Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patient Cohort
2.2. CT Acquisition
2.3. Image Reconstruction
- (a)
- B30f: Filtered back-projection with a B30f kernel for soft tissue presentation.
- (b)
- B70f: Filtered back-projection with a B70f kernel for bone or lung presentation.
- (c)
- I30f: Iterative reconstruction (SAFIRE (Siemens Healthineers, Forchheim, Germany)) with an I30f kernel for soft tissue presentation.
- (d)
- I70f: Iterative reconstruction (SAFIRE (Siemens Healthineers, Forchheim, Germany)) with an I70f kernel for bone or lung presentation.
- (e)
- P30f: PixelShine (AlgoMedica), version 1.2.104, using the reconstructed images of (a) with the parameters P214A8S.
- (f)
- P70f: PixelShine (AlgoMedica), version 1.2.104, using the reconstructed images of (b) with the parameters PB14A4L2.
2.4. Image Analysis
2.5. Analysis of Urinary Concrements
2.6. Statistical Analysis
3. Results
3.1. Patient Characteristics
3.2. Stone Size
3.3. CT-Attenuation Values of Stones
3.4. Stone Composition
3.5. CT Values and Image Noise in Tissues and Air
3.6. SNR and CNR
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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x (mm) | y (mm) | x (mm) | y (mm) | |
---|---|---|---|---|
Soft Kernels | Sharp Kernels | |||
B | 5.6 ± 3.0 AC | 4.2 ± 2.2 E | 4.4 ± 3.0 | 3.1 ± 2.0 |
I | 5.1 ± 3.1 AB | 3.8 ± 2.1 DE | 4.4 ± 3.0 | 3.1 ± 2.1 |
P | 5.4 ± 3.1 BC | 4.1 ± 2.2 D | 4.4 ± 3.0 | 3.2 ± 2.1 |
Soft Kernels | Sharp Kernels | |
---|---|---|
B | 717.6 ± 405.9 A | 1047.3 ± 490.7 C |
I | 714.4 ± 459.4 A | 986.2 ± 516.5 B |
P | 704.2 ± 424.5 | 915.9 ± 449.6 BC |
Soft Kernel Reconstruction | Sharp Kernel Reconstruction | ||||||
---|---|---|---|---|---|---|---|
Composition | n | B | I | P | B | I | P |
CaOx | 15 | 772 (523–1059) | 739 (508–1147) | 829 (531–1044) | 1316 (1045–1583) | 1251 (703–1565) | 1119 (791–1399) |
Calcium-Oxalate-carbonate apatite | 5 | 703 (663–777) | 672 (668–675) | 632 (598–699) | 1092 (1000–1339) | 1251 (1156–1254) | 850 (764–1124) |
Uric acid | 3 | 501 (430–664) | 513 (412–645) | 515 (422–562) | 460 (452–967) | 464 (434–1040) | 507 (463–972) |
Cystine | 1 | 721 | 714 | 725 | 731 | 745 | 714 |
Carbonate-Apatite-mix | 1 | 1174 | 1257 | 1202 | 1149 | 1358 | 1320 |
(a) | |||
CT Value | B | I | P |
Air (sharp) | −940.0 ± 12.0 A | −933.4 ± 11.2 AB | −939.4 ± 11.9 B |
Bone (sharp) | 188.8 ± 63.3 D | 186.0 ± 66.7 C | 196.2 ± 63.5 CD |
Liver (soft) | 44.7 ± 16.3 | 44.7 ± 16.1 | 44.6 ± 16.1 |
Muscle (soft) | 51.3 ± 6.7 EF | 51.1 ± 6.7 F | 50.8 ± 7.0 E |
Spleen (soft) | 45.4 ± 3.5 | 45.4 ± 3.4 | 45.4 ± 3.4 |
Fat (soft) | −116.1 ± 9.7 H | −116. ± 9.6 G | −115.6 ± 9.6 GH |
Results of the statistical comparisons: A–G: p < 0.001, H: p = 0.001. | |||
(b) | |||
Image Noise | B | I | P |
Air (sharp) | 77.5 ± 15.9 | 49.4 ± 13.5 | 28.5 ± 14.3 |
Bone (sharp) | 212.3 ± 40.1 | 143.0 ± 31.1 | 124.0 ± 20.7 |
Liver (soft) | 38.2 ± 7.7 | 26.2 ± 5.0 | 20.3 ± 4.8 |
Muscle (soft) | 31.6 ± 5.6 | 21.7 ± 3.9 | 14.5 ± 2.6 |
