O'Neill Alexandra G, Valdez Emely L, Lingala Sajan Goud, Pineda Angel R
Manhattan College, Department of Mathematics, New York City, New York, United States.
University of Iowa, Roy J. Carver Department of Biomedical Engineering, Iowa City, Iowa, United States.
J Med Imaging (Bellingham). 2023 Jan;10(1):015502. doi: 10.1117/1.JMI.10.1.015502. Epub 2023 Feb 25.
Task-based assessment of image quality in undersampled magnetic resonance imaging provides a way of evaluating the impact of regularization on task performance. In this work, we evaluated the effect of total variation (TV) and wavelet regularization on human detection of signals with a varying background and validated a model observer in predicting human performance.
Human observer studies used two-alternative forced choice (2-AFC) trials with a small signal known exactly task but with varying backgrounds for fluid-attenuated inversion recovery images reconstructed from undersampled multi-coil data. We used a 3.48 undersampling factor with TV and a wavelet sparsity constraints. The sparse difference-of-Gaussians (S-DOG) observer with internal noise was used to model human observer detection. The internal noise for the S-DOG was chosen to match the average percent correct (PC) in 2-AFC studies for four observers using no regularization. That S-DOG model was used to predict the PC of human observers for a range of regularization parameters.
We observed a trend that the human observer detection performance remained fairly constant for a broad range of values in the regularization parameter before decreasing at large values. A similar result was found for the normalized ensemble root mean squared error. Without changing the internal noise, the model observer tracked the performance of the human observers as the regularization was increased but overestimated the PC for large amounts of regularization for TV and wavelet sparsity, as well as the combination of both parameters.
For the task we studied, the S-DOG observer was able to reasonably predict human performance with both TV and wavelet sparsity regularizers over a broad range of regularization parameters. We observed a trend that task performance remained fairly constant for a range of regularization parameters before decreasing for large amounts of regularization.
基于任务的欠采样磁共振成像图像质量评估提供了一种评估正则化对任务性能影响的方法。在这项工作中，我们评估了总变差（TV）和小波正则化对在变化背景下人类检测信号的影响，并验证了一个模型观察者在预测人类性能方面的能力。
人类观察者研究采用二选一强制选择（2-AFC）试验，针对从欠采样多线圈数据重建的液体衰减反转恢复图像，有一个确切已知的小信号任务，但背景不同。我们使用了3.48的欠采样因子，并采用TV和小波稀疏约束。具有内部噪声的稀疏高斯差分（S-DOG）观察者被用于模拟人类观察者的检测。S-DOG的内部噪声被选择为与四名观察者在无正则化的2-AFC研究中的平均正确百分比（PC）相匹配。该S-DOG模型被用于预测一系列正则化参数下人类观察者的PC。
我们观察到一种趋势，即在正则化参数的广泛取值范围内，人类观察者的检测性能保持相当稳定，直到在较大值时下降。对于归一化总体均方根误差也发现了类似结果。在不改变内部噪声的情况下，随着正则化的增加，模型观察者跟踪了人类观察者的性能，但对于TV和小波稀疏性以及两者参数的组合，在大量正则化时高估了PC。
对于我们研究的任务，S-DOG观察者能够在广泛的正则化参数范围内，合理地预测TV和小波稀疏正则化下的人类性能。我们观察到一种趋势，即在一系列正则化参数下任务性能保持相当稳定，直到在大量正则化时下降。
