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-f_p)exp(-bD_t)+ f_pexp(-bD_p) [1], Where S₀ is signal intensity at b = 0, S, f_p, D_t and D_p are the DW signal, the perfusion fraction, tissue diffusion coefficient and pseudodiffusion coefficient in each b value, respectively. After f_p and D_p were determined, DTI model was used to determine orientation-dependent D_t,j, i.e.: S' = exp(-b_jD_t,j) [2] Where S' =[S-S₀f_pexp(-bD_p)]/[S₀(1-f_p)], b_j = bĝ_jT, ĝ_j 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.