Spleen (soft) | 34.5 ± 7.0 | 23.4 ± 4.8 | 16.4 ± 4.1 |
Fat (soft) | 29.4 ± 5.6 | 20.4 ± 4.2 | 12.8 ± 3.1 |
Statistical differences (p < 0.001) among all reconstructions within one ROI. Abbreviations: B: filtered back-projection, I: iterative reconstruction; P: PixelShine. |
Parameter | This Study | Zhang et al. [20] | Thapaliya et al. [21] | Delabie et al. [22] | ||||
---|---|---|---|---|---|---|---|---|
Vendor | Algomedica | Siemens Healthineers | Canon Medical Systems | Canon Medical Systems | GE Healthcare | |||
Techniques used | PixelShine | IR (Safire), FBP | DLR (AiCE) | HIR | DLR (AiCE, six options evaluated) | AIDR3D | DLR (TrueFidelityTM) | FBP ASiR-V |
Preprocessing techniques | FBP | Raw data | None described | Raw data | Raw data | None described | ||
Type of dataset used | CT of kidney stones in 45 patients, both soft tissue and bone kernel and window settings | CT of kidney stones in 51 patients with intra-individual comparison, soft tissue window settings; LDCT-HIR as gold standard | CT of kidney stones in 7 patients, AIDR3D as gold standard, soft tissue window | CT of kidney stones in 75 patients, soft tissue window (stone detection), bone window (stone count) | ||||
Evaluation measures | Image noise, CNR, SNR, attenuation, stone size | Radiation exposure, stone characteristics, image noise, SNR, subjective IQ | Stone detection, stone size, inter-rater reliability | Attenuation, noise measurements, SNR, contrast, CNR, detectability, IQ, stone size category; | ||||
Advantage | Higher objective IQ | Direct reconstruction | Reduced radiation exposure, higher IQ | High level of agreement with AIDR3D | Quantitative and qualitative IQ improved | |||
Disadvantage | Secondary reconstruction | Image noise | Lower sensibility | Higher sensibility | More image noise than AiCE | Contrast between kidney and spleen different to ASiR-V | Image noise | |
Recommendation | Usage of PixelShine to reduce image noise; use sharp kernel reconstructions bone window to improve differentiation between stone compositions | DLR with ultra-low dose CT to reduce dose, though it might miss stones <3mm | Usage of DLR to potentially reduce radiation exposure | Usage of DLR to improve IQ, though it still might miss stones <3mm |
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Steuwe, A.; Valentin, B.; Bethge, O.T.; Ljimani, A.; Niegisch, G.; Antoch, G.; Aissa, J. Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones. Diagnostics 2022, 12, 1627. https://doi.org/10.3390/diagnostics12071627
Steuwe A, Valentin B, Bethge OT, Ljimani A, Niegisch G, Antoch G, Aissa J. Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones. Diagnostics. 2022; 12(7):1627. https://doi.org/10.3390/diagnostics12071627
Chicago/Turabian StyleSteuwe, Andrea, Birte Valentin, Oliver T. Bethge, Alexandra Ljimani, Günter Niegisch, Gerald Antoch, and Joel Aissa. 2022. "Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones" Diagnostics 12, no. 7: 1627. https://doi.org/10.3390/diagnostics12071627