Development and Characterization of Hemoglobin Microbubbles for Acoustic Blood Oxygen Level Dependent Imaging.

Oxygen levels in tissues and organs are crucial for their normal functioning, and approaches to monitor them non-invasively have wide biological and clinical applications. In this study, we developed a method of acoustically detecting oxygenation using contrast-enhanced ultrasound (CEUS) imaging. Our approach involved the use of specially designed hemoglobin-based microbubbles (HbMBs) that reversibly bind to oxygen and alter the state-dependent acoustic response. We confirmed that the bioactivity of hemoglobin remained intact after the microbubble shell was formed, and we did not observe any significant loss of heme. We conducted passive cavitation detection (PCD) experiments to confirm whether the acoustic properties of HbMBs vary based on the level of oxygen present. The experiments involved driving the HbMBs with a 1.1 MHz focused ultrasound transducer. Through the PCD data collected, we observed significant differences in the subharmonic and harmonic responses of the HbMBs when exposed to an oxygen-rich environment versus an oxygen-depleted one. We used a programmable ultrasound system to capture high-frame rate B mode videos of HbMBs in both oxy and deoxy conditions at the same time in a two-chambered flow phantom and observed that the mean pixel intensity of deoxygenated HbMB was greater than in the oxygenated state using B-mode imaging. Finally, we demonstrated that HbMBs can circulate in vivo and are detectable by a clinical ultrasound scanner. To summarize, our results indicate that CEUS imaging with HbMB has the potential to detect changes in tissue oxygenation and could be a valuable tool for clinical purposes in monitoring regional blood oxygen levels.

Assessment of Transarterial Chemoembolization Using Super-resolution Ultrasound Imaging and a Rat Model of Hepatocellular Carcinoma.

OBJECTIVE: Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer diagnosed annually in 600,000 people worldwide. A common treatment is transarterial chemoembolization (TACE), which interrupts the blood supply of oxygen and nutrients to the tumor mass. The need for repeat TACE treatments may be assessed in the weeks after therapy with contrast-enhanced ultrasound (CEUS) imaging. Although the spatial resolution of traditional CEUS has been restricted by the diffraction limit of ultrasound (US), this physical barrier has been overcome by a recent innovation known as super-resolution US (SRUS) imaging. In short, SRUS enhances the visible details of smaller microvascular structures on the 10 to 100 µm scale, which unlocks a host of new clinical opportunities for US. METHODS: In this study, a rat model of orthotopic HCC is introduced and TACE treatment response (to a doxorubicin-lipiodol emulsion) is assessed using longitudinal SRUS and magnetic resonance imaging (MRI) performed at 0, 7 and 14 d. Animals were euthanized at 14 d for histological analysis of excised tumor tissue and determination of TACE response, that is, control, partial response or complete response. CEUS imaging was performed using a pre-clinical US system (Vevo 3100, FUJIFILM VisualSonics Inc.) equipped with an MX201 linear array transducer. After administration of a microbubble contrast agent (Definity, Lantheus Medical Imaging), a series of CEUS images were collected at each tissue cross-section as the transducer was mechanically stepped at 100 µm increments. SRUS images were formed at each spatial position, and a microvascular density metric was calculated. Microscale computed tomography (microCT, OI/CT, MILabs) was used to confirm TACE procedure success, and tumor size was monitored using a small animal MRI system (BioSpec 3T, Bruker Corp.). RESULTS: Although there were no differences at baseline (p > 0.15), both microvascular density levels and tumor size measures from the complete responder cases at 14 d were considerably lower and smaller, respectively, than those in the partial responder or control group animals. Histological analysis revealed tumor-to-necrosis levels of 8.4%, 51.1% and 100%, for the control, partial responder and complete responder groups, respectively (p < 0.005). CONCLUSION: SRUS imaging is a promising modality for assessing early changes in microvascular networks in response to tissue perfusion-altering interventions such as TACE treatment of HCC.

Faster super-resolution ultrasound imaging with a deep learning model for tissue decluttering and contrast agent localization.

Super-resolution ultrasound (SR-US) imaging allows visualization of microvascular structures as small as tens of micrometers in diameter. However, use in the clinical setting has been impeded in part by ultrasound (US) acquisition times exceeding a breath-hold and by the need for extensive offline computation. Deep learning techniques have been shown to be effective in modeling the two more computationally intensive steps of microbubble (MB) contrast agent detection and localization. Performance gains by deep networks over conventional methods are more than two orders of magnitude and in addition the networks can localize overlapping MBs. The ability to separate overlapping MBs allows use of higher contrast agent concentrations and reduces US image acquisition time. Herein we propose a fully convolutional neural network (CNN) architecture to perform the operations of MB detection as well as localization in a single model. Termed SRUSnet, the network is based on the MobileNetV3 architecture modified for 3-D input data, minimal convergence time, and high-resolution data output using a flexible regression head. Also, we propose to combine linear B-mode US imaging and nonlinear contrast pulse sequencing (CPS) which has been shown to increase MB detection and further reduce the US image acquisition time. The network was trained within silicodata and tested onin vitrodata from a tissue-mimicking flow phantom, and onin vivodata from the rat hind limb (N = 3). Images were collected with a programmable US system (Vantage 256, Verasonics Inc., Kirkland, WA) using an L11-4v linear array transducer. The network exceeded 99.9% detection accuracy onin silicodata. The average localization accuracy was smaller than the resolution of a pixel (i.e.λ/8). The average processing time on a Nvidia GeForce 2080Ti GPU was 64.5 ms for a 128 × 128-pixel image.

