We describe the virtual patient model for patients with deep brain stimulation (DBS) implants. Virtual patients are realistic computerized models of patients that allow medical-device companies to test new products earlier, helping the devices get to market more quickly and cheaply according to the Food and Drug Administration. We envision that the proposed new virtual patient simulator will enable radio frequency power dosimetry on patients with the DBS implant undergoing MRI. Future patients with DBS implants may profit from the proposed virtual patient by allowing for a MRI investigation instead of more invasive CT scans, since we will design a new MRI radio frequency pulse that will allow scanning patients with DBS implant. The virtual patient will be flexible and morphable to relate to neurological and psychiatric conditions, which benefit from DBS.

Public Health Relevance

The proposed research aims developing an ultimate toolset for MRI safety for patients with DBS implants. The development of such novel technology will allow unprecedented accuracy in bioelectromagnetic modeling potentially resulting in significant safety improvement of MRI brain imaging in a variety of patient populations with Deep Brain Stimulation devices, including those who suffer of motor disorders, epilepsy, depression and obsessive compulsive, as well as other neurological and psychiatric conditions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21EB016449-02
Application #
8711437
Study Section
Special Emphasis Panel (NOIT)
Program Officer
Peng, Grace
Project Start
2013-08-01
Project End
2015-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
2
Fiscal Year
2014
Total Cost
$210,975
Indirect Cost
$89,725
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
Makris, Nikolaos; Rathi, Yogesh; Mouradian, Palig et al. (2016) Variability and anatomical specificity of the orbitofrontothalamic fibers of passage in the ventral capsule/ventral striatum (VC/VS): precision care for patient-specific tractography-guided targeting of deep brain stimulation (DBS) in obsessive compulsive Brain Imaging Behav 10:1054-1067
Ahmadi, Emad; Katnani, Husam A; Daftari Besheli, Laleh et al. (2016) An Electrocorticography Grid with Conductive Nanoparticles in a Polymer Thick Film on an Organic Substrate Improves CT and MR Imaging. Radiology 280:595-601
Golestanirad, Laleh; Keil, Boris; Angelone, Leonardo M et al. (2016) Feasibility of using linearly polarized rotating birdcage transmitters and close-fitting receive arrays in MRI to reduce SAR in the vicinity of deep brain simulation implants. Magn Reson Med :
Serano, Peter; Angelone, Leonardo M; Katnani, Husam et al. (2015) A novel brain stimulation technology provides compatibility with MRI. Sci Rep 5:9805
Iacono, Maria Ida; Neufeld, Esra; Akinnagbe, Esther et al. (2015) MIDA: A Multimodal Imaging-Based Detailed Anatomical Model of the Human Head and Neck. PLoS One 10:e0124126
Yang, Jimmy C; Papadimitriou, George; Eckbo, Ryan et al. (2015) Multi-tensor investigation of orbitofrontal cortex tracts affected in subcaudate tractotomy. Brain Imaging Behav 9:342-52
Shenton, Martha E; Kubicki, Marek; Makris, Nikos (2014) Understanding alterations in brain connectivity in attention-deficit/hyperactivity disorder using imaging connectomics. Biol Psychiatry 76:601-2
Bonmassar, Giorgio; Makris, Nikos (2014) The Virtual Patient Simulator of Deep Brain Stimulation in the Obsessive Compulsive Disorder Based on Connectome and 7 Tesla MRI Data. Cogn Int Conf Adv Cogn Technol Appl 2014:235-238
Bonmassar, Giorgio; Angelone, Leonardo M; Makris, Nikos (2014) A Virtual Patient Simulator Based on Human Connectome and 7 T MRI for Deep Brain Stimulation. Int J Adv Life Sci 6:364-372
Iacono, Maria Ida; Makris, Nikos; Mainardi, Luca et al. (2013) MRI-based multiscale model for electromagnetic analysis in the human head with implanted DBS. Comput Math Methods Med 2013:694171