Synthetic computed tomography generation for abdominal adaptive radiotherapy using low-field magnetic resonance imaging

Hernandez, Armando Garcia; Fau, Pierre; Wojak, Julien; Mailleux, Hugues; Benkreira, Mohamed; Rapacchi, Stanislas; Adel, Mouloud

doi:10.1016/j.phro.2023.100425

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The pelvis and the head and neck can be treated using photon radiotherapy (RT) and proton therapy, which achieve high image similarity and dosimetry accuracy. As part of the feasibility phase of testing, application of DL algorithms to abdominal and thoracic positions with significant motion are showing promise ( 37 , 41 , 59 , 60 , 86 , 89 , 104 , 116 – 118 , 185 , 231 ). The MR-only simulation of pediatric patients could be extremely beneficial when their simulations are repeated since they are more radiation-sensitive than adults.…”

Section: Discussionmentioning

confidence: 99%

A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy

Sherwani,

Gopalakrishnan

2024

Front. Radiol.

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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: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ 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.

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Section: Discussionmentioning

confidence: 99%

A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy

Sherwani,

Gopalakrishnan

2024

Front. Radiol.

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“…Nevertheless, only two studies incorporated multi-view slices and none of them explored the use of low-field MR scans. The latter has only been investigated for abdomen, prostate, and lung cancer [39] , [40] , [41] , [42] , [43] , [44] , [45] , resulting in promising image similarity scores (MAE = 26–90 HU) and dose metric accuracies (DVH = 1–5%). However, and, like the HN cancer studies, none of these studies investigated the use of ViTs.…”

Section: Introductionmentioning

confidence: 99%