Research On Image Reconstruction Algorithm Based On Principal Component Analysis

PAI(Photoacoutic Imaging) has become one of the research hotspots in biomedical imaging because of its unique imaging character. PAI applies the pulse laser to irradiate the biological tissue, while the laser energy is set in the range of International Safety Standards. Compared with other imaging technologies, such as X-ray, PAI has the advantage of non-ionizing radiation.Because PAI receives the ultrasonic signal from tissues, which has a stronger penetration power and higher spatial resolution than pure optical imaging. PAI applies the collected ultrasonic signals to inverse the optical absorption of the tissues by a certain reconstruction algorithm, then PAI can get the images of optical absorption of tissues, which have a higher optical contrast than ultrasound imaging. Furthermore, the ultrasonic signals, which are detected by PAI, carrying the information of biological tissue, are able to measure the blood oxygen saturation and the concentration of hemoglobin, so PAI can be used in functional imaging area. PAI has the advantages of both optical and ultrasonic imaging, which makes it become the most rapidly developed part in biomedical imaging.Image reconstruction algorithm plays an important role in PAI. The performance of the algorithm is directly related to the speed of data acquisition and the quality of the reconstruction.In recent years, CS(Compressed Sensing) technique has been applied in PACT(photoacoustic computed tomography) successfully. It has been proved that CS has the advantage in reducing the scale of data acquisition and the cost of the imaging system. However, the reconstruction process of PACT based on CS needs hundreds of iterations, and the speed of reconstruction is very slow, which cannot achieve photoacoustic image display fast.Focusing on the issues that the data acquisition amount of PACT based on ultrasonic array is generally huge, and that the imaging process is time-consuming, a fast photoacoustic computed tomography method with Principal Component Analysis was firstly proposed to extend its applications on the field of hemodynamics etc. First, the matrix of image samples was constructed with part of full-sampling data. Second, the projection matrix representing the signal features was derived by the decomposition of the sample matrix. Finally, the high-quality three-dimensional photoacoustic images was recovered by this projection matrix under three-fold under-sampling.The in vivo imaging of the human-hand shows that, compared to the traditional back-projection method, the data acquisition amount of PACT using PCA can be greatly decreased, and the three-dimensional reconstruction speed is significantly improved. In addition,on the condition of under-sampling, the method can overcame the complexity of the CS, andsuppress the image reconstruction noise effectively. As a result, both the fast data acquisition and high-accurate three-dimensional image reconstruction are implemented successfully. It is helpful to promote the depth and breadth of photoacoustic imaging in various biomedical fields.