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Abstract #4497

The effect of variable compressed sense (CS) undersampling patterns in hyperpolarized Xenon (129Xe) diffusion-weighted MRI

Mitra Tavakkoli1,2, Sarah Svenningsen3,4, Yonni Friedlander4, Norm Konyer5, Parameswaran Nair3,4, and Michael D Noseworthy1,5,6,7
1Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, 2Imaging Research Centre, St. Joseph's Healthcare, Hamilton, ON, Canada, 3Department of Medicine, McMaster University, Hamilton, ON, Canada, 4Firestone Institute for Respiratory Health, St Joseph’s Healthcare Hamilton, Hamilton, ON, Canada, 5Imaging Research Centre, St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada, 6Biomedical Engineering, McMaster University, Hamilton, ON, Canada, 7Department of Radiology, McMaster University, Hamilton, ON, Canada

Synopsis

Keywords: Image Reconstruction, Sparse & Low-Rank Models, Compressed Sensing, Lung, 129Xenon

Compressed sensing is a fast-imaging technique capable of making high quality images with undersampled data. However, its application in hyperpolarized 129Xe diffusion-weighted MRI needs to be optimized. In this study, diffusion weighted images undersampled identically and differently, were compared with SNR, SSIM, mean ADC using repeated measures ANOVA. The application of different undersampling patterns for both diffusion-weighted and baseline images can result in ADC maps with higher quality, as compared to fully sampled equivalent parametric images. Thus, the baseline and diffusion-weighted images do not necessarily have the same optimum undersampling pattern, and optimization of each needs investigation separately.

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Abstract Presentation Video

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