3-D Microvascular Imaging Using High Frame Rate Ultrasound and ASAP Without Contrast Agents: Development and Initial In Vivo Evaluation on Nontumor and Tumor Models

Ultrafast Coherence-Based Power Doppler Estimation Using Nonlinear Compounding With Complementary Subset Transmit

OBJECTIVE

Conventional coherent plane wave compounding (CPWC) and sum-of-square power Doppler (PD) estimation lead to low contrast and high noise level in ultrafast PD imaging when the number of plane-wave angle and the ensemble length is limited. The coherence-based PD estimation using temporal-multiply-and-sum (TMAS) of high-lag autocorrelation can effectively suppress the uncorrelated noises but at the cost of signal power due to the blood flow decorrelation.

METHODS

In this study, the TMAS PD estimation is incorporated with complementary subset transmit in nonlinear compounding (DMAS-CST) to leverage the signal coherence in both angular and temporal dimensions for improvement of PD image quality. The CST correlation can be performed not only within the same Doppler ensemble (i.e., intra-correlation) but also across the adjacent Doppler ensembles (i.e., inter-correlation) to increase the number of correlation pairs in TMAS PD estimation.

RESULTS

In both simulations and experiments, DMAS-CST is capable of improving the contrast of TMAS PD image by over 10 dB compared to the nonlinear compounding alone by enhanced noise suppression and lower flow decorrelation. When the CST correlations are performed both intra and inter Doppler ensembles, the noise level further reduces in DMAS-CST.

CONCLUSION

Since the TMAS PD estimation is often limited by the loss of signal power due to temporal decorrelation, the design of complementary subsets in DMAS-CST should be carefully examined to preserve the blood flow signal. Future work of this study will focus on how to combine the conventional PD and the TMAS PD for better signal preservation and effective noise suppression.

Enhancing Row-Column Array (RCA)-Based 3D Ultrasound Vascular Imaging With Spatial-Temporal Similarity Weighting

Ultrasound vascular imaging (UVI) is a valuable tool for monitoring the physiological states and evaluating the pathological diseases. Advancing from conventional two-dimensional (2D) to three-dimensional (3D) UVI would enhance the vasculature visualization, thereby improving its reliability. Row-column array (RCA) has emerged as a promising approach for cost-effective ultrafast 3D imaging with a low channel count. However, ultrafast RCA imaging is often hampered by high-level sidelobe artifacts and low signal-to-noise ratio (SNR), which makes RCA-based UVI challenging. In this study, we propose a spatial-temporal similarity weighting (St-SW) method to overcome these challenges by exploiting the incoherence of sidelobe artifacts and noise between datasets acquired using orthogonal transmissions. Simulation, in vitro blood flow phantom, and in vivo experiments were conducted to compare the proposed method with existing orthogonal plane wave imaging (OPW), row-column-specific frame-multiply-and-sum beamforming (RC-FMAS), and XDoppler techniques. Qualitative and quantitative results demonstrate the superior performance of the proposed method. In simulations, the proposed method reduced the sidelobe level by 31.3 dB, 20.8 dB, and 14.0 dB, compared to OPW, XDoppler, and RC-FMAS, respectively. In the blood flow phantom experiment, the proposed method significantly improved the contrast-to-noise ratio (CNR) of the tube by 26.8 dB, 25.5 dB, and 19.7 dB, compared to OPW, XDoppler, and RC-FMAS methods, respectively. In the human submandibular gland experiment, it not only reconstructed a more complete vasculature but also improved the CNR by more than 15 dB, compared to OPW, XDoppler, and RC-FMAS methods. In summary, the proposed method effectively suppresses the side-lobe artifacts and noise in images collected using an RCA under low SNR conditions, leading to improved visualization of 3D vasculatures.

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.

Adaptive and Robust Vessel Quantification in Contrast-Free Ultrafast Ultrasound Microvessel Imaging

The morphological features of vasculature in diseased tissue differ significantly from those in normal tissue. Therefore, vasculature quantification is crucial for disease diagnosis and staging. Ultrasound microvessel imaging (UMI) with ultrafast ultrasound acquisitions has been determined to have potential in clinical applications given its superior sensitivity in blood flow detection. However, the presence of spatial-dependent noise caused by a low imaging signal-to-noise ratio and incoherent clutter artifacts caused by moving hyperechoic scatterers degrades the performance of UMI and the reliability of vascular quantification. To tackle these issues, we proposed an improved UMI technique along with an adaptive vessel segmentation workflow for robust vessel identification and vascular feature quantification. A previously proposed sub-aperture cross-correlation technique and a normalized cross-correlation technique were applied to equalize the spatially dependent noise level and suppress the incoherent clutter artifact. A square operator and non-local means filter were then used to better separate the blood flow signal from residual background noise. On the de-noised ultrasound microvessel image, an automatic and adaptive vessel segmentation method was developed based on the different spatial patterns of blood flow signal and background noise. The proposed workflow was applied to a CIRS phantom, to a Doppler flow phantom and to an inflammatory bowel, kidney and liver, to validate its feasibility. Results revealed that automatic adaptive, and robust vessel identification performance can be achieved using the proposed method without the subjectivity caused by radiologists/operators.

