Photoacoustic (PA) imaging has attracted increasing research interest in recent years due to its unique merit of combining light and sound. Enabling deep tissue imaging with high ultrasound spatial resolution and optical absorption contrast, PA imaging has been applied in various application scenarios including anatomical, functional and molecular imaging. However, the bulky and expensive laser source is one of the key bottlenecks that needs to address for further compact system development. Photoacoustic imaging system based on low-cost laser diode is one of the promising solutions. In this paper, we report a custom-made fingertip laser diode system enabling both pulsed and continuous modulation modes with the shortest pulse width of 30 ns, driving current of 10 A, and single modulation frequency of 3 MHz, which is suitable for both time and narrow-band frequency domain PA imaging. The experiments for generating PA signals were performed with more than 70 millivolts signals amplitude. By sweeping the pulse width, it is observed that the amplitude of PA signals is increasing due to higher laser energy. To the best of our knowledge, this may be the most compact laser source used for photoacoustic applications for PA imaging. Owing to its super-compact size, the reported laser diode system could pave the pathway to low-cost photoacoustic sensing and imaging device, even wearable photoacoustic biomedical sensors.
Nowadays, breast cancer has increasingly threatened the health of human, especially females. However, breast cancer is still hard to detect in the early stage, and the diagnostic procedure can be time-consuming with abundant expertise needed. In this paper, the main research is the application of deep learning method in the diagnosis of photoacoustic breast cancer and the comparison of the performance of the traditional machine learning classification algorithm and deep learning method in the actual scenario of the photoacoustic imaging breast cancer diagnosis. The traditional supervised learning method firstly obtains the photoacoustic images of breast cancer through preprocessing, extracts the SIFT features, and uses K-means clustering to obtain the feature dictionary. The histogram of the feature dictionary was used as the final feature of the image. Support vector machine (SVM) was used to classify the final features, achieving an accuracy of 82.14%. In the deep learning method, AlexNet and GoogLeNet were used to perform the transfer learning, achieving 88.23%, 89.23%, and 91.18% accuracy, respectively. Finally, by comparing the AUC, sensitivity, and specificity of SVM with AlexNet and GoogLeNet, it can be concluded that the combination of deep learning and photoacoustic imaging obtain a profound and important impact on clinical applications.
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