Main Page/SlicerCommunity/2020 - Revision history

Marianna: /* The Impact of Intracortical Lesions on Volumes of Subcortical Structures in Multiple Sclerosis. */

2021-07-06T18:58:20Z

The Impact of Intracortical Lesions on Volumes of Subcortical Structures in Multiple Sclerosis.

Marianna: /* Evaluation of Imaging Software Accuracy for 3-Dimensional Analysis of the Mandibular Condyle. A Comparative Study Using a Surface-to-Surface Matching Technique */

2021-07-06T18:43:27Z

Evaluation of Imaging Software Accuracy for 3-Dimensional Analysis of the Mandibular Condyle. A Comparative Study Using a Surface-to-Surface Matching Technique

Marianna at 13:23, 6 July 2021

2021-07-06T13:23:07Z

Marianna: /* 3D Slicer and Sina Appilication for Surgical Planning of Giant Invasive Spinal Schwannoma With Scoliosis: A Case Report and Literature Review */

2021-07-01T19:45:46Z

3D Slicer and Sina Appilication for Surgical Planning of Giant Invasive Spinal Schwannoma With Scoliosis: A Case Report and Literature Review

Marianna: /* The Effect of Registration on Voxel-Wise Tofts Model Parameters and Uncertainties from DCE-MRI of Early-Stage Breast Cancer Patients Using 3DSlicer */

2021-07-01T19:33:09Z

The Effect of Registration on Voxel-Wise Tofts Model Parameters and Uncertainties from DCE-MRI of Early-Stage Breast Cancer Patients Using 3DSlicer

Marianna: /* A Novel Open-Source Software-Based High-Precision Workflow for Target Definition in Cardiac Radioablation */

2021-07-01T19:15:18Z

A Novel Open-Source Software-Based High-Precision Workflow for Target Definition in Cardiac Radioablation

Marianna: /* Temporomandibular Joint Damage in K/BxN Arthritic Mice */

2021-07-01T19:11:41Z

Temporomandibular Joint Damage in K/BxN Arthritic Mice

Marianna: /* Free-to-Use DIR Solutions in Radiotherapy: Benchmark Against Commercial Platforms Through a Contour-Propagation Study */

2021-07-01T19:08:23Z

Free-to-Use DIR Solutions in Radiotherapy: Benchmark Against Commercial Platforms Through a Contour-Propagation Study

Marianna: /* The Dynamics of Hematoma Surface Regularity and Hematoma Expansion in Acute Intracerebral Hemorrhage */

2021-07-01T19:05:37Z

The Dynamics of Hematoma Surface Regularity and Hematoma Expansion in Acute Intracerebral Hemorrhage

Marianna: /* The Dynamics of Hematoma Surface Regularity and Hematoma Expansion in Acute Intracerebral Hemorrhage */

2021-07-01T19:03:19Z

The Dynamics of Hematoma Surface Regularity and Hematoma Expansion in Acute Intracerebral Hemorrhage

← Older revision		Revision as of 18:58, 6 July 2021
Line 656:		Line 656:

