Validation of a Sterilization Methods in FBG Sensors for in vivo Experiments

Karam, Leandro Zen; Franco, Ana Paula Gebert de Oliveira; Tomazinho, Paulo Henrique; Kalinowski, Hypolito José

doi:10.1364/laop.2012.lt2a.7

Cited by 4 publications

(2 citation statements)

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“…Costs of FBGs and increasingly small optical fiber cables are dropping, enabling the use of the technology in disposable medical devices [49]. Due to the robustness of the fibers, these sensors are also easily sterilizable either by autoclave, ethylene oxide, ultra-violet radiation, or other methods [50]. …”

Section: Discussionmentioning

confidence: 99%

Autonomous Real-Time Interventional Scan Plane Control With a 3-D Shape-Sensing Needle

Elayaperumal

Plata

Holbrook

et al. 2014

IEEE Trans. Med. Imaging

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This study demonstrates real-time scan plane control dependent on three-dimensional needle bending, as measured from magnetic resonance imaging (MRI)-compatible optical strain sensors. A biopsy needle with embedded fiber Bragg grating (FBG) sensors to measure surface strains is used to estimate its full 3-D shape and control the imaging plane of an MR scanner in real-time, based on the needle’s estimated profile. The needle and scanner coordinate frames are registered to each other via miniature radio-frequency (RF) tracking coils, and the scan planes autonomously track the needle as it is deflected, keeping its tip in view. A 3-D needle annotation is superimposed over MR-images presented in a 3-D environment with the scanner’s frame of reference. Scan planes calculated based on the FBG sensors successfully follow the tip of the needle. Experiments using the FBG sensors and RF coils to track the needle shape and location in real-time had an average root mean square error of 4.2 mm when comparing the estimated shape to the needle profile as seen in high resolution MR images. This positional variance is less than the image artifact caused by the needle in high resolution SPGR (spoiled gradient recalled) images. Optical fiber strain sensors can estimate a needle’s profile in real-time and be used for MRI scan plane control to potentially enable faster and more accurate physician response.

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Section: Discussionmentioning

confidence: 99%

Autonomous Real-Time Interventional Scan Plane Control With a 3-D Shape-Sensing Needle

Elayaperumal

Plata

Holbrook

et al. 2014

IEEE Trans. Med. Imaging

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“…The optical biocompatible sensors [ 14 ] used in this work were developed from a new technique of packaging with preliminary in vitro tests [ 15 ], where the viability of the technique has been demonstrated. Furthermore, with the objective of developing a package for the optical sensor, tests were performed ex vivo [ 16 ].…”

Section: Introduction and Contextualizationmentioning

confidence: 99%

In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning

Pegorini

Karam

Pitta

et al. 2015

Sensors

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Pattern classification of ingestive behavior in grazing animals has extreme importance in studies related to animal nutrition, growth and health. In this paper, a system to classify chewing patterns of ruminants in in vivo experiments is developed. The proposal is based on data collected by optical fiber Bragg grating sensors (FBG) that are processed by machine learning techniques. The FBG sensors measure the biomechanical strain during jaw movements, and a decision tree is responsible for the classification of the associated chewing pattern. In this study, patterns associated with food intake of dietary supplement, hay and ryegrass were considered. Additionally, two other important events for ingestive behavior were monitored: rumination and idleness. Experimental results show that the proposed approach for pattern classification is capable of differentiating the five patterns involved in the chewing process with an overall accuracy of 94%.

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Effects of sterilization on optical and mechanical reliability of specialty optical fibers and terminations

et al. 2014

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Validation of a Sterilization Methods in FBG Sensors for in vivo Experiments

Cited by 4 publications

References 0 publications

Autonomous Real-Time Interventional Scan Plane Control With a 3-D Shape-Sensing Needle

Autonomous Real-Time Interventional Scan Plane Control With a 3-D Shape-Sensing Needle

In Vivo Pattern Classification of Ingestive Behavior in Ruminants Using FBG Sensors and Machine Learning

Effects of sterilization on optical and mechanical reliability of specialty optical fibers and terminations

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