Nuts and bolts of 4D-MRI for radiotherapy

Stemkens, Bjorn; Paulson, E.S.; Tijssen, Rob H N

doi:10.1088/1361-6560/aae56d

Cited by 119 publications

(190 citation statements)

References 132 publications

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“…15 Four-dimensional magnetic resonance imaging (4DMRI) was recently made available to capture respiratory motion variability as a way to complement and support 4DCT. [16][17][18][19] MRI combines exquisite soft tissue contrast and radiation-free imaging, thus allowing multiple repeated acquisitions, 19,20 but it is not suitable for computing the treatment dose.…”

Section: Introductionmentioning

confidence: 99%

Virtual 4DCT from 4DMRI for the management of respiratory motion in carbon ion therapy of abdominal tumors

et al. 2020

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Purpose To evaluate a method for generating virtual four‐dimensional computed tomography (4DCT) from four‐dimensional magnetic resonance imaging (4DMRI) data in carbon ion radiotherapy with pencil beam scanning for abdominal tumors. Methods Deformable image registration is used to: (a) register each respiratory phase of the 4DMRI to the end‐exhale MRI; (b) register the reference end‐exhale CT to the end‐exhale MRI volume; (c) generate the virtual 4DCT by warping the registered CT according to the obtained deformation fields. A respiratory‐gated carbon ion treatment plan is optimized on the planning 4DCT and the corresponding dose distribution is recalculated on the virtual 4DCT. The method was validated on a digital anthropomorphic phantom and tested on eight patients (18 acquisitions). For the phantom, a ground truth dataset was available to assess the method performances from the geometrical and dosimetric standpoints. For the patients, the virtual 4DCT was compared with the planning 4DCT. Results In the phantom, the method exhibits a geometrical accuracy within the voxel size and Dose Volume Histograms deviations up to 3.3% for target V95% (mean dose difference ≤ 0.2% of the prescription dose, gamma pass rate > 98%). For patients, the virtual and the planning 4DCTs show good agreement at end‐exhale (3% median D95% difference), whereas other respiratory phases exhibit moderate motion variability with consequent dose discrepancies, confirming the need for motion mitigation strategies during treatment. Conclusions The virtual 4DCT approach is feasible to evaluate treatment plan robustness against intra‐ and interfraction motion in carbon ion therapy delivered at the abdominal site.

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Section: Introductionmentioning

confidence: 99%

Virtual 4DCT from 4DMRI for the management of respiratory motion in carbon ion therapy of abdominal tumors

et al. 2020

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“…As pointed out in a recent review on 4DMRI, 39 research is mainly focused on respiratory-correlated 4DMRI (rc-4DMRI) opposed to real-time 4DMRI (rt-4DMRI). [39][40][41][42] In contrast to 4D-CT imaging and rc-4DMRI, no retrospective sorting of projections or 2D images from different breathing cycles based on a surrogate is needed for rt-4DMRI. Fast 3D gradient echo (GRE) or steady-state free precession (SSFP) sequences using parallel imaging techniques and echo sharing are usually used for rt-4DMRI.…”

Section: Introductionmentioning

confidence: 99%

“…42 The in-plane resolution is 3-4 mm and temporal resolution is typically limited to 2 volumes per second, depending on the spatial resolution. 39 rt-4DMRI is not routinely used clinically today, but a more widespread use due to the technological advances in this area is expected in the near future. 39 The purpose of this proof-of-concept study is to demonstrate how rt-4DMRI could be used to reduce the random and systematic uncertainties associated with today's 4D-CT-based ITV definition approach.…”

Section: Introductionmentioning

confidence: 99%

Real‐time 4DMRI‐based internal target volume definition for moving lung tumors

et al. 2020

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PTVs were smaller than the ITV-based PTVs. While the SE was high for patients with small breathing pattern variations, changes of the median breathing amplitudes in different imaging sessions led to inferior SE values for the mid-ventilation PTV for one patient. In contrast, PTV 5% r and PTV 10% r showed higher SE values with a higher robustness against interfractional changes, at the cost of larger target volumes. Conclusions:The results indicate that rt-4DMRI could be valuable for the definition of target volumes based on the GTV POP to achieve a higher robustness against interfractional changes than feasible with today's 4D-CT-based target definition concepts.

