Feasibility study on 3D image reconstruction from 2D orthogonal cine‐<scp>MRI</scp> for <scp>MRI</scp>‐guided radiotherapy

Paganelli, Chiara; Lee, Danny; Kipritidis, John; Whelan, Brendan; Greer, Peter B.; Baroni, Guido; Riboldi, Marco; Keall, Paul J.

doi:10.1111/1754-9485.12713

Cited by 49 publications

(60 citation statements)

References 28 publications

(49 reference statements)

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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]. Based on single or multiple cine MRI slices, the temporally resolved 3D motion of the whole anatomy of the patient can be estimated by global motion models [30,56,57].…”

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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Section: Imaging For Motion Monitoringmentioning

confidence: 99%

Medical physics challenges in clinical MR-guided radiotherapy

et al. 2020

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“…Different approaches have been developed to image a moving organ in real time for potential applications in MRI‐guided adaptive radiation therapy. For example, several studies have proposed performing fast 2D cine imaging in three orthogonal directions simultaneously, from which pseudo‐3D images can be estimated for deriving volumetric tumor motion information . More advanced approaches aim to calculate a patient‐specific motion model that can link a 4D motion‐resolved image set acquired over multiple motion cycles (slow) with real‐time 2D cine imaging (fast), so that real‐time pseudo‐3D images can be generated with low acquisition latency .…”

Section: Introductionmentioning

confidence: 99%

MRSIGMA: Magnetic Resonance SIGnature MAtching for real‐time volumetric imaging

Feng

Tyagi

Otazo

2020

Magnetic Resonance in Med

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Purpose: To propose a real-time 3D MRI technique called MR SIGnature MAtching (MRSIGMA) for high-resolution volumetric imaging and motion tracking with very low imaging latency. Methods: MRSIGMA consists of two steps: (1) offline learning of a database of possible 3D motion states and corresponding motion signature ranges and (2) online matching of new motion signatures acquired in real time with prelearned motion states. Specifically, the offline learning step (non-real-time) reconstructs motion-resolved 4D images representing different motion states and assigns a unique motion range to each state. The online matching step (real-time) acquires motion signatures only and selects one of the prelearned 3D motion states for each newly acquired signature, which generates 3D images efficiently in real time. The MRSIGMA technique was evaluated on 15 golden-angle stack-of-stars liver data sets, and the performance of respiratory motion tracking with the online-generated real-time 3D MRI was compared with the corresponding 2D projections acquired in real time. Results: The total latency of generating each 3D image during online matching was about 300 ms, including acquisition of the motion signature data (~138 ms) and corresponding matching process (~150 ms). Linear correlation assessment suggested excellent correlation (R 2 = 0.948) between motion displacement measured from the online-generated real-time 3D images and the 2D real-time projections. Conclusion: This proof-of-concept study demonstrates the feasibility of MRSIGMA for high-resolution real-time volumetric imaging, which shifts the acquisition and reconstruction burden to an offline learning step and leaves fast online matching for online imaging with very low imaging latency. The MRSIGMA technique can potentially be used for real-time motion tracking in MRI-guided radiation therapy.

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“…One limitation of this manuscript is that the potential benefit of the proposed technique has not been evaluated in the context of template matching-based target tracking for MRIguided radiotherapy. [31][32][33][34] Template matching can be performed more rapidly than deformable registration, and it could potentially be combined with autoregression of the template centroid position to produce similar overall performance as our proposed method for rigidly moving targets. However, in the context of substantial deformations, accurate template matching-based target tracking may prove challenging.…”

Section: Discussionmentioning

confidence: 99%

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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Feasibility study on 3D image reconstruction from 2D orthogonal cine‐MRI for MRI‐guided radiotherapy

Abstract: Preliminary results on phantom and patient cases confirm the feasibility of the proposed approach in an MRI-guided scenario, especially for non-rigid tumour motion compensation.

Cited by 49 publications

References 28 publications

Medical physics challenges in clinical MR-guided radiotherapy

Medical physics challenges in clinical MR-guided radiotherapy

MRSIGMA: Magnetic Resonance SIGnature MAtching for real‐time volumetric imaging

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

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