	'''Abstract:''' Seventy-one patients with MS were included. The volumes of intracortical lesions and white matter lesions were identified on double inversion recovery and FLAIR, respectively, by using [http://www.slicer.org '''3D Slicer''']. Volumes of white matter T1 hypointensities and subcortical gray matter, thalamus, caudate, putamen, and pallidum volumes were calculated using FreeSurfer. Age, MS duration, and the Expanded Disability Status Scale score were assessed.		'''Abstract:''' Seventy-one patients with MS were included. The volumes of intracortical lesions and white matter lesions were identified on double inversion recovery and FLAIR, respectively, by using [http://www.slicer.org '''3D Slicer''']. Volumes of white matter T1 hypointensities and subcortical gray matter, thalamus, caudate, putamen, and pallidum volumes were calculated using FreeSurfer. Age, MS duration, and the Expanded Disability Status Scale score were assessed.
		+	\|}
		+
		+	==Unilateral Maxillary Central Incisor Root Resorption After Orthodontic Treatment for Angle Class II, Division 1 Malocclusion With Significant Maxillary Midline Deviation: A Possible Correlation With Root Proximity to the Incisive Canal==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://pubmed.ncbi.nlm.nih.gov/32475849 Korean J Orthod. 2020 May 25; 50(3):216–26. PMID: 32475849] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270934/pdf/KJOD-50-216.pdf PDF]
		+
		+	'''Authors:''' Imamura T, Uesugi S, Ono T.
		+
		+
		+	'''Institution:''' Department of Orthodontic Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan.
		+
		+	'''Abstract:''' The CBCT images acquired before and after active treatment were superimposed according to methods reported in a previous study. In addition, using Mimics software (version 18.0; Materialise, Leuven, Belgium) and [http://www.slicer.org '''3D Slicer'''] v.4.7.0, we confirmed the 3D configuration, amount of tooth displacement before and after treatment, and amount of root resorption by extracting and creating models of the maxillary central incisors (1,200–5,000 Hounsfield unit [HU]), incisive canal (−800 to 400 HU), and cortical bone around the incisive canal (660–2,000 HU. The periodontal ligament and root apex were relatively clear, and segregation of the root and alveolar bone could be easily performed.
		+
		+	\|}
		+
		+	==Assisting Vascular Surgery with Smartphone Augmented Reality==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://pubmed.ncbi.nlm.nih.gov/32528759 Cureus. 2020 May 8;12(5):e8020. PMID: 32528759] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282367/pdf/cureus-0012-00000008020.pdf PDF]
		+
		+	'''Authors:''' Aly O.
		+
		+
		+	'''Institution:''' General Surgery, Queen Alexandra Hospital, Portsmouth, UK.
		+
		+	'''Abstract:''' The workflow for preoperative planning utilizes cross-sectional imaging in the form of computed tomography angiogram (CTA) Digital Imaging and Communications in Medicine (DICOM®) images (National Electrical Manufacturers Association, Rosslyn, VA). These images were loaded into the [http://www.slicer.org '''3D Slicer'''] (a free open-source software application for medical image computing) to allow examination and annotation of the patient’s anatomy. Important landmarks and structures, such as the inguinal ligament and common femoral vein, were annotated using the 3D Slicer software. Tracking markers were placed on the skin to highlight optimal skin incisions that were tailored to the patient.
		+
	\|}		\|}

← Older revision		Revision as of 18:43, 6 July 2021
Line 523:		Line 523:
	\|}		\|}

		+	==Impact of Local Left Atrial Wall Thickness on the Incidence of Acute Pulmonary Vein Reconnection After Ablation Index-Guided Atrial Fibrillation Ablation==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://pubmed.ncbi.nlm.nih.gov/32642554 Int J Cardiol Heart Vasc. 2020 Jul 3;29:100574. PMID: 32642554] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334811/pdf/main.pdf PDF]
		+
		+	'''Authors:''' Mulder MJ, Kemme MJB, Hagen AMD, Hopman LHGA, van de Ven PM, Hauer HA, Tahapary GJM, Götte MJW, van Rossum AC, Allaart CP.
		+
		+
		+	'''Institution:''' Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Cardiovascular Sciences Amsterdam, The Netherlands.
		+
		+	'''Abstract:''' CT image analysis was performed blinded to ablation data using open-source software, ([http://www.slicer.org '''3D Slicer''']. LA diameter was measured in the anteroposterior direction on axial non-reformatted images. LA wall thickness measurements were performed similarly as described previously [18], and as depicted in Fig. 1. Two separate circular regions of interest were drawn on coronal images inside the LA (blood pool) and inside the ventricular myocardium. Intensity histograms were constructed from the regions of interest to define mean and standard deviation HU for blood pool and myocardium.
		+	\|}
		+
		+	==Surgical Tracking, Registration, and Navigation Characterization for Image-guided Renal Interventions==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://pubmed.ncbi.nlm.nih.gov/33019129 Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5081-4. PMID: 33019129] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8159183/pdf/nihms-1705223.pdf PDF]
		+
		+	'''Authors:''' Jackson P, Simon R, Linte C.
		+
		+	'''Institution:''' DCenter for Imaging Science, Rochester Institute of Technology Rochester, NY, USA.
		+
		+	'''Abstract:''' All image-guided navigation experiments were conducted using the [http://www.slicer.org '''3D Slicer'''] IGT platform [8] [9]. We conducted several experiments using both a custom-developed calibration device of known geometry, as well as a patient-specific 3D printed model of a kidney generated from a MR image. The calibration device consisted of a rectangular tile featuring 30 3-mm dia. hemispherical landmarks distributed in a non-symmetric pattern.
		+	\|}
		+
		+
		+
		+	==Can Surgeons Identify ACL Femoral Ridges Landmark and Optimal Tunnel Position? A 3D Model Study==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://pubmed.ncbi.nlm.nih.gov/32875301 Arthrosc Sports Med Rehabil. 2020 Jul 29;2(4):e361-e368. PMID: 32875301] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451917/pdf/main.pdf PDF]
		+
		+	'''Authors:''' Laverdiere C, Schupbach D, Schupbach J, Harvey E, Boily M, Burman M, Martineau PA.
		+
		+
		+	'''Institution:''' Department of Orthopedic Surgery, McGill University Health Centre, Montreal, Canada.
		+
		+	'''Abstract:''' The 3D MRI Digital Imaging and Communications in Medicine scans were imported in The 3D MRI Digital Imaging and Communications in Medicine scans were imported in 3D slicer software. This software enables segmentation of the Digital Imaging and Communications in Medicine scans to create a 3D model that mimics the native anatomy. software. This software enables segmentation of the Digital Imaging and Communications in Medicine scans to create a 3D model that mimics the native anatomy.
		+	\|}

