Geometric Ultrasound Localization Microscopy

Contrast-Enhanced Ultra-Sound (CEUS) has become a viable method for non-invasive, dynamic visualization in medical diagnostics, yet Ultrasound Localization Microscopy (ULM) has enabled a revolutionary breakthrough by offering ten times higher resolution. To date, Delay-And-Sum (DAS) beamformers are used to render ULM frames, ultimately determining the image resolution capability. To take full advantage of ULM, this study questions whether beamforming is the most effective processing step for ULM, suggesting an alternative approach that relies solely on Time-Difference-of-Arrival (TDoA) information. To this end, a novel geometric framework for microbubble localization via ellipse intersections is proposed to overcome existing beamforming limitations. We present a benchmark comparison based on a public dataset for which our geometric ULM outperforms existing baseline methods in terms of accuracy and robustness while only utilizing a portion of the available transducer data

Effect of agent concentration in ultrasound super- resolution imaging at clinically low frequency

Imaging of microvasculature can be valuable for the diagnosis and treatment monitoring of cancer and other diseases. Ultrasound has the potential due to its excellent spatial and temporal resolution. Super-resolution ultrasound imaging using contrast enhanced ultrasound localization microscopy is able to visualize microvasculature beyond the wave diffraction limit. The microbubble based super-resolution depends on controlling the bubble concentration, which needs to be low enough to locate isolated microbubbles. However too low a concentration will prolong the data acquisition. The aim of the thesis was to evaluate the impact of microbubble concentration on super-resolution ultrasound imaging, and to improve the signal processing in super-resolution imaging. First, various concentrations of microbubble contrast agents (6×10^3 to 1.5×10^6particles/ml) were injected into a 200 microns cross-tube flow phantom. The experimental results show that the concentration affects the resolution of the cross-tube images. When the concentration is lower than the 1.5×10^5particles/ml, two tubes in a selected region of interest near the intersection that are 370 microns apart can be separated, which is close to the expected distance (390 microns). Second, the comprehensive effects of data acquisition time and concentration on imaging resolution were studied. The preferred range of concentration was determined between 1.5×10^4 to 6×10^4particles/ml. This result can be used to better inform data acquisition in the further research. Third, a weight adaptive denoising method based on morphology was developed to remove noises which were produced in the step of microbubble detection at the preferred range of concentration. Therefore, the resolution and the accuracy of the velocity map were further improved.Open Acces

Medical Imaging of Microrobots: Toward In Vivo Applications

Medical microrobots (MRs) have been demonstrated for a variety of non-invasive biomedical applications, such as tissue engineering, drug delivery, and assisted fertilization, among others. However, most of these demonstrations have been carried out in in vitro settings and under optical microscopy, being significantly different from the clinical practice. Thus, medical imaging techniques are required for localizing and tracking such tiny therapeutic machines when used in medical-relevant applications. This review aims at analyzing the state of the art of microrobots imaging by critically discussing the potentialities and limitations of the techniques employed in this field. Moreover, the physics and the working principle behind each analyzed imaging strategy, the spatiotemporal resolution, and the penetration depth are thoroughly discussed. The paper deals with the suitability of each imaging technique for tracking single or swarms of MRs and discusses the scenarios where contrast or imaging agent's inclusion is required, either to absorb, emit, or reflect a determined physical signal detected by an external system. Finally, the review highlights the existing challenges and perspective solutions which could be promising for future in vivo applications

Improving Ultrasound Microvascular Imaging with Superharmonic Imaging and Machine Learning

