Comparing Algorithms for MR Perfusion Assessment
Author Information
Author(s): Nooralipour Niloufar, Zarinabad Amedeo, Hautvast Gilion, Schuster Andreas, Batchelor Philip, Plein Sven, Nagel Eike
Primary Institution: King's College London
Hypothesis
How do different deconvolution algorithms affect voxel-wise quantitative MR perfusion assessment?
Conclusion
The study highlights the significance of adequate signal-to-noise ratio in first-pass perfusion images and suggests that more research is needed to optimize algorithms for clinical use.
Supporting Evidence
- ARMA method analysis resulted in the lowest model curve-fit error for different levels of noise.
- Exponential deconvolution had a lower error compared to Fermi function modelling and model-independent analysis.
- The overall rest and stress error was lowest for the ARMA method.
Takeaway
This study looked at different ways to analyze heart images and found that some methods work better than others when there's a lot of noise in the images.
Methodology
The study used various deconvolution algorithms to analyze myocardial perfusion MR data in patients during adenosine-induced hyperaemia.
Limitations
Further studies are needed to determine the best algorithms for clinical use with reduced signal-to-noise ratio.
Participant Demographics
6 patients undergoing myocardial perfusion MR imaging.
Digital Object Identifier (DOI)
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