Perfusion-free Diffusion Tensor Imaging of Brain Tumors
Zhongwei Zhang1, Zhuhao Li2, Yu-Chien Wu3, Dawen Zhao4, and Mark E Schweitzer5

1Radiology, UT Southwestern Medical Center, Dallas, TX, United States, 2Radiology, The 1st Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China, People's Republic of, 3Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States, 4Biomedical Engineering and Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, United States, 5Radiology, Stony Brook University, Stony Brook, NY, United States


In conventional DTI, the quantitation of various DTI indices was strongly influenced by b-value. In this study, we proposed a new approach that perfusion-free DTI can be fulfilled using IVIM and DTI models.


MR diffusion tensor imaging (DTI) has been used to describe normal brain in characterizing tissue microanatomy as well as various disease states1. The properties of the tensor model were characterized by its eigenvectors and its eigenvalues. Typical DTI indices, derived from the diffusion tensor as rotationally-invariant parameters, include fractional anisotropy (FA), mean diffusivities (MD). However, several studies demonstrated that b-value strongly influences the quantitation of various DTI indices2. Intravoxel incoherent motion (IVIM) diffusion MRI has been proposed to evaluate perfusion in tissues because diffusion is also sensitive to perfusion and the flow of blood water in randomly oriented capillaries mimics a diffusion process3. Thus, the purpose of this study was to propose a new approach that perfusion-free DTI can be obtained using IVIM and DTI models.


A total of 7 patients who were clinically diagnosed with brain tumors were enlisted for this study. All patients with primary brain tumors were histologically diagnosed on the basis of either pre-imaging biopsy or post-imaging biopsy or resection. MR imaging was performed with a 3.0-T Verio (Siemens Medical Solutions, Erlangen, Germany) scanner. DTI was performed in the axial plane using a single-shot spin-echo echo-planar imaging (EPI) sequence with multiple b values and multiple directions acquisition. The parameters are as follows: TR/TE: 4500 ms/98ms; diffusion gradient encoding in 12 directions; b-values: 0, 50, 100, 150, 200, 300, 450, 600, 900 and 1200 s/mm2; field of view, 230mm × 230 mm; acquisition matrix, 128 × 128; 20-25 axial images with 5mm slice thickness, no gap; the number of signal averages (NSA), 1; parallel imaging: GRAPPA with a factor of 2; partial Fourier EPI scan factor: 0.75; bandwidth: 184Hz/Px. To get perfusion free DTI, IVIM model was first fitted to determine the perfusion fraction fp and blood pseudodiffusion coefficient Dp using trace-weighted images, i.e.: S/S0 = (1-fp)exp(-bDt)+ fpexp(-bDp) [1], Where S0 is signal intensity at b = 0, S, fp, Dt and Dp are the DW signal, the perfusion fraction, tissue diffusion coefficient and pseudodiffusion coefficient in each b value, respectively. After fp and Dp were determined, DTI model was used to determine orientation-dependent Dt,j, i.e.: S' = exp(-bjDt,j) [2] Where S' =[S-S0fpexp(-bDp)]/[S0(1-fp)], bj = bĝjT, ĝj is the unit vector describing the DW encoding direction. The mean diffusivity (MD) and fractional anisotropy of diffusion (FA) were generated as per standard calculations. All data were processed off-line with software procedures developed in house using Matlab (Mathworks, Natick, MA).


Representative brain tumor conventional DTI and perfusion free DTI derived parameter maps are shown in Fig.1. Upon qualitative visual inspection, MD maps obtained by both DTIs did not reveal appreciable differences in detecting tumor and brain structures, but decreased MD was observed in tumor and brain structures. FA maps showed both an increased contrast between the white matter and the surrounding gray matter and a better delineation of tumor.


Using perfusion free DTI, we have demonstrated that there are clear differences in MD and FA when compared with those of derived from conventional DTI. In addition, the improved contrast in perfusion free DTI may be useful in tumor detection and preoperative discriminations.


No acknowledgement found.


1. Alexander AL, Lee JE, Lazar M, et al. Diffusion tensor imaging of the brain. Neurotherapeutics 2007; 4:316-329.

2. Hui ES, Cheung MM, Chan KC, et al. B-value dependence of DTI quantitation and sensitivity in detecting neural tissue changes. Neuroimage 2010; 49(3):2366-74.

3. Le Bihan D, Turner R.The capillary network: a link between IVIM and classical perfusion. Magn Reson Med 1992; 27(1):171-8.


Figure: Representative conventional DTI and perfusion free DTI derived MD and FA maps of meningioma.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)