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SU‐FF‐J‐134: Slow Diffusion Enhancement for Approximated Returning to the Origin Probability (ARTOP)
Author(s) -
Chu A,
Knisely J,
Constable R,
Fulbright 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 - 10.1118/1.3181426
Subject(s) - contrast (vision) , diffusion , weighting , filter (signal processing) , probability density function , algorithm , computer science , mathematics , statistics , physics , artificial intelligence , computer vision , acoustics , thermodynamics
Purpose: This study introduces a filtering method that enhances slow/fast diffusion contrast for the q‐space analysis, ARTOP ( A pproximated R eturning T o the O rigin P robability), in clinical studies. Background: Most clinical diffusion image analyses are based on the apparent diffusion coefficient (ADC), which uses a Gaussian model for its ensemble probability density function (PDF). However an ADC‐related modality becomes problematic when it is applied to high‐diffusion MRI studies (b>3ksec/mm 2 ) due to its complexity in modeling. q‐space analysis is model‐independent; i.e. the Fourier transformation between data profile in q‐space and its displacement PDF has no modeling assumptions. Nevertheless the traditional q‐space analysis also poses some difficulties in clinical implementation. Therefore we have been developing a clinical feasible q‐space analysis, ARTOP, for high diffusion studies. This study improves ARTOP by increasing the slow‐diffusion contrast for better imaging quality, and shortens scanning time as well. Methods and Materials: The contrast of slow/fast diffusion signal is enhanced by a high‐pass filter in q‐space (b >=1ksec/mm 2 ), which most fast diffusion signals diminish over that weighting range. The effect was applied on research patient datasets. Patient datasets were collected using a Siemens Trio 3T magnet and were processed by offline homemade codes. The 9‐level diffusion weighting ranges were 1∼4k sec/mm 2 . Results: The slow/fast contrast was defined by the ratio of slow/fast ARTOP signal. The filtered ARTOP contrast is more than 7 times greater than the one without filtering; i.e. 15 versus 2 for the filtered versus non‐filtered data. Conclusion: The better imaging quality of filtered ARTOP is suitable for radiological examination or treatment planning contour, and its quantitative information can be easily retrieved from non‐filtered ARTOP map. The quantity can be used for white matter diseases, e.g. for monitoring the glioma treatment response, as in our other studies.