	==Potential Role of Convolutional Neural Network Based Algorithm in Patient Selection for DCIS Observation Trials Using a Mammogram Dataset==		==Potential Role of Convolutional Neural Network Based Algorithm in Patient Selection for DCIS Observation Trials Using a Mammogram Dataset==

@@ Line 98: / Line 98: @@
 |}
 ==Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging==
@@ Line 110: / Line 123: @@
 '''Institution:''' Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China.
-'''Abstract:''' MR images of all patients were loaded into [http://www.slicer.org '''3D Slicer'''] software v.4.10, which was used as a user-driven manual active contour-segmentation tool to segment tumor volumes. ROIs were manually drawn section by section on the T2WI, T1WI contrast-enhanced sequence, and ADC images by 2 authors (H.Z., with 7 years of clinical experience in neuroradiology, and L.T., with 5 years of clinical experience in neuroradiology). The ROIs defined in our study include both the enhancing and nonenhancing components of tumor as well as peritumoral edema. On-line Figure 1 shows an example of ROI delineation on a T1WI contrast-enhanced sequence, T2WI, and ADC maps. The intraclass correlation coefficient value was used to assess interobserver rep
+'''Abstract:''' MR images of all patients were loaded into [http://www.slicer.org '''3D Slicer'''] software v.4.10, which was used as a user-driven manual active contour-segmentation tool to segment tumor volumes. ROIs were manually drawn section by section on the T2WI, T1WI contrast-enhanced sequence, and ADC images by 2 authors (H.Z., with 7 years of clinical experience in neuroradiology, and L.T., with 5 years of clinical experience in neuroradiology). The ROIs defined in our study include both the enhancing and nonenhancing components of tumor as well as peritumoral edema. On-line Figure 1 shows an example of ROI delineation on a T1WI contrast-enhanced sequence, T2WI, and ADC maps. The intraclass correlation coefficient value was used to assess interobserver reproducibility of generated masks.
 ==3D Slicer and Sina Appilication for Surgical Planning of Giant Invasive Spinal Schwannoma With Scoliosis: A Case Report and Literature Review==
@@ Line 142: / Line 156: @@
 |}
 ==DRRGenerator: A Three-dimensional Slicer Extension for the Rapid and Easy Development of Digitally Reconstructed Radiographs==
@@ Line 325: / Line 355: @@
 '''Abstract:''' A patient-specific numerical model of the ankle joint has been developed using open-source software with realistic material properties that mimics the physiological movement of the foot during the stance phase of the gait cycle. The patient-specific ankle geometry has been segmented as a castellated surface using [http://www.slicer.org '''3D Slicer'''] from the computed tomography image scans of a subject with no congenital or acquired pathology; subsequently, the bones are smoothed, and cartilage is included as a uniform thickness extruded layer. A high-resolution Cartesian mesh has been generated using cfMesh. The material properties are assigned in the model based on the CT image Hounsfield intensities and compared to a sandwich-based material model. Gait data of the same subject was obtained and used to relatively position the tibia, talus, and calcaneus bones in the model. The stance phase of the gait cycle is simulated using a cell-centred finite-volume method implemented in open-source software OpenFOAM. The predicted peak contact pressures occur in the range of 4.85-5.53 MPa with average pressures in the range of 1.56-1.95 MPa, and the contact area ranges between 429 and 707.8 mm2 for the entire stance phase with the mid-stance phase predicting the maximum contact area. These predictions are in agreement with results from the literature. The effect of arthritis on the contact characteristics of the ankle joint has also been examined. A concentrated increase in pressure was predicted that could be manifested as pain, thereby leading to reduced motion in the ankle. The model, with continued development, has the capability to understand the effect of joint degradation and furthermore, could help provide a tool to predict the efficiency of therapeutic surgical procedures as well as guide the development of next generation ankle prostheses. The work would be made available in the University College Dublin depository (https://github.com/laxmimurali/anklejoint) as well as research gate once the article has been published.
 |}
 ==CroSSED Sequence, a New Tool for 3D Processing in Geosciences Using the Free Software 3DSlicer==

