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Rapid Diffusion Weighted Imaging with Enhanced Resolution

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Abstract

In this paper, a new, fast compressively sensed diffusion magnetic resonance image enhancement technique is presented. This algorithm aims to overcome two major obstacles—image resolution limitation and algorithm reconstruction time efficiency-by combining a highly sparse k–q-space sampling pattern with super-resolution (SR) image enhancement. Similar to the RoSA (rotating single-shot acquisition) acceleration scheme, the presented algorithm takes advantage of simultaneous k–q-space sampling procedures being able to implement directly with no hardware modifications. The method sequentially processes compressively sensed k-space’s semi-PROPELLER blades with respect to appropriately synchronized diffusion directions. The dMR image structure is expressed as a kind of minimum-spanning tree. It fades out distortions of the image’s features. Moreover, as contrasted with numerous other super-resolution algorithms, the presented method overcomes the simplifying motion model as well as blur kernel and noise estimation issues. The simulation and experimental studies have been conducted using a dMRI scanner as well as a phantom input. Combining super-resolution with time-efficient data sets resulted in a reduction of motion artifacts, improving edge delineation as well as spatial resolution.

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  1. Faculty of Electronics and Telecommunications, Poznan University of Technology, Polanka 3, Poznań, 60965, Poland

    Krzysztof Malczewski

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Malczewski, K. Rapid Diffusion Weighted Imaging with Enhanced Resolution. Appl Magn Reson 51, 221–239 (2020). https://doi.org/10.1007/s00723-019-01185-x

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  • DOI: https://doi.org/10.1007/s00723-019-01185-x

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