Ultrasound Localization Microscopy Research Articles

Whole Organ Volumetric Sensing Ultrasound Localization Microscopy for Characterization of Kidney Structure.

Glomeruli are the filtration units of the kidney and their function relies heavily on their microcirculation. Despite its obvious diagnostic importance, an accurate estimation of blood flow in the capillary bundle within glomeruli defies the resolution of conventional imaging modalities. Ultrasound Localization Microscopy (ULM) has demonstrated its ability to image in-vivo deep organs in the body. Recently, the concept of sensing ULM or sULM was introduced to classify individual microbubble behavior based on the expected physiological conditions at the micrometric scale. In the kidney of both rats and humans, it revealed glomerular structures in 2D but was severely limited by planar projection. In this work, we aim to extend sULM in 3D to image the whole organ and in order to perform an accurate characterization of the entire kidney structure. The extension of sULM into the 3D domain allows better localization and more robust tracking. The 3D metrics of velocity and pathway angular shift made glomerular mask possible. This approach facilitated the quantification of glomerular physiological parameter such as an interior traveled distance of approximately 7.5 ±0.6 microns within the glomerulus. This study introduces a technique that characterize the kidney physiology which can serve as a method to facilite pathology assessment. Furthermore, its potential for clinical relevance could serve as a bridge between research and practical application, leading to innovative diagnostics and improved patient care.

Advanced deep-tissue imaging and manipulation enabled by biliverdin reductase knockout.

We developed near-infrared (NIR) photoacoustic and fluorescence probes, as well as optogenetic tools from bacteriophytochromes, and enhanced their performance using biliverdin reductase-A knock-out model (Blvra-/-). Blvra-/- elevates endogenous heme-derived biliverdin chromophore for bacteriophytochrome-derived NIR constructs. Consequently, light-controlled transcription with IsPadC-based optogenetic tool improved up to 25-fold compared to wild-type cells, with 100-fold activation in Blvra-/- neurons. In vivo , light-induced insulin production in Blvra-/- reduced blood glucose in diabetes by ∼60%, indicating high potential for optogenetic therapy. Using 3D photoacoustic, ultrasound, and two-photon fluorescence imaging, we overcame depth limitations of recording NIR probes. We achieved simultaneous photoacoustic imaging of DrBphP in neurons and super-resolution ultrasound localization microscopy of blood vessels ∼7 mm deep in the brain, with intact scalp and skull. Two-photon microscopy provided cell-level resolution of miRFP720-expressing neurons ∼2.2 mm deep. Blvra-/- significantly enhances efficacy of biliverdin-dependent NIR systems, making it promising platform for interrogation and manipulation of biological processes.

Capillary-scale Microvessel Imaging with High-frequency Ultrasound Localization Microscopy in Mouse Brain.

Ultrasound localization microscopy is a super-resolution vascular imaging technique which has garnered substantial interest as a tool for small animal neuroimaging, neuroscience research, and the characterization of vascular pathologies. In the pursuit of increasingly high-fidelity reconstructions of microvasculature, there remains several outstanding questions concerning this sub-diffraction imaging technology, including the accurate reconstruction of microvessels approaching the capillary scale and the pragmatic challenges associated with long data acquisition times. In the context of small animal neurovascular imaging, we posit that increasing the ultrasound imaging frequency is a straightforward approach to enable higher concentrations of microbubble contrast agents, thus increasing the likelihood of microvascular/capillary mapping and decreasing the imaging duration. We demonstrate that higher frequency imaging results in improved ULM fidelity and more efficient microbubble localization due to a smaller microbubble point-spread function that is easier to localize, and which can achieve a higher localizable concentration within the same unit volume of tissue. A select example of in vivo capillary-level vascular reconstruction is demonstrated for the highest frequency imaging probe, which has substantial implications for neuroscientists investigating microvascular function in disease states, regulation, and brain development. High frequency ULM yielding a spatial resolution of 7.1μm, as measured by Fourier ring correlation, throughout the entire depth of the brain, highlighting this technology as a highly relevant tool for neuroimaging research.

