MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach

Kazemifar, Samaneh; McGuire, Sarah; Timmerman, Robert; Wardak, Zabi; Nguyen, Dan; Park, Yang; Jiang, Steve B; Owrangi, Amir

doi:10.1016/j.radonc.2019.03.026

Cited by 123 publications

(112 citation statements)

References 30 publications

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“…Magnetic resonance imaging (MRI) has superior soft tissue contrast over computed tomography (CT), allowing for improved organ‐at‐risk segmentation and target delineation for radiation therapy treatment planning . Since dose calculation algorithms rely on electron density maps generated from CT images for calculating dose, MRIs are typically registered to CT images and used alongside the CT image for treatment planning . However, the CT/MRI registration process has inherent errors, for example, a geometrical uncertainty of approximately 2 mm is present in cranial MRI .…”

Section: Introductionmentioning

confidence: 99%

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

et al. 2019

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Purpose Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI‐only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow. In this work, the authors propose a novel method for generation of sCT based on dense cycle‐consistent generative adversarial networks (cycle GAN), a deep‐learning based model that trains two transformation mappings (MRI to CT and CT to MRI) simultaneously. Methods and materials The cycle GAN‐based model was developed to generate sCT images in a patch‐based framework. Cycle GAN was applied to this problem because it includes an inverse transformation from CT to MRI, which helps constrain the model to learn a one‐to‐one mapping. Dense block‐based networks were used to construct generator of cycle GAN. The network weights and variables were optimized via a gradient difference (GD) loss and a novel distance loss metric between sCT and original CT. Results Leave‐one‐out cross‐validation was performed to validate the proposed model. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), and normalized cross correlation (NCC) indexes were used to quantify the differences between the sCT and original planning CT images. For the proposed method, the mean MAE between sCT and CT were 55.7 Hounsfield units (HU) for 24 brain cancer patients and 50.8 HU for 20 prostate cancer patients. The mean PSNR and NCC were 26.6 dB and 0.963 in the brain cases, and 24.5 dB and 0.929 in the pelvis. Conclusion We developed and validated a novel learning‐based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs. The proposed method can generate robust, high‐quality sCT in minutes. The proposed method offers strong potential for supporting near real‐time MRI‐only treatment planning in the brain and pelvis.

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Section: Introductionmentioning

confidence: 99%

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

et al. 2019

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“…Importantly, some methods of synthetic CT generation may require nonstandard sequences that increase measurement time [27]. Deep learning-based approaches are at the forefront of current research on synthetic CT and have been shown to outperform other published approaches in terms of mean absolute HU errors in a recent study [28]. These solutions could allow for fast and robust synthetic CT generation from standard MR sequences.…”

Section: Promises Of Synthetic Ct and Mr-only Planningmentioning

confidence: 99%

Magnetic resonance imaging for brain stereotactic radiotherapy

et al. 2020

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Due to its superior soft tissue contrast, magnetic resonance imaging (MRI) is essential for many radiotherapy treatment indications. This is especially true for treatment planning in intracranial tumors, where MRI has a long-standing history for target delineation in clinical practice. Despite its routine use, care has to be taken when selecting and acquiring MRI studies for the purpose of radiotherapy treatment planning. Requirements on MRI are particularly demanding for intracranial stereotactic radiotherapy, where accurate imaging has a critical role in treatment success. However, MR images acquired for routine radiological assessment are frequently unsuitable for high-precision stereotactic radiotherapy as the requirements for imaging are significantly different for radiotherapy planning and diagnostic radiology. To assure that optimal imaging is used for treatment planning, the radiation oncologist needs proper knowledge of the most important requirements concerning the use of MRI in brain stereotactic radiotherapy. In the present review, we summarize and discuss the most relevant issues when using MR images for target volume delineation in intracranial stereotactic radiotherapy.

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“…Recent advances in DNN and specifically in GANs have enabled innovations in creating a new image or composing a symphony . This discovery paradigm can be applied to various materials and provided thoughtful guidance to the synthesis of new materials . GAN is one of the nonparametric approaches for deep generative models initially proposed by Goodfellow et al The generative models can be used to create plausible molecular structures for high‐throughput screening, which is the first step in molecular discovery.…”

Section: Molecular Discoveries Using Gansmentioning

confidence: 99%

Machine learning and artificial neural network accelerated computational discoveries in materials science

Yang

Hou

Jiang

et al. 2019

WIREs Comput Mol Sci

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Artificial intelligence (AI) has been referred to as the "fourth paradigm of science," and as part of a coherent toolbox of data-driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state-of-the-art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML-based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized.

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MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach

Cited by 123 publications

References 30 publications

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

Magnetic resonance imaging for brain stereotactic radiotherapy

Machine learning and artificial neural network accelerated computational discoveries in materials science

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