Generation of synthetic megavoltage CT for MRI‐only radiotherapy treatment planning using a 3D deep convolutional neural network

Scholey, Jonathan M.; Rajagopal, Abhejit; Vasquez, Elena Grace; Sudhyadhom, Atchar; Larson, Peder E. Z.

doi:10.1002/mp.15876

Cited by 6 publications

(2 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, Scholey et al investigated the feasibility of MRI-only treatment planning by implementing a 3D U-Net to generate synthetic megavoltage CT (sMVCT) from paired T1-weighted MRI [ 71 ]. To assess the model’s performance in different tissue types, MVCT images were segmented into whole body, soft tissue, bone, and air-filled volumes.…”

Section: Resultsmentioning

confidence: 99%

“…Nonetheless, this approach could streamline a time-consuming process such as HNC planning while harmonizing the results, which are affected by a planner’s previous experience and practice. Other AI-based applications were investigated for predicting MLC leaf position errors [ 69 ], metal artifact reduction [ 70 ], and MRI-based planning [ 71 ]. Although promising, these are still in the early stages of research.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review

Franzese

Dei

Lambri

et al. 2023

JPM

View full text Add to dashboard Cite

Background: Head and neck cancer (HNC) is characterized by complex-shaped tumors and numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning, optimization, and delivery. In this review, we provided a thorough description of the applications of artificial intelligence (AI) tools in the HNC RT process. Methods: The PubMed database was queried, and a total of 168 articles (2016–2022) were screened by a group of experts in radiation oncology. The group selected 62 articles, which were subdivided into three categories, representing the whole RT workflow: (i) target and OAR contouring, (ii) planning, and (iii) delivery. Results: The majority of the selected studies focused on the OARs segmentation process. Overall, the performance of AI models was evaluated using standard metrics, while limited research was found on how the introduction of AI could impact clinical outcomes. Additionally, papers usually lacked information about the confidence level associated with the predictions made by the AI models. Conclusions: AI represents a promising tool to automate the RT workflow for the complex field of HNC treatment. To ensure that the development of AI technologies in RT is effectively aligned with clinical needs, we suggest conducting future studies within interdisciplinary groups, including clinicians and computer scientists.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review

