A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy
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A systematic literature review : deep learning techniques for synthetic medical image generation and their applications in radiotherapy. / Sherwani, Moiz Khan; Gopalakrishnan, Shyam.
I: Frontiers in Radiology, Bind 4, 1385742, 2024.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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 -
ID: 391160320