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Background: Renal multiparametric magnetic resonance imaging (MRI) is a promising tool for diagnosis, prognosis, and treatment monitoring in kidney disease., Purpose: To determine intrasubject test-retest repeatability of renal MRI measurements., Study Type: Prospective., Population: Nineteen healthy subjects aged over 40 years., Field Strength/sequences: T 1 and T 2 mapping, R 2 * mapping or blood oxygenation level-dependent (BOLD) MRI, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI), 2D phase contrast, arterial spin labelling (ASL), dynamic contrast enhanced (DCE) MRI, and quantitative Dixon for fat quantification at 3T., Assessment: Subjects were scanned twice with ~1 week between visits. Total scan time was ~1 hour. Postprocessing included motion correction, semiautomated segmentation of cortex and medulla, and fitting of the appropriate signal model., Statistical Test: To assess the repeatability, a Bland-Altman analysis was performed and coefficients of variation (CoVs), repeatability coefficients, and intraclass correlation coefficients were calculated., Results: CoVs for relaxometry (T 1 , T 2 , R 2 */BOLD) were below 6.1%, with the lowest CoVs for T 2 maps and highest for R 2 */BOLD. CoVs for all diffusion analyses were below 7.2%, except for perfusion fraction (F P ), with CoVs ranging from 18-24%. The CoV for renal sinus fat volume and percentage were both around 9%. Perfusion measurements were most repeatable with ASL (cortical perfusion only) and 2D phase contrast with CoVs of 10% and 13%, respectively. DCE perfusion had a CoV of 16%, while single kidney glomerular filtration rate (GFR) had a CoV of 13%. Repeatability coefficients (RCs) ranged from 7.7-87% (lowest/highest values for medullary mean diffusivity and cortical F P , respectively) and intraclass correlation coefficients (ICCs) ranged from -0.01 to 0.98 (lowest/highest values for cortical F P and renal sinus fat volume, respectively)., Data Conclusion: CoVs of most MRI measures of renal function and structure (with the exception of F P and perfusion as measured by DCE) were below 13%, which is comparable to standard clinical tests in nephrology., Level of Evidence: 2 TECHNICAL EFFICACY: Stage 1., (© 2020 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

To enable magnetic resonance imaging (MRI)-guided radiotherapy with real-time adaptation, motion must be quickly estimated with low latency. The motion estimate is used to adapt the radiation beam to the current anatomy, yielding a more conformal dose distribution. As the MR acquisition is the largest component of latency, deep learning (DL) may reduce the total latency by enabling much higher undersampling factors compared to conventional reconstruction and motion estimation methods. The benefit of DL on image reconstruction and motion estimation was investigated for obtaining accurate deformation vector fields (DVFs) with high temporal resolution and minimal latency. 2D cine MRI acquired at 1.5 T from 135 abdominal cancer patients were retrospectively included in this study. Undersampled radial golden angle acquisitions were retrospectively simulated. DVFs were computed using different combinations of conventional- and DL-based methods for image reconstruction and motion estimation, allowing a comparison of four approaches to achieve real-time motion estimation. The four approaches were evaluated based on the end-point-error and root-mean-square error compared to a ground-truth optical flow estimate on fully-sampled images, the structural similarity (SSIM) after registration and time necessary to acquire k-space, reconstruct an image and estimate motion. The lowest DVF error and highest SSIM were obtained using conventional methods up to [Formula: see text]. For undersampling factors [Formula: see text], the lowest DVF error and highest SSIM were obtained using conventional image reconstruction and DL-based motion estimation. We have found that, with this combination, accurate DVFs can be obtained up to [Formula: see text] with an average root-mean-square error up to 1 millimeter and an SSIM greater than 0.8 after registration, taking 60 milliseconds. High-quality 2D DVFs from highly undersampled k-space can be obtained with a high temporal resolution with conventional image reconstruction and a deep learning-based motion estimation approach for real-time adaptive MRI-guided radiotherapy.

