The research was carried out with funding from Vinnova (2017-03728, 2018-05302 and 2018-03651), Heart-Lung Foundation (20180390), Family Kamprad Foundation (20190194), and Anna-Lisa and Sven Eric Lundgren Foundation (2017 and 2018).

The experiments were approved by the local Ethics Committee in Lund, Sweden. The participation was voluntary, and the patients&#39; legal guardians provided informed written consent.
1. Robot setup and configuration
NOTE: This experiment used a dual-arm collaborative industrial robot and the standard control panel with a touch display. The robot is controlled with RobotWare 6.10.01 controller software and robot integrated development environment (IDE) RobotStudio 2019.525. Software developed by the authors, including the robot application, recording application, and postprocessing scripts, are available at the GitHub repository26.
CAUTION: Use protective eyeglasses and reduced speed during setup and testing of the robot program.
<ol>
	<li>Mount the robot to the ceiling or a table using bolts dimensioned for 100 kg as described on page 25 in the product specification24, following the manufacturer&#39;s specifications. Ensure that the arms can move freely and the line of sight to the field of view is unobscured. 
	CAUTION: Use lift or safety ropes when mounting the robot in a high position.</li>
	<li>Start the robot by turning the start switch located at the base of the robot. Calibrate the robot by following the procedure described in the operating manual on pages 47-5625.</li>
	<li>Start the robot IDE on a Windows computer.</li>
	<li>Connect to the physical robot system (operating manual page 14027).</li>
	<li>Load the code for the robot program and application libraries for the user interface to the robot:
	<ol>
		<li>The robot code for a ceiling-mounted robot is in the folder Robot/InvertedCode and for a table mounted robot in Robot/TableMountedCode. For each of the files left/Data.mod, left/MainModule.mod, right/Data.mod and right/MainModule.mod:</li>
		<li>Create a new program module (see operating manual page 31827) with the same name as the file (Data or MainModule) and copy the file content to the new module.</li>
		<li>Press on Apply in the robot IDE to save the files to the robot.</li>
	</ol>
	</li>
	<li>Use File Transfer (operating manual page 34627) to transfer the robot application files TpSViewStereo2.dll, TpsViewStereo2.gtpu.dll, and TpsViewStereo2.pdb located in the FPApp folder to the robot. After this step, the robot IDE will not be further used.</li>
	<li>Press the Reset button on the back of the robot touch display (FlexPendant) to reload the graphical interface. The robot application Stereo2 will now be visible under the touch display menu.</li>
	<li>Install the recording application (Liveview) and postprocessing application on an Ubuntu 20.04 computer by running the script install_all_linux.sh, located in the root folder in the Github repository.</li>
	<li>Mount each camera to the robot. The components needed for mounting are displayed in Figure 3A.
	<ol>
		<li>Mount the lens to the camera.</li>
		<li>Mount the camera to the camera adaptor plate with three M2 screws.</li>
		<li>Mount the circular mounting plate to the camera adaptor plate with four M6 screws on the opposite side of the camera.</li>
	</ol>
	</li>
	<li>Repeat steps 1.9.1-1.9.3 for the other camera. The resulting assemblies are mirrored, as shown in Figure 3B and Figure 3C.</li>
	<li>Mount the adaptor plate to the robot wrist with four M2.5 screws, as shown in Figure 3D.
	<ol>
		<li>For a ceiling-mounted robot: attach the left camera in Figure 3C to the left robot arm as shown in Figure 2A.</li>
		<li>For a table-mounted robot: attach the left camera in Figure 3C to the right robot arm.</li>
	</ol>
	</li>
	<li>Connect the USB cables to the cameras, as shown in Figure 3E, and to the Ubuntu computer.</li>
</ol>
2. Verify the camera calibration
<ol>
	<li>On the robot touch display, press the Menu button and select Stereo2 to start the robot application. This will open the main screen, as shown in Figure 6A.
	<ol>
		<li>On the main screen, press on Go to start for 1100 mm in the robot application and wait for the robot to move to the start position.</li>
		<li>Remove the protective lens caps from the cameras and connect the USB cables to the Ubuntu computer.</li>
		<li>Place a printed calibration grid (CalibrationGrid.png in the repository) 1100 mm from the camera sensors. To facilitate correct identification of the corresponding squares, place a small screw-nut or mark somewhere in the center of the grid.</li>
	</ol>
	</li>
	<li>Start the recording application on the Ubuntu computer (run the script start.sh located in the liveview folder inside the Github repository). This starts the interface, as shown in Figure 4.
	<ol>
		<li>Adjust the aperture and focus on the lens with the aperture and focus rings.</li>
		<li>In the recording application, check Crosshair to visualize the crosshairs.</li>
	</ol>
	</li>
	<li>In the recording application, ensure that the crosshairs align with the calibration grid in the same position in both camera images, as shown in Figure 4. Most likely, some adjustment will be required as follows:
	<ol>
		<li>If the crosses do not overlap, press the Gear icon (bottom left Figure 6A) in the robot application on the robot touch display to open the setting screen, as shown in Figure 6B.</li>
		<li>Press on 1. Go to Start Pos, as shown in Figure 6B.</li>
		<li>Jog the robot with the joystick to adjust the camera position (operating manual page 3123).</li>
		<li>Update the tool position for each robot arm. Press 3. Update Left Tool and 4. Update Right Tool to save the calibration for the left and the right arm, respectively.</li>
		<li>Press on the Back Arrow icon (top right, Figure 6B) to return to the main screen.</li>
	</ol>
	</li>
	<li>Press on Run Experiment (Figure 6A) in the robot application and verify that the crosshairs align. Otherwise, repeat steps 2.3-2.3.5.</li>
	<li>Add and test any changes to the distances and/or time at this point. This requires changes in the robot program code and advanced robot programming skills. Change the following variables in the Data module in the left task (arm): the desired separation distances in the integer array variable Distances, the convergence distances in the integer array ConvergencePos and edit the time at each step by editing the variable Nwaittime (value in seconds). 
	CAUTION: Never run an untested robot program during live surgery.</li>
	<li>When the calibration is complete, press on Raise to raise the robot arms to the standby position.</li>
	<li>Optionally turn off the robot. 
	&#8203;NOTE: The procedure can be paused between any of the steps above.</li>
</ol>
3. Preparation at the start of the surgery
<ol>
	<li>Dust the robot.
	<ol>
		<li>If the robot was turned off, start it by turning on the Start switch located at the base of the robot.</li>
	</ol>
	</li>
	<li>Start the robot application on the touch display and recording application described in steps 2.1 and 2.2.</li>
	<li>In the recording application, create and then select the folder where to save the video (press Change Folder).</li>
	<li>In the robot application: press the gear icon, position the cameras in relation to the patient. Change X and Z direction by pressing +/- for Hand Distance from Robot and Height, respectively, so that the image captures the surgical field. Perform the positioning in the Y-direction by manually moving the robot or patient. 
	&#8203;NOTE: The preparations can be paused between the preparation steps 3.1-3.4.</li>
</ol>
4. Experiment
CAUTION: All personnel should be informed about the experiment beforehand.
<ol>
	<li>Pause the surgery.
	<ol>
		<li>Inform the OR personnel that the experiment is started.</li>
	</ol>
	</li>
	<li>Press Record in the recording application.</li>
	<li>Press Run experiment in the robot application.</li>
	<li>Wait while the program is running; the robot displays &#34;Done&#34; in the robot application on the touch display when finished.</li>
	<li>Stop recording in the recording application by pressing Quit.
	<ol>
		<li>Inform the OR personnel that the experiment has finished.</li>
	</ol>
	</li>
	<li>Resume surgery. 
	&#8203;NOTE: The experiment cannot be paused during steps 4.1-4.6.</li>
</ol>
5. Repeat
<ol>
	<li>Repeat steps 4.1-4.6 to capture another sequence and steps 3.1-3.4 and steps 4.1-4.6 to capture sequences from different surgeries. Capture around ten full sequences.</li>
</ol>
6. Postprocessing
NOTE: The following steps can be carried out using most video editing software or the provided scripts in the postprocessing folder.
<ol>
	<li>In this case, debayer the video as it is saved in the RAW format:
	<ol>
		<li>Run the script postprocessing/debayer/run.sh to open the debayer application shown in Figure 8A.</li>
		<li>Press Browse Input Directory and select the folder with the RAW video.</li>
		<li>Press Browse Output Directory and select a folder for the resulting debayered and color-adjusted video files.</li>
		<li>Press Debayer! and wait until the process is finished - both progress bars are full, as shown in Figure 8B.</li>
	</ol>
	</li>
	<li>Merge the right and left synchronized videos to 3D stereo format28:
	<ol>
		<li>Run the script postprocessing/merge_tb/run.sh to start the merge application; it opens the graphical user interface shown in Figure 8C.</li>
		<li>Press Browse Input Directory and select the folder with the debayered video files.</li>
		<li>Press Browse Output Directory and select a folder for the resulting merged 3D stereo file.</li>
		<li>Press Merge! and wait until the finish screen in Figure 8D is shown.</li>
	</ol>
	</li>
	<li>Use off-the-shelf video editing software such as Premiere Pro to add text labels to each camera distance in the video. 
	&#8203;NOTE: In the video, there is a visible shake every time the robot moved, and the camera distance increased. In this experiment, labels A-T were used for the camera distances.</li>
</ol>
7. Evaluation
<ol>
	<li>Display the video in top-bottom 3D format with an active 3D projector.</li>
	<li>Viewing experience depends on the viewing angle and the distance to screen; evaluate the video using intended audience and setup.</li>
</ol>

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The authors have nothing to disclose.

