TRIO a technique for reconstruction using intensity order: application to undersampled MRI

MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering

PURPOSE

To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions.

METHODS

Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data.

RESULTS

Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively.

CONCLUSIONS

The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.

Quantitative description of the morphology and ossification center in the axial skeleton of 20-week gestation formalin-fixed human fetuses using magnetic resonance images

OBJECTIVES

Human tissues are usually studied using a series of two-dimensional visualizations of in vivo or cutout specimens. However, there is no precise anatomical description of some of the processes of human fetal development. The purpose of our study is to develop a quantitative description of the normal axial skeleton by means of high-resolution three-dimensional magnetic resonance (MR) images, collected from six normal 20-week-old human fetuses fixed in formaldehyde.

METHODS

Fetuses were collected after spontaneous abortion and subsequently fixed with formalin. They were imaged using a 1.5 T MR scanner with an isotropic spatial resolution of 200 µm. The correct tissue discrimination between ossified and cartilaginous bones was confirmed by comparing the images achieved by MR scans and computerized axial tomographies. The vertebral column was segmented out from each image using a specially developed semi-automatic algorithm.

RESULTS

Vertebral body dimensions and inter-vertebral distances were larger in the lumbar region, in agreement with the beginning of the ossification process from the thoracolumbar region toward the sacral and cephalic ends.

CONCLUSION

In this article, we demonstrate the feasibility of using MR images to study the ossification process in formalin-fixed fetal tissues. A quantitative description of the ossification centers of vertebral bodies and arches is presented.

Group sparse reconstruction using intensity-based clustering

Compressed sensing has been of great interest to speed up the acquisition of MR images. The k-t group sparse (k-t GS) method has recently been introduced for dynamic MR images to exploit not just the sparsity, as in compressed sensing, but also the spatial group structure in the sparse representation. k-t GS achieves higher acceleration factors compared to the conventional compressed sensing method. However, it assumes a spatial structure in the sparse representation and it requires a time consuming hard-thresholding reconstruction scheme. In this work, we propose to modify k-t GS by incorporating prior information about the sorted intensity of the signal in the sparse representation, for a more general and robust group assignment. This approach is referred to as group sparse reconstruction using intensity-based clustering. The feasibility of the proposed method is demonstrated for static 3D hyperpolarized lung images and applications with both dynamic and intensity changes, such as 2D cine and perfusion cardiac MRI, with retrospective undersampling. For all reported acceleration factors the proposed method outperforms the original compressed sensing method. Improved reconstruction over k-t GS method is demonstrated when k-t GS assumptions are not satisfied. The proposed method was also applied to cardiac cine images with a prospective sevenfold acceleration, outperforming the standard compressed sensing reconstruction.