TC-ULM: Temporal Correlation-based Ultrasound Localization Microscopy by Deep Learning

Abstract Ultrasound Localization Microscopy (ULM) transcends the limitations of acoustic diffraction by pinpointing minuscule microbubbles (MBs), enabling detailed visualization of microvasculature at resolutions finer than the wavelength. Current approaches in ULM, inclusive of both conventional and deep learning techniques, predominantly analyze data from the ultrasound frame plane (x-y). This leaves the valuable information in the time dimension (t), especially between consecutive frames, largely untapped. This paper introduces a novel approach, Temporal Correlation ULM (TC-ULM), which harnesses temporal correlations within the y-t plane for enhanced ULM imaging. By extracting the variations of MBs over time through a sophisticated deep learning model, TC-ULM effectively reconstructs ULM images with superior quality. Empirical evidence from numerical simulations and in vivo experiments reveals that MBs exhibit a discernible pattern of continuity and local linkage across adjacent frames in the y-t plane. This key finding allows for a significant reduction in the number of frames needed for ULM, without compromising the spatial resolution. Thus, TC-ULM emerges as an innovative technique that not only retains image quality but also potentially expedites the ULM imaging process, marking a notable shift from traditional x-y plane processing methods.

RF-ULM: Ultrasound Localization Microscopy Learned From Radio-Frequency Wavefronts.

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across a series of beamformed frames. However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) channel data, while its implications for localization remain largely unexplored. The rich contextual information embedded within RF wavefronts, including their hyperbolic shape and phase, offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. To fully exploit this data, we propose to directly localize scatterers in RF channel data. Our approach involves a custom super-resolution DNN using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block for reliable and accurate wavefront localization. Additionally, we introduce a geometric point transformation that facilitates seamless mapping to the B-mode coordinate space. To understand the impact of beamforming on ULM, we validate the effectiveness of our method by conducting an extensive comparison with State-Of-The-Art (SOTA) techniques. We present the inaugural in vivo results from a wavefront-localizing DNN, highlighting its real-world practicality. Our findings show that RF-ULM bridges the domain shift between synthetic and real datasets, offering a considerable advantage in terms of precision and complexity. To enable the broader research community to benefit from our findings, our code and the associated SOTA methods are made available at https://github.com/hahnec/rf-ulm.

Ultrasound localization microscopy based microvasculature evaluation of spinal cord injury with varying severities

Abstract The vasculature plays an important role in spinal cord function. Traumatic spinal cord injury (SCI) often results in a vascular disruption after primary insult, following secondary injury in neighbouring tissue. However, standard angiography methods such as digital subtraction angiography (DSA) still have difficulties in microvasculature imaging, especially for spinal cord with small size and non-rigid motion. The ultrasound localization microscopy (ULM) disrupts the compromise between penetration and resolution and provides super-resolution images of microvasculature. In this study, the applicability of the ULM for in vivo SCI evaluation was investigated on rat models with varying severities (different compression time durations correspond to mild and severe injury). Ultrasonic parameters including vessel saturation and blood velocity were measured via ULM. Basso, Beattie, Bresnahan (BBB) locomotion test was applied for injury severity validation and comparison with ultrasonic measurements.

An adaptive spatiotemporal filter for ultrasound localization microscopy based on density canopy clustering

Ultrasound Localization Microscopy (ULM) facilitates structural and hemodynamic imaging of microvessels with a resolution of tens of micrometers. In ULM, the extraction of effective microbubble signals is crucial for image quality. Singular Value Decomposition (SVD) is currently the most prevalent method for microbubble signal extraction in ULM. Most existing ULM studies employ a fixed SVD filter threshold using empirical values which will lead to imaging quality degradation due to the insufficient separation of blood signals. In this study, we propose an adaptive and non-threshold SVD filter based on canopy-density clustering, termed DCC-SVD. This filter automatically classifies the components of the SVD based on the density of their spatiotemporal features, eliminating the need for parameter selection. In in vitro tube phantom, DCC-SVD demonstrated its ability to adaptive separation of blood and bubble signal at varying microbubble concentrations and flow rates. We compared the proposed DCC-SVD method with the Block-match 3D (BM3D) filter and a classical adaptive method called spatial similarity matrix (SSM), using concentration-variable in vivo rat brain data, as well as open-source rat kidney and mouse tumor datasets. The proposed DCC-SVD improved the global spatial resolution by approximately 4 μm from 30.39 μm to 26.02 μm. It also captured vessel structure absent in images obtained by other methods and yielded a smoother vessel intensity profile, making it a promising spatiotemporal filter for ULM imaging.

