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Deep learning-based algorithms for low-dose CT imaging: A review.

Hongchi Chen¹, Qiuxia Li¹, Lazhen Zhou¹

¹School of Medical Information Engineering, Gannan Medical University, Ganzhou 341000, China.

European Journal of Radiology

|February 7, 2024

Summary

Deep learning significantly enhances low-dose computed tomography (CT) imaging, improving diagnostic accuracy while minimizing radiation exposure. This review explores deep learning algorithms for better low-dose CT (LDCT) image quality and clinical application.

Keywords:

Artifact reduction Deep learning Denoising Low-dose CT Reconstruction

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Area of Science:

Medical Imaging
Radiology
Artificial Intelligence in Medicine

Background:

Computed tomography (CT) utilizes high radiation doses, posing risks of radiation damage.
Reducing CT radiation dose can compromise image quality, increasing misdiagnosis risks.
High-quality imaging at reduced radiation doses remains a critical challenge in CT.

Purpose of the Study:

To review conventional and deep learning-based algorithms for low-dose CT (LDCT) image enhancement.
To analyze the application of deep neural networks in various LDCT imaging domains.
To assess the clinical and commercial viability of LDCT deep learning algorithms.

Main Methods:

Introduction to traditional CT image reconstruction algorithms.
Detailed description of deep neural network applications in projection, image, and dual domains.
Overview of direct deep learning-based reconstruction (DLR) for LDCT.

Main Results:

Deep learning algorithms show superior performance in enhancing LDCT images compared to conventional methods.
Analysis of the advantages and disadvantages of different deep learning approaches.
Presentation of commercial and clinical applications of LDCT-DLR.

Conclusions:

Deep learning-based algorithms are crucial for advancing LDCT imaging.
Further research is needed to address existing challenges and explore future algorithmic trends.
LDCT-DLR holds significant promise for safer and more accurate medical diagnostics.