Temperature monitoring during high-intensity focused ultrasound (HIFU) application is necessary to ensure effective therapy while minimizing thermal damage to adjacent tissue. In this study, we demonstrate a noninvasive approach for temperature measurement during HIFU therapy based on photoacoustic imaging (PAI). Because of the dependence of photoacoustic (PA) signal amplitude on temperature of the source tissue and the linearity of the PAI system, changes in temperature will cause changes in PA image intensity. Experiments have been conducted in ex-vivo bovine tissue to characterize the linear dependence of PA image pixel values on temperature and subsequently to convert the PA image to a real-time temperature map.
The integration of intravascular ultrasound (IVUS) and intravascular photoacoustic (IVPA) imaging produces an imaging modality with high sensitivity and specificity which is particularly needed in interventional cardiology. Conventional side-looking IVUS imaging with a single-element ultrasound (US) transducer lacks forward-viewing capability, which limits the application of this imaging mode in intravascular intervention guidance, Doppler-based flow measurement, and visualization of nearly or totally blocked arteries. For both side-looking and forward-looking imaging, the necessity to mechanically scan the US transducer limits the imaging frame rate, and therefore array-based solutions are desired. In this paper, we present a low-cost, compact, high-speed, and programmable imaging system based on a field-programmable gate array (FPGA) suitable for dual-mode forward-looking IVUS/IVPA imaging. The system has 16 US transmit and receive channels and functions in multiple modes including interleaved photoacoustic (PA) and US imaging, hardware-based high-frame-rate US imaging, software-driven US imaging, and velocity measurement. The system is implemented in the register-transfer level, and the central system controller is implemented as a finite state machine. The system was tested with a capacitive micromachined ultrasonic transducer (CMUT) array. A 170-frames-per-second (FPS) US imaging frame rate is achieved in the hardware-based high-frame-rate US imaging mode while the interleaved PA and US imaging mode operates at a 60-FPS US and a laser-limited 20-FPS PA imaging frame rate. The performance of the system benefits from the flexibility and efficiency provided by low-level implementation. The resulting system provides a convenient backend platform for research and clinical IVPA and IVUS imaging.
Photoacoustic imaging (PAI) can be used to monitor lesion formation during high-intensity focused ultrasound (HIFU) therapy because HIFU changes the optical absorption spectrum (OAS) of the tissue. However, in traditional PAI, the change could be too subtle to be observed either because the OAS does not change very significantly at the imaging wavelength or due to low signal-to-noise ratio in general. We propose a machine-learning-based method for lesion monitoring with multi-wavelength PAI (MWPAI), where PAI is repeated at a sequence of wavelengths and a stack of multi-wavelength photoacoustic (MWPA) images is acquired. Each pixel is represented by a vector and each element in the vector reflects the optical absorption at the corresponding wavelength. Based on the MWPA images, a classifier is trained to classify pixels into two categories: ablated and non-ablated. In our experiment, we create a lesion on a block of bovine tissue with a HIFU transducer, followed by MWPAI in the 690 nm to 950 nm wavelength range, with a step size of 5 nm. In the MWPA images, some of the ablated and non-ablated pixels are cropped and fed to a neural network (NN) as training examples. The NN is then applied to several groups of MWPA images and the results show that the lesions can be identified clearly. To apply MWPAI in/near real-time, sequential feature selection is performed and the number of wavelengths is decreased from 53 to 5 while retaining adequate performance. With a fast-switching tunable laser, the method can be implemented in/near real-time.
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