Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, Ontario, Canada.
Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
Magn Reson Med. 2021 Sep;86(3):1403-1419. doi: 10.1002/mrm.28812. Epub 2021 May 8.
To present a method that automatically, rapidly, and in a noniterative manner determines the regularization weighting for wavelet-based compressed sensing reconstructions. This method determines level-specific regularization weighting factors from the wavelet transform of the image obtained from zero-filling in k-space.
We compare reconstruction results obtained by our method, , to the ones obtained by the L-curve, , and the minimum NMSE, . The comparisons are done using in vivo data; then, simulations are used to analyze the impact of undersampling and noise. We use NMSE, Pearson's correlation coefficient, high-frequency error norm, and structural similarity as reconstruction quality indices.
Our method, , provides improved reconstructed image quality to that obtained by regardless of undersampling or SNR and comparable quality to at high SNR. The method determines the regularization weighting prospectively with negligible computational time.
Our main finding is an automatic, fast, noniterative, and robust procedure to determine the regularization weighting. The impact of this method is to enable prospective and tuning-free wavelet-based compressed sensing reconstructions.
提出一种自动、快速、非迭代的方法，用于确定基于小波的压缩感知重建的正则化权重。该方法从 k 空间中零填充得到的图像的小波变换中确定特定于级别的正则化权重因子。
我们将我们的方法 与 L 曲线 和最小 NMSE 得到的重建结果进行比较。通过体内数据进行比较，然后使用模拟来分析欠采样和噪声的影响。我们使用 NMSE、皮尔逊相关系数、高频误差范数和结构相似性作为重建质量指标。
我们的方法 提供了比 更好的重建图像质量，无论欠采样或 SNR 如何，并且在高 SNR 下与 具有可比的质量。该方法以可忽略的计算时间前瞻性地确定正则化权重。
我们的主要发现是一种自动、快速、非迭代和稳健的确定正则化权重的方法。该方法的影响是能够实现前瞻性和无需调整的基于小波的压缩感知重建。