@@ Line 98: / Line 98: @@
 |}
 ==3D Slicer and Sina Appilication for Surgical Planning of Giant Invasive Spinal Schwannoma With Scoliosis: A Case Report and Literature Review==
@@ Line 113: / Line 126: @@
 Conclusion: We recommend using the combination of 3D Slicer and Sina to assist surgeons in handling accurate 3D information of GISS while simultaneously simulating the surgery.
 |}
 ==A Novel CNN Algorithm for Pathological Complete Response Prediction Using an I-SPY TRIAL Breast MRI Database==

@@ Line 129: / Line 129: @@
 '''Funding:''' R38 CA231577/CA/NCI NIH HHS/United States
 |}
@@ Line 143: / Line 156: @@
 '''Abstract:''' We quantitatively investigate the influence of image registration, using open-source software (3DSlicer), on kinetic analysis (Tofts model) of dynamic contrast enhanced MRI of early-stage breast cancer patients. We also show that registration computation time can be reduced by reducing the percent sampling (PS) of voxels used for estimation of the cost function. DCE-MRI breast images were acquired on a 3T-PET/MRI system in 13 patients with early-stage breast cancer who were scanned in a prone radiotherapy position. Images were registered using a BSpline transformation with a 2 cm isotropic grid at 100, 20, 5, 1, and 0.5PS (BRAINSFit in [http://www.slicer.org '''3D Slicer''']). Signal enhancement curves were analyzed voxel-by-voxel using the Tofts kinetic model. Comparing unregistered with registered groups, we found a significant change in the 90th percentile of the voxel-wise distribution of Ktrans. We also found a significant reduction in the following: (1) in the standard error (uncertainty) of the parameter value estimation, (2) the number of voxel fits providing unphysical values for the extracellular-extravascular volume fraction (ve > 1), and (3) goodness of fit. We found no significant differences in the median of parameter value distributions (Ktrans, ve) between unregistered and registered images. Differences between parameters and uncertainties obtained using 100PS versus 20PS were small and statistically insignificant. As such, computation time can be reduced by a factor of 2, on average, by using 20PS while not affecting the kinetic fit. The methods outlined here are important for studies including a large number of post-contrast images or number of patient images.
 |}
 ==3D Slicer Craniomaxillofacial Modules Support Patient-Specific Decision-Making for Personalized Healthcare in Dental Research==

