Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique

Barrick, Thomas R.; Spilling, Catherine A.; Ingo, Carson; Madigan, Jeremy; Isaacs, Jeremy D.; Rich, Philip; Jones, Timothy L.; Magin, Richard L.; Hall, Matt G.; Howe, Franklyn A.

doi:10.1016/j.neuroimage.2020.116606

Cited by 15 publications

(41 citation statements)

References 71 publications

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“…Probing the underlying tissue structures has been a common goal of many high b ‐value diffusion models 21,22,33–43 . Unlike the simple mono‐exponential model, high b ‐value diffusion models recognize that water diffusion in heterogeneous biological tissues does not follow Gaussian diffusion behaviors.…”

Section: Discussionmentioning

confidence: 99%

“…Probing the underlying tissue structures has been a common goal of many high b-value diffusion models. 21,22,[33][34][35][36][37][38][39][40][41][42][43] Unlike the simple monoexponential model, high b-value diffusion models recognize that water diffusion in heterogeneous biological tissues does not follow Gaussian diffusion behaviors. Such non-Gaussian behaviors have been described by a diffusion kurtosis imaging (DKI) model, [44][45][46][47][48][49][50] which exhibited high diagnostic accuracy for glioma characterization.…”

Section: Discussionmentioning

confidence: 99%

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Quartile histogram assessment of glioma malignancy using high b‐value diffusion MRI with a continuous‐time random‐walk model

et al. 2021

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The purpose of this study is to investigate the feasibility of using a continuous‐time random‐walk (CTRW) diffusion model, together with a quartile histogram analysis, for assessing glioma malignancy by probing tissue heterogeneity as well as cellularity. In this prospective study, 91 patients (40 females, 51 males) with histopathologically proven gliomas underwent MRI at 3 T. The cohort included 42 grade II (GrII), 19 grade III (GrIII) and 29 grade IV (GrIV) gliomas. Echo‐planar diffusion‐weighted imaging was conducted using 17 b‐values (0–4000 s/mm2). Three CTRW model parameters, including an anomalous diffusion coefficient Dm, and two parameters related to temporal and spatial diffusion heterogeneity α and β, respectively, were obtained. The mean parameter values within the tumor regions of interest (ROIs) were computed by utilizing the first quartile of the histograms as well as the full ROI for comparison. A Bonferroni–Holm‐corrected Mann–Whitney U‐test was used for the group comparisons. Individual and combinations of the CTRW parameters were evaluated for the characterization of gliomas with a receiver operating characteristic analysis. All first‐quartile mean CTRW parameters yielded significant differences (p‐values < 0.05) between pair‐wise comparisons of GrII (Dm: 1.14 ± 0.37 μm2/ms; α: 0.904 ± 0.03, β: 0.913 ± 0.06), GrIII (Dm: 0.88 ± 0.21 μm2/ms; α: 0.888 ± 0.01, β: 0.857 ± 0.06) and GrIV gliomas (Dm: 0.73 ± 0.22 μm2/ms; α: 0.878 ± 0.01; β: 0.791 ± 0.07). The highest sensitivity, specificity, accuracy and area‐under‐the‐curve of using the combinations of the first‐quartile parameters were 84.2%, 78.5%, 75.4% and 0.76 for GrII and GrIII classification; 86.2%, 89.4%, 75% and 0.76 for GrIII and GrIV classification; and 86.2%, 85.7%, 84.5% and 0.90 for GrII and GrIV classification, respectively. Quartile‐based analysis produced higher accuracy and area‐under‐the‐curve than the full ROI‐based analysis in all classifications. The CTRW diffusion model, together with a quartile‐based histogram analysis, offers a new way for probing tumor structural heterogeneity at a subvoxel level, and has potential for in vivo assessment of glioma malignancy to complement histopathology.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quartile histogram assessment of glioma malignancy using high b‐value diffusion MRI with a continuous‐time random‐walk model

et al. 2021

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“…A key application of QDI is in imaging of the human body where diffusion of free water at body temperature is D FW = 3 × 10 −3 mm 2 s −1 and 0.5 < α < 1 in typical healthy brain tissue [1]. Figure 1 illustrates the family of signal decay curves described by (10).…”