Investigating CXCR4 expression of tumor cells and the vascular compartment: A multimodal approach

The C-X-C chemokine receptor 4 (CXCR4) is G protein-coupled receptor that upon binding to its cognate ligand, can lead to tumor progression. Several CXCR4-targeted therapies are currently under investigation, and with it comes the need for imaging agents capable of accurate depiction of CXCR4 for therapeutic stratification and monitoring. PET agents enjoy the most success, but more cost-effective and radiation-free approaches such as ultrasound (US) imaging could represent an attractive alternative. In this work, we developed a targeted microbubble (MB) for imaging of vascular CXCR4 expression in cancer. A CXCR4-targeted MB was developed through incorporation of the T140 peptide into the MB shell. Binding properties of the T140-MB and control, non-targeted MB (NT-MB) were evaluated in MDA-MB-231 cells where CXCR4 expression was knocked-down (via shRNA) through optical imaging, and in the lymphoma tumor models U2932 and SuDHL8 (high and low CXCR4 expression, respectively) by US imaging. PET imaging of [18F]MCFB, a tumor-penetrating CXCR4-targeted small molecule, was used to provide whole-tumor CXCR4 readouts. CXCR4 expression and microvessel density were performed by immunohistochemistry analysis and western blot. T140-MB were formed with similar properties to NT-MB and accumulated sensitively and specifically in cells according to their CXCR4 expression. In NOD SCID mice, T140-MB persisted longer in tumors than NT-MB, indicative of target interaction, but showed no difference between U2932 and SuDHL8. In contrast, PET imaging with [18F]MCFB showed a marked difference in tumor uptake at 40-60 min post-injection between the two tumor models (p<0.05). Ex vivo analysis revealed that the large differences in CXCR4 expression between the two models are not reflected in the vascular compartment, where the MB are restricted; in fact, microvessel density and CXCR4 expression in the vasculature was comparable between U2932 and SuDHL8 tumors. In conclusion, we successfully developed a T140-MB that can be used for imaging CXCR4 expression in the tumor vasculature.

DMAS Beamforming with Complementary Subset Transmit for Ultrasound Coherence-Based Power Doppler Detection in Multi-Angle Plane-Wave Imaging

Conventional ultrasonic coherent plane-wave (PW) compounding corresponds to Delay-and-Sum (DAS) beamforming of low-resolution images from distinct PW transmit angles. Nonetheless, the trade-off between the level of clutter artifacts and the number of PW transmit angle may compromise the image quality in ultrafast acquisition. Delay-Multiply-and-Sum (DMAS) beamforming in the dimension of PW transmit angle is capable of suppressing clutter interference and is readily compatible with the conventional method. In DMAS, a tunable p value is used to modulate the signal coherence estimated from the low-resolution images to produce the final high-resolution output and does not require huge memory allocation to record all the received channel data in multi-angle PW imaging. In this study, DMAS beamforming is used to construct a novel coherence-based power Doppler detection together with the complementary subset transmit (CST) technique to further reduce the noise level. For p = 2.0 as an example, simulation results indicate that the DMAS beamforming alone can improve the Doppler SNR by 8.2 dB compared to DAS counterpart. Another 6-dB increase in Doppler SNR can be further obtained when the CST technique is combined with DMAS beamforming with sufficient ensemble averaging. The CST technique can also be performed with DAS beamforming, though the improvement in Doppler SNR and CNR is relatively minor. Experimental results also agree with the simulations. Nonetheless, since the DMAS beamforming involves multiplicative operation, clutter filtering in the ensemble direction has to be performed on the low-resolution images before DMAS to remove the stationary tissue without coupling from the flow signal.

Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging

Delay-and-sum (DAS) beamformers, when applied to photoacoustic (PA) image reconstruction, produce strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For preclinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this article, we present PA subaperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary nonoverlapping subapertures. Then, a weighting matrix is derived by analyzing the correlation between subaperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging, and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP compared to DAS and coherence factor weighted DAS (DAS _CF ). PSAP demonstrated improved target detectability with a higher generalized contrast-to-noise (gCNR) ratio in vasculature simulations where PSAP produces 19.61% and 19.53% higher gCNRs than DAS and DAS _CF , respectively. Furthermore, PSAP provided higher image contrast quantified using contrast ratio (CR) (e.g., PSAP produces 89.26% and 11.90% higher CR than DAS and DAS _CF in vasculature simulations) and improved clutter suppression.

