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.

Magnetic resonance imaging (MRI) is increasingly being used in the radiotherapy workflow because of its superior soft tissue contrast and high flexibility in contrast. In addition to anatomical and functional imaging, MRI can also be used to characterize the physiologically induced motion of both the tumor and organs-at-risk. Respiratory-correlated 4D-MRI has gained large interest as an alternative to 4D-CT for the characterization of respiratory motion throughout the thorax and abdomen. These 4D-MRI data sets consist of three spatial dimensions and the respiratory phase or amplitude over the fourth dimension (opposed to time-resolved 4D-MRI that represents time in the fourth dimension). Over the last 15 years numerous methods have been presented in literature. This review article provides a comprehensive overview of the various 4D-MRI techniques, and describes the differences in MRI data acquisition and 4D data set generation from a methodological point of view. The current status and future perspective of these techniques are highlighted, and the requirements for safe introduction into the clinic (e.g. method validation) are discussed.

The hybrid MRI-radiotherapy machines, like the MR-linac (Elekta AB, Stockholm, Sweden) installed at the UMC Utrecht (Utrecht, The Netherlands), will be able to provide real-time patient imaging during treatment. In order to take advantage of the system's capabilities and enable online adaptive treatments, a new generation of software should be developed, ranging from motion estimation to treatment plan adaptation. In this work we present a proof of principle adaptive pipeline designed for high precision stereotactic body radiation therapy (SBRT) suitable for sites affected by respiratory motion, like renal cell carcinoma (RCC). We utilized our research MRL treatment planning system (MRLTP) to simulate a single fraction 25 Gy free-breathing SBRT treatment for RCC by performing inter-beam replanning for two patients and one volunteer. The simulated pipeline included a combination of (pre-beam) 4D-MRI and (online) 2D cine-MR acquisitions. The 4DMRI was used to generate the mid-position reference volume, while the cine-MRI, via an in-house motion model, provided three-dimensional (3D) deformable vector fields (DVFs) describing the anatomical changes during treatment. During the treatment fraction, at an inter-beam interval, the mid-position volume of the patient was updated and the delivered dose was accurately reconstructed on the underlying motion calculated by the model. Fast online replanning, targeting the latest anatomy and incorporating the previously delivered dose was then simulated with MRLTP. The adaptive treatment was compared to a conventional mid-position SBRT plan with a 3 mm planning target volume margin reconstructed on the same motion trace. We demonstrate that our system produced tighter dose distributions and thus spared the healthy tissue, while delivering more dose to the target. The pipeline was able to account for baseline variations/drifts that occurred during treatment ensuring target coverage at the end of the treatment fraction.

A robust 4D-MRI method, based on clinically available protocols, is presented and successfully applied to characterize the abdominal motion in a small number of pancreatic cancer patients.

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 . For undersampling factors , 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 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.

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 populationbased 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.

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.

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 T1 and T2 mapping, R2* 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 (T1, T2, R2*/BOLD) were below 6.1%, with the lowest CoVs for T2 maps and highest for R2*/BOLD. CoVs for all diffusion analyses were below 7.2%, except for perfusion fraction (FP), 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 FP, respectively) and intraclass correlation coefficients (ICCs) ranged from −0.01 to 0.98 (lowest/highest values for cortical FP and renal sinus fat volume, respectively). Data Conclusion CoVs of most MRI measures of renal function and structure (with the exception of FP 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