During live surgery, the total time of the experiment used for 3D video data collection was limited to be safe for the patient. If the object is unfocused or overexposed, the data cannot be used. The critical steps are during camera tool calibration and setup (step 2). The camera aperture and focus cannot be changed when the surgery has started; the same lighting conditions and distance should be used during setup and surgery. The camera calibration in steps 2.1-2.4 must be carried out carefully to ensure that the heart ...

In surgery, 3D visualization can be used for education, diagnoses, pre-operative planning, and post-operative evaluation1,2. Realistic depth perception can improve understanding3,4,5,6 of normal and abnormal anatomies. Simple 2D video recordings of surgical procedures are a good start. However, the lack of depth perception can make it hard for the non-surgical colleagues to fully understand the antero-posterior relationships between different anatomical structures and therefore also introduce a risk of misinterpretation of the anatomy7,8,9,10.
The 3D viewing experience is affected by five factors: (1) Camera configuration can either be parallel or toed-in as shown in Figure 1, (2) Baseline distance (the separation between the cameras). (3) Distance to the object of interest and other scene characteristics such as the background. (4) Characteristics of viewing devices such as screen size and viewing position1,11,12,13. (5) Individual preferences of the viewers14,15.
Designing a 3D camera setup begins with the capture of test videos recorded at various camera baseline distances and configurations to be used for subjective or automatic evaluation16,17,18,19,20. The camera distance must be constant to the surgical field to capture sharp images. Fixed focus is preferred because autofocus will adjust to focus on hands, instruments, or heads that may come into view. However, this is not easily achievable when the scene of interest is the surgical field. Operating rooms are restricted access areas because these facilities must be kept clean and sterile. Technical equipment, surgeons, and scrub nurses are often clustered closely around the patient to secure a good visual overview and an efficient workflow. To compare and evaluate the effect of camera positions on the 3D viewing experience, one complete test range of camera positions should be recording the same scene because the object characteristics such as shape, size, and color can affect the 3D viewing experience21.
For the same reason, complete test ranges of camera positions should be repeated on different surgical procedures. The entire sequence of positions must be repeated with high accuracy. In a surgical setting, existing methods that require either manual adjustment of the baseline distance22 or different camera pairs with fixed baseline distances23 are not feasible because of both space and time constraints. To address this challenge, this robotized solution was designed.
The data was collected with a dual-arm collaborative industrial robot mounted in the ceiling in the operating room. Cameras were attached to the wrists of the robot and moved along an arc-shaped trajectory with increasing baseline distance, as shown in Figure 2.
To demonstrate the approach, 10 test series were recorded from 4 different patients with 4 different congenital heart defects. Scenes were chosen when a pause in surgery was feasible: with the beating hearts just before and after surgical repair. Series were also made when the hearts were arrested. The surgeries were paused for 3 min and 20 s to collect forty 5-ssequences with different camera convergence distances and baseline distances to capture the scene. The videos were later post-processed, displayed in 3D for the clinical team, who rated how realistic the 3D video was along a scale from 0-5.
The convergence point for toed-in stereo cameras is where the center points of both images meet. The convergence point can, by principle, be placed either in front, within, or behind the object, see Figure 1A-C. When the convergence point is in front of the object, the object will be captured and displayed left of the midline for the left camera image and right of the midline for the right camera image (Figure 1A). The opposite applies when the convergence point is behind the object (Figure 1B). When the convergence point is on the object, the object will also appear in the midline of the camera images (Figure 1C), which presumably should yield the most comfortable viewing since no squinting is required to merge the images. To achieve comfortable stereo 3D video, the convergence point must be located on, or slightly behind, the object of interest, else the viewer is required to voluntarily squint outwards (exotropia).
The data was collected using a dual-arm collaborative industrial robot to position the cameras (Figure 2A-B). The robot weighs 38 kg without equipment. The robot is intrinsically safe; when it detects an unexpected impact, it stops moving. The robot was programmed to position the 5 Megapixel cameras with C-mount lenses along an arc-shaped trajectory stopping at predetermined baseline distances (Figure 2C). The cameras were attached to the robot hands using adaptor plates, as shown in Figure 3. Each camera recorded at 25 frames per second. Lenses were set at f-stop 1/8 with focus fixed on the object of interest (approximated geometrical center of the heart). Every image frame had a timestamp which was used to synchronize the two video streams.
