Résumé
Acoustic resolution photoacoustic microscopy (ARPAM) combines the advantages of high optical contrast, and high ultrasonic spatial resolution and penetration. However, in photoacoustic microscopy (PAM), the information from deep regions can be greatly affected by the shallow targets, and most importantly, the irreconcilable conflict between the lateral resolution and depth of fields has always be a major factor that limits the imaging quality. In this work, an ARPAM system was developed, in which a non-coaxial arrangement of light illumination and acoustic detection was adopted to alleviate the influence of the tissue surface on the deep targets, and a novel focal zone integral algorithm was applied with multiple axial scanning to improve the lateral resolution. Phantom experiment results show that, the build system can maintain a consistent high lateral resolution of 0.6 mm over a large range in axial direction, which is close to the theoretical calculations. The following tumor imaging results on nude mice indicate that, the proposed method can provide more in-depth information compared with the conventional back detection ARPAM method. With the development of fast repetition lasers and image scanning technologies, the proposed method may play an important role in cerebral vascular imaging, cervical cancer photoacoustic endoscopic detection, and superficial tumor imaging.
Résumé
Due to individual differences of the depth of anaesthesia (DOA) controlled objects, the drawbacks of monitoring index, the traditional PID controller of anesthesia depth could not meet the demands of nonlinear control. However, the adjustments of the rules of DOA fuzzy control often rely on personal experience and, therefore, it could not achieve the satisfactory control effects. The present research established a fuzzy closed-loop control system which takes the cerebral state index (CSI) value as a feedback controlled variable, and it also adopts the particle swarm optimization (PSO) to optimize the fuzzy control rule and membership functions between the change of CSI and propofol infusion rate. The system sets the CSI targets at 40 and 30 through the system simulation, and it also adds some Gaussian noise to imitate clinical disturbance. Experimental results indicated that this system could reach the set CSI point accurately, rapidly and stably, with no obvious perturbation in the presence of noise. The fuzzy controller based on CSI which has been optimized by PSO has better stability and robustness in the DOA closed loop control system.