OPTIMIZED DIFFUSION-WEIGHTING FOR MEASUREMENT OF APPARENT DIFFUSION-COEFFICIENT (ADC) IN HUMAN BRAIN

This work studies the effect of diffusion-weighting on the precision o f measurements of the apparent diffusion coefficient (ADC, or D) by di ffusion-weighted magnetic resonance imaging, The precision in the valu e of the ADC was described in terms of a diffusion-to-noise ratio (I)N R) which was calculated as the signal-to-noise ratio in the resultant ADC, A theoretical analysis decomposed the DNR into the signal-to-nois e ratio in the diffusion-weighted image and the sensitivity of diffusi on-weighting, ''K-D'', The latter reflects the effect of the sampling strategy in the diffusion-weighting domain on the DNR, The theoretical analysis demonstrated that optimal two-point diffusion-weighting coul d be achieved in the vicinity of xi = D(b(2)-b(1)) 1.1, where xi is a nondimensional parameter of diffusion-weighting, and b(1) and b(2) are the diffusion-weighting factors for the two-point diffusion-weighting , This approach also derived an optimised signal averaging scheme, The limitations and restrictions of the two-point scheme for in vivo ADC measurement were also considered; these included a detailed discussion on partial volume effects, The theory was verified by experiments on phantoms and on the brain of a healthy volunteer using a diffusion-wei ghted echo-planar imaging protocol, This led to an optimal two-point d iffusion-weighting for ADC measurement in human brain using b(1) = 300 , and b(2) 1550 +/- 100 s/mm(2), Such a two-point scheme successfully measured values of the ADC in gray matter, white matter and cerebrospi nal fluid in human brain, It thus offers an alternative to the commonl y used multiple-point schemes and has the advantage of requiring signi ficantly shorter imaging times, (C) 1997 Elsevier Science Inc.