USE OF PROTON MAGNETIC RESONANCE SPECTROSCOPIC IMAGING DATA IN PLANNING FOCAL RADIATION THERAPIES FOR BRAIN TUMORS
Author(s) -
Edward E. Graves,
Andrea Pirzkall,
Tracy R. McKnight,
Daniel B. Vigneron,
David A. Larson,
Lynn Verhey,
Michael McDermott,
Susan M. Chang,
Sarah J. Nelson
Publication year - 2011
Publication title -
image analysis and stereology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.237
H-Index - 27
eISSN - 1854-5165
pISSN - 1580-3139
DOI - 10.5566/ias.v21.p69-76
Subject(s) - radiosurgery , magnetic resonance imaging , magnetic resonance spectroscopic imaging , radiation treatment planning , radiation therapy , proton magnetic resonance , medicine , margin (machine learning) , medical physics , radiology , nuclear medicine , computer science , nuclear magnetic resonance , physics , machine learning
Advances in radiation therapy for malignant neoplasms have produced techniques such as Gamma Knife radiosurgery, capable of delivering an ablative dose to a specific, irregular volume of tissue. However, efficient use of these techniques requires the identification of a target volume that will produce the best therapeutic response while sparing surrounding normal brain tissue. Accomplishing this task using conventional computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) techniques has proven difficult because of the difficulties in identifying the effective tumor margin. Magnetic resonance spectroscopic imaging (MRSI) has been shown to offer a clinically-feasible metabolic assessment of the presence and extent of neoplasm that can complement conventional anatomic imaging. This paper reviews current Gamma Knife protocols and MRSI acquisition, reconstruction, and interpretation techniques, and discusses the motivation for including magnetic resonance spectroscopy findings while planning focal radiation therapies. A treatment selection and planning strategy incorporating MRSI is then proposed, which can be used in the future to assess the efficacy of spectroscopy-based therapy planning
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