Image Quality Assessment on CT Reconstruction Images: Task-specific vs. General Quality Assessment Jianmei Cai, Xiaogang Chen, Wuhao Huang and Xuanqin Mou Institute of Image Processing and Pattern Recognition Xi’an Jiaotong University June 19, 2017
Introduction Background Experimental methods Results Conclusions
Introduction Tomography Imaging Medical Diagnosis Enough pathological information Balance between noise and fine image structures Image Quality Assessment (IQA) General IQA task-specific IQA Normal dose Quarter dose The data was provided by the Mayo clinic.
Introduction ALARA[1] (As Low As Reasonably Achievable) Physical layer Algorithm layer General IQA Diagnosis layer Task-specific IQA Retrieval layer 1. Physical layer Equipment: Spiral CT, Cone-beam CT Scan protocols: KVP, mAs 2. Algorithm layer Iterative reconstruction: 牵涉到参数选择 Analytic reconstruction: FBP, 牵涉到滤波核的选择？ 3. Diagnosis layer Kinds of pathological signals (The influence of the type and shape of the lesion) 4. Retrieval layer [1] A. L. Baert, Encyclopedia of Diagnostic Imaging. Springer Berlin Heidelberg, 2008:60-60.
Background Framework of General IQA Full reference IQA: SSIM, FSIM, GMSD and NLOG-MSE No reference IQA: BIQA 为什么没有RR IQA？ General IQA是否要用图像举例，提取适当的纹理
Background Framework of Task-specific IQA Hotelling observer 列比较的公式？ Hotelling observer Nonprewhitening observer
Relationship between General and Task-specific IQA Fig.(3) has a overall better visibility than Fig.(2), while the visibility of pathological signal of Fig.(2) is better than Fig.(3). The data was provided by the Mayo clinic.
Relationship between General and Task-specific IQA General IQA and task-specific target on different optimal images The data was provided by the Mayo clinic.
Relationship between General and Task-specific IQA General IQA and task-specific observer target on the same optimal image The data was provided by the Mayo clinic.
Experimental methods Reference images Simulated Lesions: s(r) Background images: b(r) CT images reconstructed by Filter Back Projection (FBP) algorithm 512*512 pixel 𝑠(𝑟)= 𝐶 𝑡 ⋅ 1− 𝑟 𝐷 2 𝜏 示例图片， The CT image background was provided by the Mayo clinic.
Experimental methods Test images Reference images Add photons Iterative reconstructed Divided into four non-overlapping groups: D1, D2, D3, D4 Subjective experiments The visibility of pathological signal task-MOS The visibility of the whole image general-MOS Measurements SRC (Spearman rank order correlation coefficient) PCC (Pearson correlation coefficient) RMSE (Root Mean Squared Error) 打分示例图
Results Performance of general IQA models with general-MOS D1 D2 D3 D4 srocc pcc rmse SSIM 0.6049 0.6205 0.2898 0.6523 0.7080 0.3766 0.7251 0.7275 0.3659 0.8329 0.8608 0.3058 FSIM 0.5427 0.5455 0.3097 0.4355 0.4786 0.4682 0.8408 0.8351 0.2933 0.7461 0.7765 0.3786 GMSD 0.5217 0.5262 0.3143 0.3092 0.3470 0.5001 0.8510 0.8554 0.2762 0.6476 0.6482 0.4575 NLOG-MSE 0.6464 0.2801 0.5682 0.6095 0.4228 0.7857 0.8002 0.3198 0.7467 0.7685 0.3845 BIQA 0.7071 0.7023 0.2631 0.6256 0.6916 0.3851 0.7166 0.7157 0.3725 0.8471 0.8763 0.2895 Performance of general IQA models with task-MOS BIQA = 0.6458 D1 D2 D3 D4 srocc pcc rmse SSIM 0.0988 0.0980 1.0805 0.1170 0.0836 1.2068 0.0077 0.0010 0.9906 0.0412 0.0615 1.1084 FSIM 0.0729 0.0746 1.0827 0.1221 0.1219 1.2021 0.0342 0.0157 0.9905 0.0416 0.0604 1.1085 GMSD 0.0029 0.0041 1.0857 0.0940 0.0786 1.2073 0.0013 0.0192 0.9904 0.0002 0.0011 1.1105 NLOG-MSE 0.0295 0.0196 1.0855 0.0181 0.0142 1.2110 0.0892 0.1193 0.9835 0.0647 0.0632 1.1083 BIQA 0.2987 0.2093 1.0199 0.3551 0.3769 1.1218 0.3195 0.3419 0.9309 0.2500 0.2831 1.0651
Conclusions The general IQA targets on general quality assessment over the whole image, while task-specific IQA focuses on the visual perception of a specific pathological signal from its background. The relationship between general and task-specific IQA is complex. And the complexity also exists in different general IQA models.
Thank You! Institute of Image Processing and Pattern Recognition Xi’an Jiaotong University June 19, 2017