Franzese

Dei

Lambri

et al. 2023

JPM

View full text Add to dashboard Cite

show abstract

Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning

Bahloul,

Jabeen,

Benoumhani

et al. 2024

J Applied Clin Med Phys

View full text Add to dashboard Cite

BackgroundMagnetic resonance imaging (MRI) and Computed tomography (CT) are crucial imaging techniques in both diagnostic imaging and radiation therapy. MRI provides excellent soft tissue contrast but lacks the direct electron density data needed to calculate dosage. CT, on the other hand, remains the gold standard due to its accurate electron density information in radiation therapy planning (RTP) but it exposes patients to ionizing radiation. Synthetic CT (sCT) generation from MRI has been a focused study field in the last few years due to cost effectiveness as well as for the objective of minimizing side‐effects of using more than one imaging modality for treatment simulation. It offers significant time and cost efficiencies, bypassing the complexities of co‐registration, and potentially improving treatment accuracy by minimizing registration‐related errors. In an effort to navigate the quickly developing field of precision medicine, this paper investigates recent advancements in sCT generation techniques, particularly those using machine learning (ML) and deep learning (DL). The review highlights the potential of these techniques to improve the efficiency and accuracy of sCT generation for use in RTP by improving patient care and reducing healthcare costs. The intricate web of sCT generation techniques is scrutinized critically, with clinical implications and technical underpinnings for enhanced patient care revealed.PurposeThis review aims to provide an overview of the most recent advancements in sCT generation from MRI with a particular focus of its use within RTP, emphasizing on techniques, performance evaluation, clinical applications, future research trends and open challenges in the field.MethodsA thorough search strategy was employed to conduct a systematic literature review across major scientific databases. Focusing on the past decade's advancements, this review critically examines emerging approaches introduced from 2013 to 2023 for generating sCT from MRI, providing a comprehensive analysis of their methodologies, ultimately fostering further advancement in the field. This study highlighted significant contributions, identified challenges, and provided an overview of successes within RTP. Classifying the identified approaches, contrasting their advantages and disadvantages, and identifying broad trends were all part of the review's synthesis process.ResultsThe review identifies various sCT generation approaches, consisting atlas‐based, segmentation‐based, multi‐modal fusion, hybrid approaches, ML and DL‐based techniques. These approaches are evaluated for image quality, dosimetric accuracy, and clinical acceptability. They are used for MRI‐only radiation treatment, adaptive radiotherapy, and MR/PET attenuation correction. The review also highlights the diversity of methodologies for sCT generation, each with its own advantages and limitations. Emerging trends incorporate the integration of advanced imaging modalities including various MRI sequences like Dixon sequences, T1‐weighted (T1W), T2‐weighted (T2W), as well as hybrid approaches for enhanced accuracy.ConclusionsThe study examines MRI‐based sCT generation, to minimize negative effects of acquiring both modalities. The study reviews 2013‐2023 studies on MRI to sCT generation methods, aiming to revolutionize RTP by reducing use of ionizing radiation and improving patient outcomes. The review provides insights for researchers and practitioners, emphasizing the need for standardized validation procedures and collaborative efforts to refine methods and address limitations. It anticipates the continued evolution of techniques to improve the precision of sCT in RTP.

show abstract

A unified generation‐registration framework for improved MR‐based CT synthesis in proton therapy

Li,

Bellotti,

Bachtiary

et al. 2024

Medical Physics

View full text Add to dashboard Cite

BackgroundThe use of magnetic resonance (MR) imaging for proton therapy treatment planning is gaining attention as a highly effective method for guidance. At the core of this approach is the generation of computed tomography (CT) images from MR scans. However, the critical issue in this process is accurately aligning the MR and CT images, a task that becomes particularly challenging in frequently moving body areas, such as the head‐and‐neck. Misalignments in these images can result in blurred synthetic CT (sCT) images, adversely affecting the precision and effectiveness of the treatment planning.PurposeThis study introduces a novel network that cohesively unifies image generation and registration processes to enhance the quality and anatomical fidelity of sCTs derived from better‐aligned MR images.MethodsThe approach synergizes a generation network (G) with a deformable registration network (R), optimizing them jointly in MR‐to‐CT synthesis. This goal is achieved by alternately minimizing the discrepancies between the generated/registered CT images and their corresponding reference CT counterparts. The generation network employs a UNet architecture, while the registration network leverages an implicit neural representation (INR) of the displacement vector fields (DVFs). We validated this method on a dataset comprising 60 head‐and‐neck patients, reserving 12 cases for holdout testing.ResultsCompared to the baseline Pix2Pix method with MAE 124.9530.74 HU, the proposed technique demonstrated 80.987.55 HU. The unified translation‐registration network produced sharper and more anatomically congruent outputs, showing superior efficacy in converting MR images to sCTs. Additionally, from a dosimetric perspective, the plan recalculated on the resulting sCTs resulted in a remarkably reduced discrepancy to the reference proton plans.ConclusionsThis study conclusively demonstrates that a holistic MR‐based CT synthesis approach, integrating both image‐to‐image translation and deformable registration, significantly improves the precision and quality of sCT generation, particularly for the challenging body area with varied anatomic changes between corresponding MR and CT.

show abstract

Generation of synthetic megavoltage CT for MRI‐only radiotherapy treatment planning using a 3D deep convolutional neural network

Cited by 6 publications

References 99 publications

Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review

Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review

Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning

A unified generation‐registration framework for improved MR‐based CT synthesis in proton therapy

Contact Info

Product

Resources

About