The ultimate goal of the Program Project is to improve an existing infrastructure (scientific, medical, and technical) which supports coordinated multifocused, multidisciplinary development of magnetic resonance imaging (MRI) used for the guidance of interventional and surgical procedures in some well-defined clinical areas. The proposal consists of five thoroughly interrelated projects. Project 1 is designed to develop MRI-based systems and techniques for intraoperative control of thermal treatment of tissue. The target tissue will be treated with a variety of modalities including interstitial laser therapy, cryotherapy and high intensity focused ultrasound surgery. The project will address the central problem in thermal surgery, which is intraoperative control of the tissue damage. This investigation will extract the maximum amount of information from the intraoperative MR images and develop visual feedback and automated control systems for image-guided thermal ablations. Project 2 will test the hypothesis that high intensity focused ultrasound surgery guided and monitored by MRI is an accurate and safe method for noninvasive tissue coagulation. The Project includes clinical feasibility testing of breast tumor treatments. Project 3 will develop and implement high performance computer integration of preoperative MRI and CT data with intraoperative images in order to guide surgery. To accomplish this goal, the investigators will consolidate existing high performance computational hardware, networking facilities and image processing software into a system which can provide online information to surgeons during the execution of operative procedures. Project 4 will develop novel MRI encoding methods. The investigators will implement and improve the so called adaptive imaging techniques and optimize the information-gathering capability of the prototype open configuration MR therapy (MRT) scanner.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA067165-03
Application #
2545386
Study Section
Cancer Centers and Research Programs Review Committee (CCRP)
Program Officer
Torres-Anjel, Manuel J
Project Start
1995-09-30
Project End
2000-03-31
Budget Start
1997-09-30
Budget End
2000-03-31
Support Year
3
Fiscal Year
1997
Total Cost
Indirect Cost
Name
Brigham and Women's Hospital
Department
Type
DUNS #
071723621
City
Boston
State
MA
Country
United States
Zip Code
02115
Behringer, Peter A; Herz, Christian; Penzkofer, Tobias et al. (2016) Open-Source Platform for Prostate Motion Tracking During in-Bore Targeted MRI-Guided Biopsy. Clin Image Based Proced 9401:122-129
Tauscher, Sebastian; Tokuda, Junichi; Schreiber, Günter et al. (2015) OpenIGTLink interface for state control and visualisation of a robot for image-guided therapy systems. Int J Comput Assist Radiol Surg 10:285-92
Penzkofer, Tobias; Tuncali, Kemal; Fedorov, Andriy et al. (2015) Transperineal in-bore 3-T MR imaging-guided prostate biopsy: a prospective clinical observational study. Radiology 274:170-80
Yamauchi, Fernando I; Penzkofer, Tobias; Fedorov, Andriy et al. (2015) Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T(2)-mapping MRI sequence. Magn Reson Imaging 33:525-30
Tempany, Clare M C; Jayender, Jagadeesan; Kapur, Tina et al. (2015) Multimodal imaging for improved diagnosis and treatment of cancers. Cancer 121:817-27
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Tie, Yanmei; Rigolo, Laura; Ozdemir Ovalioglu, Aysegul et al. (2015) A New Paradigm for Individual Subject Language Mapping: Movie-Watching fMRI. J Neuroimaging 25:710-20
Langs, Georg; Sweet, Andrew; Lashkari, Danial et al. (2014) Decoupling function and anatomy in atlases of functional connectivity patterns: language mapping in tumor patients. Neuroimage 103:462-75
Penzkofer, Tobias; Tempany-Afdhal, Clare M (2014) Prostate cancer detection and diagnosis: the role of MR and its comparison with other diagnostic modalities--a radiologist's perspective. NMR Biomed 27:3-15
Jayender, Jagadaeesan; Chikarmane, Sona; Jolesz, Ferenc A et al. (2014) Automatic segmentation of invasive breast carcinomas from dynamic contrast-enhanced MRI using time series analysis. J Magn Reson Imaging 40:467-75

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