit for high-resolution hemodynamic imaging. Over the last decade, µDoppler-imaging has emerged as a high-resolution, contrast-free sonography-based technique which relies on High-Frame-Rate (HFR)-ultrasound and subsequent Doppler processing. In contrast to conventional millimeter-scale (Doppler) ultrasound, the µDoppler technique has a higher sensitivity to detect slow flow in the entire field-of-view which allows for unprecedented visualization of blood flow down to sub-millimeter resolution. In contrast to CEUS, µDoppler is able to image high-resolution details continuously, without being contrast bolus-dependent. Previously, our team has demonstrated the use of this technique in the context of functional brain mapping during awake brain tumor resections and surgical resections of cerebral arteriovenous malformations (AVM). However, the application of µDoppler-imaging in the context of the spinal cord has remained restricted to a handful of mostly pre-clinical animal studies. Here we describe the first application of µDoppler-imaging in the case of a patient with two thoracic spinal hemangioblastomas. We demonstrate how µDoppler is able to identify intra-operatively and with high-resolution, hemodynamic features of the lesion. In contrast to pre-operative MRA, µDoppler could identify intralesional vascular details, in real-time during the surgical procedure. Additionally, we show highly detailed post-resection images of physiological human spinal cord anatomy. Finally, we discuss the necessary future steps to push µDoppler to reach actual clinical maturity.

High-Quality Ultrafast Power Doppler Imaging Based on Spatial Angular Coherence Factor

The morphological and hemodynamic changes of microvessels are demonstrated to be related to the diseased conditions in tissues. Ultrafast power Doppler imaging (uPDI) is a novel modality with a significantly increased Doppler sensitivity, benefiting from the ultrahigh frame rate plane-wave imaging (PWI) and advanced clutter filtering. However, unfocused plane-wave transmission often leads to a low imaging quality, which degrades the subsequent microvascular visualization in power Doppler imaging. Coherence factor (CF)-based adaptive beamformers have been widely studied in conventional B-mode imaging. In this study, we propose a spatial and angular coherence factor (SACF) beamformer for improved uPDI (SACF-uPDI) by calculating the spatial CF across apertures and the angular CF across transmit angles, respectively. To identify the superiority of SACF-uPDI, simulations, in vivo contrast-enhanced rat kidney, and in vivo contrast-free human neonatal brain studies were conducted. Results demonstrate that SACF-uPDI can effectively enhance contrast and resolution and suppress background noise simultaneously, compared with conventional uPDI methods based on delay-and-sum (DAS) (DAS-uPDI) and CF (CF-uPDI). In the simulations, SACF-uPDI can improve the lateral and axial resolutions compared with those of DAS-uPDI, from 176 to [Formula: see text] of lateral resolution, and from 111 to [Formula: see text] of axial resolution. In the in vivo contrast-enhanced experiments, SACF achieves 15.14- and 5.6-dB higher contrast-to-noise ratio (CNR), 15.25- and 3.68-dB lower noise power, and 240- and 15- [Formula: see text] narrower full-width at half-maximum (FWHM) than DAS-uPDI and CF-uPDI, respectively. In the in vivo contrast-free experiments, SACF achieves 6.11- and 1.09-dB higher CNR, 11.93- and 4.01-dB lower noise power, and 528- and 160- [Formula: see text] narrower FWHM than DAS-uPDI and CF-uPDI, respectively. In conclusion, the proposed SACF-uPDI method can efficiently improve the microvascular imaging quality and has the potential to facilitate clinical applications.

Application of B-flow imaging and its enhanced mode in perforator mapping

AIM

To explore the value of B-flow (B-mode blood flow) imaging and its enhanced mode in perforator mapping.

MATERIALS AND METHODS

Before surgery, B-flow imaging, enhanced B-flow imaging, colour Doppler flow imaging (CDFI), and contrast-enhanced ultrasound (CEUS) were used to detect the skin-perforating vessels and small vessels in the fat layer of the donor site. Taking the intra-operative results as the reference standard, the diagnostic consistency and efficiency of the four modes were compared. Statistical analysis was performed using the Friedman M-test, Cochran's Q-test, and the Z-test.

RESULTS

Thirty flaps were excised, with 34 skin-perforating vessels and 25 non-skin-perforating vessels, as confirmed during surgery. In order of the number of skin-perforating vessels detected, the results showed that enhanced B-flow imaging detected more vessels than B-flow imaging and CDFI (all p<0.05), CEUS detected more vessels than B-flow imaging and CDFI (all p<0.05), B-flow imaging detected more vessels than CDFI (p<0.05). All four modes had remarkable and satisfactory diagnostic consistency and effectiveness, but B-flow imaging was the best (sensitivity 100%, specificity 92%, Youden index 0.92). In order of the number of small vessels in the fat layer detected, the results showed that enhanced B-flow imaging detected more vessels than CEUS, B-flow imaging, and CDFI (all p<0.05). CEUS detected more vessels than B-flow imaging and CDFI (all p<0.05).

CONCLUSION

B-flow imaging is an alternative method for perforator mapping. Enhanced B-flow imaging can reveal the microcirculation of flaps.

Ultrasound Ultrafast Power Doppler Imaging with High Signal-to-Noise Ratio by Temporal Multiply-and-Sum (TMAS) Autocorrelation

Coherent plane wave compounding (CPWC) reconstructs transmit focusing by coherently summing several low-resolution plane-wave (PW) images from different transmit angles to improve its image resolution and quality. The high frame rate of CPWC imaging enables a much larger number of Doppler ensembles such that the Doppler estimation of blood flow becomes more reliable. Due to the unfocused PW transmission, however, one major limitation of the Doppler estimation in CPWC imaging is the relatively low signal-to-noise ratio (SNR). Conventionally, the Doppler power is estimated by a zero-lag autocorrelation which reduces the noise variance, but not the noise level. A higher-lag autocorrelation method such as the first-lag (R(1)) power Doppler image has been developed to take advantage of the signal coherence in the temporal direction for suppressing uncorrelated random noises. In this paper, we propose a novel Temporal Multiply-and-Sum (TMAS) power Doppler detection method to further improve the noise suppression of the higher-lag method by modulating the signal coherence among the temporal correlation pairs in the higher-lag autocorrelation with a tunable pt value. Unlike the adaptive beamforming methods which demand for either receive-channel-domain or transmit-domain processing to exploit the spatial coherence of the blood flow signal, the proposed TMAS power Doppler can share the routine beamforming architecture with CPWC imaging. The simulated results show that when it is compared to the original R(1) counterpart, the TMAS power Doppler image with the pt value of 2.5 significantly improves the SNR by 8 dB for the cross-view flow velocity within the Nyquist rate. The TMAS power Doppler, however, suffers from the signal decorrelation of the blood flow, and thus, it relies on not only the pt value and the flow velocity, but also the flow direction relative to the geometry of acoustic beam. The experimental results in the flow phantom and in vivo dataset also agree with the simulations.