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. 2023 Dec 5;9(6):2158-2189.
doi: 10.3390/tomography9060169.

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Hameedur Rahman  1 , Abdur Rehman Khan  2 , Touseef Sadiq  3 , Ashfaq Hussain Farooqi  4 , Inam Ullah Khan  5 , Wei Hong Lim  6
Affiliations

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Hameedur Rahman et al. Tomography. .

Abstract

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

Keywords: 3D deep learning (3DDL); computed tomography (CT) reconstruction; systematic literature review.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
3D Reconstruction and enhanced visualization: CT scan data transformed into a detailed 3D image using deep learning techniques.
Figure 2
Figure 2
Flowchart: The study involved a comprehensive search of over 60,000 abstracts and ultimately selected 10 studies for meta-analysis and 2 studies for qualitative synthesis, with a focus on the application of 3D deep learning in computed tomography reconstruction, and presents the findings in tables summarizing diagnostic accuracy metrics for imaging and several specialties.
Figure 3
Figure 3
Global contribution to 3D deep learning research in computed tomography reconstruction over the years.
Figure 4
Figure 4
Evolution of databases utilized in 3D deep learning for computed tomography reconstruction over the years.
Figure 5
Figure 5
Evolution of 3D deep learning methods in computed tomography reconstruction over the years.
Figure 6
Figure 6
Global contribution available datasets for training and validation in 3D deep learning research in computed tomography reconstruction over the years.
Figure 7
Figure 7
Evolution of databases utilized for available datasets for training and validation in 3D deep learning for computed tomography reconstruction over the years.
Figure 8
Figure 8
Evolution of 3D deep learning datasets in computed tomography reconstruction over the years.
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