Literature DB >> 22450824

Evaluation of Interactive Visualization on Mobile Computing Platforms for Selection of Deep Brain Stimulation Parameters.

Christopher R Butson, Georg Tamm, Sanket Jain, Thomas Fogal, Jens Krüger.   

Abstract

In recent years, there has been significant growth in the use of patient-specific models to predict the effects of neuromodulation therapies such as deep brain stimulation (DBS). However, translating these models from a research environment to the everyday clinical workflow has been a challenge, primarily due to the complexity of the models and the expertise required in specialized visualization software. In this paper, we deploy the interactive visualization system ImageVis3D Mobile, which has been designed for mobile computing devices such as the iPhone or iPad, in an evaluation environment to visualize models of Parkinson's disease patients who received DBS therapy. Selection of DBS settings is a significant clinical challenge that requires repeated revisions to achieve optimal therapeutic response, and is often performed without any visual representation of the stimulation system in the patient. We used ImageVis3D Mobile to provide models to movement disorders clinicians and asked them to use the software to determine: 1) which of the four DBS electrode contacts they would select for therapy; and 2) what stimulation settings they would choose. We compared the stimulation protocol chosen from the software versus the stimulation protocol that was chosen via clinical practice (independent of the study). Lastly, we compared the amount of time required to reach these settings using the software versus the time required through standard practice. We found that the stimulation settings chosen using ImageVis3D Mobile were similar to those used in standard of care, but were selected in drastically less time. We show how our visualization system, available directly at the point of care on a device familiar to the clinician, can be used to guide clinical decision making for selection of DBS settings. In our view, the positive impact of the system could also translate to areas other than DBS.

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Year:  2012        PMID: 22450824      PMCID: PMC3686862          DOI: 10.1109/TVCG.2012.92

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  21 in total

1.  Patient-specific analysis of the volume of tissue activated during deep brain stimulation.

Authors:  Christopher R Butson; Scott E Cooper; Jaimie M Henderson; Cameron C McIntyre
Journal:  Neuroimage       Date:  2006-11-17       Impact factor: 6.556

2.  Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation.

Authors:  Christopher R Butson; Cameron C McIntyre
Journal:  Clin Neurophysiol       Date:  2005-10       Impact factor: 3.708

3.  A streaming-based solution for remote visualization of 3D graphics on mobile devices.

Authors:  Fabrizio Lamberti; Andrea Sanna
Journal:  IEEE Trans Vis Comput Graph       Date:  2007 Mar-Apr       Impact factor: 4.579

4.  Role of electrode design on the volume of tissue activated during deep brain stimulation.

Authors:  Christopher R Butson; Cameron C McIntyre
Journal:  J Neural Eng       Date:  2005-12-19       Impact factor: 5.379

Review 5.  Cicerone: stereotactic neurophysiological recording and deep brain stimulation electrode placement software system.

Authors:  S Miocinovic; A M Noecker; C B Maks; C R Butson; C C McIntyre
Journal:  Acta Neurochir Suppl       Date:  2007

Review 6.  The history and future of deep brain stimulation.

Authors:  Jason M Schwalb; Clement Hamani
Journal:  Neurotherapeutics       Date:  2008-01       Impact factor: 7.620

7.  Volumetric transformation of brain anatomy.

Authors:  G E Christensen; S C Joshi; M I Miller
Journal:  IEEE Trans Med Imaging       Date:  1997-12       Impact factor: 10.048

8.  Cognition and mood in Parkinson's disease in subthalamic nucleus versus globus pallidus interna deep brain stimulation: the COMPARE trial.

Authors:  Michael S Okun; Hubert H Fernandez; Samuel S Wu; Lindsey Kirsch-Darrow; Dawn Bowers; Frank Bova; Michele Suelter; Charles E Jacobson; Xinping Wang; Clifford W Gordon; Pam Zeilman; Janet Romrell; Pam Martin; Herbert Ward; Ramon L Rodriguez; Kelly D Foote
Journal:  Ann Neurol       Date:  2009-05       Impact factor: 10.422

9.  Reversing cognitive-motor impairments in Parkinson's disease patients using a computational modelling approach to deep brain stimulation programming.

