Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
Magn Reson Imaging. 2013 May;31(4):596-603. doi: 10.1016/j.mri.2012.09.009. Epub 2012 Dec 5.
Most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. Here, we aim to create a perceptual difference model based on Case-PDM that mimics the preference of human observers toward different artifacts.
We measured artifact disturbance to observers and calibrated the novel perceptual difference model (PDM). To tune the new model, which we call Artifact-PDM, degradations were synthetically added to three healthy brain MR data sets. Four types of artifacts (noise, blur, aliasing or "oil painting" which shows up as flattened, over-smoothened regions) of standard compressed sensing (CS) reconstruction, within a reasonable range of artifact severity, as measured by both PDM and visual inspection, were considered. After the model parameters were tuned by each synthetic image, we used a functional measurement theory pair-comparison experiment to measure the disturbance of each artifact to human observers and determine the weights of each artifact's PDM score. To validate Artifact-PDM, human ratings obtained from a Double Stimulus Continuous Quality Scale experiment were compared to the model for noise, blur, aliasing, oil painting and overall qualities using a large set of CS-reconstructed MR images of varying quality. Finally, we used this new approach to compare CS to GRAPPA, a parallel MRI reconstruction algorithm.
We found that, for the same Artifact-PDM score, the human observer found incoherent aliasing to be the most disturbing and noise the least. Artifact-PDM results were highly correlated to human observers in both experiments. Optimized CS reconstruction quality compared favorably to GRAPPA's for the same sampling ratio.
We conclude our novel metric can faithfully represent human observer artifact evaluation and can be useful in evaluating CS and GRAPPA reconstruction algorithms, especially in studying artifact trade-offs.
大多数客观图像质量指标在广泛的图像降质范围内进行平均。然而，人类临床医生对不同类型的伪影表现出偏见。在这里，我们旨在创建一个基于 Case-PDM 的感知差异模型，该模型模拟人类观察者对不同伪影的偏好。
我们测量了观察者对伪影的干扰，并校准了新的感知差异模型（PDM）。为了调整新模型，我们称之为Artifact-PDM，我们在三个健康的脑磁共振数据集上合成添加了退化。考虑了标准压缩感知（CS）重建的四种类型的伪影（噪声、模糊、混叠或“油画”，表现为平坦、过度平滑的区域），这些伪影的严重程度在 PDM 和视觉检查的合理范围内。在每个合成图像调整模型参数后，我们使用功能测量理论对比较实验来测量每个伪影对人类观察者的干扰，并确定每个伪影的 PDM 分数的权重。为了验证 Artifact-PDM，我们使用双刺激连续质量量表实验从大量不同质量的 CS 重建磁共振图像中获得的噪声、模糊、混叠、油画和整体质量的人类评分，将其与模型进行比较。最后，我们使用这种新方法比较 CS 与 GRAPPA，一种并行 MRI 重建算法。
我们发现，对于相同的 Artifact-PDM 分数，人类观察者发现不连贯的混叠最令人不安，而噪声最不令人不安。在两个实验中，Artifact-PDM 的结果与人类观察者高度相关。在相同的采样比下，优化的 CS 重建质量明显优于 GRAPPA。
我们得出结论，我们的新指标可以忠实地反映人类观察者对伪影的评估，并且可以在评估 CS 和 GRAPPA 重建算法方面有用，特别是在研究伪影权衡方面。
