Depth-dependent attenuation and backscattering characterization of optical coherence tomography by stationary iterative method

Wang, Yaning; Wei, Shuwen; Kang, Jin U.

doi:10.1117/1.jbo.28.8.085002

Cited by 2 publications

(6 citation statements)

References 42 publications

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“…MLP classifier with handcrafted features worked best for the abdominal wall, mesentery, and anterior outer layers of intestine classification; DC-CNN worked best for anterior inner layers, posterior inner layers, and posterior outer layers classification using automatically derived features from the signal intensity I and attenuation coefficients

of samples. The signal intensity I depends on the irradiance value of the system, the converting factor related to the detection quantum efficiency, the backscattering ratio, and attenuation coefficient of samples [ 30 ]. On the contrary,

are only related to the anatomical structure information of tissues.…”

Section: Resultsmentioning

confidence: 99%

“…The raw OCT image contrast is limited due to the small range of the tissue reflective index differences. OCT image deduced attenuation coefficient has proven to be useful in providing additional tissue anatomical information [ 30 ]. Here we utilized the depth-resolved reconstruction model [ 30 , 31 ] to compute the attenuation coefficient

at pixel i, given as,

where

is the OCT signal intensity at pixel i ,

is the pixel size.…”

Section: Methodsmentioning

confidence: 99%

“…OCT image deduced attenuation coefficient has proven to be useful in providing additional tissue anatomical information [ 30 ]. Here we utilized the depth-resolved reconstruction model [ 30 , 31 ] to compute the attenuation coefficient

at pixel i, given as,

where

is the OCT signal intensity at pixel i ,

is the pixel size. In practice, OCT A-line signals decay exponentially with depth, transitioning into a trailing noise floor till in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Other than the depth-resolved attenuation coefficient, the backscatter term has also successfully been used in tissue characterization [ 30 ]. Here we employed the following equation to estimate the average backscatter term

where M is the pixel length of 500.…”

Section: Methodsmentioning

confidence: 99%

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Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography

Wang,

Wei,

Zuo

et al. 2024

Biomed. Opt. Express

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Anastomosis is a common and critical part of reconstructive procedures within gastrointestinal, urologic, and gynecologic surgery. The use of autonomous surgical robots such as the smart tissue autonomous robot (STAR) system demonstrates an improved efficiency and consistency of the laparoscopic small bowel anastomosis over the current da Vinci surgical system. However, the STAR workflow requires auxiliary manual monitoring during the suturing procedure to avoid missed or wrong stitches. To eliminate this monitoring task from the operators, we integrated an optical coherence tomography (OCT) fiber sensor with the suture tool and developed an automatic tissue classification algorithm for detecting missed or wrong stitches in real time. The classification results were updated and sent to the control loop of STAR robot in real time. The suture tool was guided to approach the object by a dual-camera system. If the tissue inside the tool jaw was inconsistent with the desired suture pattern, a warning message would be generated. The proposed hybrid multilayer perceptron dual-channel convolutional neural network (MLP-DC-CNN) classification platform can automatically classify eight different abdominal tissue types that require different suture strategies for anastomosis. In MLP, numerous handcrafted features (∼1955) were utilized including optical properties and morphological features of one-dimensional (1D) OCT A-line signals. In DC-CNN, intensity-based features and depth-resolved tissues’ attenuation coefficients were fully exploited. A decision fusion technique was applied to leverage the information collected from both classifiers to further increase the accuracy. The algorithm was evaluated on 69,773 testing A-line data. The results showed that our model can classify the 1D OCT signals of small bowels in real time with an accuracy of 90.06%, a precision of 88.34%, and a sensitivity of 87.29%, respectively. The refresh rate of the displayed A-line signals was set as 300 Hz, the maximum sensing depth of the fiber was 3.6 mm, and the running time of the image processing algorithm was ∼1.56 s for 1,024 A-lines. The proposed fully automated tissue sensing model outperformed the single classifier of CNN, MLP, or SVM with optimized architectures, showing the complementarity of different feature sets and network architectures in classifying intestinal OCT A-line signals. It can potentially reduce the manual involvement of robotic laparoscopic surgery, which is a crucial step towards a fully autonomous STAR system.

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are only related to the anatomical structure information of tissues.…”

Section: Resultsmentioning

confidence: 99%

at pixel i, given as,

where

is the OCT signal intensity at pixel i ,

is the pixel size.…”

Section: Methodsmentioning

confidence: 99%

at pixel i, given as,

where

is the OCT signal intensity at pixel i ,

is the pixel size. In practice, OCT A-line signals decay exponentially with depth, transitioning into a trailing noise floor till in Fig.…”

Section: Methodsmentioning

confidence: 99%

where M is the pixel length of 500.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography

Wang,

Wei,

Zuo

et al. 2024

Biomed. Opt. Express

Self Cite

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show abstract

“…The system provided an axial resolution of approximately 6 μm in air and about 4.5 μm in biological samples, with a lateral resolution of around 12.9 μm. The numerical aperture (NA) of our OCT system was 0.05 [ 43 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Design and Evaluation of an Eye Mountable AutoDALK Robot for Deep Anterior Lamellar Keratoplasty

Opfermann,

Wang,

Kaluna

et al. 2024

Micromachines

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Partial-thickness corneal transplants using a deep anterior lamellar keratoplasty (DALK) approach has demonstrated better patient outcomes than a full-thickness cornea transplant. However, despite better clinical outcomes from the DALK procedure, adoption of the technique has been limited because the accurate insertion of the needle into the deep stroma remains technically challenging. In this work, we present a novel hands-free eye mountable robot for automatic needle placement in the cornea, AutoDALK, that has the potential to simplify this critical step in the DALK procedure. The system integrates dual light-weight linear piezo motors, an OCT A-scan distance sensor, and a vacuum trephine-inspired design to enable the safe, consistent, and controllable insertion of a needle into the cornea for the pneumodissection of the anterior cornea from the deep posterior cornea and Descemet’s membrane. AutoDALK was designed with feedback from expert corneal surgeons and performance was evaluated by finite element analysis simulation, benchtop testing, and ex vivo experiments to demonstrate the feasibility of the system for clinical applications. The mean open-loop positional deviation was 9.39 µm, while the system repeatability and accuracy were 39.48 µm and 43.18 µm, respectively. The maximum combined thrust of the system was found to be 1.72 N, which exceeds the clinical penetration force of the cornea. In a head-to-head ex vivo comparison against an expert surgeon using a freehand approach, AutoDALK achieved more consistent needle depth, which resulted in fewer perforations of Descemet’s membrane and significantly deeper pneumodissection of the stromal tissue. The results of this study indicate that robotic needle insertion has the potential to simplify the most challenging task of the DALK procedure, enable more consistent surgical outcomes for patients, and standardize partial-thickness corneal transplants as the gold standard of care if demonstrated to be more safe and more effective than penetrating keratoplasty.

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Depth-dependent attenuation and backscattering characterization of optical coherence tomography by stationary iterative method

Cited by 2 publications

References 42 publications

Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography

Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography

Design and Evaluation of an Eye Mountable AutoDALK Robot for Deep Anterior Lamellar Keratoplasty

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