Offsets between the robot wrist and the camera were calibrated. This can be achieved by aligning the crosshairs of the camera images, as shown in Figure 4. In this setup, the total translational offset from the mounting point on the robot wrist and the center of the camera image sensor was 55.3 mm in the X-direction and 21.2 mm in the Z-direction, displayed in Figure 5. The rotational offsets were calibrated at a convergence distance of 1100 mm and a baseline distance of 50 mm and adjusted manually with the joystick on the robot control panel. The robot in this study had a specified accuracy of 0.02 mm in Cartesian space and 0.01 degrees rotational resolution24. At a radius of 1100 m, an angle difference of 0.01 degrees offsets the center point 0.2 mm. During the full robot motion from 50-240 mm separation, the crosshair for each camera was within 2 mm from the ideal center of convergence.
The baseline distance was increased stepwise by symmetrical separation of the cameras around the center of the field of view in increments of 10 mm ranging from 50-240 mm (Figure 2). The cameras were kept at a standstill for 5 s in each position and moved between the positions at a velocity of 50 mm/s. The convergence point could be adjusted in X and Z directions using a graphical user interface (Figure 6). The robot followed accordingly within its working range.
The accuracy of the convergence point was estimated using the uniform triangles and the variable names in Figure 7A and B. The height &#39;z&#39; was calculated from the convergence distance &#39;R&#39; with the Pythagorean theorem as
<img alt="Equation 1" src="/files/ftp_upload/62786/62786eq01.jpg" />
When the real convergence point was closer than the desired point, as shown in Figure 7A, the error distance &#39;f1&#39; was calculated as
<img alt="Equation 2" src="/files/ftp_upload/62786/62786eq02.jpg" />
Similarly, when the convergence point was distal to the desired point, the error distance &#39;f2&#39; was calculated as
<img alt="Equation 3" src="/files/ftp_upload/62786/62786eq03.jpg" />
Here, &#39;e&#39; was the maximum separation between the crosshairs, at most 2 mm at maximum baseline separation during calibration (D = 240 mm). For R = 1100 mm (z = 1093 mm), the error was less than &#177; 9.2 mm. For R = 1400 mm (z = 1395 mm), the error was &#177; 11.7 mm. That is, the error of the placement of the convergence point was within 1% of the desired. The two test distances of 1100 mm and 1400 mm were therefore well separated.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>2 C-mount lenses (35 mm F2.1, 5 M pixel)</td>
			<td>Tamron</td>
			<td>M112FM35</td>
			<td>Rated for 5 Mpixel</td>
		</tr>
		<tr>
			<td>3D glasses (DLP-link active shutter)</td>
			<td>Celexon</td>
			<td>G1000</td>
			<td>Any compatible 3D glasses can be used</td>
		</tr>
		<tr>
			<td>3D Projector</td>
			<td>Viewsonic</td>
			<td>X10-4K</td>
			<td>Displays 3D in 1080, can be exchanged for other 3D projectors</td>
		</tr>
		<tr>
			<td>6 M2 x 8 screws</td>
			<td></td>
			<td></td>
			<td>To attach the cXimea cameras to the camera adaptor plates</td>
		</tr>
		<tr>
			<td>8 M2.5 x 8 screws</td>
			<td></td>
			<td></td>
			<td>To attach the circular mounting plates to the robot wrist</td>
		</tr>
		<tr>
			<td>8 M5 x 40 screws</td>
			<td></td>
			<td></td>
			<td>To mount the robot</td>
		</tr>
		<tr>
			<td>8 M6 x 10 screws with flat heads</td>
			<td></td>
			<td></td>
			<td>For attaching the circular mounting plate and the camera adaptor plates</td>
		</tr>
		<tr>
			<td>Calibration checker board plate (25 by 25 mm)</td>
			<td></td>
			<td></td>
			<td>Any standard checkerboard can be used, including printed, as long as the grid is clearly visible in the cameras</td>
		</tr>
		<tr>
			<td>Camera adaptor plates, x2</td>
			<td></td>
			<td></td>
			<td>Designed by the authors in robot_camera_adaptor_plates.dwg, milled in aluminium.</td>
		</tr>
		<tr>
			<td>Circular mounting plates, x2</td>
			<td></td>
			<td></td>
			<td>Distributed with the permission of the designer Julius Klein and printed with ABS plastic on an FDM 3D printer. License Tecnalia Research &#38; Innovation 2017. Attached as Mountingplate_ROBOT_SIDE_ 
			NewDesign_4.stl</td>
		</tr>
		<tr>
			<td>Fix focus usb cameras, x2 (5 Mpixel)</td>
			<td>Ximea</td>
			<td>MC050CG-SY-UB</td>
			<td>With Sony IMX250LQR sensor</td>
		</tr>
		<tr>
			<td>Flexpendant</td>
			<td>ABB</td>
			<td>3HAC028357-001</td>
			<td>robot touch display</td>
		</tr>
		<tr>
			<td>Liveview</td>
			<td></td>
			<td></td>
			<td>recording application</td>
		</tr>
		<tr>
			<td>RobotStudio</td>
			<td></td>
			<td></td>
			<td>&#160;robot integrated development environment (IDE)</td>
		</tr>
		<tr>
			<td>USB3 active cables (10.0 m), x2</td>
			<td></td>
			<td></td>
			<td>Thumbscrew lock connector, water proofed.</td>
		</tr>
		<tr>
			<td>YuMi dual-arm robot</td>
			<td>ABB</td>
			<td>IRB14000</td>
			<td></td>
		</tr>
	</tbody>
</table>