Authors:  Anneke M M Frankemolle; Jennifer Wu; Angela M Noecker; Claudia Voelcker-Rehage; Jason C Ho; Jerrold L Vitek; Cameron C McIntyre; Jay L Alberts
Journal:  Brain       Date:  2010-01-07       Impact factor: 13.501

10.  Differences among implanted pulse generator waveforms cause variations in the neural response to deep brain stimulation.

Authors:  Christopher R Butson; Cameron C McIntyre
Journal:  Clin Neurophysiol       Date:  2007-06-19       Impact factor: 3.708

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  16 in total

1.  A medical application integrating remote 3D visualization tools to access picture archiving and communication system on mobile devices.

Authors:  Longjun He; Xing Ming; Qian Liu
Journal:  J Med Syst       Date:  2014-04-05       Impact factor: 4.460

2.  Characterization of the stimulus waveforms generated by implantable pulse generators for deep brain stimulation.

Authors:  Scott F Lempka; Bryan Howell; Kabilar Gunalan; Andre G Machado; Cameron C McIntyre
Journal:  Clin Neurophysiol       Date:  2018-01-31       Impact factor: 3.708

3.  Biomedical Visual Computing: Case Studies and Challenges.

Authors:  Chris R Johnson
Journal:  Comput Sci Eng       Date:  2011-09-23       Impact factor: 2.080

4.  Anodic stimulation misunderstood: preferential activation of fiber orientations with anodic waveforms in deep brain stimulation.

Authors:  Daria Nesterovich Anderson; Gordon Duffley; Johannes Vorwerk; Alan D Dorval; Christopher R Butson
Journal:  J Neural Eng       Date:  2018-10-02       Impact factor: 5.379

5.  Interactive computation and visualization of deep brain stimulation effects using Duality.

Authors:  J Vorwerk; D McCann; J Krüger; C R Butson
Journal:  Comput Methods Biomech Biomed Eng Imaging Vis       Date:  2019-07-02

6.  Use of efficacy probability maps for the post-operative programming of deep brain stimulation in essential tremor.

Authors:  Fenna T Phibbs; Srivatsan Pallavaram; Christopher Tolleson; Pierre-François D'Haese; Benoit M Dawant
Journal:  Parkinsonism Relat Disord       Date:  2014-09-16       Impact factor: 4.891

7.  Computational medicine: translating models to clinical care.

Authors:  Raimond L Winslow; Natalia Trayanova; Donald Geman; Michael I Miller
Journal:  Sci Transl Med       Date:  2012-10-31       Impact factor: 17.956

8.  Hybrid Rendering with Scheduling under Uncertainty.

Authors:  Georg Tamm; Jens Krüger
Journal:  IEEE Trans Vis Comput Graph       Date:  2014-01-28       Impact factor: 4.579

9.  Computer-Guided Deep Brain Stimulation Programming for Parkinson's Disease.

Authors:  Dustin A Heldman; Christopher L Pulliam; Enrique Urrea Mendoza; Maureen Gartner; Joseph P Giuffrida; Erwin B Montgomery; Alberto J Espay; Fredy J Revilla
Journal:  Neuromodulation       Date:  2015-12-01

10.  Neurologist consistency in interpreting information provided by an interactive visualization software for deep brain stimulation postoperative programming assistance.

Authors:  Srivatsan Pallavaram; Fenna T Phibbs; Christopher Tolleson; Thomas L Davis; John Fang; Peter Hedera; Rui Li; Tatsuki Koyama; Benoit M Dawant; Pierre-François D'Haese
Journal:  Neuromodulation       Date:  2013-05-03
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