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 Fain^3,5,6, and Bastiaan Driehuys^4,7,8

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.

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