@@ Line 186: / Line 186: @@
 '''Abstract:''' Data from 69 patients with both bone scintigraphy and pelvic CT images were selected for this analysis. Open source software ([http://www.slicer.org '''3D Slicer'''] v.4.8.1.) was used for image analysis. Metastatic pelvic bone lesions were manually contoured, and radiomic features were extracted. As risk factors for overall survival (OS) and cause-specific survival (CSS), 105 radiomic features and clinical risk factors including age, initial prostate-specific antigen, Gleason score, TNM stage, lactate dehydrogenase (LDH), hemoglobin (Hb), alkaline phosphatase, extent of disease, visceral metastases, and radiotherapy were assessed by uni- and multivariate analyses.
 |}
@@ Line 201: / Line 214: @@
 |}
 ==A Novel Method for Observing Tumor Margin in Hepatoblastoma Based on Microstructure 3D Reconstruction==

← Older revision		Revision as of 19:11, 1 July 2021
Line 660:		Line 660:

	***************************************************************************************************		***************************************************************************************************
		+
		+	==Comprehensive Review of 3D Segmentation Software Tools for MRI Usable for Pelvic Surgery Planning==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://pubmed.ncbi.nlm.nih.gov/31236743 J Digit Imaging. 2020 Feb;33(1):99-110. PMID: 31236743] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064712/pdf/10278_2019_Article_239.pdf PDF]
		+
		+	'''Authors:''' Virzì A, Muller CO, Marret JB, Mille E, Berteloot L, Grévent D, Boddaert N, Gori P, Sarnacki S, Bloch I.
		+
		+	'''Institution:''' LTCI, Télécom Paris, Institut Polytechnique de Paris, Paris, France.
		+
		+	'''Abstract:''' Patient-specific 3D modeling is the first step towards image-guided surgery, the actual revolution in surgical care. Pediatric and adolescent patients with rare tumors and malformations should highly benefit from these latest technological innovations, allowing personalized tailored surgery. This study focused on the pelvic region, located at the crossroads of the urinary, digestive, and genital channels with important vascular and nervous structures. The aim of this study was to evaluate the performances of different software tools to obtain patient-specific 3D models, through segmentation of magnetic resonance images (MRI), the reference for pediatric pelvis examination. Twelve software tools freely available on the Internet and two commercial software tools were evaluated using T2-w MRI and diffusion-weighted MRI images. The software tools were rated according to eight criteria, evaluated by three different users: automatization degree, segmentation time, usability, 3D visualization, presence of image registration tools, tractography tools, supported OS, and potential extension (i.e., plugins). A ranking of software tools for 3D modeling of MRI medical images, according to the set of predefined criteria, was given. This ranking allowed us to elaborate guidelines for the choice of software tools for pelvic surgical planning in pediatric patients. The best-ranked software tools were Myrian Studio, ITK-SNAP, and [http://www.slicer.org '''3D Slicer'''], the latter being especially appropriate if nerve fibers should be included in the 3D patient model. To conclude, this study proposed a comprehensive review of software tools for 3D modeling of the pelvis according to a set of eight criteria and delivered specific conclusions for pediatric and adolescent patients that can be directly applied to clinical practice.
		+	\|}

	==Temporomandibular Joint Damage in K/BxN Arthritic Mice==		==Temporomandibular Joint Damage in K/BxN Arthritic Mice==