ULTRA-SR Challenge: Assessment of Ultrasound Localization and TRacking Algorithms for Super-Resolution Imaging.

With the widespread interest and uptake of super-resolution ultrasound (SRUS) through localization and tracking of microbubbles, also known as ultrasound localization microscopy (ULM), many localization and tracking algorithms have been developed. ULM can image many centimeters into tissue in-vivo and track microvascular flow non-invasively with sub-diffraction resolution. In a significant community effort, we organized a challenge, Ultrasound Localization and TRacking Algorithms for Super-Resolution (ULTRA-SR). The aims of this paper are threefold: to describe the challenge organization, data generation, and winning algorithms; to present the metrics and methods for evaluating challenge entrants; and to report results and findings of the evaluation. Realistic ultrasound datasets containing microvascular flow for different clinical ultrasound frequencies were simulated, using vascular flow physics, acoustic field simulation and nonlinear bubble dynamics simulation. Based on these datasets, 38 submissions from 24 research groups were evaluated against ground truth using an evaluation framework with six metrics, three for localization and three for tracking. In-vivo mouse brain and human lymph node data were also provided, and performance assessed by an expert panel. Winning algorithms are described and discussed. The publicly available data with ground truth and the defined metrics for both localization and tracking present a valuable resource for researchers to benchmark algorithms and software, identify optimized methods/software for their data, and provide insight into the current limits of the field. In conclusion, Ultra-SR challenge has provided benchmarking data and tools as well as direct comparison and insights for a number of the state-of-the art localization and tracking algorithms.

Nonlinear simulation for contrast ultrasound imaging.

Contrast ultrasound (CUS) has received much interest because of its sensitivity enhancement for blood flow imaging. However, there is still a lack of nonlinear simulation method for CUS, as conventional simulators cannot deal with the microbubble acoustic nonlinearity. In this paper, a nonlinear simulation method of CUS is developed based on a combination strategy of the k-space pseudospectral method and Rayleigh-Plesset Marmottant model. Different contrast pulse sequence strategies as well as the radial modulation imaging are simulated and compared using the proposed method. For blood flow imaging, simulations under different scenarios such as power Doppler and ultrasound localization microscopy are also carried out. Furthermore, a face-to-face comparison is performed between simulations and phantom experiments to validate the proposed method.

In vivo ultrasound localization microscopy for high-density microbubbles

Ultrasound Localization Microscopy (ULM) surpasses the constraints imposed by acoustic diffraction, achieving sub-wavelength resolution visualization of microvasculature through the precise localization of minute microbubbles (MBs). Nonetheless, the analysis of densely populated regions with overlapping MB point spread responses introduces significant localization errors, limiting the use of technique to low-concentration conditions. This raises a trade-off issue between localization efficiency and MB density. In this work, we present a new deep learning framework that combines Transformer and U-Net architectures, termed ULM-TransUNet. As a non-linear model, it is able to learn the complex data patterns of overlapping MBs in dense conditions for accurate localization. To evaluate the performance of ULM-TransUNet, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-TransUNet achieves high-quality ULM imaging, with improvements of 21.93 % in detection rate, 17.36 % in detection precision, and 20.53 % in detection sensitivity, compared to previous state-of-the-art deep learning (DL) method (e.g., ULM-UNet). For the in vivo experiments, ULM-TransUNet achieves the highest spatial resolution (9.4 μm) and rapid inference speed (26.04 ms/frame). Furthermore, it consistently detects more small vessels and resolves closely spaced vessels more effectively. The outcomes of this work imply that ULM-TransUNet can potentially enhance the microvascular imaging performance on high-density MB conditions.

Efficient Microbubble Trajectory Tracking in Ultrasound Localization Microscopy Using a Gated Recurrent Unit-Based Multitasking Temporal Neural Network.

