Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction
Abstract
:1. Introduction
- A novel degradation-aware deep learning framework for sparse-view CT reconstruction is proposed. The proposed framework overcomes the disadvantage of weak generalization at multiple degradation levels of previous single-degradation methods. In addition, it is beneficial for extending to more degradation levels without the growth of training parameters. Experimental results have shown the effectiveness and robustness of the proposed framework.
- A frequency-domain reconstruction module is proposed. It conducts a frequency-attention mechanism to adaptively analyze the disparity of degradation levels by employ distinct operations to each frequency. The experiments described herein illustrate its satisfactory performance on artifact removal and intensity recovery.
- An image-domain module is proposed to further capture the image space degradation characterization from the frequency-domain reconstruction results. This produces a critical-map to emphasize the contour pixels with high reconstruction errors. The experiments show the favorable achievement of the aid of an image-domain module in the structure preservation and edge enhancement.
2. Materials and Methods
2.1. Network Structure
2.2. Frequency-Domain Module
2.3. Image-Domain Module
2.4. Datasets
2.5. Network Training
3. Results
3.1. Degradation-Aware Ability Exploration
3.2. Reconstruction Performance
3.3. Ablation Study
3.4. Network Parameter Tuning
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Number of Parameters | Computational Cost (per Image) |
---|---|---|
Improved GoogLeNet | 1.25 M | 0.0032 s |
Tight frame U-Net | 31.42 M | 0.0034 s |
RED-CNN | 1.85 M | 0.0010 s |
DD-Net | 0.56 M | 0.0057 s |
FDM | 0.92 M | 0.0050 s |
Improved GoogLeNet+ | 3.75 M | 0.0032 s |
Tight frame U-Net+ | 94.26 M | 0.0034 s |
RED-CNN+ | 5.55 M | 0.0010 s |
DD-Net+ | 1.68 M | 0.0057 s |
ours | 1.63 M | 0.0062 s |
Views | Body Part | FBP | Improved GoogLeNet | Tight Frame U-Net | RED-CNN | DD-Net | FDM |
---|---|---|---|---|---|---|---|
60 | Head | 15.9478/0.3516 | 21.6936/0.4607 | 25.0216/0.6051 | 31.0674/0.8171 | 35.4189/0.9147 | 33.8139/0.8088 |
Abdomen | 14.6239/0.3977 | 19.5052/0.4886 | 25.3207/0.6907 | 32.2187/0.8760 | 35.9957/0.9237 | 35.8076/0.9028 | |
Lung | 15.7114/0.4177 | 21.1298/0.5315 | 25.1913/0.6981 | 30.2601/0.8481 | 33.9357/0.9080 | 33.4616/0.8532 | |
Esophagus | 13.8681/0.3428 | 18.2902/0.4173 | 24.0947/0.6291 | 31.9415/0.8446 | 35.6552/0.9220 | 34.8013/0.8327 | |
120 | Head | 20.5276/0.4536 | 32.3011/0.7400 | 34.5627/0.8666 | 34.5000/0.8859 | 39.4975/0.9488 | 36.5722/0.8386 |
Abdomen | 18.6596/0.4940 | 29.1715/0.7059 | 33.0186/0.9035 | 35.2896/0.9210 | 39.3417/0.9496 | 38.4492/0.9184 | |
Lung | 20.0424/0.5333 | 29.9168/0.7671 | 31.6234/0.8894 | 33.0130/0.8990 | 37.0283/0.9388 | 36.7272/0.8877 | |
Esophagus | 17.2796/0.4285 | 27.0452/0.6057 | 32.2867/0.8630 | 34.7532/0.8904 | 38.8522/0.9518 | 37.1417/0.8544 | |
240 | Head | 26.5085/0.5865 | 32.4706/0.8255 | 36.7117/0.9150 | 37.1478/0.9052 | 42.6465/0.9660 | 38.4318/0.8443 |
Abdomen | 25.4890/0.6368 | 31.2142/0.8480 | 34.9420/0.9465 | 36.7007/0.9384 | 41.8674/0.9654 | 41.0545/0.9425 | |
Lung | 25.6982/0.6791 | 29.5797/0.8113 | 33.2487/0.9263 | 35.1415/0.9260 | 39.1089/0.9560 | 38.6735/0.9058 | |
Esophagus | 23.0284/0.5516 | 34.1418/0.8526 | 34.6916/0.9156 | 37.3571/0.9097 | 41.3469/0.9687 | 39.7422/0.8852 | |
Views | Body Part | SART | Improved GoogLeNet+ | Tight Frame U-Net+ | RED-CNN+ | DD-Net+ | Ours |
60 | Head | 23.4269/0.6964 | 28.0190/0.7486 | 25.8151/0.6176 | 31.4201/0.8158 | 35.5668/0.9256 | 36.0998/0.9421 |
Abdomen | 18.4496/0.6381 | 28.3975/0.7401 | 25.3411/0.6758 | 32.1736/0.8687 | 36.0104/0.9338 | 36.8327/0.9434 | |