Evaluation of Nonlinear Contrast Pulse Sequencing for Use in Super-Resolution Ultrasound Imaging.

The use of super-resolution ultrasound (SR-US) imaging greatly improves visualization of microvascular structures, but clinical adoption is limited by long imaging times. This method depends on detecting and localizing isolated microbubbles (MBs), forcing the use of a dilute contrast agent concentration. Contrast-enhanced ultrasound (CEUS) image acquisition times as long as minutes arise as the localization of thousands of MBs are acquired to form a complete SR-US image. In this article, we explore the use of nonlinear CEUS strategies using nonlinear fundamental contrast pulse sequencing (CPS) to increase the contrast-to-tissue ratio (CTR) and compare MB detection effectiveness to linear B-mode CEUS imaging. The CPS compositions of amplitude modulation (AM), pulse inversion (PI), and a combination of the two (AMPI) were studied. A simulation study combined the Rayleigh-Plesset-Marmottant (RPM) model of MB characteristics and a nonlinear tissue model using the k-Wave toolbox for MATLAB (MathWorks Inc., Natick, MA, USA). Validation was conducted using an in vitro flow phantom and in vivo in the rat hind-limb. Imaging was performed with a programmable US scanner (Vantage 256, Verasonics Inc., Kirkland, WA, USA) and customized to transmit a set of basis US pulses from which both B-mode US (frame rate (FR) of 800 Hz) and multiple nonlinear CPS compositions (FR of 200 Hz) could be assessed from identical in vitro and in vivo datasets using a near simultaneous method. The simulations suggest that MB characteristics, such as diameter and motion, help to predict which US imaging strategy will enhance MB detection. The in vitro and in vivo US imaging studies revealed that different subpopulations of polydisperse MB contrast agents were detected by linear imaging and by each different nonlinear CPS composition. The most effective single imaging strategy at a 200-Hz FR was found to be B-mode US imaging. However, a combination of B-mode US imaging with a nonlinear CPS imaging strategy was more effective in detecting MBs in vivo at all depths and was shown to shorten image acquisition time by an average of 33.3%-76.7% when combining one or more CPS sequences.

Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging.

Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures down to tens of micrometers. The image processing methods for the spatiotemporal filtering needed in SR-US, such as singular value decomposition (SVD), are computationally burdensome and performed offline. Deep learning has been applied to many biomedical imaging problems, and trained neural networks have been shown to process an image in milliseconds. The goal of this study was to evaluate the effectiveness of deep learning to realize a spatiotemporal filter in the context of SR-US processing. A 3-D convolutional neural network (3DCNN) was trained on in vitro and in vivo data sets using SVD as ground truth in tissue clutter reduction. In vitro data were obtained from a tissue-mimicking flow phantom, and in vivo data were collected from murine tumors of breast cancer. Three training techniques were studied: training with in vitro data sets, training with in vivo data sets, and transfer learning with initial training on in vitro data sets followed by fine-tuning with in vivo data sets. The neural network trained with in vitro data sets followed by fine-tuning with in vivo data sets had the highest accuracy at 88.0%. The SR-US images produced with deep learning allowed visualization of vessels as small as [Formula: see text] in diameter, which is below the diffraction limit (wavelength of [Formula: see text] at 14 MHz). The performance of the 3DCNN was encouraging for real-time SR-US imaging with an average processing frame rate for in vivo data of 51 Hz with GPU acceleration.

Tree balance, time slices, and evolutionary turnover in cretaceous planktonic foraminifera.

Studies of phylogenetic tree shape often concentrate on the balance of phylogenies of extant taxa. Paleontological phylogenies (which include extinct taxa) can contain additional useful information and can directly document changes in tree shape through evolutionary time. Unfortunately, the inclusion of extinct taxa lowers the power of direct examinations of tree balance because it increases the range of tree shapes expected under null models of evolution (with equal rates of speciation and extinction across lineages). A promising approach for the analysis of tree shape in paleontological phylogenies is to break the phylogeny down into time slices, examining the shape of the phylogeny of taxa alive at each time slice and changes in that shape between successive time slices. This method was illustrated with 57 time slices through a stratophenetic phylogeny of the Cretaceous planktonic foraminiferal superfamily Globotruncanacea. At 3 of 56 intervals between time slices, 93-92.5 million years ago (MYA), 89-88.5 MYA, and 85.5-84 MYA, the group showed steep increases in imbalance. Although none of these increases were significant after Bonferroni correction, these points in the history of the Globotruncanacea were nevertheless identified as deserving of further macroevolutionary investigation. The 84 MYA time slice coincides with a peak in species turnover for the superfamily. Time slices through phylogenies may prove useful for identifying periods of time when evolution was proceeding in a nonstochastic manner.