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This is a Quantitative Study study.

Deep learning in CT image reconstruction and processing: Techniques, performance evaluation, radiation dose, and future perspective

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This overview demonstrates deep learning's impact on image quality and radiation dose reduction in CT scans.

DLR techniques effectively reduce image noise while maintaining texture similar to traditional methods, enhancing diagnostic utility.
Performance evaluations from phantom studies and patient images indicate varying degrees of radiation dose reduction depending on tasks.
Hybrid techniques and virtual imaging trials show promise for improving diagnostic performance while minimizing radiation exposure.
Challenges like low-contrast lesion detection remain, highlighting the importance of robust monitoring strategies for DLR methods.

Yu et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

This overview demonstrates deep learning's impact on image quality and radiation dose reduction in CT scans.

DLR techniques effectively reduce image noise while maintaining texture similar to traditional methods, enhancing diagnostic utility.
Performance evaluations from phantom studies and patient images indicate varying degrees of radiation dose reduction depending on tasks.
Hybrid techniques and virtual imaging trials show promise for improving diagnostic performance while minimizing radiation exposure.
Challenges like low-contrast lesion detection remain, highlighting the importance of robust monitoring strategies for DLR methods.

Yu et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark