Advanced Angiographic Examination Method with Real-Time Instrument Tracking

The present inventive concept relates to an improved angiographic examination method that utilizes machine learning, deep learning, and physics-based simulations to enhance the accuracy and efficiency of medical instrument tracking during vascular interventions.

The original patent (U.S. Pat. No. 7,500,784 B2) describes an angiographic examination method using a C-arm system. However, this method has limitations in accurately tracking medical instruments in real-time, which can lead to complications during interventions. The present inventive concept addresses these limitations by introducing advanced algorithms and simulations to predict the optimal insertion path of medical instruments and provide real-time feedback to the user.

The inventive concept comprises a system for angiographic examination, including an X-ray emitter and detector attached to a C-arm, a patient positioning couch, and a processor configured to generate a 3D volume data set and perform real-time tracking of a medical instrument. The processor uses machine learning algorithms to predict the optimal insertion path of the instrument based on the 3D volume data set and the real-time tracking data. Additionally, the system can utilize deep learning-based object detection algorithms to detect the medical instrument in 2D/3D overlay images or physics-based simulations to predict the deformation of the instrument during insertion. The inventive concept also encompasses methods for angiographic examination, including capturing 3D volume data sets, generating 2D/3D overlay images, and detecting medical instruments using various algorithms.

The present inventive concept's novelty lies in the integration of advanced algorithms, such as machine learning and deep learning, with physics-based simulations to enable real-time tracking of medical instruments during vascular interventions. This combination provides a non-obvious improvement over the original patent, as it allows for more accurate and efficient instrument tracking, reducing the risk of complications.

Alternative embodiments of the inventive concept may include using different machine learning algorithms, such as reinforcement learning or transfer learning, or incorporating additional sensors, such as electromagnetic tracking systems, to enhance the accuracy of instrument tracking. Variations of the inventive concept may also include adapting the system for use in other medical interventions, such as neurointerventions or orthopedic procedures.

The present inventive concept has significant commercial potential in the medical device industry, particularly in the areas of vascular interventions and image-guided procedures. The market for angiographic examination systems is expected to grow, driven by the increasing demand for minimally invasive procedures and the need for improved accuracy and efficiency in medical instrument tracking.