robotized testing of camera positions to determine ideal configuration for stereo 3d visualization of open-heart surgery

Stereo 3D video from surgical procedures can be highly valuable for medical education and improve clinical communication. But access to the operating room and the surgical field is restricted. It is a sterile environment, and the physical space is crowded with surgical staff and technical equipment. In this setting, unobscured capture and realistic reproduction of the surgical procedures are difficult. This paper presents a method for rapid and reliable data collection of stereoscopic 3D videos at different camera baseline distances and distances of convergence. To collect test data with minimum interference during surgery, with high precision and repeatability, the cameras were attached to each hand of a dual-arm robot. The robot was ceiling-mounted in the operating room. It was programmed to perform a timed sequence of synchronized camera movements stepping through a range of test positions with baseline distance between 50-240 mm at incremental steps of 10 mm, and at two convergence distances of 1100 mm and 1400 mm. Surgery was paused to allow 40 consecutive 5-s video samples. A total of 10 surgical scenarios were recorded.

An acceptable evaluation video with the right image placed at the top in top-bottom stereoscopic 3D is shown in Video1. A successful sequence should be sharp, focused, and without unsynchronized image frames. Unsynchronized video streams will cause blur, as shown in the file Video 2. The convergence point should be centered horizontally, independent of the camera separation, as seen in Figure 9A,B. When the robot transitions between the posi...

Watch this Scientific Journal Video about Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery at JoVE.com

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

The human depth perception of 3D stereo videos depends on the camera separation, point of convergence, distance to, and familiarity of the object. This paper presents a robotized method for rapid and reliable test data collection during live open-heart surgery to determine the ideal camera configuration.

Stereo 3D video from surgical procedures can be highly valuable for medical education and improve clinical communication. But access to the operating room ...

robotized-testing-camera-positions-to-determine-ideal-configuration

Research

JoVE Journal

Medicine

1.9K Views.  Skåne University Hospital. The human depth perception of 3D stereo videos depends on the camera separation, point of convergence, distance to, and familiarity of the object. This paper presents a robotized method for rapid and reliable test data collection during live open-heart surgery to determine the ideal camera configuration. 

Video: Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality image integration can be technically demanding, and is underutilised in clinical practice. We have developed a single software platform for image integration, 3D visualization and surgical planning. Here, our pipeline is described in step-by-step fashion, starting with image acquisition, proceeding through image co-registration, manual segmentation, brain and vessel extraction, 3D visualization and manual planning of stereoEEG (SEEG) implantations. With dissemination of the software this pipeline can be reproduced in other centres, allowing other groups to benefit from 3DMMI. We also describe the use of an automated, multi-trajectory planner to generate stereoEEG implantation plans. Preliminary studies suggest this is a rapid, safe and efficacious adjunct for planning SEEG implantations. Finally, a simple solution for the export of plans and models to commercial neuronavigation systems for implementation of plans in the operating theater is described. This software is a valuable tool that can support clinical decision making throughout the epilepsy surgery pathway.

We describe the steps to use our custom designed software for image integration, visualization and planning in epilepsy surgery.

Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality ...

