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Improving Vessel Delineation in Brain Using Susceptibility Weighted MRI and Group Sparse Reconstruction

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W Stefan

W Stefan1*, K Hwang2 , J Hazle4 , R Stafford5 , (1) UT MD Anderson Cancer Center, Houston, Texas, (2) General Electric Company, Houston, TX, (3) UT MD Anderson Cancer Center, Houston, Texas, (4) UT MD Anderson Cancer Center, Houston, TX, (5) UT MD Anderson Cancer Center, Houston, TX


SU-E-I-32 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: To optimize small vessel delineation on non-contrast enhanced susceptibility weighted MRI protocol for MRI guided intervention in the brain.

Methods: Multiple 5mm slices of the brain in a healthy volunteer were acquired with a 16-echo gradient echo sequence and a 32 channel head coil on a 3T scanner. K-space was under-sampled by a factor of 2. Images were reconstructed using 1) the vendor ASSET algorithm, 2) sparsity enforcing TV regularization applied to each slice and echo individually, and 3) group sparse reconstruction on each slice individually but all echoes simultaneously. The group sparse reconstruction increases the signal-to-noise ratio SNR of later echoes by utilizing the shared edges of brain structure between the echoes, which include the blood vessels. Quantitative PPM maps are computed from the complex images and are post processed by a method that removes background off-resonance effects. Magnitude images and corrected PPM maps are compared.

Results: PPM maps show blood vessels with high contrast. The magnitude images and ppm maps using method 1 appear blurry compared to methods 2 and 3 because of anti-ringing filters. While small vasculatures appear very sharp in the ppm maps of methods 2 and 3, method 3 appears to have less smoothed magnitude images..

Conclusion: To obtain useful blood vessel delineation, low noise susceptibility weighted images are needed. Standard methods like SWI or SWAN require the combination of several slices using a minimum intensity projection to delineate vessels. The method considered here delineates vasculature with high spatial resolution and high SNR in a single slice. Iterative reconstruction methods have superior image quality but also require much more computation time. By reconstructing all echoes simultaneously, group sparse reconstruction produces the sharpest and clearest images.

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