Multislice motion modeling for<scp>MRI</scp>‐guided radiotherapy gating

Ginn, John S.; Ruan, Dan; Low, Daniel A.; Lamb, James

doi:10.1002/mp.13350

Cited by 13 publications

(10 citation statements)

References 36 publications

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“…The average registration consistency was 0.77 mm. These values are similar to those reported in our previous publications …”

Section: Resultssupporting

confidence: 93%

“…Notably, the other authors estimated motion across multiple slices whereas the present study predicted motion in a single imaging plane . Our technique is generalizable and could be combined with a LLE‐based multi‐slice motion modeling method to perform multi‐slice motion prediction …”

Section: Discussionmentioning

confidence: 93%

“…16 Our technique is generalizable and could be combined with a LLE-based multi-slice motion modeling method to perform multi-slice motion prediction. 28…”

Section: Discussionmentioning

confidence: 99%

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An image regression motion prediction technique for MRI‐guided radiotherapy evaluated in single‐plane cine imaging

et al. 2019

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Purpose To develop and evaluate a novel motion prediction method for magnetic resonance image (MRI)‐guided radiotherapy applications. This method, which we deem “image regression,” predicts future tissue motion based on a weighted combination of previously observed motion states. Motion predictions are derived from a sliding window of recent motion states which are defined by a temporal sequence of images. A key advantage of this method compared to other motion prediction methods is that its computational complexity scales weekly with the number of spatial points predicted. Applications of gating latency reduction and improvement in deformable registration‐based target tracking are demonstrated. Methods The image regression (IR) motion prediction method was developed and evaluated using 26.9 h of real‐time imaging acquired from eight healthy volunteers and 13 patients using a 0.35 T MRI‐guided radiotherapy system. Motion predictions were performed 0.25–0.33 s into the future using a weighted sum of previously observed motion states with image similarity‐derived weights. The set of previously observed motion states were continuously updated to incorporate the changes in breathing patterns. The accuracy of the predicted radiotherapy gating decision, beam‐on positive predictive value (PPV), and predicted vs ground‐truth target centroid position errors are reported. The IR technique was compared against no prediction, linear extrapolation, and an established autoregressive linear prediction algorithm. The usage of IR to initialize the deformable registration and enhance the target tracking was demonstrated in the healthy volunteer studies. Deformable registration with IR initialization was compared to the initialization performed by current clinical software: no initialization, previous image registration initialization and linear motion extrapolation initialization. Results The average IR‐predicted radiation beam gating decision accuracy was 95.8%, with a PPV of 95.7%, and median and 95th percentile centroid position errors of 0.63 and 2.08 mm, respectively. Compared to the autoregressive linear prediction method, gating accuracy was 1.15% greater, PPV was 1.61% greater, and median and 95th percentile centroid distances were 0.21 and 0.23 mm smaller. The IR‐initialized registration on average converged within 0.50 mm of the ground‐truth position in fewer than 10 iterations whereas the next best initialization method required more than 25 iterations. Conclusions Image regression motion prediction has the potential to reduce the gating latencies and improve the speed and accuracy of deformable registration‐based target tracking in MRI‐guided radiotherapy.

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“…The average registration consistency was 0.77 mm. These values are similar to those reported in our previous publications …”

Section: Resultssupporting

confidence: 93%

Section: Discussionmentioning

confidence: 93%

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An image regression motion prediction technique for MRI‐guided radiotherapy evaluated in single‐plane cine imaging

et al. 2019

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“…As out-of-plane motion can occur when a single 2D slice is used, the acquisition of several parallel slices [12,49] or the interleaved [50][51][52][53] or simultaneous [54,55] acquisition of orthogonal slices has been investigated. Acquiring imaging data from different orientations could indeed enable the real-time reconstruction of the anatomy in 3D [53].…”

Section: Imaging For Motion Monitoringmentioning

confidence: 99%

Medical physics challenges in clinical MR-guided radiotherapy

et al. 2020

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The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART. Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation. Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing. The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

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“…Note that this process is currently 2D, due to a lack of commercially available real-time 3D imaging sequences. Recent studies however showed promising approaches towards realtime 3D MR image acquisition (53)(54)(55)(56) and reconstruction (57).…”

Section: Real-time Mrimentioning

confidence: 99%

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

Thorwarth

Low

2021

Front. Oncol.

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In the past few years, radiotherapy (RT) has experienced a major technological innovation with the development of hybrid machines combining magnetic resonance (MR) imaging and linear accelerators. This new technology for MR-guided cancer treatment has the potential to revolutionize the field of adaptive RT due to the opportunity to provide high-resolution, real-time MR imaging before and during treatment application. However, from a technical point of view, several challenges remain which need to be tackled to ensure safe and robust real-time adaptive MR-guided RT delivery. In this manuscript, several technical challenges to MR-guided RT are discussed. Starting with magnetic field strength tradeoffs, the potential and limitations for purely MR-based RT workflows are discussed. Furthermore, the current status of real-time 3D MR imaging and its potential for real-time RT are summarized. Finally, the potential of quantitative MR imaging for future biological RT adaptation is highlighted.

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Multislice motion modeling forMRI‐guided radiotherapy gating

Cited by 13 publications

References 36 publications

An image regression motion prediction technique for MRI‐guided radiotherapy evaluated in single‐plane cine imaging

An image regression motion prediction technique for MRI‐guided radiotherapy evaluated in single‐plane cine imaging

Medical physics challenges in clinical MR-guided radiotherapy

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

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