Dataset Information

HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging.

ABSTRACT: The parameter selection for diffusion MRI experiments is dominated by the "k-q tradeoff" whereby the Signal to Noise Ratio (SNR) of the images is traded for either high spatial resolution (determined by the maximum k-value collected) or high diffusion sensitivity (effected by b-value or the q vector) but usually not both. Furthermore, different brain regions (such as gray matter and white matter) likely require different tradeoffs between these parameters due to the size of the structures to be visualized or the length-scale of the microstructure being probed. In this case, it might be advantageous to combine information from two scans - a scan with high q but low k (high angular resolution in diffusion but low spatial resolution in the image domain) to provide maximal information about white matter fiber crossing, and one low q but high k (low angular resolution but high spatial resolution) for probing the cortex. In this study, we propose a method, termed HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging, for acquiring and combining the information from these two complementary types of scan with the goal of studying diffusion in the cortex without compromising white matter fiber information. The white-gray boundary and pial surface obtained from anatomical scans are incorporated as prior information to guide the fusion. We study the complementary advantages of the fused datasets, and assess the quality of the HIBRID data compared to either alone.

SUBMITTER: Fan Q

PROVIDER: S-EPMC5501959 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging.

Fan Qiuyun Q Nummenmaa Aapo A Polimeni Jonathan R JR Witzel Thomas T Huang Susie Y SY Wedeen Van J VJ Rosen Bruce R BR Wald Lawrence L LL

NeuroImage 20170207

The parameter selection for diffusion MRI experiments is dominated by the "k-q tradeoff" whereby the Signal to Noise Ratio (SNR) of the images is traded for either high spatial resolution (determined by the maximum k-value collected) or high diffusion sensitivity (effected by b-value or the q vector) but usually not both. Furthermore, different brain regions (such as gray matter and white matter) likely require different tradeoffs between these parameters due to the size of the structures to be ...[more]

PMID: 28188913

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Project description:BACKGROUND AND PURPOSE:Imaging-based tumor grading is highly desirable but faces challenges in sensitivity, specificity, and diagnostic accuracy. A recently proposed diffusion imaging method by using a fractional order calculus model offers a set of new parameters to probe not only the diffusion process itself but also intravoxel tissue structures, providing new opportunities for noninvasive tumor grading. This study aimed to demonstrate the feasibility of using the fractional order calculus model to differentiate low- from high-grade gliomas in adult patients and illustrate its improved performance over a conventional diffusion imaging method using ADC (or D). MATERIALS AND METHODS:Fifty-four adult patients (18-70 years of age) with histology-proved gliomas were enrolled and divided into low-grade (n = 24) and high-grade (n = 30) groups. Multi-b-value diffusion MR imaging was performed with 17 b-values (0-4000 s/mm(2)) and was analyzed by using a fractional order calculus model. Mean values and SDs of 3 fractional order calculus parameters (D, ?, and ?) were calculated from the normal contralateral thalamus (as a control) and the tumors, respectively. On the basis of these values, the low- and high-grade glioma groups were compared by using a Mann-Whitney U test. Receiver operating characteristic analysis was performed to assess the performance of individual parameters and the combination of multiple parameters for low- versus high-grade differentiation. RESULTS:Each of the 3 fractional order calculus parameters exhibited a statistically higher value (P ? .011) in the low-grade than in the high-grade gliomas, whereas there was no difference in the normal contralateral thalamus (P ? .706). The receiver operating characteristic analysis showed that ? (area under the curve = 0.853) produced a higher area under the curve than D (0.781) or ? (0.703) and offered a sensitivity of 87.5%, specificity of 76.7%, and diagnostic accuracy of 82.1%. CONCLUSIONS:The study demonstrated the feasibility of using a non-Gaussian fractional order calculus diffusion model to differentiate low- and high-grade gliomas. While all 3 fractional order calculus parameters showed statistically significant differences between the 2 groups, ? exhibited a better performance than the other 2 parameters, including ADC (or D).

Dataset Information

HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging.

Publications

HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging.

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