A Respiratory Signal Monitoring Method Based on Dual-Pathway Deep Learning Networks in Image-Guided Robotic-Assisted Intervention System.

Background: Percutaneous puncture procedures, guided by image-guided robotic-assisted intervention (IGRI) systems, are susceptible to disruptions in patients' respiratory rhythm due to factors such as pain and psychological distress.

Methods: We developed an IGRI system with a coded structured light camera and a binocular camera. Our system incorporates dual-pathway deep learning networks, combining convolutional long short-term memory (ConvLSTM) and point long short-term memory (PointLSTM) modules for real-time respiratory signal monitoring.

Embedding expertise knowledge into inverse treatment planning for low-dose-rate brachytherapy of hepatic malignancies.

Background: Leveraging the precision of its radiation dose distribution and the minimization of postoperative complications, low-dose-rate (LDR) permanent seed brachytherapy is progressively adopted in addressing hepatic malignancies.

Purpose: The present study endeavors to devise a sophisticated treatment planning system (TPS) to optimize LDR brachytherapy for hepatic lesions.

Methods: Our TPS encompasses four integral modules: multi-organ segmentation, seed distribution initialization, puncture pathway selection, and inverse dose planning.