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., (© 2019 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

For successful abdominal radiotherapy it is crucial to have a clear tumor definition and an accurate characterization of the motion. While dynamic contrast-enhanced (DCE) MRI aids tumor visualization, it is often hampered by motion artifacts. 4D-MRI characterizes this motion, but often lacks the contrast to clearly visualize the tumor. This dual requirement is challenging due to time constraints. Here, we propose combining both into a single acquisition by reconstructing the data in various ways in order to achieve both high spatio-temporal resolution DCE-MRI and accurate 4D-MRI motion estimates. A 5 min T 1 -weigthed DCE acquisition was collected in five renal-cell carcinoma patients and simulated in a digital phantom. Data were acquired continuously using a 3D golden angle radial stack-of-stars acquisition. This enabled three types of reconstruction; (1) a high spatio-temporal resolution DCE time series, (2) a 5D reconstruction and (3) a contrast-enhanced 4D-MRI for motion characterization. Motion extracted from the 4D- and 5D-MRI was compared with a separately acquired 4D-MRI and additional 2D cine MR imaging. Simulations on XCAT showed that 5D reconstructions severely underestimated motion ([Formula: see text]), whereas contrast-enhanced 4D-MRI only underestimated motion by [Formula: see text]. This was confirmed in the in vivo data where motion of the contrast-enhanced 4D-MRI was approximately [Formula: see text] smaller than the motion in the 2D cine MRI (5.8 mm versus 6.5 mm), but equal to a separately acquired 4D-MRI (5.8 mm versus 5.9 mm). 5D reconstructions underestimated the motion by more than [Formula: see text], but minimized respiratory-induced blurring in the contrast enhanced images. DCE time-series demonstrated clear tumor visualization and contrast enhancement throughout the entire field-of-view. DCE- and 4D-MRI can be integrated into a single acquisition that enables different reconstructions with complementary information for abdominal radiotherapy planning and, in an MRI-guided treatment, precise motion information, input for motion models, and rapid feedback on the contrast enhancement.

Online adaptive MR-guided radiation therapy improves treatment quality at the expense of considerable longer treatment time. The treatment lengthening partially originates from the preparatory (pre-beam) MR imaging required to encode all the information needed for contour propagation, contour adaptation and replanning. MRI requires several minutes of scan time before the encoded information is converted to usable images, which results in long idle times before the first clinical tasks are performed. In this study we propose a novel imaging sequence, called MR-RIDDLE, that reduces the idle time and therefore speeds-up the workflow in online MR-guided radiation therapy. MR-RIDDLE enables multiresolution image reconstruction to commence during data acquisition where low resolution images are available within one minute, after which the data collection continuous for subsequent high-resolution image updates. We demonstrate that the low resolution images can be used to accurately propagate contours from the pre-treatment scan. For abdominothoracic tumours MR-RIDDLE inherently captures a motion-blurred representation of the mid-position, which we were able to deblur using a combination of an internal motion surrogate and auto-adaptive soft-gating filters. Our results demonstrate that MR-RIDDLE provides a robust, flexible and time-efficient strategy for pre-beam imaging, even for cases with large respiratory movements or baseline shifts within the acquisition. We anticipate that this novel concept of parallelising the MR imaging and the clinical tasks has the potential to considerably speed-up and streamline the online MR-guided radiation therapy workflow.

