Over the last decade MR diffusion imaging has evolved into an immensely useful tool to assess stroke, tissue fiber architecture, and characteristic tissue water diffusion. Nevertheless, compared with other commonly applied MR image contrasts, state-of-the art MR diffusion imaging exhibits significant deficiencies. The widely used 2D single-shot diffusion imaging techniques tend to produce images of inferior quality, often suffering from distorted image geometry and poor signal-to-noise ratio (SNR). In particular, distorted image geometries preclude direct comparisons with images of different contrast or different imaging modalities. In the quest for better MR diffusion imaging pulse sequences, numerous approaches have been suggested, like reduced field-of-view imaging, line and slab scan diffusion imaging (LSDI and SSDI), single-shot parallel diffusion imaging, and segmented multi-shot diffusion imaging. While all of these methods reduce to some extent the excessive distortions often present in diffusion images, such improvements invariably come at the expense of increased scan time and/or reduced SNR. In the present work, the main incentive is to greatly alleviate the SNR problem by extending the image acquisition to 3D. Until now, this extension to 3D has been hampered by prolonged scan times and proneness for motion artifacts. The proposed research introduces novel methods, which promise to overcome these disadvantages.
The specific aims are: 1) To develop SNR-neutral parallelization in 3D for the typically slower, but very artifact- resistant LSDI and SSDI sequences. 2) To parallelize multi-shot segmented 2D along the third dimension and to develop 3D diffusion imaging with 3D navigator correction of motion-related phase errors. 3) To develop novel acceleration schemes to keep 3D diffusion imaging scan times within practical limits, while also reducing distortion artifacts. 4) To test these developments in phantoms, in normal volunteers, and in neuroepithelial tumor patients, who undergo clinical scans with the purpose of surgical treatment planning.
The capability of magnetic resonance imaging to measure and image molecular diffusion has provided a new source of image contrast and has proven extremely useful in assessing stroke-related damage and the fiber architecture of nerves and muscles. While 3D diffusion imaging would present significant advantages over 2D imaging, it has up to now been impeded by technical problems, such as long acquisition times and motion artifacts. The present application aims at solving these problems, to enable robust and practical 3D diffusion imaging.
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