Ms. Linda-Sophie Schneider Profile

Linda-Sophie Schneider

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SPIE Journal Paper | 22 January 2025 Open Access

EAGLE: an edge-aware gradient localization enhanced loss for CT image reconstruction

Yipeng Sun, Yixing Huang, Zeyu Yang, Linda-Sophie Schneider, Mareike Thies, Mingxuan Gu, Siyuan Mei, Siming Bayer, Frank Zöllner, Andreas Maier

JMI, Vol. 12, Issue 01, 014001, (January 2025) https://doi.org/10.1117/12.10.1117/1.JMI.12.1.014001

KEYWORDS: Computed tomography, CT reconstruction, Education and training, Image restoration, Image quality, Visualization, Image sharpness, Image enhancement, Medical image reconstruction, Super resolution

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PurposeWe aim to enhance deep learning-based computed tomography (CT) image reconstructions. Conventional loss functions such as mean squared error (MSE) yield blurry images, and alternative methods may introduce artifacts. To address these limitations, we propose Eagle-Loss, designed to improve sharpness and edge definition without increasing computational complexity. Eagle-Loss leverages spectral analysis of localized gradient variations to enhance visual quality and quantification in CT imaging.ApproachEagle-Loss enhances CT reconstructions by segmenting gradient maps into patches and calculating intra-block variance to create a variance map. This map is analyzed in the frequency domain to identify critical features. We evaluate Eagle-Loss on two public datasets for low-dose CT reconstruction and field-of-view (FOV) extension and on a private photon counting CT (PCCT) dataset for super-resolution. Eagle-Loss is integrated into various deep learning models and used as a regularizer in ART, demonstrating effectiveness across reconstruction methods.ResultsOur experiments show that Eagle-Loss outperforms existing methods across all evaluated tasks, consistently improving the visual quality of reconstructed CT images. It achieves better performance compared with current top-performing loss functions when used with different network architectures while maintaining similar speed without adding extra computational costs. For low-dose CT reconstruction, Eagle-Loss achieved the highest SSIM scores of 0.958 for the TF-FBP model and 0.972 for RED-CNN. In the CT FOV task, our method reached a best SSIM of 0.966. For PCCT super-resolution, Eagle-Loss attained a top SSIM score of 0.998.ConclusionsEagle-Loss effectively mitigates blurring and artifacts prevalent in current CT reconstruction methods by significantly improving image sharpness and edge definition. Our evaluation confirms that Eagle-Loss can be successfully integrated into various deep learning models and reconstruction techniques without incurring additional computational costs, underscoring its robustness and model-independent nature. This significant enhancement in visual quality is important for achieving more accurate diagnoses and improved clinical outcomes.

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