The future of image-guided radiotherapy will be MR guided

Pollard, Julianne M.; Wen, Zhifei; Sadagopan, R; Wang, Jihong; Ibbott, Geoffrey S.

doi:10.1259/bjr.20160667

Cited by 168 publications

(138 citation statements)

References 82 publications

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“…Recently, hybrid systems that incorporate magnetic resonance imaging (MRI) with a linear accelerator (Linac) have been developed to enable real‐time guidance and adaptive radiation treatment . This hybrid system is a promising technique that allows clinicians to dynamically monitor tumor shape and position, so that effective image‐guided radiotherapy can be used .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, hybrid systems that incorporate magnetic resonance imaging (MRI) with a linear accelerator (Linac) have been developed to enable real-time guidance and adaptive radiation treatment. 1,2 This hybrid system is a promising technique that allows clinicians to dynamically monitor tumor shape and position, so that effective image-guided radiotherapy can be used. 3,4 In order to provide sufficient space for the patient, the Australian 1.0 Tesla MRI-Linac scanner splits the gradient coils into two halves with a large central gap (50 cm).…”

Section: Introductionmentioning

confidence: 99%

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Geometric distortion characterization and correction for the 1.0 T Australian MRI‐linac system using an inverse electromagnetic method

et al. 2020

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Purpose The magnetic resonance imaging (MRI)‐Linac system combines a MRI scanner and a linear accelerator (Linac) to realize real‐time localization and adaptive radiotherapy for tumors. Given that the Australian MRI‐Linac system has a 30‐cm diameter of spherical volume (DSV) with a shimmed homogeneity of ±4.05 parts per million (ppm), a gradient nonlinearity (GNL) of <5% can only be assured within 15 cm from the system's isocenter. GNL increases from the isocenter and escalates close to and outside of the edge of the DSV. Gradient nonlinearity can cause large geometric distortions, which may provide inaccurate tumor localization and potentially degrade the radiotherapy treatment. In this study, we aimed to characterize and correct the geometric distortions both inside and outside of the DSV. Methods On the basis of phantom measurements, an inverse electromagnetic (EM) method was developed to reconstitute the virtual current density distribution that could generate gradient fields. The obtained virtual EM source was capable of characterizing the GNL field both inside and outside of the DSV. With the use of this GNL field information, our recently developed “GNL‐encoding” reconstruction method was applied to correct the distortions implemented in the k‐space domain. Results Both phantom and in vivo human images were used to validate the proposed method. The results showed that the maximal displacements within an imaging volume of 30 cm × 30 cm × 30 cm after using the fifth‐order spherical harmonic (SH) method and the proposed method were 6.1 ± 0.6 mm and 1.8 ± 0.6 mm, respectively. Compared with the fifth‐order SH‐based method, the new solution decreased the percentage of markers (within an imaging volume of 30 cm × 30 cm × 30 cm) with ≥1.5‐mm distortions from 6.3% to 1.3%, indicating substantially improved geometric accuracy. Conclusions The experimental results indicated that the proposed method could provide substantially improved geometric accuracy for the region outside of the DSV, when comparing with the fifth‐order SH‐based method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geometric distortion characterization and correction for the 1.0 T Australian MRI‐linac system using an inverse electromagnetic method

et al. 2020

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“…The use of magnetic resonance imaging (MRI) in radiation therapy (RT) has grown in recent years due to a desire to take advantage of the attractive properties of the modality, especially with the advent of MRI‐based delivery systems . Compared to x‐ray computed tomography (CT), MRI offers superior soft tissue contrast without exposing the patient to ionizing radiation .…”

Section: Introductionmentioning

confidence: 99%

“…Compared to x‐ray computed tomography (CT), MRI offers superior soft tissue contrast without exposing the patient to ionizing radiation . Taking full advantage of these attractive properties with MRI‐based therapy delivery systems allows for numerous applications including motion tracking and management as well as online adaptive therapy . The increasing use of MRI for tissue and target delineation makes an MRI‐only RT workflow, one in which MRI is the sole imaging modality employed for treatment planning and dose calculations, a favorable option for MR image‐guided RT (MR‐IGRT) applications.…”

