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

Bruijnen, Tom; Stemkens, Bjorn; Berg, C.A.T. Van den; Tijssen, Rob H N

doi:10.1002/mrm.28097

Cited by 16 publications

(16 citation statements)

References 29 publications

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“…All MR-STAT experiments in the current work have been performed with linear, Cartesian sampling strategies. This sampling strategy offers important advantages in the form of robustness to hardware imperfections (e.g., eddy currents, especially for gradient-balanced sequences 43,44 ), less susceptibility to ΔB 0 related blurring artefacts, 45 17 More advanced iterative MRF reconstructions 13,14,48,49 might perform better with Cartesian sampling than the currently used MRF reconstructions (low-rank inversion followed by low-rank dictionary matching), and an in-depth comparison will be the subject of further studies. It should also be noted that neither the MR-STAT framework nor the currently proposed reconstruction algorithm is restricted to Cartesian sampling and further research is also aimed at incorporating non-Cartesian trajectories into MR-STAT.…”

Section: Discussionmentioning

confidence: 99%

“…All MR‐STAT experiments in the current work have been performed with linear, Cartesian sampling strategies. This sampling strategy offers important advantages in the form of robustness to hardware imperfections (e.g., eddy currents, especially for gradient‐balanced sequences), less susceptibility to

normalΔ B_{0}

related blurring artefacts, and direct availability on clinical MR systems. Within the conventional MRF framework, it is more challenging to work with Cartesian sampling strategies, as demonstrated using the simulation experiments.…”

Section: Discussionmentioning

confidence: 99%

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High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

et al. 2020

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MR‐STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time‐domain data simultaneously, without relying on the fast Fourier transform (FFT). To do this at high resolution, specialized algorithms are required to solve the underlying large‐scale nonlinear optimisation problem. We propose a matrix‐free and parallelized inexact Gauss–Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high‐performance computing cluster and is demonstrated to be able to generate high‐resolution (1 mm × 1 mm in‐plane resolution) quantitative parameter maps in simulation, phantom, and in vivo brain experiments. Reconstructed T1 and T2 values for the gel phantoms are in agreement with results from gold standard measurements and, for the in vivo experiments, the quantitative values show good agreement with literature values. In all experiments, short pulse sequences with robust Cartesian sampling are used, for which MR fingerprinting reconstructions are shown to fail.

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Section: Discussionmentioning

confidence: 99%

normalΔ B_{0}

Section: Discussionmentioning

confidence: 99%

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

et al. 2020

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“…Imaging data were acquired using the MRF sequence described by Jiang et al (Jiang et al 2015), which consists of an adiabatic inversion pulse and a sinusoidal flip angle train. One radial line was acquired per time-point (Cloos et al 2016) and subsequent readouts were azimuthally incremented using the tiny golden angle to minimize eddy current effects (Wundrak et al 2015, Bruijnen et al 2019) (Fig. 2).…”

Section: Mrf Pulse Sequence and Reconstruction Methodsmentioning

confidence: 99%

Technical feasibility of Magnetic Resonance Fingerprinting on a 1.5T MRI-Linac

Bruijnen,

van der Heide,

Intven

et al. 2020

Preprint

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Hybrid MRI-linac (MRL) systems enable daily multiparametric quantitative MRI to assess tumor response to radiotherapy. Magnetic Resonance Fingerprinting (MRF) may provide time efficient means of rapid multiparametric quantitative MRI. The accuracy of MRF, however, relies on adequate control over system imperfections, such as eddy currents and B + 1 , which are different and not as well established on MRL systems compared to diagnostic systems. In this study we investigate the technical feasibility of gradient spoiled 2D MRF on a 1.5T MRL. We show with phantom experiments that the MRL generates reliable MRF signals that are temporally stable during the day and have good agreement with spin-echo reference measurements. Subsequent in-vivo MRF scans in healthy volunteers and a patient with a colorectal liver metastasis showed good image quality, where the quantitative values of selected organs corresponded with the values reported in literature. Therefore we conclude that gradient spoiled 2D MRF is feasible on a 1.5T MRL with similar performance as on a diagnostic system. The precision and accuracy of the parametric maps are sufficient for further investigation of the clinical utility of MRF for online quantitatively MRI-guided radiotherapy.

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“…It can be observed that the motion-fields are mostly smooth in time, except for small high frequency fluctuations which are mostly visible in end-exhale for both volunteers. These fluctuations may be caused by hardware imperfections [30] or by sensitivity to cardiac motion, which manifest themselves as high frequency fluctuations on top of respiratory motion.…”

Section: ) Online Inference Phasementioning

confidence: 99%

Real-time non-rigid 3D respiratory motion estimation for MR-guided radiotherapy using MR-MOTUS

Huttinga,

Bruijnen,

Berg

et al. 2021

Preprint

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The MR-Linac is a combination of an MRscanner and radiotherapy linear accelerator (Linac) which holds the promise to increase the precision of radiotherapy treatments with MR-guided radiotherapy by monitoring motion during radiotherapy with MRI, and adjusting the radiotherapy plan accordingly. Optimal MR-guidance for respiratory motion during radiotherapy requires MRbased 3D motion estimation with a latency of 200-500 ms. Currently this is still challenging since typical methods rely on MR-images, and are therefore limited to the 3D MRimaging latency. In this work, we present a method to perform non-rigid 3D respiratory motion estimation with 170 ms latency, including both acquisition and reconstruction. The proposed method called real-time low-rank MR-MOTUS reconstructs motion-fields directly from k-space data, and leverages an explicit low-rank decomposition of motionfields to split the large scale 3D+t motion-field reconstruction problem posed in our previous work into two parts: (I) a medium-scale offline preparation phase and (II) a smallscale online inference phase which exploits the results of the offline phase for real-time computations. The method was validated on free-breathing data of five volunteers, acquired with a 1.5T Elekta Unity MR-Linac. Results show that the reconstructed 3D motion-fields are anatomically plausible, highly correlated with a self-navigation motion surrogate (R = 0.975 ± 0.0110), and can be reconstructed with a total latency of 170 ms that is sufficient for real-time MR-guided abdominal radiotherapy.

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

Cited by 16 publications

References 29 publications

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

High‐resolution in vivo MR‐STAT using a matrix‐free and parallelized reconstruction algorithm

Technical feasibility of Magnetic Resonance Fingerprinting on a 1.5T MRI-Linac

Real-time non-rigid 3D respiratory motion estimation for MR-guided radiotherapy using MR-MOTUS

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