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Featured researches published by Guillaume Pernelle.


Proceedings of SPIE | 2014

EM-navigated catheter placement for gynecologic brachytherapy: an accuracy study

Alireza Mehrtash; Antonio L. Damato; Guillaume Pernelle; Lauren Barber; Nabgha Farhat; Akila N. Viswanathan; Robert A. Cormack; Tina Kapur

Gynecologic malignancies, including cervical, endometrial, ovarian, vaginal and vulvar cancers, cause significant mortality in women worldwide. The standard care for many primary and recurrent gynecologic cancers consists of chemoradiation followed by brachytherapy. In high dose rate (HDR) brachytherapy, intracavitary applicators and /or interstitial needles are placed directly inside the cancerous tissue so as to provide catheters to deliver high doses of radiation. Although technology for the navigation of catheters and needles is well developed for procedures such as prostate biopsy, brain biopsy, and cardiac ablation, it is notably lacking for gynecologic HDR brachytherapy. Using a benchtop study that closely mimics the clinical interstitial gynecologic brachytherapy procedure, we developed a method for evaluating the accuracy of image-guided catheter placement. Future bedside translation of this technology offers the potential benefit of maximizing tumor coverage during catheter placement while avoiding damage to the adjacent organs, for example bladder, rectum and bowel. In the study, two independent experiments were performed on a phantom model to evaluate the targeting accuracy of an electromagnetic (EM) tracking system. The procedure was carried out using a laptop computer (2.1GHz Intel Core i7 computer, 8GB RAM, Windows 7 64-bit), an EM Aurora tracking system with a 1.3mm diameter 6 DOF sensor, and 6F (2 mm) brachytherapy catheters inserted through a Syed-Neblett applicator. The 3D Slicer and PLUS open source software were used to develop the system. The mean of the targeting error was less than 2.9mm, which is comparable to the targeting errors in commercial clinical navigation systems.


Medical Image Analysis | 2017

Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images

Andre Mastmeyer; Guillaume Pernelle; Ruibin Ma; Lauren Barber; Tina Kapur

HighlightsSegmentation and catheter identification in MRI images for brachytherapy.ONE CLICK user interaction per catheter.Coupling of mechanical model and image features.Outlier detection and correction.93% accuracy and 0.29 mm precision error. Graphical abstract Figure. No caption available. ABSTRACT The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external‐beam radiotherapy followed by high dose rate brachytherapy is the standard‐of‐care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image‐features, and is guided by a catheter‐specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49 mm; 51 of the outliers deviated more than two catheter widths (3.4 mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed–Neblett template. In a multi‐user simulation experiment for evaluating RMS precision by simulating varying manually‐provided superior tip positions, 3&sgr; maximum errors were 2.44 mm. The average segmentation time for a single catheter was 3 s on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR‐guidance in gynecologic brachytherapy and other catheter‐based interventional procedures.


PLOS Computational Biology | 2018

Gap junction plasticity as a mechanism to regulate network-wide oscillations

Guillaume Pernelle; Wilten Nicola; Claudia Clopath

Cortical oscillations are thought to be involved in many cognitive functions and processes. Several mechanisms have been proposed to regulate oscillations. One prominent but understudied mechanism is gap junction coupling. Gap junctions are ubiquitous in cortex between GABAergic interneurons. Moreover, recent experiments indicate their strength can be modified in an activity-dependent manner, similar to chemical synapses. We hypothesized that activity-dependent gap junction plasticity acts as a mechanism to regulate oscillations in the cortex. We developed a computational model of gap junction plasticity in a recurrent cortical network based on recent experimental findings. We showed that gap junction plasticity can serve as a homeostatic mechanism for oscillations by maintaining a tight balance between two network states: asynchronous irregular activity and synchronized oscillations. This homeostatic mechanism allows for robust communication between neuronal assemblies through two different mechanisms: transient oscillations and frequency modulation. This implies a direct functional role for gap junction plasticity in information transmission in cortex.