← Older revision		Revision as of 19:08, 1 July 2021
Line 441:		Line 441:
	'''Abstract:''' The aim of this study was to assess the accuracy of 3D rendering of the mandibular condylar region obtained from different semi-automatic segmentation methodology. A total of 10 Cone beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of the condyles by using three free-source software (Invesalius, version 3.0.0, Centro de Tecnologia da Informação Renato Archer, Campinas, SP, Brazil; ITK-Snap, version2.2.0; [http://www.slicer.org '''3D Slicer'''], version 4.10.2) and one commercially available software Dolphin 3D (Dolphin Imaging, version 11.0, Chatsworth, CA, USA). The same models were also manually segmented (Mimics, version 17.01, Materialise, Leuven, Belgium) and set as ground truth. The accuracy of semi-automatic segmentation was evaluated by (1) comparing the volume of each semi-automatic 3D rendered condylar model with that obtained with manual segmentation, (2) deviation analysis of each 3D rendered mandibular models with those obtained from manual segmentation. No significant differences were found in the volumetric dimensions of the condylar models among the tested software (p > 0.05). However, the color-coded map showed underestimation of the condylar models obtained with ITK-Snap and Slicer 3D, and overestimation with Dolphin 3D and Invesalius. Excellent reliability was found for both intra-observer and inter-observer readings. Despite the excellent reliability, the present findings suggest that data of condylar morphology obtained with semi-automatic segmentation should be taken with caution when an accurate definition of condylar boundaries is required.		'''Abstract:''' The aim of this study was to assess the accuracy of 3D rendering of the mandibular condylar region obtained from different semi-automatic segmentation methodology. A total of 10 Cone beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of the condyles by using three free-source software (Invesalius, version 3.0.0, Centro de Tecnologia da Informação Renato Archer, Campinas, SP, Brazil; ITK-Snap, version2.2.0; [http://www.slicer.org '''3D Slicer'''], version 4.10.2) and one commercially available software Dolphin 3D (Dolphin Imaging, version 11.0, Chatsworth, CA, USA). The same models were also manually segmented (Mimics, version 17.01, Materialise, Leuven, Belgium) and set as ground truth. The accuracy of semi-automatic segmentation was evaluated by (1) comparing the volume of each semi-automatic 3D rendered condylar model with that obtained with manual segmentation, (2) deviation analysis of each 3D rendered mandibular models with those obtained from manual segmentation. No significant differences were found in the volumetric dimensions of the condylar models among the tested software (p > 0.05). However, the color-coded map showed underestimation of the condylar models obtained with ITK-Snap and Slicer 3D, and overestimation with Dolphin 3D and Invesalius. Excellent reliability was found for both intra-observer and inter-observer readings. Despite the excellent reliability, the present findings suggest that data of condylar morphology obtained with semi-automatic segmentation should be taken with caution when an accurate definition of condylar boundaries is required.

		+	\|}
		+
		+
		+	==Potential Role of Convolutional Neural Network Based Algorithm in Patient Selection for DCIS Observation Trials Using a Mammogram Dataset==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://pubmed.ncbi.nlm.nih.gov/31526687 Acad Radiol. 2020 Jun;27(6):774-9. PMID: 31526687]
		+
		+	'''Authors:''' Mutasa S, Chang P, Van Sant EP, Nemer J, Liu M, Karcich J, Patel G, Jambawalikar S, Ha R.
		+
		+	'''Institution:''' Department of Medical Physics and Radiology, Columbia University Medical Center, New York, NY, USA.
		+
		+	'''Abstract:''' An IRB-approved retrospective study was performed. 246 unique images from 123 patients were used for our CNN algorithm. In total, 164 images in 82 patients diagnosed with DCIS by stereotactic-guided biopsy of calcifications without any upgrade at the time of surgical excision (pure DCIS group). A total of 82 images in 41 patients with mammographic calcifications yielding occult invasive carcinoma as the final upgraded diagnosis on surgery (occult invasive group). Two standard mammographic magnification views (CC and ML/LM) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform [http://www.slicer.org '''3D Slicer'''] and resized to fit a 128 × 128 pixel bounding box. A 15 hidden layer topology was used to implement the neural network. The network architecture contained five residual layers and dropout of 0.25 after each convolution. Five-fold cross validation was performed using training set (80%) and validation set (20%). Code was implemented in open source software Keras with TensorFlow on a Linux workstation with NVIDIA GTX 1070 Pascal GPU.
	\|}		\|}

← Older revision		Revision as of 19:05, 1 July 2021
Line 548:		Line 548:

