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Abstract #2700

Evaluation of Image Quality Improvement using Wavelet Denoising Based on Stein's Unbiased Risk Estimate (SURE)

Tao Zhang1, Peng Lai2, Shreyas Vasanawala3, Robert Herfkens3, Kedar Khare4, Luca Marinelli4, Kevin F. King5, Anja Brau2

1Electrical Engineering, Stanford University, Stanford, CA, United States; 2Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States; 3Radiology, Stanford University, Stanford, CA, United States; 4GE Global Research Center, Niskayuna, NY, United States; 5GE Healthcare, Waukesha, WI, United States

Low SNR in MRI images can usually be found in cases with parallel imaging and acquisitions with high spatial resolution. Image denoising methods can increase SNR. But some of them will introduce artifacts such as image blurring. In this work, wavelet denosing based on Steins Unbiased Risk Estimate (SURE) was evaluated on various MRI applications. Comparisons on overall image quality and image sharpness were carried out on these applications. Based on the evaluation results from radiologists, wavelet denoising based on SURE can effectively increase SNR without introducing image blurring.