The purpose of this study was to quantitatively evaluate diffusion-weighted signals and ADC values from two different DWI sequences – echo planar spin echo (EPSE) and read-out segmented echo planar (RESOLVE) imaging - and to perform a comparative analysis of the two. Image acquisition: The participants included 30 normal subjects with no medical history of neurological diseases (mean age = 64.4, standard deviation = 14.2, 15 males and 15 females). MR imaging was performed on a 3T MR system (Siemens). A single-shot diffusion weighted image (ssDWI) was acquired using an echo-planar spin echo (EPSE) sequence (TR = 6800ms, TE = 100ms, voxel dimension = 1.15 x 1.15 x 6 mm³), and a high resolution multi-shot DWI (msDWI) was obtained from a readout-segmented echo planner imaging (RESOLVE) sequence (TR = 4900ms, TE = 68ms, voxel dimension = 1.15 x 1.15 x 6 mm³, number of readout segment per image = 11, echo-spacing = 300µs) at b-values of 0 and 1000s/mm². The axial T2-weighted image was also acquired for image registration and segmentation (TR = 4900ms, TE = 68ms, voxel dimension = 0.47x 0.47 x 6mm³). Image processing: All pre-processing and statistical analysis steps were performed using a Statistical Parametric Mapping version 8 (SPM8) program and MATLAB software. The T2-weighted images were co-registered to msDWI at b = 0 s/mm² and were segmented into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) to generate masking templates. The calculated ADC maps, msDWI and ssDWI of b0 and 1000 s/mm² were segmented into GM, WM and CSF using the masking templates, and mean ADC values from voxels containing more than 90% of GM, WM, CSF and combination of the three were estimated. Statistical analysis: A correlation analysis was performed between the two mean ADC values. The ADC maps of all subjects were spatially normalized with the resized voxel dimensions of 1.5 x 1.5 x 3mm³ and were smoothed for a voxel-based statistical analysis. The full-width at half maximum (FWHM) of the Gaussian smoothing kernel was chosen to be 4 x 4 x 8 mm³. A paired t-test was performed between the two ADC maps of each subject to identify voxels with significant differences. A false discovery rate (FDR) correction was performed for those voxels representing significant differences, and the p-value less than 0.05 was chosen. Figure 1 shows the mean ADCs of the whole brain, GM only, WM only and CSF only, respectively. The mean ADC values from ssDWIs were greater in all components. The ADC difference between single-shot and multi-shot DWIs was smallest in CSF and was largest in WM. Figure 2illustrates the brain regions, in which the differences between ADC values from msDWI and ssDWI were statistically significant (p < 0.05, FDR corrected). As the color indicates, the ADC values from msDWI and ssDWI were statistically different over the whole brain regions. Figure 3 illustrates the correlation analysis results between ADCs from msDWI and ssDWI. Our results showed that the two ADCs were highly correlated (R² = 0.989), with the correlation coefficient of 0.897 and a residual constant of 65.368 given that ADC from msDWI was a dependent variable.

Although a number of studies compared image quality and detectability of lesions between ssDWI and msDWI [1-3], no study has been conducted to quantitatively analyze ADCs from the two sequences. As an initial study, we compared ADCs from normal subjects in different components of the brain and discovered that although they were significantly different over the whole brain regions, the two ADCs were highly correlated (R² = 0.989). A correlation coefficient and a residual constant were also estimated to find a relationship between the two ADCs in normal brains. Although msDWI has a superior signal-to-noise ratio than ssDWI does, it suffers from a long acquisition time, which might not be suitable for certain patients. Evaluating diseased tissues such as stoke infarcts [4] should enable us to compare the feasibility of ADCs calculated from each sequence and possibly suggest a complimentary role of ssDWI to msDWI for characterizing certain types of diseases.