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SU‐FF‐J‐140: Multiple Aspects of Approximated‐Returning‐To‐The‐Origin Probability (ARTOP) Diffusion Tensor Imaging for Better Observation High‐Graded Gliomas Treatment Responses
Author(s) -
Chu A,
Knisely J,
Fulbright R,
Constable R,
Nath R
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3181432
Subject(s) - diffusion mri , nuclear medicine , diffusion , medicine , glioma , magnetic resonance imaging , nuclear magnetic resonance , materials science , radiology , physics , cancer research , thermodynamics
Purpose: This study is to provide multiple aspects of physio‐pathological information for early detecting cancer treatment responses and better clinical prognosis by using diffusion MRI modalities. The conventional a pparent d iffusion c oefficient (ADC) is known as a sensitive tool to detect early treatment responses. In this study, the diffusion weighting range is further extended and analyzed by novel a pproximated r eturning t o the o rigin p robability (ARTOP) for observing slow diffusion compartments for high graded gliomas. Reports had shown higher‐graded gliomas correlating to slower water diffusing among the denser cells, the pathologically‐recognized pseudopalisading necroses, caused by degenerating vessels in progressive gliomas in early stage. Furthermore, slower water diffusion also occurs as malignant gliomas infiltrate preferentially along myelinated fiber‐tracks. Method and Material: The data were collected by Siemens Trio 3T magnet, and processed by 2 nd order diffusion tensor with ARTOP q‐space analysis developed by homemade MatLab‐codes. The 5‐minute echo‐planar sequence with 2 averages for (∼40 slices, 112×128 pixels per slice) is equipped by 9‐level diffusion weighting (1∼4k sec/mm 2 ), and each level is with 6‐direction of diffusion tensor imaging (DTI) encoding. Data are gathered from three scans on non‐resection Glioblastoma patients before, during and after radiation‐ and chemo‐therapies. The imaging registrations were performed by rigid‐body affine transformation. Results: ARTOP does not only clearly underline the slower‐diffusing water signals (i.e. higher possibility at its origin), but it's also feasible for clinical routine unlike other q‐space algorithms. So far, the newly migrating palisading glioma cells (as higher ARTOP and lower ADC) have been observed in the data of 3 out of 4 Glioblastoma patients. The increased extracellular spaces due to treatment illustrated as lower ARTOP, higher ADC, impaired fibers in fractional‐anisotropy (FA) map with T2‐weighting highlights all in one data set. Conclusion: The multi‐face diffusion information, ARTOP‐, ADC‐, and FA‐maps could suggest adaptive treatment in the future.