Purpose: To quantify intrafractional motion to determine population-based radiotherapy treatment margins for head-and-neck tumors., Methods: Cine MR imaging was performed in 100 patients with head-and-neck cancer on a 3T scanner in a radiotherapy treatment setup. MR images were analyzed using deformable image registration (optical flow algorithm) and changes in tumor contour position were used to calculate the tumor motion. The tumor motion was used together with patient setup errors (450 patients) to calculate population-based PTV margins., Results: Tumor motion was quantified in 84 patients (12/43/29 nasopharynx/oropharynx/larynx, 16 excluded). The mean maximum (95th percentile) tumor motion (swallowing excluded) was: 2.3 mm in superior, 2.4 mm in inferior, 1.8 mm in anterior and 1.7 mm in posterior direction. PTV margins were: 2.8 mm isotropic for nasopharyngeal tumors, 3.2 mm isotropic for oropharyngeal tumors and 4.3 mm in inferior-superior and 3.2 mm in anterior-posterior for laryngeal tumors, for our institution., Conclusions: Intrafractional head-and-neck tumor motion was quantified and population-based PTV margins were calculated. Although the average tumor motion was small (95th percentile motion <3.0 mm), tumor motion varied considerably between patients (0.1-12.0 mm). The intrafraction motion expanded the CTV-to-PTV with 1.7 mm for laryngeal tumors, 0.6 mm for oropharyngeal tumors and 0.2 mm for nasopharyngeal tumors., (Copyright © 2018 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: One of the major challenges in stereotactic body radiation therapy (SBRT) of renal cell carcinoma is internal motion during treatment. Previous literature has aimed to mitigate the effects of motion by expanding the treatment margins or respiratory tracking. Online magnetic resonance imaging (MRI)-guided radiation therapy has the potential to further improve the treatment of renal cell carcinoma by direct visualization of the tumor during treatment. The efficacy of 2 motion management techniques were assessed: tumor trailing and respiratory tracking. The simulation of a single-fraction, MRI-based SBRT was performed to quantify intrafraction motion and assess the efficacy of the different motion management strategies., Methods and Materials: Fifteen patients were included in the study. At the beginning and end of the scanning protocol, 2 cine-MRI scans were acquired to assess cyclic respiratory motion. In addition, 3-dimensional spoiled gradient echo scans were acquired at 4 different time points to assess the slow drifts over 25 minutes. The systematic and random errors owing to intrafraction drift were calculated, as well as the random error induced by respiratory motion. The motion margins were calculated for tumor trailing and respiratory tracking and compared with the margin when no motion compensation would be performed to assess the relative efficacy of each technique., Results: The largest respiratory tumor motion was observed along the caudo-cranial direction with a median 95% maximum amplitude of approximately 12 mm. Σ DRIFT , σ DRIFT , and σ RESP were determined to be 1.0 mm 1.8 mm, and 3.8 mm, respectively. Without mechanical immobilization, intrafraction drift accounted for 75% of the total intrafraction motion margin for online midposition-based SBRT treatments., Conclusions: The contribution of intrafraction drift to the total internal motion margin is much larger than periodic respiratory motion. This makes tumor trailing a viable option to consider on the MRI linac because it allows for 3-dimensional MRI acquisitions during beam delivery, which simplifies the introduction of new techniques, such as dose accumulation and online intrafraction replanning., (Copyright © 2018. Published by Elsevier Inc.)

Objective: To develop and evaluate a combined motion-assisted/gated MRHIFU heating strategy designed to accelerate the treatment procedure by reducing the required number of sonications to ablate a target volume in the pancreas. Methods: A planning method for combined motion-assisted/gated MRHIFU using 4D-MRI and motion characterization is introduced. Six healthy volunteers underwent 4D-MRI for target motion characterization on a 3.0-T clinical scanner. Using displacement patterns, simulations were performed for all volunteers for three sonication approaches: gated, combined motion-assisted/gated, and static. The number of sonications needed to ablate the pancreas head was compared. The influence of displacement amplitude and target volume size was investigated. Spherical target volumes (8, 15, 20 and 34 mL) and displacement amplitudes ranging from 5 to 25 mm were evaluated. For this case, the number of sonications required to ablate the whole target was determined. Results: The number of required sonications was lowest for a static target, 62 on average (range 49-78). The gated approach required most sonications, 126 (range 97-159). The combined approach was almost as efficient as the hypothetical static case, with an average of 78 (range 53-123). Simulations showed that with a 5-mm displacement amplitude, the target could be treated by making use of motion-assisted MRHIFU sonications only. In that case, this approach allowed the lowest number of sonication, while for 10 mm and above, the number of required sonications increased. Conclusion: The use of a combined motion-assisted/gated MRHIFU strategy may accelerate tumor ablation in the pancreas when respiratory-induced displacement amplitudes are between 5 and 10 mm.

