Meeting Banner
Abstract #4473

The Effect of Signal to Noise Ratio on Linear-binning and Adaptive k-means Quantification of Hyperpolarized 129Xe Ventilation MRI

Mu He1, Fei Tan2, Wei Zha3, Leith Rankine4, Sean Fain3,5,6, and Bastiaan Driehuys4,7,8

1Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States, 2Biomedical Engineering Department, Duke University, Durham, NC, United States, 3Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 4Department of Medical Physics, Duke University, Durham, NC, United States, 5Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, 6Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 7Department of Biomedical Engineering, Duke University, Durham, NC, United States, 8Department of Radiology, Duke University, Durham, NC, United States

129Xe ventilation MRI lacks a reference standard. Here, we circumvent this problem in part by comparing and evaluating the robustness of two independent methods for quantifying ventilation – linear-binning and adaptive k-means - by adding MR noise to the source images until the algorithms fail. Results at different noise levels were compared to that quantified using the original high signal-to-noise ratio (SNR) ventilation image. We found that both methods provide robust quantification until SNR is less than 1.7 ± 0.8 for the linear-binning method, and 2.1 ± 1.2 for the uncorrected adaptive k-means method.

This abstract and the presentation materials are available to members only; a login is required.

Join Here