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This paper presents a visually-guided autonomous needle driving algorithm for autonomous robotic surgical suturing. Surgical needle tracking, needle path planning, and optimum needle grasp selection algorithms are employed. The procedure is performed in 5 major steps: needle grasp, needle hand-off, needle drive, needle regrasp, and needle pull. The performance of the procedure is experimentally evaluated using the physical da Vinci® surgical robotic system and da Vinci Research Kit (dVRK). Initial results suggest that the dVRK can successfully perform needle driving with visual guidance.

The development of autonomous or semiautonomous surgical robots stands to improve the performance of existing teleoperated equipment but requires fine hand-eye calibration between the free-moving endoscopic camera and patient-side manipulator arms (PSMs). A novel method of solving this problem for the da Vinci robotic surgical system and kinematically similar systems is presented. First, a series of image-processing and optical-tracking operations are performed to compute the coordinate transformation between the endoscopic camera view frame and an optical-tracking marker permanently affixed to the camera body. Then, the kinematic properties of the PSM are exploited to compute the coordinate transformation between the kinematic base frame of the PSM and an optical marker permanently affixed thereto. Using these transformations, it is then possible to compute the spatial relationship between the PSM and the endoscopic camera using only one tracker snapshot of the two markers. The effectiveness of this calibration is demonstrated by successfully guiding the PSM end-effector to points of interest identified through the camera. Additional tests on a surgical task, namely, grasping a surgical needle, are also performed to validate the proposed method. The resulting visually guided robot positioning accuracy is better than the earlier hand-eye calibration results reported in the literature for the da Vinci system while supporting the intraoperative update of the calibration and requiring only devices that are already commonly used in the surgical environment. Note to Practitioners —The problem of hand-eye calibration for the da Vinci robotic surgical system and kinematically similar systems is addressed in this article. Existing approaches have insufficient accuracy to automate low-level surgical subtasks and often require external patterns or subjective human intervention, none of which are applicable to practical robotic minimally invasive surgery (RMIS) scenarios. This article breaks down the calibration procedure into systematic steps to reduce error accumulation. Most of the time-consuming steps are performed offline, allowing them to be retained between movements. Each time the passive joints of the manipulator or the endoscope move, all that needs to be done is to refresh the transformation between the fixed markers. This key idea enables intraoperative updates of the hand-eye calibration to be performed online without sacrificing precision. The calibration method presented here demonstrates that the achieved accuracy is sufficient for automating basic surgical manipulation tasks, such as grasping a suturing needle. The hand-eye calibration will be incorporated into a visually guided manipulation framework to perform high-precision autonomous surgical tasks.

This paper presents an approach to surgical tool tracking using stereo vision for the da Vinci(®) Surgical Robotic System. The proposed method is based on robot kinematics, computer vision techniques and Bayesian state estimation. The proposed method employs a silhouette rendering algorithm to create virtual images of the surgical tool by generating the silhouette of the defined tool geometry under the da Vinci(®) robot endoscopes. The virtual rendering method provides the tool representation in image form, which makes it possible to measure the distance between the rendered tool and real tool from endoscopic stereo image streams. Particle Filter algorithm employing the virtual rendering method is then used for surgical tool tracking. The tracking performance is evaluated on an actual da Vinci(®) surgical robotic system and a ROS/Gazebo-based simulation of the da Vinci(®) system.

This paper presents algorithms for three-dimensional tracking of surgical needles using the stereo endoscopic camera images obtained from the da Vinci(®) Surgical Robotic System. The proposed method employs Bayesian state estimation, computer vision techniques, and robot kinematics. A virtual needle rendering procedure is implemented to create simulated images of the surgical needle under the da Vinci(®) robot endoscope, which makes it possible to measure the similarity between the rendered needle image and the real needle. A particle filter algorithm using the mentioned techniques is then used for tracking the surgical needle. The performance of the tracking is experimentally evaluated using an actual da Vinci(®) surgical robotic system and quantitatively validated in a ROS/Gazebo simulation thereof.

This work evaluated the performance of a detector-based spectral CT system by obtaining objective reference data, evaluating attenuation response of iodine and accuracy of iodine quantification, and comparing conventional CT and virtual monoenergetic images in three common phantoms. Scanning was performed using the hospital's clinical adult body protocol. Modulation transfer function (MTF) was calculated for a tungsten wire and visual line pair targets were evaluated. Image noise power spectrum (NPS) and pixel standard deviation were calculated. MTF for monoenergetic images agreed with conventional images within 0.05 lp cm−1. NPS curves indicated that noise texture of 70 keV monoenergetic images is similar to conventional images. Standard deviation measurements showed monoenergetic images have lower noise except at 40 keV. Mean CT number and CNR agreed with conventional images at 75 keV. Measured iodine concentration agreed with true concentration within 6% for inserts at the center of the phantom. Performance of monoenergetic images at detector based spectral CT is the same as, or better than, that of conventional images. Spectral acquisition and reconstruction with a detector based platform represents the physical behaviour of iodine as expected and accurately quantifies the material concentration.