Ultrasound Localization Microscopy (ULM), an emerging medical imaging technique, effectively resolves the classical trade-off between resolution and penetration inherent in traditional ultrasound imaging, opening up new avenues for noninvasive observation of the microvascular system. However, traditional microbubble tracking methods encounter various practical challenges. These methods typically entail multiple processing stages, including intricate steps like pairwise correlation and trajectory optimization, rendering real-time applications unfeasible. Furthermore, existing deep learning-based tracking techniques neglect the temporal aspects of microbubble motion, leading to ineffective modeling of their dynamic behavior. To address these limitations, this study introduces a novel approach called the Gated Recurrent Unit (GRU)-based Multitasking Temporal Neural Network (GRU-MT). GRU-MT is designed to simultaneously handle microbubble trajectory tracking and trajectory optimization tasks. Additionally, we enhance the nonlinear motion model initially proposed by Piepenbrock et al. to better encapsulate the nonlinear motion characteristics of microbubbles, thereby improving trajectory tracking accuracy. In this study, we perform a series of experiments involving network layer substitutions to systematically evaluate the performance of various temporal neural networks, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), GRU, Transformer, and its bidirectional counterparts, on the microbubble trajectory tracking task. Concurrently, the proposed method undergoes qualitative and quantitative comparisons with traditional microbubble tracking techniques. The experimental results demonstrate that GRU-MT exhibits superior nonlinear modeling capabilities and robustness, both in simulation and in vivo dataset. Additionally, it achieves reduced trajectory tracking errors in shorter time intervals, underscoring its potential for efficient microbubble trajectory tracking. Model code is open-sourced at https://github.com/zyt-Lib/GRU-MT.

Dynamic Ultrasound Localization Microscopy Without ECG-Gating

ObjectiveDynamic Ultrasound Localization Microscopy (DULM) has first been developed for non-invasive Pulsatility measurements in the rodent brain. DULM relies on the localization and tracking of microbubbles (MBs) injected into the bloodstream, to obtain highly resolved velocity and density cine-loops. Previous DULM techniques required ECG-gating, limiting its application to specific datasets, and increasing acquisition time. The objective of this study is to eliminate the need for ECG-gating in DULM experiments by introducing a motion-matching method for time registration. MethodsWe developed a motion-matching algorithm based on tissue Doppler that leverages the cyclic tissue motion within the brain. Tissue Doppler was estimated for each group of frames in the acquisitions, at multiple locations identified as local maxima in the skin above the skull. Subsequently, each group of frames was time-registered to a reference group by delaying it based on the maximum correlation value between their respective tissue Doppler signals. This synchronization ensured that each group of frames aligned with the brain tissue motion of the reference group, and consequently, with its cardiac cycle. As a result, velocities of MBs could be averaged to retrieve flow velocity variations over time. ResultsInitially validated in ECG-gated acquisitions in a rat model (n = 1), the proposed method was successfully applied in a mice model in 2D (n = 3) and in a feline model in 3D (n = 1). Performing time-registration with the proposed motion-matching method or by using ECG-gating leads to similar results. For the first time, dynamic velocity and density cine-loops were extracted without the need for any information on the animal ECG, and complex dynamic markers such as the Pulsatility index were estimated. ConclusionResults suggest that DULM can be performed without external gating, enabling the use of DULM on any ULM dataset where enough MBs are detectable. Time registration by motion-matching represents a significant advancement in DULM techniques, making DULM more accessible by simplifying its experimental complexity.

Novel intravital approaches to quantify deep vascular structure and perfusion in the aging mouse brain using ultrasound localization microscopy (ULM)

Intra-vital visualization of deep cerebrovascular structures and blood flow in the aging brain has been a difficult challenge in the field of neurovascular research, especially when considering the key role played by the cerebrovasculature in the pathogenesis of both vascular cognitive impairment and dementia (VCID) and Alzheimer’s disease (AD). Traditional imaging methods face difficulties with the thicker skull of older brains, making high-resolution imaging and cerebral blood flow (CBF) assessment challenging. However, functional ultrasound (fUS) imaging, an emerging non-invasive technique, provides real-time CBF insights with notable spatial-temporal resolution. This study introduces an enhanced longitudinal fUS method for aging brains. Using elderly (24-month C57BL/6) mice, we detail replacing the skull with a polymethylpentene window for consistent fUS imaging over extended periods. Ultrasound localization mapping (ULM), involving the injection of a microbubble (<<10 μm) suspension allows for recording of high-resolution microvascular vessels and flows. ULM relies on the localization and tracking of single circulating microbubbles in the blood flow. A FIJI-based analysis interprets these high-quality ULM visuals. Testing on older mouse brains, our method successfully unveils intricate vascular specifics even in-depth, showcasing its utility for longitudinal studies that require ongoing evaluations of CBF and vascular aspects in aging-focused research.

