1. 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

2. Introduction
Background
Experimental methods
Results
Conclusions

3. 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.

4. 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.

5. 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

6. Background Framework of Task-specific IQA Hotelling observer
列比较的公式？
Hotelling observer
Nonprewhitening observer

7. 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.

8. 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.

9. 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.

10. 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 𝜏
示例图片，
The CT image background was provided by the Mayo clinic.

11. 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

12. Results Performance of general IQA models with general-MOSD1 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 = 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

13. 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.

14. Thank You!
Institute of Image Processing and Pattern Recognition Xi’an Jiaotong University
June 19, 2017