Updates on AURIS' ARES

The most recent patent of AURIS on ARES - Navigation of Tubular Networks - reveals some details about their system and control approach:

"Methods and apparatuses provide improved navigation through tubular networks such as lung airways by providing improved estimation of location and orientation information of a medical instrument (e.g., an endoscope) within the tubular network. Various input data such as image data, EM data, and robot data are used by different algorithms to estimate the state of the medical instrument, and the state information is used to locate a specific site within a tubular network and/or to determine navigation information for what positions/orientations the medical instrument should travel through to arrive at the specific site. Probability distributions together with confidence values are generated corresponding to different algorithms are used to determine the medical instrument's estimated state.

The methods and apparatus disclosed herein provide improved navigation through tubular networks such as lung airways by providing improved estimation of location and orientation information of a medical instrument like a flexible or rigid elongated medical instrument (e.g., an endoscope) within the tubular network.

As one example, the apparatus is a robotic endoscopic tool to acquire “raw” location and orientation information (collectively, input data) of a desired anatomical site or of the endoscopic tool within the tubular network. The endoscopic tool includes a flexible tip and an instrument device manipulator (IDM) coupled to the endoscopic tool. Devices such as an electromagnetic sensor (EM sensor), an imaging device (e.g., optical sensor), and a robotic control system controlling the medical instrument are coupled to the instrument tip to collect the input data as the endoscopic tool enters and navigates through the tubular network. The IDM is used to control movement and position of different robotic components (e.g., the endoscopic tool) of the surgical robotic system. A processor is coupled to the endoscopic tool to receive the input data to determine moment-by-moment movements and location and orientation information of the medical instrument (e.g., a instrument tip) within the tubular network.

The processor is instructed by a navigation configuration system to use the input data to estimate the state of the medical instrument, which may include information such as position, orientation, relative and absolute depth, branch selection, etc. The processor may be further instructed to use the estimated state to locate a specific site within a tubular network and/or to determine navigation information for what positions/orientations the medical instrument should travel through to arrive at the specific site, which may be referred to as the output data or navigation data.

The navigation configuration system further includes multiple algorithm modules employing various navigation algorithms for providing the estimated state and navigation data. Example algorithms used include EM-based algorithms, image-based algorithms, and robot-based algorithms. The estimated state and navigation data generated after employing these various algorithms makes use of any one or more of the EM-based input data, image-based input data, and robot-based input data.

In some embodiments, probability distributions together with confidence values are generated by the algorithm modules, which are used to determine the medical instrument's estimated state. The “probability” of the “probability distribution”, as used herein, refers to a likelihood of an estimation or identification of location and/or orientation of the medical instrument being correct. For example, different probabilities may be calculated indicating the relative likelihood that the medical instrument is in one of several different possible airways within the lung. In contrast, the “confidence value, as used herein, reflects a measure of confidence in the estimation of the state provided by one of the algorithms. For example, relatively close to the airway opening, a particular algorithm may have a high confidence in its estimations of medical instrument position and orientation; but further into the bottom of the lung the medical instrument travels, that confidence value may drop. Generally, the confidence value is based on one or more “external” factors relating to the process by which a result is determined, whereas probability is a relative measure that arises when trying to determine possible results from a single algorithm. The algorithms, probabilities, and confidence values may be variously combined to arrive at the estimated state and navigation data.

In one embodiment, before executing an actual surgical operation on a patient, a sequence of pre-operative steps employing the improved navigation of surgical instruments (e.g., endoscopic) within a tubular network of the patient may be taken. Initially, a CT scan of the tubular network is obtained to generate a 3D model of the tubular network. A target area (e.g., a lesion to biopsy) within the tubular network is selected and a corresponding path for a surgical instrument to travel through the tubular network to reach the target area is automatically planned and displayed to a user (e.g., a physician responsible for the surgical operation). After the path is determined, a virtual endoscopic may be applied to travel through the tubular network to arrive at the target area. In the actual surgical operation, the CT scan, the generated 3D model as well as other input data (e.g., image data, EM data, robot data collected over the duration of the surgery) is combined and repeatedly analyzed during the surgery via the surgical configuration system to provide an estimation of the real-time movement information and location/orientation information of the surgical instrument (e.g., the endoscope) within the tubular network along with navigation information, which allows for more convenient operations by the physician."

Source: Auris