Sirui Li1, Wenbo Sun1, Yuan Zheng2, Qing Wei3, Samo Lasic4, Shihong Han3, Shuheng Zhang3, Danielle van Westen5, Karin Karin Bryskhe4, Daniel Topgaard4,5, and Haibo Xu1
1Zhongnan Hospital of Wuhan University, Wuhan, China, 2UIH America, Inc., Houston, TX, United States, 3United Imaging Healthcare, Shanghai, China, 4Random Walk Imaging, Lund, Sweden, 5Lund University, Lund, Sweden
Synopsis
Diffusion kurtosis decomposition (DKD) is a novel advanced diffusion MRI
modality relying on customized pulse sequences and high-performance hardware to
assess cell shapes and density heterogeneity via the anisotropic and isotropic
mean kurtosis parameters, MKa and MKi, which are fundamentally different
microstructural properties that are inextricably entangled in conventional
diffusion kurtosis imaging (DKI). We have investigated DKD imaging of gliomas
in a clinical setting, and for the first time established the correlations
between MKa, MKi, and tumor grade. In comparison to conventional diffusion methods,
DKD more accurately describes the microstructural changes and provides a useful
tool for glioma diagnosis.
Introduction
Glioma is the most common malignant primary brain tumor.1 Progression
from low to high grade glioma (LGG and HGG) coincides with microstructural
changes related to cell shape and density heterogeneity which may be assessed
via the anisotropic and isotropic mean kurtosis parameters, MKa and MKi,
obtained from the advanced diffusion MRI modality diffusion kurtosis decomposition
(DKD).2-5 MKa characterizes the microscopic anisotropy independent
of the subvoxel structure alignment, while MKi measures the isotropic
diffusivity dispersion which is often related to the cell density variation
(Fig. 1). The fundamentally different microstructural properties captured in
the MKa and MKi metrics are entangled in the DKI6 parameter mean
kurtosis (MK), which is generally interpreted in terms of “tissue complexity”.1
As opposed to the diffusion tensor imaging (DTI) metrics MD and FA,7
the MK parameter from DKI is significantly higher in HGG than in LGG.1
While DTI and DKI can be performed with off-the-shelf pulse sequences, the
additional information in DKD relies on customized sequences and
high-performance hardware allowing for high-amplitude and rapidly slewing
gradient waveforms in three dimensions. Here we investigate DKD imaging of
gliomas in a clinical setting, and establish correlations between MKa, MKi, and
tumor grade. In comparison to conventional diffusion methods, DKD more
accurately describes the microstructural changes and provides a useful tool for
glioma diagnosis.Methods
Forty-one glioma patients (13 LGG and 28 HGG) underwent MRI and
subsequent surgery within one month. Glioma grade was determined from
histological and molecular analysis of the resected tumor according to the 2016
WHO guidelines and tumors were classified as LGG (grades I and II) or HGG
(grades III and IV).
MRI was performed on a uMR 790 3.0 T scanner (United Imaging Healthcare,
Shanghai, China) with a 24-channel head coil and 100 mT/m maximum gradient. Images
were acquired with a custom spin-echo EPI sequence using TR = 3750 ms, TE =
84.9 ms, FA = 90°, slice thickness = 4 mm, whole brain coverage, FOV = 224×224
mm, matrix = 112×112, bandwidth = 1560 Hz/pixel, partial Fourier factor = 0.75,
in-plane parallel imaging factor = 2 (anterior-posterior). Diffusion encoding was
performed with 40 directional and 40 isotropic encodings8 using
optimized gradient waveforms9,10 for b = 100, 700, 1400, 2000
s/mm2, and 6, 6, 12, 16 directions or averages, giving a total scan
time of ~5 min. After motion and eddy current correction, the per-voxel
signals were powder-averaged and analyzed with the Gamma distribution model to
extract MKa, MKi, and total mean kurtosis MKt=MKa+MKi parameter maps.2-5
ROIs delineating solid tumor were defined by two radiologists in
consensus using T1 images for contrast-enhancing tumors and T2/T2
FLAIR images for non-enhancing ones. Non-linear image registration was
performed between anatomical and EPI images to project ROIs onto the parameter
maps.11
DKD parameters with normal/non-normal distribution (by Shapiro-Wilk
test) were compared between groups using two sample t-test or Mann–Whitney U
test, p < 0.05 was
considered significant. ROC analysis was applied to determine optimal cutoff
values for classifying HGG/LGG according to the maximal Youden index.Results & Discussion
Fig. 2 shows parameter maps of a grade IV tumor. While high values of MKi
(1.70 ± 0.14) in the tumor correspond to high intra-voxel heterogeneity in cell
density, likely related to cell proliferation, the low but finite values of MKa
(0.38 ± 0.11) indicate that the cells are more elongated in the tumor than in its surroundings. The fundamentally different microstructural information from
MKa and MKi are combined in the MKt parameter (2.09 ± 0.19).
DKD metrics of the tumors are summarized in Table 1 and Fig. 3. While MKa,
MKi, and MKt are all significantly higher in HGG than in LGG (p < 0.001, p <
0.001, p < 0.001 respectively), MKi is larger than MKa in both LGG (p = 0.001)
and HGG (p < 0.001) by Wilcoxon signed-rank test. The ratio MKa/MKt is
significantly higher in HGG than in LGG (p < 0.001). Correspondingly,
MKi/MKt is significantly lower in HGG than in LGG (p < 0.001).
ROC curves for MKa, MKi, and MKt are shown in Fig. 4, with AUC 0.89,
0.89, and 0.90, respectively. The optimal cutoff values for classification are 1.07,
0.16, and 0.79, respectively.Conclusions
We have used a clinically feasible 5-min acquisition to non-invasively study
cell shape and density heterogeneity represented by the parameters MKa and MKi in
order to determine glioma grade (LGG versus HGG). Higher MKa and MKi were
observed in HGG than in LGG. Cell density heterogeneity, captured by MKi, is
the dominant contribution to the total mean kurtosis MKt especially in the LGG.
The ratio MKi/MKt could therefore provide an important index to distinguish HGG
and LGG. The novel metrics unveiled valuable microstructural information about
gliomas, thereby providing exciting new opportunities for non-invasive glioma grading.Acknowledgements
Data collection was approved by the Wuhan hospital ethics committee and
with written informed consent from all participants. This work was financially supported by the national key research and development plan of China (2017YFC0108803), the Swedish Foundation for
Strategic Research (AM13-0090, ITM17-0267) and the Swedish Research Council
(2018-03697). Daniel Topgaard owns shares in Random Walk Imaging (RWI) AB
(Lund, Sweden, http://www.rwi.se/), holding patents related to the described
methods. Markus Nilsson and Filip Szczepankiewicz are gratefully acknowledged
for generously sharing know-how on pulse sequence setup acquired during their
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