Section: Introductionmentioning

confidence: 99%

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR‐only breast radiotherapy

et al. 2019

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Purpose The superior soft‐tissue contrast achieved using magnetic resonance imaging (MRI) compared to x‐ray computed tomography (CT) has led to the popularization of MRI‐guided radiation therapy (MR‐IGRT), especially in recent years with the advent of first and second generation MRI‐based therapy delivery systems for MR‐IGRT. The expanding use of these systems is driving interest in MRI‐only RT workflows in which MRI is the sole imaging modality used for treatment planning and dose calculations. To enable such a workflow, synthetic CT (sCT) data must be generated based on a patient’s MRI data so that dose calculations may be performed using the electron density information derived from CT images. In this study, we propose a novel deep spatial pyramid convolutional framework for the MRI‐to‐CT image‐to‐image translation task and compare its performance to the well established U‐Net architecture in a generative adversarial network (GAN) framework. Methods Our proposed framework utilizes atrous convolution in a method named atrous spatial pyramid pooling (ASPP) to significantly reduce the total number of parameters required to describe the model while effectively capturing rich, multi‐scale structural information in a manner that is not possible in the conventional framework. The proposed framework consists of a generative model composed of stacked encoders and decoders separated by the ASPP module, where atrous convolution is applied at increasing rates in parallel to encode large‐scale features. The performance of the proposed method is compared to that of the conventional GAN framework in terms of the time required to train the model and the image quality of the generated sCT as measured by the root mean square error (RMSE), structural similarity index (SSIM), and peak signal‐to‐noise ratio (PSNR) depending on the size of the training data set. Dose calculations based on sCT data generated using the proposed architecture are also compared to clinical plans to evaluate the dosimetric accuracy of the method. Results Significant reductions in training time and improvements in image quality are observed at every training data set size when the proposed framework is adopted instead of the conventional framework. Over 1042 test images, values of 17.7 ± 4.3 HU, 0.9995 ± 0.0003, and 71.7 ± 2.3 are observed for the RMSE, SSIM, and PSNR metrics, respectively. Dose distributions calculated based on sCT data generated using the proposed framework demonstrate passing rates equal to or greater than 98% using the 3D gamma index with a 2%/2 mm criterion. Conclusions The deep spatial pyramid convolutional framework proposed here demonstrates improved performance compared to the conventional GAN framework that has been applied to the image‐to‐image translation task of sCT generation. Adopting the method is a first step toward an MRI‐only RT workflow that enables widespread clinical applications for MR‐IGRT including online adaptive therapy.

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“…Such improved visualization makes MR an attractive modality for radiation therapy. 3 Therefore, fast and accurate tumor segmentation on magnetic resonance imaging (MRI) could help to deliver high-dose radiation while reducing treatment complications to normal structures.…”

Section: Introductionmentioning

confidence: 99%

Cross‐modality (CT‐MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets

Jiang

Tyagi

et al. 2019

Medical Physics

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Purpose Accurate tumor segmentation is a requirement for magnetic resonance (MR)‐based radiotherapy. Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross‐modality (MR‐CT) deep learning segmentation approach that augments training data using pseudo MR images produced by transforming expert‐segmented CT images was developed. Methods Eighty‐one T2‐weighted MRI scans from 28 patients with non‐small cell lung cancers (nine with pretreatment and weekly MRI and the remainder with pre‐treatment MRI scans) were analyzed. Cross‐modality model encoding the transformation of CT to pseudo MR images resembling T2w MRI was learned as a generative adversarial deep learning network. This model was used to translate 377 expert segmented non‐small cell lung cancer CT scans from the Cancer Imaging Archive into pseudo MRI that served as additional training set. This method was benchmarked against shallow learning using random forest, standard data augmentation, and three state‐of‐the art adversarial learning‐based cross‐modality data (pseudo MR) augmentation methods. Segmentation accuracy was computed using Dice similarity coefficient (DSC), Hausdorff distance metrics, and volume ratio. Results The proposed approach produced the lowest statistical variability in the intensity distribution between pseudo and T2w MR images measured as Kullback–Leibler divergence of 0.069. This method produced the highest segmentation accuracy with a DSC of (0.75 ± 0.12) and the lowest Hausdorff distance of (9.36 mm ± 6.00 mm) on the test dataset using a U‐Net structure. This approach produced highly similar estimations of tumor growth as an expert (P = 0.37). Conclusions A novel deep learning MR segmentation was developed that overcomes the limitation of learning robust models from small datasets by leveraging learned cross‐modality information using a model that explicitly incorporates knowledge of tumors in modality translation to augment segmentation training. The results show the feasibility of the approach and the corresponding improvement over the state‐of‐the‐art methods.

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The future of image-guided radiotherapy will be MR guided

Cited by 168 publications

References 82 publications

Geometric distortion characterization and correction for the 1.0 T Australian MRI‐linac system using an inverse electromagnetic method

Geometric distortion characterization and correction for the 1.0 T Australian MRI‐linac system using an inverse electromagnetic method

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR‐only breast radiotherapy

Cross‐modality (CT‐MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets

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