title: Reduced encoding diffusion spectrum imaging implemented with a bi-Gaussian model.
abstract: Diffusion spectrum imaging (DSI) can map complex fiber microstructures in tissues by characterizing their 3-D water diffusion spectra. However, a long acquisition time is required for adequate q-space sampling to completely reconstruct the 3-D diffusion probability density function. Furthermore, to achieve a high q-value encoding for sufficient spatial resolution, the diffusion gradient duration and the diffusion time are usually lengthened on a clinical scanner, resulting in a long echo time and low signal-to-noise ratio of diffusion-weighted images. To bypass long acquisition times and strict gradient requirements, the reduced-encoding DSI (RE-DSI) with a bi-Gaussian diffusion model is presented in this study. The bi-Gaussian extrapolation kernel, based on the assumption of the bi-Gaussian diffusion signal curve across biological tissue, is applied to the reduced q-space sampling data in order to fulfill the high q-value requirement. The crossing phantom model and the manganese-enhanced rat model served as standards for accuracy assessment in RE-DSI. The errors of RE-DSI in estimating fiber orientations were close to the noise limit. Meanwhile, evidence from a human study demonstrated that RE-DSI significantly decreased the acquisition time required to resolve complex fiber orientations. The presented method facilitates the application of DSI analysis on a clinical magnetic resonance imaging system.
authors.affiliation: Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Chun-Hung
authors.initials: CH
authors.lastname: Yeh
authors.forename: Kuan-Hung
authors.initials: KH
authors.lastname: Cho
authors.forename: Hsuan-Cheng
authors.initials: HC
authors.lastname: Lin
authors.forename: Jiun-Jie
authors.initials: JJ
authors.lastname: Wang
authors.forename: Ching-Po
authors.initials: CP
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D016011:Normal Distribution; D009900:Optic Nerve / Q000033:anatomy & histology*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity