Interlaced photoacoustic and ultrasound imaging system with real-time coregistration for ovarian tissue characterization

Coregistered ultrasound (US) and photoacoustic imaging are emerging techniques for mapping the echogenic anatomical structure of tissue and its corresponding optical absorption. We report a 128-channel imaging system with real-time coregistration of the two modalities, which provides up to 15 coregistered frames per second limited by the laser pulse repetition rate. In addition, the system integrates a compact transvaginal imaging probe with a custom-designed fiber optic assembly for in vivo detection and characterization of human ovarian tissue. We present the coregistered US and photoacoustic imaging system structure, the optimal design of the PC interfacing software, and the reconfigurable field programmable gate array operation and optimization. Phantom experiments of system lateral resolution and axial sensitivity evaluation, examples of the real-time scanning of a tumor-bearing mouse, and ex vivo human ovaries studies are demonstrated.

Recognition algorithm for assisting ovarian cancer diagnosis from coregistered ultrasound and photoacoustic images: ex vivo study

Unique features and the underlining hypotheses of how these features may relate to the tumor physiology in coregistered ultrasound and photoacoustic images of ex vivo ovarian tissue are introduced. The images were first compressed with wavelet transform. The mean Radon transform of photoacoustic images was then computed and fitted with a Gaussian function to find the centroid of a suspicious area for shift-invariant recognition process. Twenty-four features were extracted from a training set by several methods, including Fourier transform, image statistics, and different composite filters. The features were chosen from more than 400 training images obtained from 33 ex vivo ovaries of 24 patients, and used to train three classifiers, including generalized linear model, neural network, and support vector machine (SVM). The SVM achieved the best training performance and was able to exclusively separate cancerous from non-cancerous cases with 100% sensitivity and specificity. At the end, the classifiers were used to test 95 new images obtained from 37 ovaries of 20 additional patients. The SVM classifier achieved 76.92% sensitivity and 95.12% specificity. Furthermore, if we assume that recognizing one image as a cancer is sufficient to consider an ovary as malignant, the SVM classifier achieves 100% sensitivity and 87.88% specificity.