The future of MRI in radiation therapy: Challenges and opportunities for the MR community.

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.

Nonrigid 3D motion estimation at high temporal resolution from prospectively undersampled k-space data using low-rank MR-MOTUS.

PURPOSE: With the recent introduction of the MR-LINAC, an MR-scanner combined with a radiotherapy LINAC, MR-based motion estimation has become of increasing interest to (retrospectively) characterize tumor and organs-at-risk motion during radiotherapy. To this extent, we introduce low-rank MR-MOTUS, a framework to retrospectively reconstruct time-resolved nonrigid 3D+t motion fields from a single low-resolution reference image and prospectively undersampled k-space data acquired during motion. THEORY: Low-rank MR-MOTUS exploits spatiotemporal correlations in internal body motion with a low-rank motion model, and inverts a signal model that relates motion fields directly to a reference image and k-space data. The low-rank model reduces the degrees-of-freedom, memory consumption, and reconstruction times by assuming a factorization of space-time motion fields in spatial and temporal components. METHODS: Low-rank MR-MOTUS was employed to estimate motion in 2D/3D abdominothoracic scans and 3D head scans. Data were acquired using golden-ratio radial readouts. Reconstructed 2D and 3D respiratory motion fields were, respectively, validated against time-resolved and respiratory-resolved image reconstructions, and the head motion against static image reconstructions from fully sampled data acquired right before and right after the motion. RESULTS: Results show that 2D+t respiratory motion can be estimated retrospectively at 40.8 motion fields per second, 3D+t respiratory motion at 7.6 motion fields per second and 3D+t head-neck motion at 9.3 motion fields per second. The validations show good consistency with image reconstructions. CONCLUSIONS: The proposed framework can estimate time-resolved nonrigid 3D motion fields, which allows to characterize drifts and intra and inter-cycle patterns in breathing motion during radiotherapy, and could form the basis for real-time MR-guided radiotherapy.

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

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.

Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy.

Respiratory motion during radiotherapy causes uncertainty in the tumor's location, which is typically addressed by an increased radiation area and a decreased dose. As a result, the treatments' efficacy is reduced. The recently proposed hybrid MR-linac scanner holds the promise to efficiently deal with such respiratory motion through real-time adaptive MR-guided radiotherapy (MRgRT). For MRgRT, motion-fields should be estimated from MR-data and the radiotherapy plan should be adapted in real-time according to the estimated motion-fields. All of this should be performed with a total latency of maximally 200 ms, including data acquisition and reconstruction. A measure of confidence in such estimated motion-fields is highly desirable, for instance to ensure the patient's safety in case of unexpected and undesirable motion. In this work, we propose a framework based on Gaussian Processes to infer 3D motion-fields and uncertainty maps in real-time from only three readouts of MR-data. We demonstrated an inference frame rate up to 69 Hz including data acquisition and reconstruction, thereby exploiting the limited amount of required MR-data. Additionally, we designed a rejection criterion based on the motion-field uncertainty maps to demonstrate the framework's potential for quality assurance. The framework was validated in silico and in vivo on healthy volunteer data (n=5) acquired using an MR-linac, thereby taking into account different breathing patterns and controlled bulk motion. Results indicate end-point-errors with a 75th percentile below 1 mm in silico, and a correct detection of erroneous motion estimates with the rejection criterion. Altogether, the results show the potential of the framework for application in real-time MR-guided radiotherapy with an MR-linac.

Real-Time Non-Rigid 3D Respiratory Motion Estimation for MR-Guided Radiotherapy Using MR-MOTUS.

The MR-Linac is a combination of an MR-scanner 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 MR-based 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 by the 3D MR-imaging 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 motion-fields 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 small-scale 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-field 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.

A mask-compatible, radiolucent, 8-channel head and neck receive array for MRI-guided radiotherapy treatments and pre-treatment simulation.

Immobilization masks are used to prevent patient movement during head and neck (H&N) radiotherapy. Motion restriction is beneficial both during treatment, as well as in the pre-treatment simulation phase, where magnetic resonance imaging (MRI) is often used for target definition. However, the shape and size of the immobilization masks hinder the use of regular, close-fitting MRI receive arrays. In this work, we developed a mask-compatible 8-channel H&N array that consists of a single-channel baseplate, on which the mask can be secured, and a flexible 7-channel anterior element that follows the shape of the mask. The latter uses high impedance coils to achieve its flexibility and radiolucency. A fully-functional prototype was manufactured, its radiolucency was characterized, and the gain in imaging performance with respect to current clinical setups was quantified. Dosimetry measurements showed an overall dose change of -0.3%. Small, local deviations were up to -2.7% but had no clinically significant impact on a full treatment plan, as gamma pass rates (3%/3 mm) only slightly reduced from 97.9% to 97.6% (clinical acceptance criterion: ≥95%). The proposed H&N array improved the imaging performance with respect to three clinical setups. The H&N array more than doubled (+123%) and tripled (+246%) the signal-to-noise ratio with respect to the clinical MRI-simulation and MR-linac setups, respectively.G-factors were also lower with the proposed H&N array. The improved imaging performance resulted in a clearly visible signal-to-noise ratio improvement of clinically used TSE and DWI acquisitions. In conclusion, the 8-channel H&N array improves the imaging performance of MRI-simulation and MR-linac acquisitions, while dosimetry suggests that no clinically significant dose changes are induced.

