Curvilinear endocavity ultrasound images capture a wide field of view with a miniature probe. In adapting photoacoustic imaging (PAI) to work with such ultrasound systems, light delivery is challenged by the tradeoff between image quality and laser safety concerns. Here, we present two novel designs based on cylindrical lenses that are optimized for transvaginal PAI B-scan imaging. Our simulation and experimental results demonstrate that, compared to conventional light delivery methods for PAI imaging, the proposed designs are safer for higher pulse energies and provide deeper imaging and a wider lateral field of view. The proposed designs could also improve the performance of endoscopic co-registered ultrasound/ photoacoustic imaging in other clinical applications.
Colorectal cancer ranks as the second most prevalent global malignancy and stands as the fourth leading cause of cancer-related deaths in the United States. The ability to accurately monitor rectal cancer treatment responses poses a significant challenge given the limitations of existing imaging modalities in confirming pathological complete response after chemoradiation. Non-invasive confirmation of complete response can offer improved quality of life, reduced medical costs, and decreased strain on the healthcare system for patients. Previous findings from our research group highlighted the potential of co-registered acoustic-resolution Photoacoustic Microscopy (ARPAM) and ultrasound (ARPAM/US) in monitoring treatment response, revealing the recovery of regular microvascular patterns in the tumor bed through photoacoustic microscopy in treatment responders. In this presentation, we introduce a second-generation compact and robust ARPAM/US system designed for monitoring rectal cancer treatment responses in an endoscopy unit suitable for repeated imaging. We will present a comparative analysis between normal tissue and tumor bed with and without residual tumor after chemoradiation.
We present results of rectal cancer treatment response assessment using co-registered ultrasound and photoacoustic imaging from over 20 in vivo patients. We develop a deep learning model based on co-registered dual-modality images with individualized prior information. Compared to models using only ultrasound images, our model identifies complete treatment responders with significantly higher accuracy. We achieve a 3-class classification accuracy (normal, cancer, and image artifact) of 89.1±0.8%. To facilitate surgeons’ decision-making, we generate localized hotspots to indicate suspicious cancer regions based on model predictions. We conclude that the addition of photoacoustic imaging to conventional ultrasound improves treatment response assessment.
KEYWORDS: Acquisition tracking and pointing, Machine learning, Medical image reconstruction, Interpolation, Image restoration, Ultrasound transducers, Signal detection, Reconstruction algorithms, Monte Carlo methods, 3D vision
Photoacoustic tomography (PAT) is a valuable tool in characterizing ovarian lesions for accurate diagnosis. However, limited view problem degrades the quality of PAT reconstruction severely, especially for transvaginal transducer which only partially encloses the target. To address this issue, we compensated limited view information loss by co-registered PAT and US machine learning method. The simulation and phantom results showed that the details of the target were recovered by proposed method, compared with delay-and-sum reconstruction method.
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