Biomedical ultrasound imaging devices are safe, portable, relatively inexpensive, and produce high-resolution images of soft tissues in real time, making ultrasound the ideal modality for a variety of point-of-care applications. While ultrasound is useful for noninvasively locating suspicious lesions in organs such as the breast and prostate, a tissue biopsy is required to make an accurate diagnosis in most cases. The majority of biopsies are benign, and the procedures are typically invasive and uncomfortable (e.g., a typical prostate biospy involves the removal of 12 tissue samples). Cancer cells are abnormal and divide uncontrollably, causing the formation of a tumor if the cancer cells are located in soft tissue or bone. This rapid growth is accompanied by a considerable increase in angiogenesis, or the formation of new blood vessels. The normal balance of pro- and anti-angiogenic factors is disrupted, resulting in a dense and disorderly network of vessels. The effect is so pronounced that this phenomenon has been described as one of the "hallmarks of cancer", and many clinical and preclinical studies have suggested that the recruitment of new blood vessels precedes the appearance of solid tumors. For these reasons, angiogeneis is an attractive target for both the treatment and early detection of cancer. This knowledge is one of the motivations behind the research of ultrasound vascular imaging methods. Recently, two notable technologies have been developed: acoustic angiography and ultrasound localization microscopy. Both of these techniques are capable of noninvasively imaging the small blood vessels associated with tumor growth, and a variety of preclinical studies have demonstrated that these images can be used to detect small tumors and monitor response to treatment. However, a number of roadblocks remain for cancer screening in humans with ultrasound imaging. Specifically, these imaging technologies require an excellent signal to noise ratio, and the current methods for processing each image is quite labor intensive. In this dissertation, we explore four new ideas with the aim of improving the existing methods for ultrasound microvascular imaging. First, we explore superharmonic imaging for ultrasound localization microscopy, and show that this approach is robust to physiological motion and improves signal quality for small blood vessels. Then, we adapt this methodology to achieve super-resolution acoustic molecular imaging in vivo for the first time, paving the way for a new mode of quantitative cancer imaging. Afterwards, we apply deep learning to improve the detection of contrast agents for ultrasound localization microscopy, improving resolution in the presence of noise and image artifacts. Finally, we train convolutional neural networks to accurately detect tumors in acoustic angiography images in real time.Doctor of Philosoph

Echo Particle Image/Tracking Velocimetry: Technical Development and In Vivo Applications in Cardiovascular and Cerebrovascular Flows

Contrast-enhanced ultrasound (CEUS) imaging utilizes intravascular echogenic microbubbles (1-5μm in diameter) to visualize the blood flow in various organs. In this dissertation, we develop and implement techniques for analyzing the motions of microbubbles to quantify cardiovascular and cerebrovascular flows. Obtaining accurate bubble center locations from noisy CEUS images is a primary challenge. Since the bubble trace is typically modeled as a point scatter convolved with a point spread function (PSF), techniques including blind deconvolution, supervised, and self-supervised learning are introduced and calibrated for identifying the PSF and locating the bubble center. The enhanced CEUS images enable echo particle image velocimetry (echo-PIV) for characterizing 2D cardiovascular flows, and the global-optimized Kalman filter-based echo particle tracking velocimetry (echo-PTV) for determining bubble trajectories which are subsequently used for mapping the cerebral and ocular microcirculation at a spatial resolution of 20μm. These techniques are applied to two applications. First, echo-PIV is used for monitoring the aortic root flow in an adult pig undergoing veno-arterial extracorporeal membrane oxygenation (VA-ECMO), a life support technology whose parameters can be optimized based on the aortic root hemodynamics. Phase-averaged and instantaneous flow fields show that, for the pig with severe myocardial ischemia, the cardiac ejection velocity, velocity-time integral, and mean arterial pressure (MAP) reach their peak at an ECMO flow rate of 3.0L/min, indicating an optimal flow rate that provides adequate support. Second, we investigate non-invasive methods for estimating intracranial pressure (ICP), a critical parameter for hydrocephalus patients that cannot be invasively measured safely. Echo-PTV is used to map cerebral and ocular microcirculation of pediatric hydrocephalus porcine models for inferring ICP. Results show that accounting for pulse pressure, highly correlated relationships between ICP and cortical microcirculation density are obtained with correlation coefficients beyond 0.85. For cerebral ischemia, nondimensionalized cortical micro-perfusion decreases by an order of magnitude when the ICP exceeds 50% of MAP. Moreover, retinal microcirculation also shows a highly correlated relationship with ICP when accounting for pulse pressure. These findings suggest that CEUS-based microcirculation measurement is a plausible noninvasive method for evaluating the ICP and detecting brain ischemia