Lisha Yuan^{1}, Yi-Cheng Hsu^{2}, Dan Wu^{1}, Hongjian He^{1}, and Jianhui Zhong^{1,3}

^{1}Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China, ^{2}MR Collaboration, Siemens Healthcare Ltd., Shanghai, China, ^{3}Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

Compared to traditional echo-planar imaging (EPI)-based schemes, spatiotemporal encoding (SPEN) is largely insensitive to magnetic field and chemical shift heterogeneities. However, excitation gradient has different effects for each position, thus the interaction between imaging and diffusion gradients introduces spatial-dependent diffusion weightings along the SPEN axis. A new method named polarities average mode (PAM) was proposed to obtain accurate apparent diffusion coefficient (ADC) map, with two acquisitions of different polarities between excitation and diffusion gradients. Simulation, phantom, and human experiments were designed to assess method performance. The proposed method enables SPEN to obtain ADC maps easily and accurately.

This study examines the possibility to obtain DWI with uniform b-values and accurate apparent diffusion coefficient (ADC). One end of the sample along the SPEN-axis, which is excited last but refocused first, has a minimal effective TE value and a minimal effective area of G

The frequency of chirp pulse is chosen as linearly decreasing with time. For simplicity, only diffusion weightings along the SPEN-axis are considered. Two acquisitions with opposite directions of the diffusion gradient are shown in Figures 1a-b.

i. Case 1: G

ii. Case 2: G

The reconstructed images have the same spatially dependent TE(y) due to their same excitation/refocusing order. Simulations of the spatial-dependent b(y) curves from the two cases show an opposite but symmetrical trend (Figures 1c-d). After averaging the corresponding b(y) curves for each expected b-value, a new curve which is spatial-independent was obtained.

The diffusion effect for Case1, Case 2, and combined image can be described by the following equations,

$${I_1} = {I_0}*{e^{ - {b_1}(y)*ADC}}{\kern 1pt}{\kern 1pt}[1]$$

$${I_2} = {I_0}*{e^{ - {b_2}(y)*ADC}}{\kern 1pt}{\kern 1pt}[2]$$

$$\sqrt {{I_1}*{I_2}} = {I_0}*{e^{ - \frac{{{b_1}(y) + {b_2}(y)}}{2}*ADC}}{\kern 1pt}{\kern 1pt}[3]$$

By integrating two DWIs with different polarities, namely polarities average mode (PAM), the combined $$$\sqrt {{I_1}*{I_2}}$$$ has an expected b-value in all positions along the SPEN-axis (Figure 1e). In addition to spatial-independent diffusion weighting, the combined image has an improved signal-to-noise ratio (SNR) and could be used to produce an improved ADC map. ADC map was calculated from b=0 and b=800 DWIs, and the results were compared among , i) dEPI (Case 1), ii) dSPEN (Case 1), iii) dSPEN (Case 2), and iv) dSPEN (PAM).

A simulation was designed as shown in Figure 2. The simulated object was encoded by two parts: i) diffusion simulation, in which the ADC values within a circular ROI were pre-defined as 2.10*10

All simulations and real experiments shared the following parameters: field of view (FOV)=220×220 mm

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