ASME 2011 Summer Bioengineering Conference, Parts A and B | 2011

Assessment of Minimally Invasive Device That Provides Simultaneous Adjustable Cardiac Support and Active Synchronous Assist in an Acute Heart Failure Model

Michael R. Moreno; Saurabh Biswas; Lewis D. Harrison; Guillaume Pernelle; Matthew W. Miller; Theresa W. Fossum; David A. Nelson; John C. Criscione

Congestive heart failure (CHF) is a debilitating disease that is generally initiated by some index cardiac event and ultimately characterized by left ventricular (LV) remodeling which dramatically alters the mechanical environment about the heart. It is well established that mechanical stimuli (e.g., stress or strain) are important epigenetic factors in cardiovascular development, adaptation, and disease.1–3 Interestingly, abnormal cardiac kinematics is often considered a symptom of heart failure when in actuality it is likely a primary contributing factor to the relentless progression of the disease.4 Cellular responses to pathologic mechanical factors lead to further pathologic remodeling and a positive feedback loop emerges such that eventually a threshold is reached wherein the neurohormal compensatory mechanisms activated to maintain homeostasis following the initial cardiac event are no longer sufficient to deter further progression of the disease. Consequently, treatment strategies that fail to remedy the aberrant mechanical environment become increasingly ineffective as the disease progresses.Copyright


medical image computing and computer assisted intervention | 2013

Validation of Catheter Segmentation for MR-Guided Gynecologic Cancer Brachytherapy

Guillaume Pernelle; Alireza Mehrtash; Lauren Barber; Antonio L. Damato; Wei Wang; Ravi T. Seethamraju; Ehud J. Schmidt; Robert A. Cormack; William M. Wells; Akila N. Viswanathan; Tina Kapur


Brachytherapy | 2013

Robust Applicator Registration for Interstitial Gynecologic Brachytherapy

Guillaume Pernelle; Jan Egger; Carolina Vale; Xiaojun Chen; Franz Irlinger; Tim C. Lueth; William M. Wells; Ron Kikinis; Akila N. Viswanathan; Tina Kapur


arXiv: Computer Vision and Pattern Recognition | 2017

Model-based Catheter Segmentation in MRI-images.

Andre Mastmeyer; Guillaume Pernelle; Lauren Barber; Steve Pieper; Dirk Fortmeier; Sandy Wells; Heinz Handels; Tina Kapur


Archive | 2012

Segmentation of Pelvic Structures for Gynecologic Brachytherapy

Jan Egger; Neha Agrawal; Tyler Blevins; Nabgha Farhat; Guillaume Pernelle; Xiaojun Chen; Yi Gao; William M. Wells; Tobias Penzkofer; Tina Kapur; Akila N. Viswanathan


IEEE Transactions on Medical Imaging | 2018

Automatic Needle Segmentation and Localization in MRI with 3D Convolutional Neural Networks: Application to MRI-targeted Prostate Biopsy

Alireza Mehrtash; Mohsen Ghafoorian; Guillaume Pernelle; Alireza Ziaei; Friso G. Heslinga; Kemal Tuncali; Andriy Fedorov; Ron Kikinis; Clare M. Tempany; William M. Wells; Purang Abolmaesumi; Tina Kapur


arXiv: Neurons and Cognition | 2017

Gap junction plasticity can lead to spindle oscillations

Guillaume Pernelle; Wilten Nicola; Claudia Clopath

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Tina Kapur

Brigham and Women's Hospital

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Alireza Mehrtash

Brigham and Women's Hospital

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William M. Wells

Brigham and Women's Hospital

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Antonio L. Damato

Memorial Sloan Kettering Cancer Center

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Ehud J. Schmidt

Brigham and Women's Hospital

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Nabgha Farhat

Brigham and Women's Hospital

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Robert A. Cormack

Brigham and Women's Hospital

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Ron Kikinis

Wisconsin Alumni Research Foundation

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