Safe Motion Planning for Steerable Needles Using Cost Maps Automatically Extracted from Pulmonary Images.

Area of Science:

• Medical imaging
• Robotics
• Pulmonary medicine

Background:

• Lung cancer diagnosis relies on biopsy, but current methods have limitations in safety and accessibility, especially for peripheral nodules.
• Early diagnosis is crucial for improving lung cancer survival rates.

Purpose of the Study:

• To develop an automated method for generating a cost map from pulmonary CT images to guide steerable needle biopsies.
• To enable safe and efficient navigation of a steerable needle to peripheral lung nodules.

Main Methods:

• Automatic extraction of a cost map from CT scans, encoding obstacles (pleura, airways, vessels) and blood vessel likelihood.
• Development of a motion planning algorithm utilizing the cost map to find safe, low-cost paths for a steerable needle.

Main Results:

• The proposed method generates a cost map that effectively represents anatomical hazards and vessel presence.
• The motion planning approach successfully plans safe paths for a steerable needle within the lung using the cost map.

Conclusions:

• This automated cost map generation and motion planning system offers a promising approach for safer and more accessible lung nodule biopsies.
• Improved accessibility to peripheral lung nodules can enhance early lung cancer detection and patient outcomes.