# Robustness of Single- and Dual-Energy Deep-Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. COVID-19 CT Image Database

#### 2.2. Monte Carlo Simulations to Generate Training and Validation Dataset

#### 2.3. CNN Architecture

#### 2.4. Evaluation of Scatter Estimation Models on Simulated CXRs

#### 2.5. Evaluation of Scatter Correction on True CXRs

## 3. Results

#### 3.1. Accuracy of Scatter Correction on Simulated CXRs

#### 3.2. Study of Contrast Improvement after Scatter Correction

#### 3.3. Study of Scatter Correction on True CXRs

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

**Table A1.**Contrast value (Equation (5)) between the COVID-19-affected region and the healthy lung for the soft-tissue test images in the uncorrected image, the scatter-corrected ground-truth image and the scatter-corrected images estimated by the single-energy and dual-energy models. For patients with both lungs affected, each lung has been considered to be a different case.

Patient | Ground Truth Uncorrected Image | Ground Truth Scatter-Corrected Image | Scatter-Corrected with Single Energy | Scatter-Corrected with 1-Output Dual Energy | Scatter-Corrected with 2-Output Dual Energy |
---|---|---|---|---|---|

Case 1 | 1.216 | 1.385 | 1.352 | 1.374 | 1.380 |

Case 2 | 1.165 | 1.282 | 1.268 | 1.285 | 1.278 |

Case 3 | 1.032 | 1.101 | 1.072 | 1.080 | 1.095 |

Case 4 | 1.292 | 1.430 | 1.419 | 1.404 | 1.455 |

Case 5 | 1.187 | 1.299 | 1.289 | 1.293 | 1.299 |

Case 6 | 1.047 | 1.018 | 1.045 | 1.048 | 1.014 |

Case 7 | 1.147 | 1.239 | 1.235 | 1.231 | 1.255 |

Case 8 | 1.297 | 1.448 | 1.453 | 1.439 | 1.434 |

Case 9 | 1.096 | 1.301 | 1.244 | 1.252 | 1.284 |

Case 10 | 1.301 | 1.553 | 1.499 | 1.531 | 1.526 |

Case 11 | 1.154 | 1.201 | 1.195 | 1.198 | 1.190 |

Case 12 | 1.045 | 1.159 | 1.104 | 1.145 | 1.149 |

Case 13 | 0.795 | 0.645 | 0.648 | 0.684 | 0.627 |

Case 14 | 1.209 | 1.454 | 1.403 | 1.434 | 1.436 |

Case 15 | 1.305 | 1.575 | 1.532 | 1.549 | 1.573 |

Case 16 | 1.128 | 1.176 | 1.170 | 1.198 | 1.184 |

Case 17 | 0.960 | 0.931 | 0.922 | 0.953 | 0.928 |

Case 18 | 1.104 | 1.202 | 1.173 | 1.205 | 1.213 |

Case 19 | 1.037 | 1.035 | 1.052 | 1.045 | 1.040 |

Case 20 | 0.879 | 0.890 | 0.864 | 0.861 | 0.913 |

Case 21 | 1.110 | 1.115 | 1.113 | 1.103 | 1.114 |

Case 22 | 1.021 | 1.056 | 1.054 | 1.063 | 1.058 |

Case 23 | 0.945 | 0.987 | 0.967 | 0.972 | 1.001 |

Case 24 | 1.098 | 1.165 | 1.148 | 1.163 | 1.162 |

Case 25 | 1.209 | 1.324 | 1.327 | 1.333 | 1.325 |

Case 26 | 1.183 | 1.279 | 1.293 | 1.287 | 1.291 |

Case 27 | 0.962 | 0.886 | 0.898 | 0.899 | 0.851 |

Case 28 | 0.841 | 0.719 | 0.719 | 0.723 | 0.717 |

Case 29 | 0.872 | 0.816 | 0.795 | 0.817 | 0.791 |

Case 30 | 0.908 | 0.912 | 0.876 | 0.875 | 0.872 |

Case 31 | 1.316 | 1.532 | 1.529 | 1.562 | 1.544 |

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**Figure 1.**Simulated chest X-rays for two cases considered (low energy = 60 kVp; high energy = 130 kVp). The simulation with scatter (

**left**) can be decomposed into a direct component (“without scatter”, (

**center**)) and the scatter contribution (

**right**).

**Figure 3.**Diagram of the MultiResUNet architecture used in this work to train the neural networks. The input of the network is the image affected by scatter (i.e., uncorrected-scatter image), and the output is the fraction of the image of scatter with respect to the uncorrected-scatter image.