Section: Quasi-diffusion Imagingmentioning

confidence: 99%

“…Experimental diffusion times were δ = 23.5 ms and ∆ = 43.7 ms giving an effective diffusion time of ∆ = 35.9 ms. Data were acquired on a clinical 3T MR scanner at St George's, University of London (SGUL) with a voxel size of 1.5 mm ×1.5 mm ×5 mm in 35 min 12 s. Full image acquisition parameters are given in Appendix A. Data analysis were performed using the technique described in [1] to estimate D 1,2 and α values in each diffusion encoding direction and their mean values within each image voxel. The top row of Figure 2 shows the exceptional quality of fit of the quasi-diffusion model to observed data in individual grey (Figure 2a) and white matter voxels (Figure 2b) across the full range of b-factors.…”

Section: Quasi-diffusion Imagingmentioning

confidence: 99%

“…Quasi-diffusion imaging [1] is a novel diffusion magnetic resonance imaging (dMRI) technique based on a special case of the continuous time random walk (CTRW) diffusion model [2][3][4][5]. The diffusion dynamics are represented by an effective normal diffusion where the mean squared-displacement of the diffusing particles is proportional to time,…”

Section: Introductionmentioning

confidence: 99%

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The Mathematics of Quasi-Diffusion Magnetic Resonance Imaging

et al. 2021

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Quasi-diffusion imaging (QDI) is a novel quantitative diffusion magnetic resonance imaging (dMRI) technique that enables high quality tissue microstructural imaging in a clinically feasible acquisition time. QDI is derived from a special case of the continuous time random walk (CTRW) model of diffusion dynamics and assumes water diffusion is locally Gaussian within tissue microstructure. By assuming a Gaussian scaling relationship between temporal () and spatial () fractional exponents, the dMRI signal attenuation is expressed according to a diffusion coefficient, (in mm2 s−1), and a fractional exponent, . Here we investigate the mathematical properties of the QDI signal and its interpretation within the quasi-diffusion model. Firstly, the QDI equation is derived and its power law behaviour described. Secondly, we derive a probability distribution of underlying Fickian diffusion coefficients via the inverse Laplace transform. We then describe the functional form of the quasi-diffusion propagator, and apply this to dMRI of the human brain to perform mean apparent propagator imaging. QDI is currently unique in tissue microstructural imaging as it provides a simple form for the inverse Laplace transform and diffusion propagator directly from its representation of the dMRI signal. This study shows the potential of QDI as a promising new model-based dMRI technique with significant scope for further development.

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Optimization of quasi‐diffusion magnetic resonance imaging for quantitative accuracy and time‐efficient acquisition

Spilling

Howe

Barrick

2022

Magnetic Resonance in Med

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Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, D 1,2 in mm 2 s −1 and a fractional exponent, α, defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. Methods: Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between b = 0 and 5000 s mm −2 . The effects of varying maximum b-value (b max ), number of b-value shells, and the effects of Rician noise were investigated. Results: QDTI measures showed b max dependence, most significantly for α in white matter, which monotonically decreased with higher b max leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of D 1,2 and underestimation of α in white matter, and overestimation of D 1,2 and α anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at b max = 5000 s mm −2 , and 4 b-value shells at b max = 3960 s mm −2 , providing minimal bias in D 1,2 and α compared to the MbR. Conclusion: A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.

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Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique

Cited by 15 publications

References 71 publications

Quartile histogram assessment of glioma malignancy using high b‐value diffusion MRI with a continuous‐time random‐walk model

Quartile histogram assessment of glioma malignancy using high b‐value diffusion MRI with a continuous‐time random‐walk model

The Mathematics of Quasi-Diffusion Magnetic Resonance Imaging

Optimization of quasi‐diffusion magnetic resonance imaging for quantitative accuracy and time‐efficient acquisition

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