Characterization of imaging latency for real-time MRI-guided radiotherapy

Borman, P.; Tijssen, Rob H N; Bos, Clemens; Moonen, Chrit; Raaymakers, B W; Glitzner, M

doi:10.1088/1361-6560/aad2b7

Cited by 39 publications

(54 citation statements)

References 29 publications

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“…Lin‐PE was selected because of its widespread usage in clinical protocols and robustness to eddy current effects. Rnd‐PE was selected because it resembles the relatively large jumps in k‐space that are commonly seen in highly undersampled acquisitions for compressed sensing, low‐high profile ordering for low latency imaging or k − t sampling patterns for dynamic imaging. GA‐Rad was selected to represent non‐Cartesian with widespread utility in dynamic imaging .…”

Section: Methodsmentioning

confidence: 99%

Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

Bruijnen

Stemkens

Berg

et al. 2019

Magnetic Resonance in Med

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Purpose To propose an explicit Balanced steady‐state free precession (bSSFP) signal model that predicts eddy current‐induced steady‐state disruptions and to provide a prospective, practical, and general eddy current compensation method. Theory and Methods Gradient impulse response functions (GIRF) were used to simulate trajectory‐specific eddy current‐induced phase errors at the end of a repetition block. These phase errors were included in bloch simulations to establish a bSSFP signal model to predict steady‐state disruptions and their corresponding image artifacts. The signal model was embedded in the MR system and used to compensate the phase errors by prospectively modifying the phase cycling scheme of the RF pulse. The signal model and eddy current compensation method were validated in phantom and in vivo experiments. In addition, the signal model was used to analyze pre‐existing eddy current mitigation methods, such as 2D tiny golden angle radial and 3D paired phase encoded Cartesian acquisitions. Results The signal model predicted eddy current‐induced image artifacts, with the zeroth‐order GIRF being the primary factor to predict the steady‐state disruption. Prospective RF phase cycling schemes were automatically computed online and considerably reduced eddy current‐induced image artifacts. The signal model provides a direct relationship for the smoothness of k‐space trajectories, which explains the effectiveness of phase encode pairing and tiny golden angle trajectory. Conclusions The proposed signal model can accurately predict eddy current‐induced steady‐state disruptions for bSSFP imaging. The signal model can be used to derive the eddy current‐induced phase errors required for trajectory‐specific RF phase cycling schemes, which considerably reduce eddy current‐induced image artifacts.

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Section: Methodsmentioning

confidence: 99%

Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

Bruijnen

Stemkens

Berg

et al. 2019

Magnetic Resonance in Med

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show abstract

“…However, based on their results, template matching provides the best compromise between flexibility, speed and accuracy. In a study conducted by (Glitzner et al 2015b), the delay attributed to the multi-leaf collimator adaptation was shown to be a minor contributor to the overall feedback chain as compared to the impact of imaging components such as MRI acquisition and processing (Borman et al 2018), which therefore require mitigation strategies to predict tumour motion (Yun et al 2012, Seregni et al 2016, Krauss, Nill and Oelfke 2011.…”

Section: Intra-fraction Motion Managementmentioning

confidence: 99%

MRI-guidance for motion management in external beam radiotherapy: current status and future challenges

et al. 2018

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“…In the clinic, adaptive radiotherapy requires the MRI-Linac system to monitor the target tumor in real-time using continuously acquired MR images. 46 Thus, the latency for correcting geometric distortions, including GNL field characterization and undistorted image reconstruction, should be as short as possible. The superior efficiency of the proposed method enables the GNL field correction to be implemented with an acceptable latency.…”

Section: B Computational Complexitymentioning

confidence: 99%

Fast geometric distortion correction using a deep neural network: Implementation for the 1 Tesla MRI‐Linac system

Shan

Chandra

et al. 2020

Medical Physics

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Purpose: Combining high-resolution magnetic resonance imaging (MRI) with a linear accelerator (Linac) as a single MRI-Linac system provides the capability to monitor intra-fractional motion and anatomical changes during radiotherapy, which facilitates more accurate delivery of radiation dose to the tumor and less exposure to healthy tissue. The gradient nonlinearity (GNL)-induced distortions in MRI, however, hinder the implementation of MRI-Linac system in image-guided radiotherapy where highly accurate geometry and anatomy of the target tumor is indispensable. Methods: To correct the geometric distortions in MR images, in particular, for the 1 Tesla (T) MRI-Linac system, a deep fully connected neural network was proposed to automatically learn the intricate relationship between the undistorted (theoretical) and distorted (real) space. A dataset, consisting of spatial samples acquired by phantom measurement that covers both inside and outside the working diameter of spherical volume (DSV), was utilized for training the neural network, which offers the ability to describe subtle deviations of the GNL field within the entire region of interest (ROI). Results: The performance of the proposed method was evaluated on MR images of a three-dimensional (3D) phantom and the pelvic region of an adult volunteer scanned in the 1T MRI-Linac system. The experimental results showed that the severe geometric distortions within the entire ROI had been successfully corrected with an error less than the pixel size. Also, the presented network is highly efficient, which achieved significant improvement in terms of computational efficiency compared to existing methods. Conclusions: The feasibility of the presented deep neural network for characterizing the GNL field deviations in the 1T MRI-Linac system was demonstrated in this study, which shows promise in facilitating the MRI-Linac system to be routinely implemented in real-time MRI-guided radiotherapy.

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Characterization of imaging latency for real-time MRI-guided radiotherapy

Cited by 39 publications

References 29 publications

Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

MRI-guidance for motion management in external beam radiotherapy: current status and future challenges

Fast geometric distortion correction using a deep neural network: Implementation for the 1 Tesla MRI‐Linac system

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