Real-time 3D motion estimation from undersampled MRI using multi-resolution neural networks.

PURPOSE: To enable real-time adaptive magnetic resonance imaging-guided radiotherapy (MRIgRT) by obtaining time-resolved three-dimensional (3D) deformation vector fields (DVFs) with high spatiotemporal resolution and low latency ( < 500  ms). Theory and Methods: Respiratory-resolved T 1 -weighted 4D-MRI of 27 patients with lung cancer were acquired using a golden-angle radial stack-of-stars readout. A multiresolution convolutional neural network (CNN) called TEMPEST was trained on up to 32 × retrospectively undersampled MRI of 17 patients, reconstructed with a nonuniform fast Fourier transform, to learn optical flow DVFs. TEMPEST was validated using 4D respiratory-resolved MRI, a digital phantom, and a physical motion phantom. The time-resolved motion estimation was evaluated in-vivo using two volunteer scans, acquired on a hybrid MR-scanner with integrated linear accelerator. Finally, we evaluated the model robustness on a publicly-available four-dimensional computed tomography (4D-CT) dataset. RESULTS: TEMPEST produced accurate DVFs on respiratory-resolved MRI at 20-fold acceleration, with the average end-point-error < 2  mm, both on respiratory-sorted MRI and on a digital phantom. TEMPEST estimated accurate time-resolved DVFs on MRI of a motion phantom, with an error < 2  mm at 28 × undersampling. On two volunteer scans, TEMPEST accurately estimated motion compared to the self-navigation signal using 50 spokes per dynamic (366 × undersampling). At this undersampling factor, DVFs were estimated within 200 ms, including MRI acquisition. On fully sampled CT data, we achieved a target registration error of 1.87 ± 1.65 mm without retraining the model. CONCLUSION: A CNN trained on undersampled MRI produced accurate 3D DVFs with high spatiotemporal resolution for MRIgRT.

Intrafraction motion quantification and planning target volume margin determination of head-and-neck tumors using cine magnetic resonance imaging.

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.

Multiresolution radial MRI to reduce IDLE time in pre-beam imaging on an MR-Linac (MR-RIDDLE).

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.

A dual-purpose MRI acquisition to combine 4D-MRI and dynamic contrast-enhanced imaging for abdominal radiotherapy planning.

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 T1-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.

Accelerated magnetic resonance fingerprinting using soft-weighted key-hole (MRF-SOHO).

OBJECT: To develop a novel approach for highly accelerated Magnetic Resonance Fingerprinting (MRF) acquisition. MATERIALS AND METHODS: The proposed method combines parallel imaging, soft-gating and key-hole approaches to highly accelerate MRF acquisition. Slowly varying flip angles (FA), commonly used during MRF acquisition, lead to a smooth change in the signal contrast of consecutive time-point images. This assumption enables sharing of high frequency data between different time-points, similar to what is done in some dynamic MR imaging methods such as key-hole. The proposed approach exploits this information using a SOft-weighted key-HOle (MRF-SOHO) reconstruction to achieve high acceleration factors and/or increased resolution without compromising image quality or increasing scan time. MRF-SOHO was validated on a standard T1/T2 phantom and in in-vivo brain acquisitions reconstructing T1, T2 and proton density parametric maps. RESULTS: Accelerated MRF-SOHO using less data per time-point and less time-point images enabled a considerable reduction in scan time (up to 4.6x), while obtaining similar T1 and T2 accuracy and precision when compared to zero-filled MRF reconstruction. For the same number of spokes and time-points, the proposed method yielded an enhanced performance in quantifying parameters than the zero-filled MRF reconstruction, which was verified with 2, 1 and 0.7 (sub-millimetre) resolutions. CONCLUSION: The proposed MRF-SOHO enabled a 4.6x scan time reduction for an in-plane spatial resolution of 2x2 mm2 when compared to zero-filled MRF and enabled sub-millimetric (0.7x0.7 mm2) resolution MRF.

Effect of a stiff lifting belt on spine compression during lifting.

STUDY DESIGN: An in vivo study on weightlifters. OBJECTIVES: To determine if and how a stiff back belt affects spinal compression forces in weightlifting. SUMMARY OF BACKGROUND DATA: In weightlifting, a back belt has been reported to enhance intraabdominal pressure (IAP) and to reduce back muscle EMG and spinal compression forces. METHODS: Nine experienced weightlifters lifted barbells up to 75% body weight while inhaling and wearing a belt, inhaling and not wearing a belt, and exhaling and wearing a belt. IAP, trunk muscle EMG, ground reaction forces, and kinematics were measured. An EMG-assisted trunk model, including IAP effects, was used to calculate spinal compression and shear forces and to reveal the contribution of back muscles, abdominal muscles, and IAP to moment generation. RESULTS: The belt reduced compression forces by about 10%, but only when inhaling before lifting. The moment generated by IAP increased when wearing a belt and inhaling, but this moment was small and the increase was largely negated by the flexing moment generated by abdominal muscles. CONCLUSIONS: Wearing a tight and stiff back belt while inhaling before lifting reduces spine loading. This is caused by a moment generated by the belt rather than by the IAP.