- Poster presentation
- Open Access
High resolution 3D cardiac perfusion imaging using compartment based k-t PCA
© Vitanis et al; licensee BioMed Central Ltd. 2010
- Published: 21 January 2010
- Spatial Weighting
- Coarse Spatial Resolution
- Reconstruction Equation
- Bolus Arrival
- Undersampled Data
k-t PCA  was recently introduced as an expansion of k-t SENSE, aiming at improving the temporal fidelity of accelerated dynamic MRI. Its advantage originates from the fact that it decomposes the training and undersampled data using optimized temporal basis functions, leading to better temporal fidelity in the reconstructed data.
k-t PCA is based on the assumption that the unaliased signal is a linear combination of temporal basis functions with corresponding spatial weightings (Eq.1). The temporal basis functions are derived by the low-resolution training data (Eq.2), where the spatial weightings are calculated by solving Eq.3.
The presented compartment based k-t PCA reconstruction addresses inaccuracies associated with the coarse spatial resolution of the training data. Excluding certain training voxels from the calculation of the temporal basis functions can improve reconstruction, leading to more accurate perfusion curves and subsequently to more reliable semi-quantitative perfusion analyses.