Non-Cartesian magnetic resonance imaging (MRI) sequences have shown great promise for abdominal examination during free breathing, but break down in the presence of bulk patient motion (i.e. voluntary or involuntary patient movement resulting in translation, rotation or elastic deformations of the body). This work describes a data-consistency-driven image stabilization technique that detects and excludes bulk movements during data acquisition. Bulk motion is identified from changes in the signal intensity distribution across different elements of a multi-channel receive coil array. A short free induction decay signal is acquired after excitation and used as a measure to determine alterations in the load distribution. The technique has been implemented on a clinical MR scanner and evaluated in the abdomen. Six volunteers were scanned and two radiologists scored the reconstructions. To show the applicability to other body areas, additional neck and knee images were acquired. Data corrupted by bulk motion were successfully detected and excluded from image reconstruction. An overall increase in image sharpness and reduction of streaking and shine-through artifacts were seen in the volunteer study, as well as in the neck and knee scans. The proposed technique enables automatic real-time detection and exclusion of bulk motion during MR examinations without user interaction. It may help to improve the reliability of pediatric MRI examinations without the use of sedation., (© 2017 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

Stereotactic body radiation therapy (SBRT) has shown great promise in increasing local control rates for renal-cell carcinoma (RCC). Characterized by steep dose gradients and high fraction doses, these hypo-fractionated treatments are, however, prone to dosimetric errors as a result of variations in intra-fraction respiratory-induced motion, such as drifts and amplitude alterations. This may lead to significant variations in the deposited dose. This study aims to develop a method for calculating the accumulated dose for MRI-guided SBRT of RCC in the presence of intra-fraction respiratory variations and determine the effect of such variations on the deposited dose. For this, RCC SBRT treatments were simulated while the underlying anatomy was moving, based on motion information from three motion models with increasing complexity: (1) STATIC, in which static anatomy was assumed, (2) AVG-RESP, in which 4D-MRI phase-volumes were time-weighted, and (3) PCA, a method that generates 3D volumes with sufficient spatio-temporal resolution to capture respiration and intra-fraction variations. Five RCC patients and two volunteers were included and treatments delivery was simulated, using motion derived from subject-specific MR imaging. Motion was most accurately estimated using the PCA method with root-mean-squared errors of 2.7, 2.4, 1.0 mm for STATIC, AVG-RESP and PCA, respectively. The heterogeneous patient group demonstrated relatively large dosimetric differences between the STATIC and AVG-RESP, and the PCA reconstructed dose maps, with hotspots up to [Formula: see text] of the D99 and an underdosed GTV in three out of the five patients. This shows the potential importance of including intra-fraction motion variations in dose calculations.

Respiratory motion introduces substantial uncertainties in abdominal radiotherapy for which traditionally large margins are used. The MR-Linac will open up the opportunity to acquire high resolution MR images just prior to radiation and during treatment. However, volumetric MRI time series are not able to characterize 3D tumor and organ-at-risk motion with sufficient temporal resolution. In this study we propose a method to estimate 3D deformation vector fields (DVFs) with high spatial and temporal resolution based on fast 2D imaging and a subject-specific motion model based on respiratory correlated MRI. In a pre-beam phase, a retrospectively sorted 4D-MRI is acquired, from which the motion is parameterized using a principal component analysis. This motion model is used in combination with fast 2D cine-MR images, which are acquired during radiation, to generate full field-of-view 3D DVFs with a temporal resolution of 476 ms. The geometrical accuracies of the input data (4D-MRI and 2D multi-slice acquisitions) and the fitting procedure were determined using an MR-compatible motion phantom and found to be 1.0-1.5 mm on average. The framework was tested on seven healthy volunteers for both the pancreas and the kidney. The calculated motion was independently validated using one of the 2D slices, with an average error of 1.45 mm. The calculated 3D DVFs can be used retrospectively for treatment simulations, plan evaluations, or to determine the accumulated dose for both the tumor and organs-at-risk on a subject-specific basis in MR-guided radiotherapy.