Accelerated T2 mapping

Parameter mapping requires multiple images acquired with different timings usually resulting in long acquisition times. While acquisition time can be reduced by acquiring undersampled data, obtaining accurate estimates of parameters from undersampled data is a challenging problem, in particular for structures with high spatial frequency content. With the understanding of the signal models and the advances in Compressed Sensing, we were able to generate a T2 map and T2w images using the same acquisition time as needed for conventional T2w images.

We used principal component analysis combined with a modelbased algorithm to reconstruct maps of selected principal component coefficients from highly undersampled radial MRI data. This novel approach linearizes the cost function of the optimization problem yielding a more accurate and reliable estimation of MR parameter maps. The proposed algorithm - reconstruction of principal component coefficient maps using compressed sensing - is demonstrated in phantoms and in vivo and compared with two other algorithms previously developed for undersampled data.

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Fast T2 fitting with indirect echo compensation

Indirect echoes (such as stimulated echoes) are a source of signal contamination in a multi-echo spin-echo T2 quantification, and can lead to T2 overestimation if a conventional exponential T2 decay model is assumed. Recently, nonlinear least square fitting of a slice-resolve extended phase graph (SEPG) signal model has been shown to provide accurate T2 estimates with indirect echo compensation. However, the iterative nonlinear least square fitting is computationally expensive and the T2 map generation time is long.

we present a pattern recognition T2 mapping technique based on the SEPG model that can be performed with a single pre-computed dictionary for any arbitrary echo spacing. Almost identical T2 and B1 maps were obtained from in vivo data using the proposed technique compared to conventional iterative nonlinear least square fitting, while the computation time was reduced by more than 14 fold.

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Accelerated T2 reconstruction with indirect echo compensation

Because single-echo spin-echo T2 mapping requires long acquisition times, its translation to the clinic has been limited by its time inefficiency. To reduce the acquisition times it is customary to use multi-echo spinecho (MESE) pulse sequences, where several echo time (TE) points are acquired per pulse repetition time (TR) period by using a train of 180 degree refocusing pulses after the initial 90 degree excitation pulse. To further accelerate T2 data acquisition, a fast (or turbo) spin-echo approach where several k-space lines of data are acquired per TR period is commonly used. Recently, the focus has been on model-based T2 mapping algorithms. Most of these models rely on the assumption that the signal follows an exponential decay. However, in MESE acquisitions the decay is generally contaminated by indirect echoes (echoes leading to signal generation after more than one refocusing pulse such as stimulated echoes) including differences in the signal intensities between even and odd echoes, thus, altering the single exponential nature of the T2 decay observed in a single-echo spin-echo experiment. The indirect echoes are the result of refocusing pulses not attaining the ideal 180. flip angle (FA) due to nonrectangular slice profiles, static (B0) and transmit field (B1) inhomogeneity, and B1 calibration errors

By using a more relistic Slice-resolve Extend Phase Graph (SEPG) signal model, we extended the PCA-based approach used in the REPCOM algorithm to linearize the SEPG model. The proposed CUrve Reconstruction via pca-based Linearization with Indirect Echo compensation (CURLIE) algorithm aims to obtain accurate T2 decay curves from highly undersampled data in the presence of B1 imperfections or non-180 degree refocusing pulses. The T2 values of the reconstructed curves can then be obtained by applying SEPG fitting.

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Why quantitative MRI?

Conventionally, magnetic resonance imaging (MRI) is used in a qualitative manner. When an image is obtained using the MRI scanner, radiologists look at the images with varies contrasts (Proton Density, T1 weighted, T2 weighted, etc) and identify regions with abnormal contrast to naked eye. For example, a region showing higher contrast in a T2-weighted image will be reported as hyperintense on a T2w image.

The story of quantitative MRI dates back to 1971, it was discovered that some tumors have higher relaxation times than regular tissue. While some tissue changes can be easily identified on a conventional MRI, other tissue with subtle T1 or T2 changes can not be picked up by naked eye. These changes can be identified with quantitative MRI.

Quantitative MRI offers several advantages including higher sensititive, better accuracy and higher reproducibility.

If quantitative MRI is this great, what is the reason it is not used every day in clinical MRI (yet)? The main problem is the significant longer acquisition time to obtain the quantitative maps (T1 map, T2 map, etc). For example, the conventional way to obtain a T2 map requires the acquisition of 4-32 T2w images. This effectly increase the acquisition time by 4-32 folds which is not acceptable in a clinical setting. This is where my research comes in.