bioRxiv · 10.1101/2022.07.13.499856
Optimization of 4D Combined Angiography and Perfusion using Radial Imaging and Arterial Spin Labeling
Abstract
PurposeTo extend and optimize a non-contrast MRI technique to obtain whole head 4D (time-resolved 3D) angiographic and perfusion images from a single scan. Methods4D combined angiography and perfusion using radial imaging and arterial spin labeling (CAPRIA) uses pseudocontinuous labeling with a 3D golden ratio ("koosh ball") readout to continuously image the blood water as it travels through the arterial system and exchanges into the tissue. High spatial/temporal resolution angiograms and low spatial/temporal resolution perfusion images can be flexibly reconstructed from the same raw k-space data at any timepoint within the readout. A constant flip angle (CFA) and a quadratic variable flip angle (VFA) excitation schedule were optimized through simulations and tested in healthy volunteers. A conventional sensitivity encoding (SENSE) reconstruction was compared against a locally low rank (LLR) reconstruction, which leverages spatiotemporal correlations to improve reconstruction quality. Differences in image quality were assessed through split-scan repeatability. ResultsThe optimized VFA schedule (2-9{degrees}) reduced initial signal attenuation whilst boosting the signal at later timepoints, resulting in a significant (p < 0.001) improvement in image quality (up to 84%), particularly for the lower SNR perfusion images. The LLR reconstruction provided effective denoising without biasing the signal timecourses, significantly improving angiographic and perfusion imaging repeatability (up to 143%, p < 0.001) and enabling the clear depiction of small distal vessels and late tissue perfusion. Conclusion4D CAPRIA can be optimized using a VFA schedule and LLR reconstruction to yield whole head 4D angiograms and perfusion images from a single scan.
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Okell, T. W., Chiew, M.. 2022-07-14. Optimization of 4D Combined Angiography and Perfusion using Radial Imaging and Arterial Spin Labeling. https://doi.org/10.1101/2022.07.13.499856
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