Abstract
Perfusion imaging (PI) is clinically used to assess strokes and brain tumors. Commonly used PI approaches based on magnetic resonance imaging (MRI) or X-ray computed tomography (CT) measure the effect of a contrast agent moving through blood vessels and into tissue. Contrast-agent free approaches, for example, based on intravoxel incoherent motion, also exist, but are not routinely used clinically. MR or CT perfusion imaging based on contrast agents relies on the estimation of the arterial input function (AIF) to approximately model tissue perfusion, neglecting spatial dependencies. Reliably estimating the AIF is also non-trivial, leading to difficulties with standardizing perfusion measures. In this work we propose a data-assimilation approach (PIANO) which estimates the velocity and diffusion fields of an advection-diffusion model best explaining the contrast dynamics. PIANO accounts for spatial dependencies and neither requires estimating the AIF nor relies on a particular contrast agent bolus shape. Specifically, we propose a convenient parameterization of the estimation problem, a numerical estimation approach, and extensively evaluate PIANO. We demonstrate that PIANO can successfully resolve velocity and diffusion field ambiguities and results in sensitive measures for the assessment of stroke, comparing favorably to conventional measures of perfusion.
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Notes
- 1.
Our dataset is acquired axially, but BCs could be modified for different acquisition formats as needed. This BC essentially replaces determining the AIF.
- 2.
While a paired test between corresponding voxels is possible and results in similar measures, we opt for the unpaired test to avoid voxel-level correspondence issues.
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Acknowledgment
Research reported in this work was supported by the National Institutes of Health (NIH) under award number NIH 2R42NS086295. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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Liu, P., Lee, Y.Z., Aylward, S.R., Niethammer, M. (2020). PIANO: Perfusion Imaging via Advection-Diffusion. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_67
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