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“…Respiratory‐related tumor motion can be detected using time‐resolved 4D MRI and cine MRI . Time‐resolved 4D MRI provides high‐spatial resolution 3D MRI datasets with multiple respiratory amplitudes/phases.…”

Section: Introductionmentioning

confidence: 99%

“…11,12 Respiratory-related tumor motion can be detected using time-resolved 4D MRI and cine MRI. [13][14][15][16] Time-resolved 4D MRI provides high-spatial resolution 3D MRI datasets with multiple respiratory amplitudes/phases. For example, in motion studies, iterative reconstruction techniques in 4D MRI improved image quality while reducing motion artifacts.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: Real‐time 3D MRI in the presence of motion for MRI‐guided radiotherapy: 3D Dynamic keyhole imaging with super‐resolution

et al. 2019

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Purpose The purpose of this study was to present real‐time three‐dimensional (3D) magnetic resonance imaging (MRI) in the presence of motion for MRI‐guided radiotherapy (MRgRT) using dynamic keyhole imaging for high‐temporal acquisition and super‐resolution generative (SRG) model for high‐spatial reconstruction. Method We propose a unique real‐time 3D MRI technique by combining a data sharing technique (3D dynamic keyhole imaging) with a SRG model using cascaded deep learning technique. 3D dynamic keyhole imaging utilizes the data sharing mechanism by combining keyhole central k‐space data acquired in real‐time with high‐spatial, high‐temporal resolution prior peripheral k‐space data at various motion positions prepared by the SRG model. The efficacy of the 3D dynamic keyhole imaging with super‐resolution (SR_dKeyhole) was compared to the ground‐truth super‐resolution images with the original full k‐space data. It was also compared with the zero‐filling reconstruction (zero‐filling), conventional keyhole reconstruction with low‐spatial high‐temporal prior data (LR_cKeyhole), and conventional keyhole reconstruction with super‐resolution prior data (SR_cKeyhole). Results High‐spatial, high‐temporal resolution 3D MRI datasets (1.5 × 1.5 × 6 mm3) were generated from low‐spatial, high‐temporal resolution 3D MRI datasets (6 × 6 × 6 mm3) using the cascaded deep learning SRG framework (<100 ms/volume). 3D dynamic keyhole imaging with the SRG model provided high‐spatial, high‐temporal resolution images (1.5 × 1.5 × 6 mm3, 455 ms) with the highest similarity to the ground‐truth SR images without any noticeable artifacts. Structural similarity indices (SSIM) of the reconstructed 3D MRI to the original SR 3D MRI were 0.65, 0.66, 0.86, and 0.89 for zero‐filling, LR_cKeyhole, SR_cKeyhole, and SR_dKeyhole, respectively (1 for identical image). In addition, average value of image relative error (IRE) of the reconstructed 3D MRI to the original SR 3D MRI were 0.169, 0.191, 0.079, and 0.067 for zero‐filling, LR_cKeyhole, SR_cKeyhole, and SR_dKeyhole, respectively (0 for identical image). Conclusions We demonstrated that high‐spatial, high‐temporal resolution 3D MRI was feasible by combing 3D dynamic keyhole imaging with a SRG model in terms of image quality and imaging time. The proposed technique can be utilized for real‐time 3D MRgRT.

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Nuts and bolts of 4D-MRI for radiotherapy

Cited by 119 publications

References 132 publications

Virtual 4DCT from 4DMRI for the management of respiratory motion in carbon ion therapy of abdominal tumors

Virtual 4DCT from 4DMRI for the management of respiratory motion in carbon ion therapy of abdominal tumors

Real‐time 4DMRI‐based internal target volume definition for moving lung tumors

Technical Note: Real‐time 3D MRI in the presence of motion for MRI‐guided radiotherapy: 3D Dynamic keyhole imaging with super‐resolution

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