Transthoracic ultrasound localization microscopy of myocardial vasculature in patients

Myocardial microvasculature and haemodynamics are indicative of potential microvascular diseases for patients with symptoms of coronary heart disease in the absence of obstructive coronary arteries. However, imaging microvascular structure and flow within the myocardium is challenging owing to the small size of the vessels and the constant movement of the patient’s heart. Here we show the feasibility of transthoracic ultrasound localization microscopy for imaging myocardial microvasculature and haemodynamics in explanted pig hearts and in patients in vivo. Through a customized data-acquisition and processing pipeline with a cardiac phased-array probe, we leveraged motion correction and tracking to reconstruct the dynamics of microcirculation. For four patients, two of whom had impaired myocardial function, we obtained super-resolution images of myocardial vascular structure and flow using data acquired within a breath hold. Myocardial ultrasound localization microscopy may facilitate the understanding of myocardial microcirculation and the management of patients with cardiac microvascular diseases.

Time Efficient Ultrasound Localization Microscopy Based on A Novel Radial Basis Function 2D Interpolation.

Ultrasound localization microscopy (ULM) allows for the generation of super-resolved (SR) images of the vasculature by precisely localizing intravenously injected microbubbles. Although SR images may be useful for diagnosing and treating patients, their use in the clinical context is limited by the need for prolonged acquisition times and high frame rates. The primary goal of our study is to relax the requirement of high frame rates to obtain SR images. To this end, we propose a new time-efficient ULM (TEULM) pipeline built on a cutting-edge interpolation method. More specifically, we suggest employing Radial Basis Functions (RBFs) as interpolators to estimate the missing values in the 2-dimensional (2D) spatio-temporal structures. To evaluate this strategy, we first mimic the data acquisition at a reduced frame rate by applying a down-sampling (DS = 2, 4, 8, and 10) factor to high frame rate ULM data. Then, we up-sample the data to the original frame rate using the suggested interpolation to reconstruct the missing frames. Finally, using both the original high frame rate data and the interpolated one, we reconstruct SR images using the ULM framework steps. We evaluate the proposed TEULM using four in vivo datasets, a Rat brain (dataset A), a Rat kidney (dataset B), a Rat tumor (dataset C) and a Rat brain bolus (dataset D), interpolating at the in-phase and quadrature (IQ) level. Results demonstrate the effectiveness of TEULM in recovering vascular structures, even at a DS rate of 10 (corresponding to a frame rate of sub-100Hz). In conclusion, the proposed technique is successful in reconstructing accurate SR images while requiring frame rates of one order of magnitude lower than standard ULM.

Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy

Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.

Inside Front Cover: Super‐resolution ultrasound localization microscopy for the non‐invasive imaging of human testicular microcirculation and its differential diagnosis role in male infertility (View 2/2024)

Inside Front Cover: Super‐resolution ultrasound localization microscopy for the non‐invasive imaging of human testicular microcirculation and its differential diagnosis role in male infertility (View 2/2024)

Abstract 4164: Hybrid photoacoustic imaging and fast ultrasound localization microscopy to probe the tumor microenvironment