Simultaneous Noise Suppression and Incoherent Artifact Reduction in Ultrafast Ultrasound Vascular Imaging

Ultrasound vascular imaging based on ultrafast plane wave imaging and singular value decomposition (SVD) clutter filtering has demonstrated superior sensitivity in blood flow detection. However, ultrafast ultrasound vascular imaging is susceptible to electronic noise due to the weak penetration of unfocused waves, leading to a lower signal-to-noise ratio (SNR) at larger depths. In addition, incoherent clutter artifacts originating from strong and moving tissue scatterers that cannot be completely removed create a strong mask on top of the blood signal that obscures the vessels. Herein, a method that can simultaneously suppress the background noise and incoherent artifacts is proposed. The method divides the tilted plane or diverging waves into two subgroups. Coherent spatial compounding is performed within each subgroup, resulting in two compounded data sets. An SVD-based clutter filter is applied to each data set, followed by a correlation between the two data sets to produce a vascular image. Uncorrelated noise and incoherent artifacts can be effectively suppressed with the correlation process, while the coherent blood signal can be preserved. The method was evaluated in wire-target simulations and phantom, in which around 7-10-dB SNR improvement was shown. Consistent results were found in a flow channel phantom with improved SNR by the proposed method (39.9 ± 0.2 dB) against conventional power Doppler (29.1 ± 0.6 dB). Last, we demonstrated the effectiveness of the method combined with block-wise SVD clutter filtering in a human liver, breast tumor, and inflammatory bowel disease data sets. The improved blood flow visualization may facilitate more reliable small vessel imaging for a wide range of clinical applications, such as cancer and inflammatory diseases.

Ultrafast Power Doppler Imaging Using Frame-Multiply-and-Sum-Based Nonlinear Compounding

Ultrafast power Doppler imaging based on coherent compounding (UPDI-CC) has become a promising technique for microvascular imaging due to its high sensitivity to slow blood flows. However, since this method utilizes a limited number of plane-wave or diverging-wave transmissions for high-frame-rate imaging, it suffers from degraded image quality because of the low contrast resolution. In this article, an ultrafast power Doppler imaging method based on a nonlinear compounding framework, called frame-multiply-and-sum (UPDI-FMAS), is proposed to improve contrast resolution. In UPDI-FMAS, unlike conventional channel-domain delay-multiply-and-sum (DMAS) beamforming, the signal coherence is estimated based on autocorrelation function over plane-wave angle frames. To avoid phase distortion of blood flow signals during the autocorrelation process, clutter filtering is preferentially applied to individual beamformed plane-wave data set. Therefore, only coherent blood flow signals are emphasized, while incoherent background noise is suppressed. The performance of the UPDI-FMAS was evaluated with simulation, phantom, and in vivo studies. For the simulation and phantom studies with a constant laminar flow, the UPDI-FMAS showed improvements in the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) to those of UPDI-CC, i.e., over 10 and 7 dB for 13 plane waves, respectively, and the performances were improved as the number of plane waves increased. Moreover, the enhancement of the image quality due to the increased SNR and CNR in UPDI-FMAS was more clearly depicted with the in vivo study, in which a human kidney and a tumor-bearing mouse were evaluated. These results indicate that the FMAS compounding can improve the image quality of UPDI for microvascular imaging without loss of temporal resolution.

Current Concepts in Multi-Modality Imaging of Solid Tumor Angiogenesis

Simple Summary

The recent increase in the use of targeted molecular therapy including anti-angiogenetic agents in cancer treatment necessitate the use of robust tools to assess and guide treatment. Angiogenesis, the formation of new disorganized blood vessels, is used by tumor cells to grow and spread using different mechanisms that could be targeted by anti-angiogenetic agents. In this review, we discuss the biological principles of tumor angiogenesis and the imaging modalities that could provide information beyond gross tumor size and morphology to capture the efficacy of anti-angiogenetic therapeutic response.

Abstract

There have been rapid advancements in cancer treatment in recent years, including targeted molecular therapy and the emergence of anti-angiogenic agents, which necessitate the need to quickly and accurately assess treatment response. The ideal tool is robust and non-invasive so that the treatment can be rapidly adjusted or discontinued based on efficacy. Since targeted therapies primarily affect tumor angiogenesis, morphological assessment based on tumor size alone may be insufficient, and other imaging modalities and features may be more helpful in assessing response. This review aims to discuss the biological principles of tumor angiogenesis and the multi-modality imaging evaluation of anti-angiogenic therapeutic responses.