Biomedical subjects

Arjun Arunachalam

Publications and source records attributed to Arjun Arunachalam.

2 recordsLinked to original sources

PubMed2006

Background suppression in time-resolved MR angiography without mask acquisition or operator intervention.

PURPOSE: To suppress static and background tissue in time-resolved MRA studies of the full thorax or abdomen without the need of a mask image or operator intervention. MATERIALS AND METHODS: The time course of each voxel is projected onto the orthogonal complement space of a matrix that spans static and linearly enhancing signal vectors. The norm of the solution, or the projection length, acts as a confidence measure for segmenting vascular and nonvascular tissue. Voxels whose confidence measures fall below an automatically detected threshold value are considered nonvascular. These voxels undergo an increasing level of suppression as the distance of the confidence measure from the threshold grows. RESULTS: MIPs of processed volunteer studies were compared to the original unaltered studies to assess the improvement in clarity of vascular structures. Qualitative and quantitative comparisons of full body MIPs verify excellent suppression of nonvascular tissue in time-resolved three-dimensional image volumes. CONCLUSION: Contrast increased by an average factor of 13 in five volunteer studies, quantitatively emphasizing the improvement in MIP processing achieved by this method. Improvement in the clarity of vascular structures in subvolume MIPs is also demonstrated to emphasize the significant increase in ease with which regions of interest can be identified.

Abdomen↗

PubMed2006

Advances in locally constrained k-space-based parallel MRI.

In this article, several theoretical and methodological developments regarding k-space-based, locally constrained parallel MRI (pMRI) reconstruction are presented. A connection between Parallel MRI with Adaptive Radius in k-Space (PARS) and GRAPPA methods is demonstrated. The analysis provides a basis for unified treatment of both methods. Additionally, a weighted PARS reconstruction is proposed, which may absorb different weighting strategies for improved image reconstruction. Next, a fast and efficient method for pMRI reconstruction of data sampled on non-Cartesian trajectories is described. In the new technique, the computational burden associated with the numerous matrix inversions in the original PARS method is drastically reduced by limiting direct calculation of reconstruction coefficients to only a few reference points. The rest of the coefficients are found by interpolating between the reference sets, which is possible due to the similar configuration of points participating in reconstruction for highly symmetric trajectories, such as radial and spirals. As a result, the time requirements are drastically reduced, which makes it practical to use pMRI with non-Cartesian trajectories in many applications. The new technique was demonstrated with simulated and actual data sampled on radial trajectories.

Abdomen↗