Endoscopic surgery has been widely adopted across specialties because it reduces patient trauma, risk, and recovery time as compared to open procedures. The foremost challenge of endoscopic surgery is the inability to see in three dimensions, a disadvantage that significantly increases procedure time and uncertainty by inhibiting depth perception as well as localization and measurement capability. State-of-the-art approaches in endoscopic 3D measurement are largely based on a combination of structured light and stereo vision, but are limited in their robustness and applicability to clinical workflow. Both approaches require a large baseline and are not supported by most handheld off-the-shelf endoscopes; stereo methods specifically are sensitive to scenes lacking features or containing disturbances such as smoke and specular reflections, which are common in surgery. To address these issues, we propose an alternative method for 3D measurement based on time-of-flight (TOF), a depth sensing modality which is inherently monocular and known to be insensitive to featureless scenes and many types of disturbances. Specifically, we develop a TOF imaging module adapter compatible with off-theshelf endoscopes and demonstrate single-shot, sub-millimeter 3D measurement accuracy on animal tissue. These results are significant because they suggest widespread compatibility with existing operating room equipment and workflow and applicability to a wide variety of clinically relevant surgical tasks including measurement of tumors, hernias, and anastomoses, intra-operative registration of the surgical scene to high-quality static 3D imaging data, and control of surgical robots.
Anastomosis, the connection of two structures, is a critical procedure for reconstructive surgery with over 1 million cases/year for visceral indication alone. However, complication rates such as strictures and leakage affect up to 19% of cases for colorectal anastomoses and up to 30% for visceral transplantation anastomoses. Local ischemia plays a critical role in anastomotic complications, making blood perfusion an important indicator for tissue health and predictor for healing following anastomosis. In this work, we apply a real time multispectral imaging technique to monitor impact on tissue perfusion due to varying interrupted suture spacing and suture tensions. Multispectral tissue images at 470, 540, 560, 580, 670 and 760 nm are analyzed in conjunction with an empirical model based on diffuse reflectance process to quantify the hemoglobin oxygen saturation within the suture site. The investigated tissues for anastomoses include porcine small (jejunum and ileum) and large (transverse colon) intestines. Two experiments using interrupted suturing with suture spacing of 1, 2, and 3 mm and tension levels from 0 N to 2.5 N are conducted. Tissue perfusion at 5, 10, 20 and 30 min after suturing are recorded and compared with the initial normal state. The result indicates the contrast between healthy and ischemic tissue areas and assists the determination of suturing spacing and tension. Therefore, the assessment of tissue perfusion will permit the development and intra-surgical monitoring of an optimal suture protocol during anastomosis with less complications and improved functional outcome.
Intestinal anastomosis is a surgical procedure that restores bowel continuity after surgical resection to treat intestinal malignancy, inflammation, or obstruction. Despite the routine nature of intestinal anastomosis procedures, the rate of complications is high. Standard visual inspection cannot distinguish the tissue subsurface and small changes in spectral characteristics of the tissue, so existing tissue anastomosis techniques that rely on human vision to guide suturing could lead to problems such as bleeding and leakage from suturing sites. We present a proof-of-concept study using a portable multispectral imaging (MSI) platform for tissue characterization and preoperative surgical planning in intestinal anastomosis. The platform is composed of a fiber ring light-guided MSI system coupled with polarizers and image analysis software. The system is tested on ex vivo porcine intestine tissue, and we demonstrate the feasibility of identifying optimal regions for suture placement.
The observation and 3D quantification of arbitrary scenes using optical imaging systems is challenging, but increasingly necessary in many fields. This paper provides a technical basis for the application of plenoptic cameras in medical and medical robotics applications, and rigorously evaluates camera integration and performance in the clinical setting. It discusses plenoptic camera calibration and setup, assesses plenoptic imaging in a clinically relevant context, and in the context of other quantitative imaging technologies. We report the methods used for camera calibration, precision and accuracy results in an ideal and simulated surgical setting. Afterwards, we report performance during a surgical task. Test results showed the average precision of the plenoptic camera to be 0.90mm, increasing to 1.37mm for tissue across the calibrated FOV. The ideal accuracy was 1.14mm. The camera showed submillimeter error during a simulated surgical task.