Abstract Photoacoustic (PA) imaging can map the physiological conditions of tissues and track the biodistribution of contrast agents. Ultrasound localization microscopy (ULM) with microbubbles provides deep-tissue super-resolution blood vessel images and blood velocity maps. The integration of these techniques offers a potential tool for oncological applications, such as visualizing tumor hypoxia and vasculature. Additionally, because PA imaging and ULM can share the same ultrasound detector and data-acquisition system, PA images can seamlessly fuse with ULM images without requiring post co-registration processing. However, the acquisition time of ULM is considerably longer (several minutes per frame) compared to PA imaging (10 milliseconds to 100 milliseconds per frame), limiting the simultaneous acquisition of the two imaging modalities. To address this issue, we developed a deep learning model based on U-Net to accelerate the frame rate of ULM using information from sparse microbubble locations and power Doppler images. By enabling fast ULM acquisition, we developed an interleaved PA/fast ULM imaging technique to simultaneously capture photoacoustic and ULM images. Our experiments demonstrated up to a 16 times improvement in acquisition speed with U-Net, utilizing in vivo mouse brain and skin tumor data. The hybrid imaging sequence achieved super-resolution vascular and tissue oxygenation imaging with less than 2 seconds of acquisition time per frame in vivo. The implementation of this rapid dual imaging scheme enables the longitudinal monitoring of 3D tumor microvasculature and hypoxia conditions using a common linear ultrasound transducer. We monitored tumor growth of epidermoid carcinoma (A431) in a mouse model from Day 7 to Day 17 of tumor implantation, and the results reveal a significant decrease in tumor vasculature density and oxygen saturation as the tumor grew larger over time. The results indicate a positive relationship between oxygen saturation and vessel density. Our findings showcase the potential of this innovative technique for advancing deep tissue, high-resolution, multi-biological parameters imaging in oncological research. Citation Format: Shensheng Zhao, Sayantani Basu, Roy H. Campbell, Yang Zhao, Yun-Sheng Chen. Hybrid photoacoustic imaging and fast ultrasound localization microscopy to probe the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4164.

Vasa vasorum interna in the carotid wall of active forms of Takayasu arteritis evidenced by ultrasound localization microscopy.

Introduction: Takayasu arteritis (TA) is associated with microvascularization of the wall of large arteries and is related to inflammation. Ultrasound localization microscopy (ULM), combining ultrafast ultrasound imaging with microbubble (MB) injection, can track the path of MBs within the arterial wall and thus provide imaging of the vasa vasorum. From the analysis of MB tracks in the common carotid arteries of patients with active TA, we report the presence of microvessels in connection with the carotid lumen (i.e., vasa vasorum interna [VVI]). Methods: ULM maps were obtained on five patients with active disease in the observational single-center series of the TAK-UF study. MB tracks connected to the carotid lumen were automatically identified, allowing the reconstruction of VVI. Results: MB tracking allows us to observe a microvascular network on the inner part of the wall, with some vessels in communication with the carotid lumen. This type of vessel was identified in all patients with active TA (n = 5) with a median of 2.2 [1.1-3.0] vessels per acquisition (2D longitudinal view of 3 cm of the common carotid artery). The blood flow within these vessels is mainly centrifugal; that is, toward the adventitia (88% [54-100] of MB tracks with flow directed to the outer part of the wall). Conclusion: VVI are present in humans in the case of active TA and emphasize the involvement of the intima in the pathological process. ClinicalTrials.gov Identifier: NCT03956394.

Monitoring of neoadjuvant chemotherapy response of breast cancer with ultrasound localization microscopy

Neoadjuvant chemotherapy (NAC) is the standard treatment for high-risk breast cancer, but less than 30% of patients exhibit a complete response, necessitating better preselection and monitoring. Ultrasound localization microscopy (ULM) offers potential improvements by providing super-resolution images of vasculature and functional features. To assess its efficacy, we applied ULM to breast cancer patients undergoing NAC, comparing their responses based on ULM features. Patients were evaluated before their first, second, and fourth NAC cycles, assessing tumor volume and employing contrast-enhanced ultrasound (CEUS) in split-screen mode. CEUS videos underwent postprocessing, involving MB localization and tracking, and motion compensation. ULM images, derived from tracked MBs, generated parameter maps for analyzing vascular changes and differences between responders and non-responders. Preliminary results of 14 patients revealed that during the first three cycles of NAC, the tumor volume as well as vessel coverage (i.e., ratio of vessel to tumor surface) of responders noticeably dropped. In contrast, non-responders showed only minor tumor size reduction and overall no decrease in coverage. Furthermore, the mean distance to the closest track was noticeably higher in non-responders. These findings, though preliminary, hint at ULM's potential for enhancing preselection and monitoring of NAC patients.