Accelerating MR diffusion tensor imaging via filtered reduced‐encoding projection‐reconstruction

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Abstract

MR diffusion tensor imaging (DTI) is a promising tool for characterizing the microstructure of ordered tissues. However, its practical applications are hampered by relatively low signal-to-noise-ratio and spatial and temporal resolution. Reduced-encoding imaging (REI) via k-space sharing with constrained reconstruction has previously been shown to be effective for accelerating DTI, although the implementation was based on rectilinear k-space sampling. Due to the intrinsic oversampling of central k-space and allowance for isotropic downsampling, projection-reconstruction (PR) imaging may be better suited for REI. In this study, regularization procedures, including radial filtering and baseline signal correction to adequately reconstruct reduced encoded PR imaging data, are investigated. The proposed filtered reduced-encoding projection-reconstruction (FREPR) technique is applied to DTI tissue fiber orientation and fractional anisotropy (FA) measurements. Results show that FREPR offers improved reconstructions of the reduced encoded images and on an equal total scan-time basis provides more accurate fiber orientation and FA measurements compared to rectilinear k-space sampling-based REI methods or a control experiment consisting of only fully encoded images. These findings suggest a potentially significant role of FREPR in accelerating repeated imaging and improving the data acquisition-time efficiency of DTI experiments.

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