T₂ relaxometry with indirect echo compensation from highly undersampled data

Abstract

Purpose To develop an algorithm for fast and accurate T2 estimation from highly undersampled multi-echo spin-echo (MESE) data. Methods The algorithm combines a model-based reconstruction with a signal decay based on the slice-resolved extended phase graph (SEPG) model with the goal of reconstructing T2 maps from highly undersampled radial MESE data with indirect echo compensation. To avoid problems associated with the nonlinearity of the SEPG model, principal component decomposition is used to linearize the signal model. The proposed CUrve Reconstruction via pca-based Linearization with Indirect Echo compensation (CURLIE) algorithm is used to estimate T2 curves from highly undersampled data. T2 maps are obtained by fitting the curves to the SEPG model. Results Results on phantoms showed T2 biases (1.9% to 18.4%) when indirect echoes are not taken into account. The T2 biases were reduced (<3.2%) when the CURLIE reconstruction was performed along with SEPG fitting even for high degrees of undersampling (4% sampled). Experiments in vivo for brain, liver and heart followed the same trend as the phantoms. Conclusion The CURLIE reconstruction combined with SEPG fitting enables accurate T2 estimation from highly undersampled MESE radial data thus, yielding a fast T2 mapping method without errors caused by indirect echoes.

Accelerated and motion‐robust in vivo T2 mapping from radially undersampled data using bloch‐simulation‐based iterative reconstruction

“…Notwithstanding the good correlation between the T 2 maps produced by the EMC model‐based reconstruction and the reference Cartesian values, this type of reconstruction is still challenging to optimize. This is mostly reflected in the sensitivity of the iterative procedure to the relative scaling between the fitted variables, affecting both convergence accuracy and speed . In practice, we found the relative scaling to have lower significance when processing the phantom and brain T 2 maps, while having higher effect on the spinal cord data.…”

T2 shuffling: Sharp, multicontrast, volumetric fast spin‐echo imaging

“…If a max-column norm is used, then the tolerance can be interpreted as the worst-case error on any signal evolution. By choosing the ensemble X to match the distribution of ( T 1 , T 2 ) values in the tissue of interest, a suitable subspace that minimizes the Frobenius norm can be generated using PCA (19, 20, 44). …”

Pattern recognition for rapid T2 mapping with stimulated echo compensation

“…The technique can also be combined with a recently published method (CURLIE-SEPG [15]) which is an SEPG model-based algorithm designed to recover TE images from highly undersampled (~ 4% sampled) MESE data while preserving the signal from indirect echoes. The CURLIE-SEPG method yields accurate T 2 maps with high spatial resolution from rapidly acquired data (i.e., a breath hold).…”