	Materials and methods: [http://www.slicer.org '''3D Slicer'''] software (free open-source software) was used to calculate with precision the intra-peritoneal and intra-hernia volumes, and to create a 3D reconstruction of both volumes. The measurement technique is described step by step using detailed figures and videos.		Materials and methods: [http://www.slicer.org '''3D Slicer'''] software (free open-source software) was used to calculate with precision the intra-peritoneal and intra-hernia volumes, and to create a 3D reconstruction of both volumes. The measurement technique is described step by step using detailed figures and videos.
		+	\|}
		+
		+	==Computed Tomographic Portography with Esophageal Variceal Measurements in the Evaluation of Esophageal Variceal Severity and Assessment of Esophageal Variceal Volume Efficacy==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://pubmed.ncbi.nlm.nih.gov/31303576 Acad Radiol. 2020 Apr;27(4):528-35. PMID: 31303576]
		+
		+	'''Authors:''' Wan S, Wei Y, Yu H, Li Y, Yao S, Song B.
		+
		+	'''Institution:''' Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
		+
		+	'''Abstract:''' A total of 53 EV patients examined by CTP within 4 weeks of upper endoscopy were evaluated, the patients were divided into a nonconspicuous EV group (mild-to-moderate EV, n = 28) and a conspicuous EV group (severe EV, n = 25) according to endoscopy results. The diameter, cross-sectional surface area (CSA), and volume of EV were measured independently using [http://www.slicer.org '''3D Slicer'''] (Boston) by two experienced abdominal radiologists blinded to endoscopy findings. The averaged values measured by the two observers were used in the final dataset, these indicators' predictive performances were studied by using receiver operating characteristic curve analysis, and the area under the curve (Az) and the cutoff values were calculated to distinguish mild-to-moderate from severe EV.
	\|}		\|}

← Older revision		Revision as of 19:03, 1 July 2021
Line 533:		Line 533:

	'''Abstract:''' Normal CT thoracic spine obtained from CT thorax data of five patients were used in the [http://www.slicer.org '''3D Slicer'''] environment using PSS for simulation. Entry points and axial trajectory as described by Fennell et al. and a sagittal trajectory parallel to the superior endplate were used for simulating the freehand technique using EA (entry angle) mode in the PSS. An ideal trajectory through the midsection of the pedicle from the same entry point and a sagittal trajectory parallel to the superior endplate were simulated using the ET (Entry Target) mode. Angle predicted by the software for an ideal axial trajectory was compared with the Fennell technique and this angle difference was noted at all the levels. Presence of pedicle breach was noted while simulating the Fennell technique.		'''Abstract:''' Normal CT thoracic spine obtained from CT thorax data of five patients were used in the [http://www.slicer.org '''3D Slicer'''] environment using PSS for simulation. Entry points and axial trajectory as described by Fennell et al. and a sagittal trajectory parallel to the superior endplate were used for simulating the freehand technique using EA (entry angle) mode in the PSS. An ideal trajectory through the midsection of the pedicle from the same entry point and a sagittal trajectory parallel to the superior endplate were simulated using the ET (Entry Target) mode. Angle predicted by the software for an ideal axial trajectory was compared with the Fennell technique and this angle difference was noted at all the levels. Presence of pedicle breach was noted while simulating the Fennell technique.
		+	\|}
		+
		+	==New, Simple and Reliable Volumetric Calculation Technique in Incisional Hernias With Loss of Domain==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://pubmed.ncbi.nlm.nih.gov/31218439 Hernia. 2020 Apr;24(2):403-9. PMID: 31218439]
		+
		+	'''Authors:''' Martre P, Sarsam M, Tuech JJ, Coget J, Schwarz L, Khalil H.
		+
		+	'''Institution:''' Department of Digestive Surgery, Rouen University Hospital, 1 rue de Germont, 76000, Rouen, France.
		+
		+	'''Abstract:''' The management of hernias with loss of domain is a challenging problem. It has been shown that the volume of the incisional hernia/peritoneal volume ratio < 20% was a predictive factor for tension-free fascia closure, after pre-operative pneumoperitoneum preparation (Goni Moreno technique). In this study, we propose an easy, reliable and fast technique to perform volumetric calculation, by the surgeon alone.
		+
		+	Materials and methods: [http://www.slicer.org '''3D Slicer'''] software (free open-source software) was used to calculate with precision the intra-peritoneal and intra-hernia volumes, and to create a 3D reconstruction of both volumes. The measurement technique is described step by step using detailed figures and videos.
	\|}		\|}