This paper addresses the stochastic cell formation problem with a newly proposed stochastic genetic algorithm (SGA) approach considering stochastic demand and processing times, thus capacity requirements. A stochastic nonlinear mathematical model [SNMM, proposed by Egilmez et al. (2012)] and the newly proposed SGA approaches are compared based on the solution quality and execution times on 10, 20 and 30-product problems. SGA approach is used to experiment with various GA parameters including number of generation, population size, probability and type of crossover and mutation, which resulted in 456 combinations with 10-replications each. The results of the proposed SGA model indicated that the optimal solution is guaranteed with the 10 product problem and average gaps of 1.75% and 3.70% were obtained from 20 and 30-product problems, respectively. The execution times were significantly reduced by the proposed SGA model, where reductions of 87.2%, 98.3% and 99.5% were achieved in computation times.

Purpose: To evaluate the attenuation response of iodine and the accuracy of iodine quantification on a detector-based spectral CT scanner. Methods: A Gammex 461A phantom was scanned using a dual-layer detector (IQon, Philips) at 120 kVp using helical acquisition with a CDTIvol of 15 mGy to approximate the hospital's clinical body protocol. No modifications to the standard protocol were necessary to enable spectral imaging. Iodine inserts at 6 concentrations (2, 5, 7.5, 10, 15, 20 mg/ml) were scanned individually at the center of the phantom and the 20 mg/ml insert was additionally scanned at the 3, 6, and 12 o'clock positions. Scans were repeated 10 times. Conventional, virtual monoenergetic (40–200 keV) and iodine-no-water images (with pixel values equal to iodine concentration of corresponding tissue) were reconstructed from acquired data. A circular ROI (diameter=30 pixels) was used in each conventional and monoenergetic image to measure the mean and standard deviation of the CT number in HU and in each iodine-no-water image to measure iodine concentration in mg/ml. Results: Mean CT number and contrast-to-noise ratio (CNR) measured from monoenergetic images increased with decreasing keV for all iodine concentrations and matched measurements from conventional images at 75 keV. Measurements from the 20 ml insert showed the CT number is independent of location and CNR is a function only of noise, which was higher in the center. Measured concentration from iodine-no-water images matched phantom manufacturer suggested concentration to within 6% on average for inserts at the center of the phantom. Measured concentrations were systematically higher due to optimization of iodine quantification parameters for clinical mixtures of iodine and blood/tissue. Conclusion: Spectral acquisition and reconstruction with a dual-layer detector represents the physical behavior of iodine as expected and accurately quantifies the material concentration. This should permit a variety of clinical applications including lesion characterization, vessel patency, and myocardial perfusion. This study was performed as part of a research agreement among Philips Healthcare, University Hospitals of Cleveland, and Case Western Reserve University.

Purpose: To obtain objective reference data for the spectral performance on a dual-layer detector CT platform (IQon, Philips) and compare virtual monoenergetic to conventional CT images. Methods: Scanning was performed using the hospital's clinical adult body protocol: helical acquisition at 120kVp, with CTDIvol=15mGy. Multiple modules (591, 515, 528) of a CATPHAN 600 phantom and a 20 cm diameter cylindrical water phantom were scanned. No modifications to the standard protocol were necessary to enable spectral imaging. Both conventional and virtual monoenergetic images were generated from acquired data. Noise characteristics were assessed through Noise Power Spectra (NPS) and pixel standard deviation from water phantom images. Spatial resolution was evaluated using Modulation Transfer Functions (MTF) of a tungsten wire as well as resolution bars. Low-contrast detectability was studied using contrast-to-noise ratio (CNR) of a low contrast object. Results: MTF curves of monoenergetic and conventional images were almost identical. MTF 50%, 10%, and 5% levels for monoenergetic images agreed with conventional images within 0.05lp/cm. These observations were verified by the resolution bars, which were clearly resolved at 7lp/cm but started blurring at 8lp/cm for this protocol in both conventional and 70 keV images. NPS curves indicated that, compared to conventional images, the noise power distribution of 70 keV monoenergetic images is similar (i.e. noise texture is similar) but exhibit a low frequency peak at keVs higher and lower than 70 keV. Standard deviation measurements show monoenergetic images have lower noise except at 40 keV where it is slightly higher. CNR of monoenergetic images is mostly flat across keV values and is superior to that of conventional images. Conclusion: Values for standard image quality metrics are the same or better for monoenergetic images compared to conventional images. Results indicate virtual monoenergetic images can be used without any loss in image quality or noise penalties relative to conventional images. This study was performed as part of a research agreement among Philips Healthcare, University Hospitals of Cleveland, and Case Western Reserve University.

Manufacturing system design is one of the most crucial steps of business processes. Several approaches have been proposed and implemented to increase productivity and profitability due to the change in customer characteristics, market condition and economy. Cellular Manufacturing (CM) is one of the approaches that emerged as an application of Group Technology in the late 70s due to the increase in product variety and demand variance. Group Technology (GT) is a product-oriented manufacturing approach to group similar products for smaller batch size production. As an application of GT, CM is the physical or virtual division of manufacturing facilities into manufacturing cells. A manufacturing cell is a small group of machines and/or workers ideally arranged in a flow layout to produce “similar items”, in other words “product families”.