**Figure 4.**Scheme of input and output images corresponding to the 3 neural network models presented in this work. The NNs differ in the amount of input and output channels used.

**Figure 5.**Soft-tissue dual-energy subtraction images: (

**a**) Calculated from uncorrected-scatter CXRs of 60 kVp and 130 kVp. (

**b**) Calculated from ground-truth scatter-corrected images of 60 kVp and 130 kVp. (

**c**) Difference pixel by pixel between the uncorrected-scatter soft-tissue image and the soft-tissue scatter-corrected image.

**Figure 6.**(

**a**) Mask of the region affected by COVID-19 (blue) over the CXR. (

**b**) Mask of the healthy region in the lung (red). The range of values of these images was obtained after the normalization procedure explained in Section 2.2.

**Figure 7.**(

**a**) Scatter-corrected image estimated by the single-energy model. (

**b**) Scatter-corrected image estimated by the 1-output dual-energy model. (

**c**) Scatter-corrected image estimated by the 2-output dual-energy model. (

**d**) Difference pixel by pixel between the scatter-corrected ground-truth image (represented in Figure 1) and the scatter-corrected image estimated by the single-energy model. (

**e**) Difference pixel by pixel between the scatter-corrected ground-truth image (represented in Figure 1) and the scatter-corrected image estimated by the 1-output dual-energy model. (

**f**) Difference pixel by pixel between the scatter-corrected ground-truth image (represented in Figure 1) and the scatter-corrected image estimated by the 2-output dual-energy model.

**Figure 8.**Box plot of the MSE, MAPE, SSIM, and relative error for the 22 test cases at different source-to-detector distances. A blue dashed line represents the median value of the metric for each SDD. The box extends from the lower to the upper quartile values of the data, while the range (also referred to as whiskers) shows the rest of the distribution.

**Figure 9.**(

**a**) Graphic representation of the percentage contrast improvement factor for ground-truth image, single-energy model estimation, 1-output dual-energy model, and 2-output dual-energy model estimation. (

**b**) Relative difference in the contrast value between ground truth and deep-learning-based estimations. In both graphics, the red solid line represents the average value, while the blue dash-dotted line represents the median value.

**Figure 10.**Original CXR (with scatter) with pixel value conversion (

**left**); estimation of the scatter-corrected CXR (

**center**); and estimation of the scatter contribution on real CXR (

**right**) of three of the real chest X-ray images used to test the single-energy model of scatter correction.

Parameter | Specification |
---|---|

Source-Detector Distance (cm) | 180 |

X-ray Detector Size (cm) | $41\times 41$ |

X-ray Detector Resolution (pixel) | $2050\times 2050$ |

**Table 2.**Ratio between a region of the lung with and without rib in the original, real CXRs and in the scatter-corrected CXRs yielded by the single-energy algorithm for the 10 CXRs taken as test set (listed as C1–C10), and the resulting average value.

C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | Avg | |
---|---|---|---|---|---|---|---|---|---|---|---|

Original | 2.71 | 1.66 | 1.36 | 1.11 | 1.35 | 1.16 | 1.71 | 1.47 | 1.28 | 1.59 | 1.54 |

Scatter-corrected | 3.80 | 2.02 | 1.54 | 1.17 | 1.49 | 1.23 | 2.14 | 1.68 | 1.41 | 1.64 | 1.81 |

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## Share and Cite

**MDPI and ACS Style**

Freijo, C.; Herraiz, J.L.; Arias-Valcayo, F.; Ibáñez, P.; Moreno, G.; Villa-Abaunza, A.; Udías, J.M.
Robustness of Single- and Dual-Energy Deep-Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays. *Algorithms* **2023**, *16*, 565.
https://doi.org/10.3390/a16120565

**AMA Style**

Freijo C, Herraiz JL, Arias-Valcayo F, Ibáñez P, Moreno G, Villa-Abaunza A, Udías JM.
Robustness of Single- and Dual-Energy Deep-Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays. *Algorithms*. 2023; 16(12):565.
https://doi.org/10.3390/a16120565

**Chicago/Turabian Style**

Freijo, Clara, Joaquin L. Herraiz, Fernando Arias-Valcayo, Paula Ibáñez, Gabriela Moreno, Amaia Villa-Abaunza, and José Manuel Udías.
2023. "Robustness of Single- and Dual-Energy Deep-Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays" *Algorithms* 16, no. 12: 565.
https://doi.org/10.3390/a16120565