Accurate real-time depth control for CP-SSOCT distal sensor based handheld microsurgery tools
Identifieur interne : 000B38 ( Main/Exploration ); précédent : 000B37; suivant : 000B39Accurate real-time depth control for CP-SSOCT distal sensor based handheld microsurgery tools
Auteurs : Gyeong Woo Cheon [États-Unis] ; Yong Huang [États-Unis] ; Jaepyeng Cha [États-Unis] ; Peter L. Gehlbach [États-Unis] ; Jin U. Kang [États-Unis]Source :
- Biomedical Optics Express [ 2156-7085 ] ; 2015.
Abstract
This paper presents a novel intuitive targeting and tracking scheme that utilizes a common-path swept source optical coherence tomography (CP-SSOCT) distal sensor integrated handheld microsurgical tool. To achieve micron-order precision control, a reliable and accurate OCT distal sensing method is required; simultaneously, a prediction algorithm is necessary to compensate for the system delay associated with the computational, mechanical and electronic latencies. Due to the multi-layered structure of retina, it is necessary to develop effective surface detection methods rather than simple peak detection. To achieve this, a shifted cross-correlation method is applied for surface detection in order to increase robustness and accuracy in distal sensing. A predictor based on Kalman filter was implemented for more precise motion compensation. The performance was first evaluated using an established dry phantom consisting of stacked cellophane tape. This was followed by evaluation in an
Url:
DOI: 10.1364/BOE.6.001942
PubMed: 26137393
PubMed Central: 4467719
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>This paper presents a novel intuitive targeting and tracking scheme that utilizes a common-path swept source optical coherence tomography (CP-SSOCT) distal sensor integrated handheld microsurgical tool. To achieve micron-order precision control, a reliable and accurate OCT distal sensing method is required; simultaneously, a prediction algorithm is necessary to compensate for the system delay associated with the computational, mechanical and electronic latencies. Due to the multi-layered structure of retina, it is necessary to develop effective surface detection methods rather than simple peak detection. To achieve this, a shifted cross-correlation method is applied for surface detection in order to increase robustness and accuracy in distal sensing. A predictor based on Kalman filter was implemented for more precise motion compensation. The performance was first evaluated using an established dry phantom consisting of stacked cellophane tape. This was followed by evaluation in an <italic>ex-vivo</italic>
bovine retina model to assess system accuracy and precision. The results demonstrate highly accurate depth targeting with less than 5 μm RMSE depth locking.</p>
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