Abstract:Photoacoustic spectrum analysis (PASA) has been demonstrated as a new method for quantitative tissue imaging and characterization. The ability of PASA in evaluating microsize tissue features was limited by the bandwidth of detectors for photoacoustic (PA) signal acquisition. We improve upon such a limit, and report on developments of PASA facilitated by an optical ultrasonic detector based on micro-ring resonator. The detector's broad and flat frequency response significantly improves the performance of PASA a… Show more
“…Increasing absorber size decreases the SS regardless of concentration, suggesting that the PA RF spectra contain information about the absorber size. This is consistent with previous studies in which the SS was used to quantify microstructure in biological tissue and for red blood cell aggregation in blood suspensions [32,[55], [56], [57]]. Fig.…”
In ultrasound imaging, fully-developed speckle arises from the spatiotemporal superposition of pressure waves backscattered by randomly distributed scatterers. Speckle appearance is affected by the imaging system characteristics (lateral and axial resolution) and the random-like nature of the underlying tissue structure. In this work, we examine speckle formation in acoustic-resolution photoacoustic (PA) imaging using simulations and experiments. Numerical and physical phantoms were constructed to demonstrate that PA speckle carries information related to unresolved absorber structure in a manner similar to ultrasound speckle and unresolved scattering structures. A fractal-based model of the tumor vasculature was used to study PA speckle from unresolved cylindrical vessels. We show that speckle characteristics and the frequency content of PA signals can be used to monitor changes in average vessel size, linked to tumor growth. Experimental validation on murine tumors demonstrates that PA speckle can be utilized to characterize the unresolved vasculature in acoustic-resolution photoacoustic imaging.
“…Increasing absorber size decreases the SS regardless of concentration, suggesting that the PA RF spectra contain information about the absorber size. This is consistent with previous studies in which the SS was used to quantify microstructure in biological tissue and for red blood cell aggregation in blood suspensions [32,[55], [56], [57]]. Fig.…”
In ultrasound imaging, fully-developed speckle arises from the spatiotemporal superposition of pressure waves backscattered by randomly distributed scatterers. Speckle appearance is affected by the imaging system characteristics (lateral and axial resolution) and the random-like nature of the underlying tissue structure. In this work, we examine speckle formation in acoustic-resolution photoacoustic (PA) imaging using simulations and experiments. Numerical and physical phantoms were constructed to demonstrate that PA speckle carries information related to unresolved absorber structure in a manner similar to ultrasound speckle and unresolved scattering structures. A fractal-based model of the tumor vasculature was used to study PA speckle from unresolved cylindrical vessels. We show that speckle characteristics and the frequency content of PA signals can be used to monitor changes in average vessel size, linked to tumor growth. Experimental validation on murine tumors demonstrates that PA speckle can be utilized to characterize the unresolved vasculature in acoustic-resolution photoacoustic imaging.
“…Increasing absorber size decreases the SS regardless of concentration, suggesting that the PA RF spectra contain information about the absorber size. This is consistent with previous studies in which the SS was used to quantify microstructure in biological tissue and for red blood cell aggregation in blood suspensions[145],[185]-[187].…”
<div>This dissertation describes the development of functional and structural photoacoustic (PA) imaging biomarkers that can be used to monitor cancer treatment response and potentially predict treatment outcome. An imaging method that can indicate individualized treatment success could improve therapeutic outcome. Assessing the effectiveness of therapies as early as possible may spare the patient from unnecessary treatments and save precious clinical resources. In order for PA imaging to enter mainstream radiology and become a treatment monitoring tool, rigorous development of biomarkers that are easy-to-use and representative of the treatment-induced changes in the tumor microenvironment are needed. In this work, I have developed imaging biomarkers that rely on the analysis of the radiofrequency signals in acoustic resolution PA imaging. Specifically, I show through simulations and experiments that biomarkers sensitive to the size, number density and spacing of tumor blood vessels can be extracted through time and frequency domain analysis of PA signals. This information is encoded in the speckle that forms during diffuse optical illumination, which was previously thought to be noise. Moreover, I demonstrate that PA imaging can detect the response of a thermosensitive liposome by measuring a >10% drop in the oxygenation of the tumor as early as 30 minutes post-treatment. This change in oxygenation is due to vascular disruption, a phenomenon that can be detected through frequency analysis of the PA signals. The spectral slope parameter decreases by as much as 73% in 2 hours post-treatment and can be used to differentiate alongside the oxygenation biomarker between responders and non-responders. Lastly, I demonstrate that these PA imaging biomarkers correlate well with the histologically measured biophysical changes of two novel, bubble-based cancer treatments. In this dissertation, PA imaging biomarkers for cancer treatment monitoring are developed, advancing the modality towards clinical translation.</div>
“…4(f) shows the spectrum of the US waves of the blood vessel and its RA at 532 and 800 nm. The oscillation in the spectrum is similar to the observation in [17]. The spectrum of the signal at the wavelength of 532 nm is broader and thus experiences attenuation differently compared to the signal at the wavelength of 800 nm.…”
One of the remaining challenges of bringing photoacoustic imaging to clinics is the occurrence of reflection artifacts. Previously, we proposed a method using multi-wavelength excitation to identify and remove the RAs. However, this method requires at least 3 wavelengths. Here we improve the method further by reducing the required number of wavelengths to 2. We experimentally demonstrate this new method and compare it with the previous one. Results show that this new method holds great feasibility for identifying reflection artifacts in addition to preserving all advantages of the previous method.
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