Lung | 17.1589/0.6097 | 27.2493/0.7354 | 25.6127/0.6899 | 30.1739/0.8490 | 33.7070/0.9152 | 34.9458/0.9291 | |
Esophagus | 18.5389/0.6526 | 29.2958/0.7119 | 24.2923/0.6177 | 31.7161/0.8389 | 35.1918/0.9291 | 36.4700/0.9428 | |
120 | Head | 28.6282/0.7748 | 30.8313/0.7143 | 30.0908/0.6645 | 35.4605/0.8824 | 39.5932/0.9517 | 40.2681/0.9630 |
Abdomen | 23.1838/0.7338 | 28.2309/0.6705 | 27.7061/0.6966 | 35.7323/0.9180 | 39.4556/0.9521 | 40.1679/0.9606 | |
Lung | 21.6494/0.7158 | 27.6462/0.6789 | 28.7425/0.7372 | 33.3552/0.9009 | 37.1235/0.9433 | 38.3021/0.9525 | |
Esophagus | 22.7435/0.7335 | 26.2278/0.5736 | 25.9611/0.6196 | 35.0883/0.8977 | 39.0283/0.9550 | 39.5306/0.9625 | |
240 | Head | 34.5998/0.8424 | 35.8046/0.8199 | 34.5272/0.7890 | 38.4972/0.9242 | 43.3962/0.9727 | 43.8769/0.9755 |
Abdomen | 29.7504/0.8250 | 35.0612/0.8505 | 32.7387/0.8281 | 37.7709/0.9488 | 42.3253/0.9712 | 43.0279/0.9724 | |
Lung | 27.7240/0.8141 | 33.6383/0.8704 | 31.9236/0.8353 | 35.7299/0.9385 | 39.9550/0.9641 | 40.7020/0.9670 | |
Esophagus | 28.4788/0.8112 | 32.4359/0.7369 | 30.5312/0.7478 | 37.2861/0.9355 | 41.8500/0.9743 | 42.2515/0.9745 |
Metric | Views | Standard Deviation | Confidence Interval |
---|---|---|---|
PSNR | 60 | 3.0346 | (35.8188 ± 0.1883) |
120 | 3.2308 | (39.2702 ± 0.2005) | |
240 | 3.5828 | (42.0365 ± 0.2223) | |
SSIM | 60 | 0.0214 | (0.9368 ± 0.0013) |
120 | 0.0181 | (0.9577 ± 0.0011) | |
240 | 0.0164 | (0.9708 ± 0.0010) |
Compared Method | 60 Views | 120 Views | 240 Views | |||
---|---|---|---|---|---|---|
t_psnr | t_ssim | t_psnr | t_ssim | t_psnr | t_ssim | |
Improved GoogLeNet | 118.2965 | 122.8433 | 72.5626 | 58.2919 | 73.6602 | 72.7600 |
Tight frame U-Net | 116.1232 | 93.8206 | 85.0838 | 55.3261 | 93.7110 | 40.0548 |
RED-CNN | 55.4372 | 56.7843 | 62.7069 | 54.5588 | 65.8841 | 44.3951 |
DD-Net | 19.9694 | 63.3336 | 18.4503 | 36.6697 | 20.1678 | 22.4695 |
Improved GoogLeNet+ | 54.7750 | 54.1022 | 100.2554 | 82.4739 | 66.5465 | 40.6918 |
Tight frame U-Net+ | 115.1382 | 94.8125 | 105.0866 | 80.6470 | 98.7682 | 58.7093 |
RED-CNN+ | 59.2286 | 61.5391 | 58.8350 | 55.3430 | 55.2423 | 43.2324 |
DD-Net+ | 20.3308 | 40.7363 | 16.3571 | 45.8754 | 10.0287 | 10.1868 |
Views | Body Part | NDM | DM | FDM* |
---|---|---|---|---|
60 | Head | 32.8563 | 32.5425 | 33.7495 |
Abdomen | 32.8446 | 32.6188 | 35.6840 | |
Lung | 32.9257 | 32.6978 | 33.1673 | |
Esophagus | 32.8197 | 32.6037 | 33.7173 | |
120 | Head | 35.7321 | 35.5704 | 36.5904 |
Abdomen | 35.8170 | 35.6162 | 38.1990 | |
Lung | 35.6267 | 35.5710 | 36.1079 | |
Esophagus | 35.7173 | 35.5953 | 36.1940 | |
240 | Head | 37.6943 | 37.5759 | 38.4270 |
Abdomen | 37.9056 | 37.6344 | 40.3256 | |
Lung | 37.6960 | 37.4694 | 38.2643 | |
Esophagus | 37.6851 | 37.3774 | 38.1505 |
Views | Body Part | No_Image_Domain | Ours |
60 | Head | 0.9396 | 0.9421 |
Abdomen | 0.9415 | 0.9434 | |
Lung | 0.9268 | 0.9291 | |
Esophagus | 0.9404 | 0.9428 | |
120 | Head | 0.9619 | 0.9630 |
Abdomen | 0.9596 | 0.9606 | |
Lung | 0.9510 | 0.9525 | |
Esophagus | 0.9612 | 0.9625 | |
240 | Head | 0.9751 | 0.9755 |
Abdomen | 0.9719 | 0.9724 | |
Lung | 0.9660 | 0.9670 | |
Esophagus | 0.9739 | 0.9745 |
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Sun, C.; Liu, Y.; Yang, H. Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction. Tomography 2021, 7, 932-949. https://doi.org/10.3390/tomography7040077
Sun C, Liu Y, Yang H. Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction. Tomography. 2021; 7(4):932-949. https://doi.org/10.3390/tomography7040077
Chicago/Turabian StyleSun, Chang, Yitong Liu, and Hongwen Yang. 2021. "Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction" Tomography 7, no. 4: 932-949. https://doi.org/10.3390/tomography7040077
APA StyleSun, C., Liu, Y., & Yang, H. (2021). Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction. Tomography, 7(4), 932-949. https://doi.org/10.3390/tomography7040077