- Workshop presentation
- Open Access
Improved characterization of infarct heterogeneity from high resolution T1* maps using compressed sensing and temporal PCA with weighted total variation
© Zhang et al; licensee BioMed Central Ltd. 2015
- Published: 3 February 2015
- Compressed Sensing
- Spatial Resolution Image
- Total Variation Regularization
- High Spatial Resolution Image
- Cardiac Coil Array
Characterization of infarct heterogeneity can inform therapeutic strategies for arrhythmia management of patients with prior myocardial infarction (MI). Multi-contrast late-enhancement (MCLE)  images along the signal relaxation curve, acquired in a breath-hold ECG-gated scan, offer better visualization of MI than IR-GRE. A T1* map and steady state image Mss are then used to quantitatively characterize infarct heterogeneity. However, motion and constrained imaging durations typically yield low spatial resolution images and blurry anatomical borders in infarcted regions on a T1* map. This work explores the feasibility of accelerating the MCLE acquisition using Compressed Sensing and temporal Principal Component Analysis (CS-tPCA) to achieve higher spatial resolution images and T1* maps, while preserving anatomical edges using weighted total variation (TV) in the reconstruction, to eventually improve infarct heterogeneity characterization.
Reperfused MI was induced in 2 pigs by complete occlusion of the LAD artery for 90 min. At four weeks, the animals were imaged in vivo using MCLE after injecting 0.2 mmol/kg Gadolinium-DTPA. The fully sampled dataset of each slice was acquired, using a four-channel anterior cardiac coil array, over a 24-s breath-hold to achieve an in-plane spatial resolution of 1.25 mm (monitored HR = 92 beats/min), and was then retrospectively undersampled in the outer k-space region to yield a net acceleration factor of 2.67. With PCA performed on a low-rank Casorati matrix formed from the central region of k-t space, the principal components (PC) of the temporal signal evolution were extracted. The weighted TV regularization was applied in the CS framework to reconstruct the PC coefficient maps from undersampled datasets. The MCLE images were then obtained by a coefficient-weighted sum of PCs and used to obtain the T1* and Mss maps from a non-linear least squares parameter fitting, which were then used in a fuzzy c-means clustering algorithm for tissue classification. For comparison, an alternative reconstruction, REPCOM , was also implemented.
We successfully demonstrated that improving characterization of infarct heterogeneity is feasible in a high-spatial-resolution acquisition using compressed sensing and temporal PCA, with edge preservation in infarcted regions using weighted TV in the reconstruction.
GE Healthcare and CIHR grant.
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