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.

Feature Extraction of Ultrasound Radiofrequency Data for the Classification of the Peripheral Zone of Human Prostate.

Prostate cancer ranks among the most prevalent types of cancer in males, prompting a demand for early detection and noninvasive diagnostic techniques. This paper explores the potential of ultrasound radiofrequency (RF) data to study different anatomic zones of the prostate. The study leverages RF data's capacity to capture nuanced acoustic information from clinical transducers. The research focuses on the peripheral zone due to its high susceptibility to cancer. The feasibility of utilizing RF data for classification is evaluated using ex-vivo whole prostate specimens from human patients. Ultrasound data, acquired using a phased array transducer, is processed, and correlated with B-mode images. A range filter is applied to highlight the peripheral zone's distinct features, observed in both RF data and 3D plots. Radiomic features were extracted from RF data to enhance tissue characterization and segmentation. The study demonstrated RF data's ability to differentiate tissue structures and emphasizes its potential for prostate tissue classification, addressing the current limitations of ultrasound imaging for prostate management. These findings advocate for the integration of RF data into ultrasound diagnostics, potentially transforming prostate cancer diagnosis and management in the future.

Hemoglobin Microbubbles and the Prediction of Different Oxygen Levels Using RF Data and Deep Learning.

Ultrasound contrast agents (UCA) are gas-encapsulated microspheres that oscillate volumetrically when exposed to an ultrasound field producing backscattered signals efficiently, which can be used for improved ultrasound imaging and drug delivery applications. We developed a novel oxygen-sensitive hemoglobin-shell microbubble designed to acoustically detect blood oxygen levels. We hypothesize that structural change in hemoglobin caused due to varying oxygen levels in the body can lead to mechanical changes in the shell of the UCA. This can produce detectable changes in the acoustic response that can be used for measuring oxygen levels in the body. In this study, we have shown that oxygenated hemoglobin microbubbles can be differentiated from deoxygenated hemoglobin microbubbles using a 1D convolutional neural network using radiofrequency (RF) data. We were able to classify RF data from oxygenated and deoxygenated hemoglobin microbubbles into the two classes with a testing accuracy of 90.15%. The results suggest that oxygen content in hemoglobin affects the acoustical response and may be used for determining oxygen levels and thus could open many applications, including evaluating hypoxic regions in tumors and the brain, among other blood-oxygen-level-dependent imaging applications.

Identifying Unique Acoustic Signatures from Chemically-Crosslinked Microbubble Clusters Using Deep Learning.

Ultrasound contrast agents (UCA) are gas encapsulated microspheres that oscillate volumetrically when exposed to an ultrasound field producing a backscattered signal which can be used for improved ultrasound imaging and drug delivery. UCA's are being used widely for contrast-enhanced ultrasound imaging, but there is a need for improved UCAs to develop faster and more accurate contrast agent detection algorithms. Recently, we introduced a new class of lipid based UCAs called Chemically Cross-linked Microbubble Clusters (CCMCs). CCMCs are formed by the physical tethering of individual lipid microbubbles into a larger aggregate cluster. The advantages of these novel CCMCs are their ability to fuse together when exposed to low intensity pulsed ultrasound (US), potentially generating unique acoustic signatures that can enable better contrast agent detection. In this study, our main objective is to demonstrate that the acoustic response of CCMCs is unique and distinct when compared to individual UCAs using deep learning algorithms. Acoustic characterization of CCMCs and individual bubbles was performed using a broadband hydrophone or a clinical transducer attached to a Verasonics Vantage 256. A simple artificial neural network (ANN) was trained and used to classify raw 1D RF ultrasound data as either from CCMC or non-tethered individual bubble populations of UCAs. The ANN was able to classify CCMCs at an accuracy of 93.8% for data collected from broadband hydrophone and 90% for data collected using Verasonics with a clinical transducer. The results obtained suggest the acoustic response of CCMCs is unique and has the potential to be used in developing a novel contrast agent detection technique.