Methods for Reducing Ring Artifacts in Tomographic Images Using Wavelet Decomposition and Averaging Techniques
Abstract
:1. Introduction
2. Materials and Methods
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Authors (year) | Methods | Data | Results |
---|---|---|---|
Selim et al. (2022) [14] | Sparsity-based method, during iterative reconstruction algorithms | Real data and simulations | 0.9682 (SSIM) |
Sijbers et al. (2004) [13] | Post-reconstruction filtration method | Real data | Only visulal results, no quantitative data |
Tang et al. (2001) [12] | Wawelet analysis pre-reconstruction method | Callibration cells | Only visulal results, no quantitative data |
Kim et al. (2014) [4] | Pre-reconstruction method of calculating the ratio of adjacent detector elements in the projection data, termed the line ratio | Sheep–Logan phantom and human organs models | 7.13 × 10−7 (MSE) for Sheep-Logan phantom |
Abu et al. (2011) [2] | Comparison five ring artifact removal methods: Modified Wavelet (MWPN) [12], Wavelet-Fourier (WF) method [11], Ring corrections using homogeneity test (RCHT) [13], Ring correction in polar coordinate (RCP) [1], and Strength based ring correction (SBRC) method [15] | Head Phantom and real data | quantitative data for head Phantom (MSSIM): MWPN—0.97 WF—0.81 RCHT—0.82 RCP—0.77 SBRC—0.98 |
PSNR (dB) | MSE | SSIM | MS-SSIM | |
---|---|---|---|---|
No Filtration 20% noise | 21.99288 | 0.07074 | 0.37611 | 0.60218 |
Filtered 20% noise | 27.09104 | 0.02187 | 0.80090 | 0.82214 |
No Filtration 10% noise | 27.08707 | 0.02189 | 0.47612 | 0.76688 |
Filtered 10% noise | 29.35344 | 0.01299 | 0.80226 | 0.82219 |
No Filtration 5% noise | 34.59004 | 0.00389 | 0.61120 | 0.80143 |
Filtered 5% noise | 27.08509 | 0.02190 | 0.80265 | 0.82261 |
No Filtration 2% noise | 37.74795 | 0.00188 | 0.79428 | 0.88367 |
Filtered 2% noise | 27.09303 | 0.02186 | 0.80534 | 0.82340 |
Noise-free reconstruction | 38.61433 | 0.00154 | 0.92164 | 0.98685 |
Wavelets Type | PSNR (dB) | RMSE | SSIM | MS-SSIM |
---|---|---|---|---|
Haar | 29.0471 | 0.1477 | 0.8031 | 0.8231 |
“Discrete” Meyer | 28.9729 | 0.1502 | 0.6140 | 0.7211 |
Coiflets 1 | 29.0710 | 0.1469 | 0.7898 | 0.8155 |
Coiflets 5 | 29.0279 | 0.1483 | 0.6337 | 0.7244 |
Coiflets 10 | 29.,0420 | 0.1479 | 0.6533 | 0.7484 |
Coiflets 15 | 29.0320 | 0.1482 | 0.6434 | 0.7320 |
Biorthogonal 1.5 | 29.0743 | 0.1468 | 0.6708 | 0.7485 |
Biorthogonal 2.2 | 29.0660 | 0.1470 | 0.7631 | 0.8052 |
Biorthogonal 2.8 | 29.0713 | 0.1469 | 0.6531 | 0.7320 |
Biorthogonal 3.1 | 29.0513 | 0.1475 | 0.7833 | 0.8125 |
Biorthogonal 3.9 | 29.0886 | 0.1463 | 0.6601 | 0.7470 |
Biorthogonal 4.4 | 29.1073 | 0.1457 | 0.7111 | 0.7823 |
Biorthogonal 5.5 | 29.0492 | 0.1476 | 0.6543 | 0.7451 |
Biorthogonal 6.8 | 29.0364 | 0.1481 | 0.6295 | 0.7083 |
Daubechies 2 | 29.0415 | 0.1479 | 0.7700 | 0.8080 |
Daubechies 10 | 29.0353 | 0.1481 | 0.6633 | 0.7551 |
Daubechies 20 | 29.0182 | 0.1487 | 0.6293 | 0.7366 |
Daubechies 30 | 28.9943 | 0.1495 | 0.6604 | 0.7428 |
Symlets 4 | 29.0808 | 0.1465 | 0.7276 | 0.7685 |
Symlets 8 | 29.0652 | 0.1471 | 0.6702 | 0.7565 |
Symlets 12 | 29.0140 | 0.1488 | 0.6183 | 0.7119 |
Symlets 16 | 29.0317 | 0.1482 | 0.6243 | 0.7255 |
Symlets 20 | 29.0633 | 0.1471 | 0.6309 | 0.7397 |
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Lipowicz, P.; Borowska, M.; Dardzińska-Głębocka, A. Methods for Reducing Ring Artifacts in Tomographic Images Using Wavelet Decomposition and Averaging Techniques. Appl. Sci. 2024, 14, 7292. https://doi.org/10.3390/app14167292
Lipowicz P, Borowska M, Dardzińska-Głębocka A. Methods for Reducing Ring Artifacts in Tomographic Images Using Wavelet Decomposition and Averaging Techniques. Applied Sciences. 2024; 14(16):7292. https://doi.org/10.3390/app14167292
Chicago/Turabian StyleLipowicz, Paweł, Marta Borowska, and Agnieszka Dardzińska-Głębocka. 2024. "Methods for Reducing Ring Artifacts in Tomographic Images Using Wavelet Decomposition and Averaging Techniques" Applied Sciences 14, no. 16: 7292. https://doi.org/10.3390/app14167292