Gambarota, G. | Search bioRxiv

bioRxiv2021

Multi T1-weighted contrast imaging and T1 mapping with Compressed sensing FLAWS at 3T

The Fluid And White matter Suppression (FLAWS) MRI sequence allows for the acquisition of multiple T1-weighted contrasts in a single sequence acquisition. However, its acquisition time is prohibitive for use in clinical practice when the k-space is linearly downsampled and reconstructed using the Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) technique. This study proposes a FLAWS sequence optimization tailored to allow for the acquisition of FLAWS images with a Cartesian phyllotaxis k-space undersampling and compressed sensing (CS) reconstruction at 3T. The CS FLAWS sequence parameters were determined using a method previously employed to optimize FLAWS imaging at 1.5T and 7T. In-vivo experiments show that the proposed CS FLAWS optimization allows to reduce the FLAWS sequence acquisition time from 8 mins to 6 mins without decreasing the FLAWS image quality. In addition, this study demonstrates for the first time that T1-weighted imaging with low B1 sensitivity and T1 mapping can be performed with the FLAWS sequence at 3T for both GRAPPA and CS reconstructions. The FLAWS T1 mapping was validated using in-silico, in-vitro and in-vivo experiments with comparison against the inversion recovery turbo spin echo and MP2RAGE T1 mappings. These new results suggest that the recent advances in FLAWS imaging allow to combine the MP2RAGE imaging benefits (T1-weigthed imaging with low B1 sensitivity and T1 mapping) and with the previous version of FLAWS imaging benefits (multi T1-weighted contrast imaging) in a single 6 mins sequence acquisition. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

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bioRxiv2021

Avoiding Data Loss: Synthetic MRIs Generated from Diffusion Imaging Can Replace Corrupted Structural Acquisitions For Freesurfer-Seeded Tractography

1Magnetic Resonance Imaging (MRI) motion artefacts frequently complicate structural and diffusion MRI analyses. While diffusion imaging is easily scrubbed of motion affected volumes, the same is not true for structural images. Structural images are critical to most diffusion-imaging pipelines thus their corruption can lead to disproportionate data loss. To enable diffusion-image processing when structural images have been corrupted, we propose a means by which synthetic structural images can be generated from diffusion MRI. This technique combines multi-tissue constrained spherical deconvolution, which is central to many existing diffusion analyses, with the Bloch equations which allow simulation of MRI intensities given scanner parameters and magnetic resonance (MR) tissue properties. We applied this technique to 32 scans, including those acquired on different scanners, with different protocols and with pathology present. The resulting synthetic T1w and T2w images were visually convincing and exhibited similar tissue contrast to acquired structural images. These were also of sufficient quality to drive a Freesurfer-based tractographic analysis. In this analysis, probabilistic tractography connecting the thalamus to the primary sensorimotor cortex was delineated with Freesurfer, using either real or synthetic structural images. Tractography for real and synthetic conditions was largely identical in terms of both voxels encountered (Dice 0.88 - 0.95) and mean fractional anisotropy (intrasubject absolute difference 0.00 - 0.02). We provide executables for the proposed technique in the hope that these may aid the community in analysing datasets where structural image corruption is common, such as studies of children or cognitively impaired persons. HighlightsO_LIWe propose a simple means of synthesizing T1w and T2w images from diffusion data C_LIO_LIThe proposed method worked well for a variety of acquisitions C_LIO_LISynthetic images showed tissue contrast akin to acquired images C_LIO_LISynthetic images were high enough quality to be used for Freesurfer seeded diffusion tractography C_LIO_LIThis method enables analysis of datasets where motion has corrupted acquired structural MRIs C_LI

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