X-ray Scatter Estimation Using Deep Splines

• Project Description

X-ray scatter is a significant source of image degradation in medical imaging, especially in computed tomography (CT) scans. Estimating and correcting X-ray scatter is a critical step in improving the quality of CT images. Recently, a method called Deep Scatter Estimation (DSE) was proposed to estimate X-ray scatter using deep neural networks. However, DSE requires a large amount of training data and has limited generalization capability.

To address these limitations, a new method called Deep Spline Scatter Estimation (DSSE) was proposed. DSSE combines deep learning with spline regression, which enables efficient estimation of scatter from a limited number of training samples. The DSSE method uses a deep neural network to estimate the initial scatter distribution, which is then refined using spline regression to achieve high accuracy.

DSSE has been shown to outperform DSE on both synthetic and real CT data. DSSE achieved a mean absolute error (MAE) of 6.0% compared to 9.1% for DSE on synthetic data, and an MAE of 6.4% compared to 7.4% for DSE on real data. DSSE also showed better generalization capability than DSE, achieving accurate scatter estimation even on data that was significantly different from the training data.

In summary, DSSE is a promising method for estimating X-ray scatter in medical imaging. Its combination of deep learning and spline regression enables efficient and accurate estimation of scatter from limited training data, and it has been shown to outperform existing methods on both synthetic and real data.

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