Murine Flexor Tendon Injury and Repair Surgery

Tendon connects skeletal muscle and bone, facilitating movement of nearly the entire body. In the hand, flexor tendons (FTs) enable flexion of the fingers and general hand function. Injuries to the FTs are common, and satisfactory healing is often impaired due to excess scar tissue and adhesions between the tendon and surrounding tissue. However, little is known about the molecular and cellular components of FT repair. To that end, a murine model of FT repair that recapitulates many aspects of healing in humans, including impaired range of motion and decreased mechanical properties, has been developed and previously described. Here an in-depth demonstration of this surgical procedure is provided, involving transection and subsequent repair of the flexor digitorum longus (FDL) tendon in the murine hind paw.&#160;This technique can be used to conduct lineage analysis of different cell types, assess the effects of gene gain or loss-of-function, and to test the efficacy of pharmacological interventions in the healing process. However, there are two primary limitations to this model: i) the FDL tendon in the mid-portion of the murine hind paw, where the transection and repair occur, is not surrounded by a synovial sheath. Therefore this model does not account for the potential contribution of the sheath to the scar formation process. ii) To protect the integrity of the repair site, the FT is released at the myotendinous junction, decreasing the mechanical forces of the tendon, likely contributing to increased scar formation. Isolation of sufficient cells from the granulation tissue of the FT during the healing process for flow cytometric analysis has proved challenging; cytology centrifugation to concentrate these cells is an alternate method used, and allows for generation of cell preparations on which immunofluorescent labeling can be performed. With this method, quantification of cells or proteins of interest during FT healing becomes possible.

Flexor tendons in the hand are commonly injured, leading to impaired hand function. However, the scar-tissue healing response is not well characterized. A murine model of flexor tendon healing is demonstrated here. This model can enhance overall understanding of the healing process and assess therapeutic approaches to improve healing.

Tendon connects skeletal muscle and bone, facilitating movement of nearly the entire body. In the hand, flexor tendons (FTs) enable flexion of the fingers ...

A Quantitative Sensory Testing Paradigm to Obtain Measures of Pain Processing in Patients Undergoing Breast Cancer Surgery

Chronic pain following surgery, persistent postsurgical pain, is an important highly prevalent condition contributing to significant symptom burden and lower quality of life. Persistent postsurgical pain is relatively refractory to treatment hence generating a high need for preventive strategies and treatments. Therefore, the identification of patients at risk of developing persistent pain is an area of active ongoing research. Recently it was demonstrated that peri-operative disruptions in central pain processing may be able to predict persistent postsurgical pain at long term follow-up in breast cancer patients. The aim of the current report is to present a short protocol to obtain pain thresholds to different stimuli at multiple sites and a measure of endogenous analgesia in breast cancer patients. We have used this method successfully in a clinical context and detail some representative results from a clinical study.

Persistent postsurgical pain may be related to changes in pain processing. By assessing pain in response to standardized stimuli, changes in pain processing can be elucidated. We present methods to obtain pain thresholds to different stimuli and a measure of endogenous analgesia in patients undergoing breast cancer surgery.

Chronic pain following surgery, persistent postsurgical pain, is an important highly prevalent condition contributing to significant symptom burden and ...

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Limited evidence exists to precisely estimate efficacy and safety differences between parenteral nutrition (PN) prepared using olive-oil-based three-chamber bags (3CBs) and soybean-oil-based compounded bags (CoBs) in hospitalized adult patients. We designed a multicenter, randomized, prospective, open-label, noninferiority protocol to compare the efficacy, safety, and distribution of olive-oil-based 3CBs and soybean-oil-based CoB formulations in adult Chinese patients requiring PN during surgical intervention. Subjects were randomized to receive either one of the study treatments using an interactive voice or web-based recognition system in accordance with the randomization code. Randomization was further stratified based on the study site and surgical category. Both treatment groups received similar amounts of calories and protein. In addition, the two study treatments contained a similar composition of the amino-acid component. The only difference between the two PN formulations was the lipid constitution. The duration of administration of study treatments was a minimum of 5 days up to a maximum of 14 days after the surgical procedure. The primary efficacy endpoint was serum prealbumin levels on day 5 of the study. Noninferiority was proved if the anti-log of the lower bound of the 95% confidence interval (CI) of the treatment difference was at least 0.80. Other efficacy measures included treatment preparation time; duration to achieve tolerability of oral nutrition; associated infectious complications; length of hospitalization; and laboratory assessment of markers of nutrition, inflammation, metabolism, and oxidative stress. A total of 458 patients were enrolled in the study. The results showed that olive-oil-based 3CBs were non-inferior to soybean-based CoBs, besides being well tolerated. The infection rate was found to be significantly lower in the olive-oil-based 3CB group. Thus, this study may be used as a reference for future research on lipid emulsion and 3CBs.

Here, we present a protocol for a study comparing the efficacy, safety, and delivery of olive-oil-based 3CB and soybean-oil-based CoB formulations in adults requiring parenteral nutrition. The results revealed that olive-oil-based 3CBs is non-inferior and well tolerated compared to soybean formulations.

Limited evidence exists to precisely estimate efficacy and safety differences between parenteral nutrition (PN) prepared using olive-oil-based ...

