TY - JOUR

T1 - A systematic literature review

T2 - deep learning techniques for synthetic medical image generation and their applications in radiotherapy

AU - Sherwani, Moiz Khan

AU - Gopalakrishnan, Shyam

N1 - Publisher Copyright: 2024 Sherwani and Gopalakrishnan.

PY - 2024

Y1 - 2024

N2 - The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: (Formula presented.) MR-based treatment planning and synthetic CT generation techniques. (Formula presented.) Generation of synthetic CT images based on Cone Beam CT images. (Formula presented.) Low-dose CT to High-dose CT generation. (Formula presented.) Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.

AB - The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: (Formula presented.) MR-based treatment planning and synthetic CT generation techniques. (Formula presented.) Generation of synthetic CT images based on Cone Beam CT images. (Formula presented.) Low-dose CT to High-dose CT generation. (Formula presented.) Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.

KW - convolutional neural network

KW - deep learning

KW - generative adversarial network

KW - photon therapy

KW - proton therapy

KW - radiotherapy

KW - synthetic CT

U2 - 10.3389/fradi.2024.1385742

DO - 10.3389/fradi.2024.1385742

M3 - Journal article

C2 - 38601888

AN - SCOPUS:85190123694

VL - 4

JO - Frontiers in Radiology

JF - Frontiers in Radiology

SN - 2673-8740

M1 - 1385742

ER -