Real-time and multimodality image-guided intelligent HIFU therapy for uterine fibroid

Rationale: High-intensity focused ultrasound (HIFU) therapy represents a noninvasive surgical approach to treat uterine fibroids. The operation of HIFU therapy relies on the information provided by medical images. In current HIFU therapy, all operations such as positioning of the lesion in magnetic resonance (MR) and ultrasound (US) images are manually performed by specifically trained doctors. Manual processing is an important limitation of the efficiency of HIFU therapy. In this paper, we aim to provide an automatic and accurate image guidance system, intelligent diagnosis, and treatment strategy for HIFU therapy by combining multimodality information. Methods: In intelligent HIFU therapy, medical information and treatment strategy are automatically processed and generated by a real-time image guidance system. The system comprises a novel multistage deep convolutional neural network for preoperative diagnosis and a nonrigid US lesion tracking procedure for HIFU intraoperative image-assisted treatment. In the process of intelligent therapy, the treatment area is determined from the autogenerated lesion area. Based on the autodetected treatment area, the HIFU foci are distributed automatically according to the treatment strategy. Moreover, an image-based unexpected movement warning and other physiological monitoring are used during the intelligent treatment procedure for safety assurance. Results: In the experiment, we integrated the intelligent treatment system on a commercial HIFU treatment device, and eight clinical experiments were performed. In the clinical validation, eight randomly selected clinical cases were used to verify the feasibility of the system. The results of the quantitative experiment indicated that our intelligent system met the HIFU clinical tracking accuracy and speed requirements. Moreover, the results of simulated repeated experiments confirmed that the autodistributed HIFU focus reached the level of intermediate clinical doctors. Operations performed by junior- or middle-level operators with the assistance of the proposed system can reach the level of operation performed by senior doctors. Various experiments prove that our proposed intelligent HIFU therapy process is feasible for treating common uterine fibroid cases. Conclusion: We propose an intelligent HIFU therapy for uterine fibroid which integrates multiple medical information processing procedures. The experiment results demonstrated that the proposed procedures and methods can achieve monitored and automatic HIFU diagnosis and treatment. This research provides a possibility for intelligent and automatic noninvasive therapy for uterine fibroid.