Convolutional autoencoder-based reconstruction of vascular structures in photoacoustic images

In medical imaging, a number of imaging modalities have been used to visualize the structural information of different internal organs in the human body and some modalities can even visualize structures at a cellular level for diagnostic and treatment purposes. Optical resolution photoacoustic microscopy (OR-PAM) is one of the emerging imaging modalities to analyze the anatomy and functionality of tissues non-invasive. It is a hybrid imaging technology that combines photoacoustic (PA) contrast and acoustic resolution to reconstruct images of tissues in humans and animals. However, in OR-PAM the received ultrasonic signal by the thermal expansion of tissues has a low signal-to-noise ratio because most of the signal power is lost in the conversion process from light to acoustic waves which makes it difficult to visualize the structural information in the PA images. Traditional denoising methods such as wiener filter, bandpass filter, wavelet-based denoising, noise reduction by SVD and dictionary-based denoising methods can denoise PA images to some extent but it is still a difficult task to preserve structural information in the images by such methods. In this research, a convolutional autoencoder (CAE) based model is proposed to denoise and learn the structural patterns of blood vessels in PA images. To achieve this task, a CAE model is first trained between noisy and Gabor filtered sub-images, those contain the patterns of different vascular structures. Then, the trained model is used to approximate the denoise version of the input noisy sub-images of blood vessels. The proposed model is trained and tested on PA images of blood vessels of a mouse ear, acquired by the OR-PAM imaging system and the results show that our proposed method can effectively approximate and reconstruct the noisy vascular structures than traditional images denoising filtering methods.