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How Many Gradients are Sufficient in High-Angular Resolution Diffusion Imaging (HARDI)? Liang Zhan 1 , Ming-Chang Chiang 1 , Marina Barysheva 1 , Arthur W. Toga 1 , Katie L. McMahon 2 , Greig I. de Zubicaray 2 , Matthew Meredith 2 , Margaret J. Wright 3 , Paul M. Thompson 1 1 Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 2 Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia 3 Queensland Institute of Medical Research, Brisbane, Australia Introduction: High-angular resolution diffusion imaging (HARDI) is a powerful extension of MRI that maps the directional diffusion of water in the brain, based on applying diffusion-sensitized gradients in 30-100 or more different directions. In the Diffusion Tensor (DT) model, a tensor is fitted to the set of diffusion images, and maps of fiber integrity such as fractional or relative anisotropy (FA, RA), mean diffusivity (MD) or volume ratio (VR), may be computed from the tensors’ eigenvalues. Given the trade-off between the available signal-to-noise (SNR) and the time required to collect more gradient images, some studies argue that 20 gradient directions are sufficient to accurately compute FA [1], while tractography applications require more gradients to accurately resolve fiber directions and crossings. Here we computed how the SNR increases, depending on the number of gradients acquired, for several widely used DTI measures, to evaluate the trade-off between image quality and acquisition time. Methods: 105-gradient HARDI data were acquired from 10 healthy adult subjects (age: 23.9±1.4SD years; 6 men/4 women) on a 4 Tesla Bruker Medspec MRI scanner using an optimized diffusion tensor sequence [2]. 105 images were acquired: 11 baseline (b 0 ) images with no diffusion sensitization (i.e., T2-weighted images) and 94 diffusion- weighted images (b-value 1159 s/mm 2 ) in which gradient directions were evenly distributed on the hemisphere [2]. Imaging parameters were: TR/TE 92.3/8250 ms, 55 x 2mm contiguous slices, FOV = 23 cm. The reconstruction matrix was 128x128, yielding a 1.8x1.8 mm 2 in-plane resolution. The total scan time was 14.5 minutes. For each subject, DT images (denoted by D ij , 1≤ i, j ≤3) and FA, RA MD, and VR maps were computed from the HARDI signals using standard formulae (http://www-sop.inria.fr/asclepios/software/MedINRIA ; Figure 1 ). We artificially reduced the angular sampling of the 105-gradient sequence by optimizing the angular distribution energy [2] on subsets of size 1≤N≤94 of the diffusion-sensitized gradient images. SNR was computed using all optimized gradient subsets and all 11 baseline (b 0 ) images, to measure SNR effects with a constant number of b 0 images (this conservative approach slightly favors sequences with fewer gradients, for which fewer than 11 baseline images would typically be collected). Results: SNR rose sharply for FA, MD, RA and VR reached 90% of the available SNR with 22, 19, 31 and 64 of the available 94 diffusion-sensitized gradients. By increasing the number of diffusion-sensitized gradients from 20 to 94, SNR improved by 69.23% and 19.93% for VR and RA, and by 12.24% and 8.77% for FA and MD (Figure 1). Measures involving products of 3 eigenvalues (e.g., VR) were noisier, requiring more gradients to determine, but even FA SNR rose rapidly with more gradients than are routinely collected. Conclusions: HARDI offers increased SNR even for routine brain mapping studies using tensor-derived measures, such as FA, which requires only 7 gradients to determine analytically. Further studies of scanner field strength, spatial resolution, tolerability, motion artifacts, and clinical effect sizes will clarify the added benefit of this SNR for neuroscientific studies. References:

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Page 1: How Many Gradients are Sufficient in High-Angular ...users.loni.usc.edu/~thompson/OHBM2008/Liang-HARDI-OHBM08.pdf · HARDI offers increased SNR even for routine brain mapping studies

How Many Gradients are Sufficient inHigh-Angular Resolution Diffusion Imaging (HARDI)?

Liang Zhan1, Ming-Chang Chiang1, Marina Barysheva1, Arthur W. Toga1, Katie L. McMahon2, Greig I. de Zubicaray2,Matthew Meredith2, Margaret J. Wright3, Paul M. Thompson1

1Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA2Functional MRI Laboratory, Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia

3Queensland Institute of Medical Research, Brisbane, Australia

Introduction:High-angular resolution diffusion imaging (HARDI) is a powerful extension of MRI that maps the directionaldiffusion of water in the brain, based on applying diffusion-sensitized gradients in 30-100 or more differentdirections. In the Diffusion Tensor (DT) model, a tensor is fitted to the set of diffusion images, and maps of fiberintegrity such as fractional or relative anisotropy (FA, RA), mean diffusivity (MD) or volume ratio (VR), may becomputed from the tensors’ eigenvalues. Given the trade-off between the available signal-to-noise (SNR) and thetime required to collect more gradient images, some studies argue that 20 gradient directions are sufficient toaccurately compute FA [1], while tractography applications require more gradients to accurately resolve fiberdirections and crossings. Here we computed how the SNR increases, depending on the number of gradientsacquired, for several widely used DTI measures, to evaluate the trade-off between image quality and acquisitiontime.

Methods:105-gradient HARDI data were acquired from 10 healthy adult subjects (age: 23.9±1.4SD years; 6 men/4 women)on a 4 Tesla Bruker Medspec MRI scanner using an optimized diffusion tensor sequence [2]. 105 images wereacquired: 11 baseline (b0) images with no diffusion sensitization (i.e., T2-weighted images) and 94 diffusion-weighted images (b-value 1159 s/mm2) in which gradient directions were evenly distributed on the hemisphere[2]. Imaging parameters were: TR/TE 92.3/8250 ms, 55 x 2mm contiguous slices, FOV = 23 cm. Thereconstruction matrix was 128x128, yielding a 1.8x1.8 mm2 in-plane resolution. The total scan time was 14.5minutes. For each subject, DT images (denoted by Dij, 1≤ i, j ≤3) and FA, RA MD, and VR maps were computedfrom the HARDI signals using standard formulae (http://www-sop.inria.fr/asclepios/software/MedINRIA ; Figure1). We artificially reduced the angular sampling of the 105-gradient sequence by optimizing the angulardistribution energy [2] on subsets of size 1≤N≤94 of the diffusion-sensitized gradient images. SNR was computedusing all optimized gradient subsets and all 11 baseline (b0) images, to measure SNR effects with a constantnumber of b0 images (this conservative approach slightly favors sequences with fewer gradients, for which fewerthan 11 baseline images would typically be collected).

Results:SNR rose sharply for FA, MD, RA and VR reached 90% of the available SNR with 22, 19, 31 and 64 of theavailable 94 diffusion-sensitized gradients. By increasing the number of diffusion-sensitized gradients from 20 to94, SNR improved by 69.23% and 19.93% for VR and RA, and by 12.24% and 8.77% for FA and MD (Figure1). Measures involving products of 3 eigenvalues (e.g., VR) were noisier, requiring more gradients to determine,but even FA SNR rose rapidly with more gradients than are routinely collected.

Conclusions:HARDI offers increased SNR even for routine brain mapping studies using tensor-derived measures, such as FA,which requires only 7 gradients to determine analytically. Further studies of scanner field strength, spatialresolution, tolerability, motion artifacts, and clinical effect sizes will clarify the added benefit of this SNR forneuroscientific studies.

References: