Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905, USA.
Magn Reson Med. 2012 Apr;67(4):1022-32. doi: 10.1002/mrm.23084. Epub 2011 Aug 25.
The application of sparsity-driven reconstruction methods to MRI to date has largely focused on situations where high-contrast features (e.g., gadolinium-enhanced vessels) are of primary interest. In clinical practice, however, low contrast features such as subtle lesions are often of equal or greater interest. Using an American College of Radiology MR quality assurance phantom and test, we describe a novel framework for systematically and automatically evaluating the low-contrast object detectability performance of different undersampled image reconstruction methods. This platform is used to evaluate three such methods, two based on classic Tikhonov regularization and one sparsity-driven method based on ℓ(1) -norm minimization (which is commonly used in compressive sensing, also known as compressed sensing, applications), across a wide range of sampling rates and parameterizations. Both the automated evaluation system and a manual evaluation of anatomical images with numerically-generated low contrast inserts demonstrate that sparse reconstructions exhibit superior low-contrast object detectability performance compared to both Tikhonov-regularized reconstructions. The implications of this result, and potential applications of both the described low-contrast object detectability platform and generalizations of it are then discussed.
迄今为止，基于稀疏驱动的重建方法在磁共振成像中的应用主要集中在高对比度特征（例如钆增强血管）是主要关注点的情况。然而，在临床实践中，细微病变等低对比度特征通常同样或更受关注。我们使用美国放射学院磁共振质量保证体模和测试，描述了一种新颖的框架，用于系统地和自动评估不同欠采样图像重建方法的低对比度物体检测性能。该平台用于评估三种此类方法，两种基于经典的 Tikhonov 正则化，一种基于稀疏驱动的方法基于ℓ(1)范数最小化（常用于压缩感知，也称为压缩感知，应用），涵盖了广泛的采样率和参数化范围。自动评估系统和手动评估带有数值生成的低对比度插入物的解剖图像都表明，稀疏重建与 Tikhonov 正则化重建相比，具有更好的低对比度物体检测性能。然后讨论了这一结果的意义，以及所描述的低对比度物体检测平台及其推广的潜在应用。
