ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2025
Device-Specific Calibration Methods for Optical Frequency Domain Reflectometry-Based Shape Sensing in Catheters and Surgical Needles.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Minimally invasive procedures for diagnosing and treating occlusive arterial diseases and prostate cancer face significant challenges due to the complexity of navigating within occluded arteries and precisely positioning surgical needles. Fiber optic sensors, coupled with optical frequency domain reflectometry (OFDR), offer promising solutions to the accuracy limitations of traditional imaging methods in complex anatomies. This work proposes custom calibration techniques of fiber optic sensors for vascular catheters and prostate surgical needles, addressing device-specific characteristics that can cause shape sensing inaccuracies, making precise and reliable calibration crucial. We assessed how calibration, tool characteristics, and spatial resolution affect shape reconstruction accuracy, with the catheter calibration protocol yielding a root-mean-squared-error (RMSE) of 1.67 ± 0.77 mm (0.4% ± 0.2%), and the needle calibration protocol achieving 0.23 ± 0.14 mm (0.2% ± 0.1%). Although the impact of spatial resolution wasn't significant, it's crucial to consider as it varies with the specific medical device and application.Clinical relevance-The proposed calibration methods enhance the safety and precision of fiber optic minimally invasive procedures by reducing reliance on imaging like fluoroscopy, minimizing tool placement errors across various medical devices and clinical domains. We demonstrate potential for automation to improve both clinical outcomes and workflow efficiency.
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