Mild hyperthermia - defined as raising the human body temperature to 39-42 Celsius - has been shown to improve the effectiveness of systemic therapies for cancer treatment by improving tumor oxygenation and blood flow. High intensity focused ultrasound (HIFU) is a non-invasive, thermal ablative therapy that can be used to induce mild hyperthermia in a small area around the focus. When used in the presence of microbubbles (an ultrasound contrast agent), referred to as bubble-enhanced heating (BEH) HIFU becomes more efficient and increases the treatment area. Further research is required to study the mechanisms of BEH and better understand the complex relationship between microbubble dynamics and the ultrasound parameters. In this in vitro study, I fabricated gel and liquid tissue-mimicking phantoms to perform heating experiments in. The experimental setup consisted of a focused ultrasound transducer aligned to two thermocouples that were placed inside the phantom, one at the focus and one pre-focally. An imaging probe was used to image the phantoms before and after HIFU exposure. During heating experiments, I measured the temperature of the phantom at a single point via thermocouples for 30 s of continuous ultrasound exposure followed by 30 s after exposure has been stopped. I originally hypothesized that as microbubble concentration increases, the temperature elevation would also increase. However, the results showed that for both the gel and liquid phantoms measured at the focus, a higher microbubble concentration does not always result in a higher temperature elevation. This is due to the phenomenon of acoustic shadowing, where the concentration of microbubbles impedes the propagation of sound through the phantom, altering where most of the heat deposition occurs. Future experiments will be performed to confirm these results and investigate further microbubble concentrations and acoustic pressures in order to optimize BEH treatment for future clinical applications.

Therapeutic ultrasound with microbubble contrast agents induces biological effects that can be utilized for various clinical applications, and its non-invasiveness enables targeted treatments without harming tissue around the target by concentrating the acoustic energy of ultrasound to a specific location. In cancer therapy, ultrasound can enhance the delivery of chemotherapy by priming tumors or directly destroy cancer cells without surgical risks. While Averkiou lab investigates the effects of ultrasound pulses with microbubbles to enhance the efficiency of drug delivery into cancer cells, this project focuses on studying microbubble behavior during ultrasound-microbubble therapy and developing a technique to monitor their response and effects on surrounding tissues. A tissue-mimicking phantom with a wall-less channel will be used to simulate a vascular environment, allowing for controlled observation of microbubble cavitation. Passive cavitation detection (PCD) will be employed to monitor microbubble responses, with one transducer delivering ultrasound pulses to excite microbubbles and another transducer passively recording the resulting scattered signals. Additionally, this study will explore how excitation pulse nonlinearity influences microbubble behavior by modifying the acoustic conditions. While prior research has primarily focused on peak negative amplitudes when transmitting acoustic pressure, this project will examine the effects of both peak negative and positive amplitudes, potentially revealing new insights into microbubble dynamics and therapeutic ultrasound applications. Differences in microbubble responses to these excitation pulses will be analyzed experimentally and compared to theoretical predictions using MATLAB-based computational simulations. The findings of this study could contribute to optimizing ultrasound-mediated drug delivery and broadening the clinical applications of therapeutic ultrasound.

Internal pressure sensing gives healthcare providers essential information regarding patient health and can help determine risk factors for many diseases. The current method for this involves the insertion of a catheter to the location where pressure is being measured (e.g. portal vein, cranium, spine), which can be an invasive and potentially dangerous surgical procedure. A promising alternative is to use ultrasound contrast imaging and microbubbles as a pressure sensor. Studies have shown that the magnitude of the subharmonic component of scattered signals from microbubbles varies as ambient pressure changes. However, many acoustic parameters can induce this effect and it is still unknown how to optimize the parameters to maximize the subharmonic response. I perform experiments to determine the ideal acoustic parameters to sense these changes in ambient pressure and apply this knowledge to develop an ultrasound imaging system that can predict these pressures in vitro.

Liver cancer can be diagnosed in the clinic with contrast-enhanced ultrasound (CEUS). This method of diagnosis is qualitative and relies on the comparison of blood flow in the suspected tumor to the rest of the liver. However, observer biases in this method can result in inaccurate diagnoses and delays in treatment. To reduce observer bias, our lab developed a comprehensive and repeatable method of quantifying blood flow in liver tumors from CEUS scans. One problem that reduces the accuracy of this quantitative CEUS method is that tumor blood flow metrics are highly impacted by the motion of the liver, stemming from both breathing and sonographer movement. To solve this problem, there needs to be a standardized method to both detect and correct the motion of the tumor on the CEUS scan. I created an automated MATLAB algorithm to measure the motion of a suspected liver tumor on a CEUS scan and identify frames that cannot be analyzed quantitatively. Compared to a manual realignment and deletion of frames done by an expert (a very time-consuming process), as well as a current motion reduction algorithm based only on respiratory gating, my algorithm was simpler, faster, required less input, and produced similar blood flow parameters. This suggests that my MATLAB algorithm can be used in combination with quantitative CEUS processing to help clinicians diagnose liver cancer more rapidly and accurately.