Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 26, Issue 11, Pages 1464-1471Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2007.907552
Keywords
deconvolution; diffusion -weighted magnetic; resonance imaging (DW-MRI); multifiber reconstruction; nonnegative least squares (NNLS)
Categories
Funding
- NCRR NIH HHS [P41 RR016105, P41-RR16105] Funding Source: Medline
- NIBIB NIH HHS [R01 EB007082, EB007082, R01 EB007082-03] Funding Source: Medline
- NINDS NIH HHS [R01 NS042075-04, R01 NS036992, R01-NS36992, R01 NS042075] Funding Source: Medline
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Diffusion magnetic resonance imaging (MRI) is a relatively new imaging modality which is capable of measuring the diffusion of water molecules in biological systems noninvasively. The measurements from diffusion MRI provide unique clues for extracting orientation information of brain white matter fibers and can be potentially used to infer the brain connectivity in vivo using tractography techniques. Diffusion tensor imaging (DTI), currently the most widely used technique, fails to extract multiple fiber orientations in regions with complex microstructure. In order to overcome this limitation of DTI, a variety of reconstruction algorithms have been introduced in the recent past. One of the key ingredients in several model-based approaches is deconvolution operation which is presented in a unified deconvolution framework in this paper. Additionally' some important computational issues in solvin the deconvolution problem that are not addressed adequately in previous studies are described in detail here. Further, we investigate several deconvolution schemes towards achieving stable, sparse, and accurate solutions. Experimental results on both simulations and real data are presented. The comparisons empirically suggest that nonnegative least squares method is the technique of choice for the multifiber reconstruction problem in the presence of intravoxel orientational heterogeneity.
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