A novel imaging system that recommends potential suture placement for anastomosis to surgeons is developed. This is
achieved by a multispectral imaging system coupled with polarizers and image analysis software. We performed
preliminary imaging of ex vivo porcine intestine to evaluate the system. Vulnerable tissue regions including blood
vessels were successfully identified and segmented. Thickness of different tissue areas is visualized. Strategies towards
optimal points for suture placements have been discussed. Preliminary data suggest our imaging platform and analysis
algorithm may be useful in avoiding blood vessels, identifying optimal regions for suture placements to perform safer
operations in possibly reduced time.
Accurate optical characterization of different tissue types is an important tool for potentially guiding surgeons
and enabling automated robotic surgery. Multispectral imaging and analysis have been used in the literature to detect
spectral variations in tissue reflectance that may be visible to the naked eye. Using this technique, hidden structures can
be visualized and analyzed for effective tissue classification. Here, we investigated the feasibility of automated tissue
classification using multispectral tissue analysis. Broadband reflectance spectra (200-1050 nm) were collected from nine
different ex vivo porcine tissues types using an optical fiber-probe based spectrometer system. We created a
mathematical model to train and distinguish different tissue types based upon analysis of the observed spectra using total
principal component regression (TPCR). Compared to other reported methods, our technique is computationally
inexpensive and suitable for real-time implementation. Each of the 92 spectra was cross-referenced against the nine
tissue types. Preliminary results show a mean detection rate of 91.3%, with detection rates of 100% and 70.0% (inner
and outer kidney), 100% and 100% (inner and outer liver), 100% (outer stomach), and 90.9%, 100%, 70.0%, 85.7%
(four different inner stomach areas, respectively). We conclude that automated tissue differentiation using our
multispectral tissue analysis method is feasible in multiple ex vivo tissue specimens. Although measurements were
performed using ex vivo tissues, these results suggest that real-time, in vivo tissue identification during surgery may be
possible.
Automating surgery using robots requires robust visual tracking. The surgical environment often has poor light
conditions where several organs have similar visual appearances. In addition, the field of view might be occluded
by blood or tissue. In this paper, the feasibility of near-infrared (NIR) fluorescent marking and imaging for
vision-based robot control is studied. The NIR region of the spectrum has several useful properties including
deep tissue penetration. We study the optical properties of a clinically-approved NIR fluorescent dye, indocyanine
green (ICG), with different concentrations and quantify image positioning error of ICG marker when obstructed
by tissue.
The development of image guided robotic and mechatronic platforms for medical applications requires a phantom
model for initial testing. Finding an appropriate phantom becomes challenging when the targeted patient
population is pediatrics, particularly infants, neonates or fetuses. Our group is currently developing a pediatricsized
surgical robot that operates under fused MRI and laparoscopic video guidance. To support this work, we
describe a method for designing and manufacturing silicone rubber organ phantoms for the purpose of testing
the robotics and the image fusion system. A surface model of the organ is obtained and converted into a mold
that is then rapid-prototyped using a 3D printer. The mold is filled with a solution containing a particular
ratio of silicone rubber to slacker additive to achieve a specific set of tactile and imaging characteristics in
the phantom. The expected MRI relaxation times of different ratios of silicone rubber to slacker additive are
experimentally quantified so that the imaging properties of the phantom can be matched to those of the organ
that it represents. Samples of silicone rubber and slacker additive mixed in ratios ranging from 1:0 to 1:1.5 were
prepared and scanned using inversion recovery and spin echo sequences with varying TI and TE, respectively,
in order to fit curves to calculate the expected T1 and T2 relaxation times of each ratio. A set of infantsized
abdominal organs was prepared, which were successfully sutured by the robot and imaged using different
modalities.
We propose a novel neuro-fuzzy hybrid transformation model for deformable image registration in intra-operative image
guided procedures involving large soft tissue deformation. The hybrid model consists of two parts: a physics-based
model and a mathematical approximation model. The physics-based model is based on elastic solid mechanics to model
major deformation patterns of the central part of organs, and the mathematical approximation model depicts the
deformation of the residual part along organ boundary. A neuro-fuzzy technique is employed to seamlessly integrate the
two parts into a unified hybrid model. Its unique feature is to incorporate domain knowledge of soft tissue deformation
patterns and significantly reduce the number of transformation parameters. We demonstrate the effectiveness of our
hybrid model to register liver magnetic resonance (MR) images in human subject study. This technique has the potential
to significantly improve intra-operative image guidance in abdominal and thoracic procedures.
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