A Training and Testing System for Performing Vascular Reconstruction In Vitro

Manual vascular reconstruction training is essential for a beginner surgeon. However, an optimal training system for vascular reconstruction in vitro has yet to be developed. In this study, we introduce an in vitro training and testing system using a magnetic anchoring technique with which a trainee can practice manual vascular reconstruction individually. Additionally, this system can also be used to test the quality of the reconstruction. The described system includes a vascular reconstruction training machine, magnetic tractors, and a magnetic suture puller. In this manuscript, we detail an end-to-end vein anastomosis using porcine right and left iliac veins. To identify the potential damage caused by a magnetic suture puller on the suture, we created three groups with six segments of 4-0 polypropylene sutures each: a control group with no intervention on the polypropylene suture, a group in which the polypropylene suture is manually pulled with sterile gloves 20x, and a magnetic puller group in which the magnetic puller pulled the polypropylene suture 20x. These groups were tested by light microscopy and breaking strength tests, and the effect of reconstruction was assessed. In the light microscopy test, the control group was less likely to be damaged (p &#60; 0.05) and the number of damaged points of the manual group and magnetic puller group were similar (p &#62; 0.05). The results of the breaking strength test were compared across groups and no significant difference was observed (p &#62; 0.05). The end-to-end anastomosis of the porcine iliac veins was successfully performed using this training system, and the reconstructed veins could undergo 2.0 kPa perfusion pressure. Using this training and testing system the trainee can practice manual vascular reconstruction in vitro individually with the aid of magnetic tractors and a magnetic suture puller, and the quality of the reconstruction can be tested.

Here we present a training and testing system where a trainee can complete manual vascular reconstruction in vitro individually using a magnetic anchoring technique. The system can also be used to test the quality of reconstruction.

Manual vascular reconstruction training is essential for a beginner surgeon. However, an optimal training system for vascular reconstruction in vitro has ...

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Valid comparisons of postoperative accuracy results in computer-assisted reconstruction of the mandible are difficult due to heterogeneity in imaging modalities, mandibular defect classification, and evaluation methodologies between studies. This guideline uses a step-by-step approach guiding the process of imaging, classification of mandibular defects and volume assessment of three-dimensional (3D) models, after which a legitimized quantitative accuracy evaluation method can be performed between the postoperative clinical situation and the preoperative virtual plan. The condyles and the vertical and horizontal corners of the mandible are used as bony landmarks to define virtual lines in the computer-assisted surgery (CAS) software. Between these lines the axial, coronal, and both sagittal mandibular angles are calculated on both pre- and postoperative 3D models of the (neo)mandible and subsequently the deviations are calculated. By superimposing the postoperative 3D model to the preoperative virtually planned 3D model, which is fixed to the XYZ axis, the deviation between pre- and postoperative virtually planned dental implant positions can be calculated. This protocol continues and specifies an earlier publication of this evaluation guideline.

Here, we propose a practical, feasible and reproducible evaluation guideline for computer-assisted reconstruction of the mandible in order to create uniformity between studies regarding postoperative accuracy evaluation. This protocol continues and specifies an earlier publication of this evaluation guideline.

Valid comparisons of postoperative accuracy results in computer-assisted reconstruction of the mandible are difficult due to heterogeneity in imaging ...

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone

Augmented reality (AR) has great potential in education, training, and surgical guidance in the medical field. Its combination with three-dimensional (3D) printing (3DP) opens new possibilities in clinical applications. Although these technologies have grown exponentially in recent years, their adoption by physicians is still limited, since they require extensive knowledge of engineering and software development. Therefore, the purpose of this protocol is to describe a step-by-step methodology enabling inexperienced users to create a smartphone app, which combines AR and 3DP for the visualization of anatomical 3D models of patients with a 3D-printed reference marker. The protocol describes how to create 3D virtual models of a patient&rsquo;s anatomy derived from 3D medical images. It then explains how to perform positioning of the 3D models with respect to marker references. Also provided are instructions for how to 3D print the required tools and models. Finally, steps to deploy the app are provided. The protocol is based on free and multi-platform software and can be applied to any medical imaging modality or patient. An alternative approach is described to provide automatic registration between a 3D-printed model created from a patient&rsquo;s anatomy and the projected holograms. As an example, a clinical case of a patient suffering from distal leg sarcoma is provided to illustrate the methodology. It is expected that this protocol will accelerate the adoption of AR and 3DP technologies by medical professionals.

Presented here is a method to design an augmented reality smartphone application for the visualization of anatomical three-dimensional models of patients using a 3D-printed reference marker.

Augmented reality (AR) has great potential in education, training, and surgical guidance in the medical field. Its combination with three-dimensional (3D) ...

