CS: k-t FOCUSSS: a general compressed sensing framework for high resolution dynamic MRI, Radial single-shot STEAM MRI

It now seems that we understand what we see with MRI or fMRI when imaging the brain: Astrocytes activity (more here). I am personally surprised it took that long. Talking about fMRI here is a new and I think an important paper providing some insight about how compressed sensing can handle the time dimension. H. Jung, Kyunghyun Sung, Krishna Nayak, Eung Yeop Kim, and Jong Chul Ye just released k-t FOCUSSS: a general compressed sensing framework for high resolution dynamic MRI. The abstract reads:

A model-based dynamic MRI called k-t BLAST/SENSE has drawn significant attention from the MR imaging community due to its improved spatio-temporal resolution. Recently, we showed that the k-t BLAST/SENSE corresponds to the special case of a new dynamic MRI algorithm called k-t FOCUSS that is optimal from a compressed sensing perspective. The main contribution of this paper is an extension of k-t FOCUSS to a more general framework with prediction and residual encoding, where the prediction provides an initial estimate and the residual encoding takes care of the remaining residual signals. Two prediction methods, RIGR and motion estimation/compensation scheme, are proposed, which significantly sparsify the residual signals. Then, using a more sophisticated random sampling pattern and optimized temporal transform, the residual signal can be effectively estimated from a very small number of k-t samples. Experimental results show that excellent reconstruction can be achieved even from severely limited k-t samples without aliasing artifacts.

MRI is so advanced in the area of undersampling that they already have several techniques looks at the time dimension. With video we have barely touched the subject in such thourough manner (the only ones I could find are the following: Vladimir Stankovic, Lina Stankovic, and Samuel Cheng on Compressive video sampling, Compressive Coded Aperture Video Reconstruction by Roummel Marcia and Rebecca Willett).


also of interest are the following papers: Radial single-shot STEAM MRI by Tobias Block, Jens Frahm. The abstract reads:

Rapid MR imaging using the stimulated echo acquisition mode (STEAM) technique yields single-shot images without any sensitivity to resonance offset effects. However, the absence of susceptibility-induced signal voids or geometric distortions is at the expense of a somewhat lower signal-to-noise ratio than EPI. As a consequence, the achievable spatial resolution is limited when using conventional Fourier encoding. To overcome the problem, this study combined single-shot STEAM MRI with radial encoding. This approach exploits the efficient undersampling properties of radial trajectories with use of a previously developed iterative image reconstruction method that compensates for the incomplete data by incorporating a priori knowledge. Experimental results for a phantom and human brain in vivo demonstrate that radial single-shot STEAM MRI may exceed the resolution obtainable by a comparable Cartesian acquisition by a factor of four.

while the paper is not available, a poster with the same title is here. Many other interesting papers can be viewed in Tobias Block's publication page such as the one featuring the ability to quantify the sensitivity of the coils with image quality: Martin Uecker, Thorsten Hohage, Kai Tobias Block, and Jens Frahm, Image Reconstruction by Regularized Nonlinear Inversion - Joint Estimation of Coil Sensitivities and Image Content,

The use of parallel imaging for scan time reduction in MRI faces problems with image degradation when using GRAPPA or SENSE for high acceleration factors. While an inherent loss of SNR in parallel MRI is inevitable due to the reduced measurement time, the sensitivity to image artifacts that result from severe undersampling can be ameliorated by alternative reconstruction methods. While the introduction of GRAPPA and SENSE extended MRI reconstructions from a simple orthogonal transformation (Fourier transform) to the inversion of an ill-conditioned linear system, the next logical step is the use of a nonlinear inversion. Here,a respective algorithm based on a Newton-type method with appropriate regularization terms is demonstrated to improve the performance of autocalibrating parallel MRI { mainly due to a better estimation of the coil sensitivity profiles. The approach yields images with considerably reduced artifacts for high acceleration factors and/or a low number of reference lines.

Credit: NASA/JPL-Caltech/University of Arizona/Texas A&M. Sol 24, Dodo trench, the white is disappearing under our own eyes and they think it is because this is ice.