Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Signal and Image Processing Institute, University of Southern California, Los Angeles, California, USA.
Magn Reson Med. 2021 Oct;86(4):1873-1887. doi: 10.1002/mrm.28828. Epub 2021 Jun 3.
Modern methods for MR image reconstruction, denoising, and parameter mapping are becoming increasingly nonlinear, black-box, and at risk of "hallucination." These trends mean that traditional tools for judging confidence in an image (visual quality assessment, point-spread functions (PSFs), g-factor maps, etc.) are less helpful than before. This paper describes and evaluates an approach that can help with assessing confidence in images produced by arbitrary nonlinear methods.
We propose to characterize nonlinear methods by examining the images they produce before and after applying controlled perturbations to the measured data. This results in functions known as local perturbation responses (LPRs) that can provide useful insight into sensitivity, spatial resolution, and aliasing characteristics. LPRs can be viewed as generalizations of classical PSFs, and are are very flexible-they can be applied to arbitary nonlinear methods and arbitrary datasets across a range of different reconstruction, denoising, and parameter mapping applications. Importantly, LPRs do not require a ground truth image.
Impulse-based and checkerboard-pattern LPRs are demonstrated in image reconstruction and denoising scenarios. We observe that these LPRs provide insights into spatial resolution, signal leakage, and aliasing that are not available with other methods. We also observe that popular reference-based image quality metrics (eg, mean-squared error and structural similarity) do not always correlate with good LPR characteristics.
LPRs are a useful tool that can be used to characterize and assess confidence in nonlinear MR methods, and provide insights that are distinct from and complementary to existing quality assessments.
磁共振（MR）图像重建、去噪和参数映射的现代方法变得越来越非线性、黑箱化，并且存在“幻觉”的风险。这些趋势意味着，用于判断图像置信度的传统工具（视觉质量评估、点扩散函数（PSF）、g 因子图等）不如以前有用。本文描述并评估了一种可用于评估任意非线性方法生成的图像置信度的方法。
我们建议通过检查对测量数据施加受控扰动前后生成的图像来对非线性方法进行特征描述。这会产生被称为局部扰动响应（LPR）的函数，这些函数可以提供有关灵敏度、空间分辨率和混叠特性的有用见解。LPR 可以被视为经典 PSF 的推广，并且非常灵活-它们可以应用于任意非线性方法和各种不同的重建、去噪和参数映射应用中的任意数据集。重要的是，LPR 不需要真实图像。
在图像重建和去噪场景中演示了基于脉冲和棋盘模式的 LPR。我们观察到，这些 LPR 提供了关于空间分辨率、信号泄漏和混叠的见解，而这些见解是其他方法无法提供的。我们还观察到，流行的基于参考的图像质量指标（例如，均方误差和结构相似性）并不总是与良好的 LPR 特征相关。
LPR 是一种有用的工具，可以用于描述和评估非线性 MR 方法的置信度，并提供与现有质量评估不同且互补的见解。