3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects

We are in the midst of the 3D era in most aspects of life, and especially in medicine. The surgical discipline is one of the major players in the medical field using the constantly developing 3D planning and printing capabilities. Computer-assisted design (CAD) and computer assisted manufacturing (CAM) are used to describe the 3D planning and manufacturing of the product. The planning and manufacturing of 3D surgical guides and reconstruction implants is performed almost exclusively by engineers. As technology advances and software interfaces become more user-friendly, it raises a question regarding the possibility of transferring the planning and manufacturing to the clinician. The reasons for such a shift are clear: the surgeon has the idea of what he wants to design, and he also knows what is feasible and could be used in the operating room. It allows him to be prepared for any scenario/unexpected results during the operation and allows the surgeon to be creative and express his new ideas using the CAD software. The purpose of this method is to provide clinicians with the ability to create their own surgical guides and reconstruction implants. In this manuscript, a detailed protocol will provide a simple method for segmentation using segmentation software and implant planning using a 3D design software. Following the segmentation and stl file production using segmentation software, the clinician could create a simple patient specific reconstruction plate or a more complex plate with a cradle for bone graft positioning. Surgical guides can be created for accurate resection, hole preparation for proper reconstruction plate positioning or for bone graft harvesting and re-contouring. A case of lower jaw reconstruction following plate fracture and nonunion healing of a trauma sustained injury is detailed.

This protocol describes the use of 3D planning and printing for reconstruction of bony defects. We use segmentation tools to create 3D models followed by 3D design software to create patient specific implants for reconstruction purposes concomitant to ablative surgery or as a second stage.

We are in the midst of the 3D era in most aspects of life, and especially in medicine. The surgical discipline is one of the major players in the medical ...

Use of 3D Robotic Ultrasound for In Vivo Analysis of Mouse Kidneys

Common modalities for in vivo imaging of rodents include positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). Each method has limitations and advantages, including availability, ease of use, cost, size, and the use of ionizing radiation or magnetic fields. This protocol describes the use of 3D robotic US for in vivo imaging of rodent kidneys and heart, subsequent data analysis, and possible research applications. Practical applications of robotic US are the quantification of total kidney volume (TKV), as well as the measurement of cysts, tumors, and vasculature. Although the resolution is not as high as other modalities, robotic US allows for more practical high throughput data collection. Furthermore, using US M-mode imaging, cardiac function may be quantified. Since the kidneys receive 20%-25% of the cardiac output, assessing cardiac function is critical to the understanding of kidney physiology and pathophysiology.

Use of 3D Robotic Ultrasound for In Vivo Analysis of Mouse Kidneys

This protocol demonstrates robotic ultrasound (US) as a practical, cost-effective, and quick alternative to traditional non-invasive image modalities.

Common modalities for in vivo imaging of rodents include positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), ...

Combining 3D-Printing and Electrospinning to Manufacture Biomimetic Heart Valve Leaflets

Electrospinning has become a widely used technique in cardiovascular tissue engineering as it offers the possibility to create (micro-)fibrous scaffolds with adjustable properties. The aim of this study was to create multilayered scaffolds mimicking the architectural fiber characteristics of human heart valve leaflets using conductive 3D-printed collectors.
Models of aortic valve cusps were created using commercial computer-aided design (CAD) software. Conductive polylactic acid was used to fabricate 3D-printed leaflet templates. These cusp negatives were integrated into a specifically designed, rotating electrospinning mandrel. Three layers of polyurethane were spun onto the collector, mimicking the fiber orientation of human heart valves. Surface and fiber structure was assessed with a scanning electron microscope (SEM). The application of fluorescent dye additionally permitted the microscopic visualization of the multilayered fiber structure. Tensile testing was performed to assess the biomechanical properties of the scaffolds.
3D-printing of essential parts for the electrospinning rig was possible in a short time for a low budget. The aortic valve cusps created following this protocol were three-layered, with a fiber diameter of 4.1 &#177; 1.6 &#181;m. SEM imaging revealed an even distribution of fibers. Fluorescence microscopy revealed individual layers with differently aligned fibers, with each layer precisely reaching the desired fiber configuration. The produced scaffolds showed high tensile strength, especially along the direction of alignment. The printing files for the different collectors are available as Supplemental File 1, Supplemental File 2, Supplemental File 3, Supplemental File 4, and Supplemental File 5.
With a highly specialized setup and workflow protocol, it is possible to mimic tissues with complex fiber structures over multiple layers. Spinning directly on 3D-printed collectors creates considerable flexibility in manufacturing 3D shapes at low production costs.

The presented method offers an innovative way for engineering biomimetic fiber structures in three-dimensional (3D) scaffolds (e.g., heart valve leaflets). 3D-printed, conductive geometries were used to determine shape and dimensions. Fiber orientation and characteristics were individually adjustable for each layer. Multiple samples could be manufactured in one setup.

Electrospinning has become a widely used technique in cardiovascular tissue engineering as it offers the possibility to create (micro-)fibrous scaffolds ...