Jemma Brown

3-D In Vitro Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles

The structure of microvasculature cannot be resolved using standard clinical ultrasound (US) imaging frequencies due to the fundamental diffraction limit of US waves. In this work, we use a standard clinical US system to perform in vivo sub-diffraction imaging on a CD1, female mouse aged eight weeks by localizing isolated US signals from microbubbles flowing within the ear microvasculature, and compare our results to optical microscopy. Furthermore, we develop a new technique to map blood velocity at super-resolution by tracking individual bubbles through the vasculature. Resolution is improved from a measured lateral and axial resolution of 112 μm and 94 μm respectively in original US data, to super-resolved images of microvasculature where vessel features as fine as 19 μm are clearly visualized. Velocity maps clearly distinguish opposing flow direction and separated speed distributions in adjacent vessels, thereby enabling further differentiation between vessels otherwise not spatially separated in the image. This technique overcomes the diffraction limit to provide a noninvasive means of imaging the microvasculature at super-resolution, to depths of many centimeters. In the future, this method could noninvasively image pathological or therapeutic changes in the microvasculature at centimeter depths in vivo.

Cone beam CT in dental practice

Cone Beam Computed Tomography is a relatively new three-dimensional imaging technology, which has been specifically developed for imaging of the teeth and jaws. The aim of this paper is to acquaint the dental team with various forms of this technology and its potential applications. An understanding of the underlying principles will allow the users of this technology to tailor the imaging protocol to the patients individual needs to achieve appropriate imaging at the lowest radiation dose.

Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Acoustic super-resolution imaging has allowed the visualization of microvascular structure and flow beyond the diffraction limit using standard clinical ultrasound systems through the localization of many spatially isolated microbubble signals. The determination of each microbubble position is typically performed by calculating the centroid, finding a local maximum, or finding the peak of a 2-D Gaussian function fit to the signal. However, the backscattered signal from a microbubble depends not only on diffraction characteristics of the waveform, but also on the microbubble behavior in the acoustic field. Here, we propose a new axial localization method by identifying the onset of the backscattered signal. We compare the accuracy of localization methods using in vitro experiments performed at 7-cm depth and 2.3-MHz center frequency. We corroborate these findings with simulation results based on the Marmottant model. We show experimentally and in simulations that detecting the onset of the returning signal provides considerably increased accuracy for super-resolution. Resulting experimental cross-sectional profiles in super-resolution images demonstrate at least 5.8 times improvement in contrast ratio and more than 1.8 times reduction in spatial spread (provided by 90% of the localizations) for the onset method over centroiding, peak detection, and 2-D Gaussian fitting methods. Simulations estimate that these latter methods could create errors in relative bubble positions as high as

Two Stage Sub-Wavelength Motion Correction in Human Microvasculature for CEUS Imaging

900~mu text{m}

Acoustic wave sparsely-activated localization microscopy (AWSALM): super-resolution ultrasound imaging using acoustic activation and deactivation of nanodroplets

at these experimental settings, while the onset method reduced the interquartile range of these errors by a factor of over 2.2. Detecting the signal onset is, therefore, expected to considerably improve the accuracy of super-resolution.

Localisation of multiple non-isolated microbubbles with frequency decomposition in super-resolution imaging

The structure of microvasculature cannot be resolved using clinical B-mode or contrast-enhanced ultrasound (CEUS) imaging due to the fundamental diffraction limit at clinical ultrasound frequencies. It is possible to overcome this resolution limitation by localizing individual microbubbles through multiple frames and forming a super-resolved image. However, ultrasound super-resolution creates its unique problems since the structures to be imaged are on the order of 10s of μm. Tissue movement much larger than 10 μm is common in clinical imaging, which can significantly reduce the accuracy of super-resolution images created from microbubble locations gathered through hundreds of frames. This study investigated an existing motion estimation algorithm from magnetic resonance imaging for ultrasound super-resolution imaging. Its correction accuracy is evaluated using simulations with increasing complexity of motion. Feasibility of the method for ultrasound super-resolution in vivo is demonstrated on clinical ultrasound images. For a chosen microvessel, the super-resolution image without motion correction achieved a sub-wavelength resolution; however after the application of proposed two-stage motion correction method the size of the vessel was reduced to half.

Microbubble localization errors in ultrasonic super-resolution imaging

Photo-activated localization microscopy (PALM) has revolutionized the field of fluorescence microscopy by breaking the diffraction limit in spatial resolution. In this study, “acoustic wave sparsely activated localization microscopy (AWSALM),” an acoustic counterpart of PALM, is developed to super-resolve structures which cannot be resolved by conventional B-mode imaging. AWSALM utilizes acoustic waves to sparsely and stochastically activate decafluorobutane nanodroplets by acoustic vaporization and to simultaneously deactivate the existing vaporized nanodroplets via acoustic destruction. In this method, activation, imaging, and deactivation are all performed using acoustic waves. Experimental results show that sub-wavelength micro-structures not resolvable by standard B-mode ultrasound images can be separated by AWSALM. This technique is flow independent and does not require a low concentration of contrast agents, as is required by current ultrasound super resolution techniques. Acoustic activation and deactivation can be controlled by adjusting the acoustic pressure, which remains well within the FDA approved safety range. In conclusion, this study shows the promise of a flow and contrast agent concentration independent super-resolution ultrasound technique which has potential to be faster and go beyond vascular imaging.

Automated super-resolution image processing in ultrasound using machine learning

Sub-diffraction imaging, also known as ultrasound localization microscopy, is a novel method that can overcome the fundamental diffraction limit by localizing spatially isolated microbubbles. This method requires the use of a low concentration of microbubbles to ensure that they are spatially isolated. For in vivo microvascular imaging, especially for cancer tissue with high microvascular density, spatial isolation cannot be always achieved, since vessels are close to each other and the speed of flow is slow. This study proposes a frequency decomposition method that uses the polydisperse nature of commercial contrast agents to separate spatially non-isolated microbubbles with different acoustic signatures. Zero-phase filters were applied to ensure that there is no relative phase delay between decomposed signals. Results showed that a super-resolution image after frequency decomposition can be generated with three times lower number of acquisitions without sacrificing image quality.

Super-resolution ultrasound to aid testicular lesion characterisation

Recently, acoustic super-resolution (SR) imaging has allowed visualization of microvascular structure and flow beyond the diffraction limit through the localization of many isolated microbubble signals. Each bubble position is typically estimated by calculating the centroid, finding a local maximum, or finding the peak of a 2-D Gaussian function fit. However, the backscattered signal from a microbubble depends not only on diffraction characteristics of the waveform, but also on the bubble behavior in the acoustic field, which if not accounted for, may cause localization errors.

Ultrasound super-resolution with microbubble contrast agents

Clinical implementation of super-resolution (SR) ultrasound imaging requires accurate single microbubble detection, and would benefit greatly from automation in order to minimize time requirements and user dependence. We present a machine learning based post-processing tool for the application of SR ultrasound imaging, where we utilize superpixelation and support vector machines (SVMs) for foreground detection and signal differentiation.