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Introduction: Respiratory motion models establish a correspondence between respiratory-correlated (RC) 4-dimensional (4D) imaging and respiratory surrogates, to estimate time-resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine-MRI and (ii) additional 4DMRI data for testing intra-/inter-fraction breathing motion variability., Methods: Repeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter-fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine-MRI and 4DMRI respiratory phases, respectively., Results: The median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm 3 ), with higher values in the presence of large inter-fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks' position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra- and inter-fraction motion, respectively. The range of motion described in the cine-MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter-fraction baseline variations occurred., Conclusions: Results suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI-guided radiotherapy., (© 2021 The Authors. Journal of Medical Imaging and Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Royal Australian and New Zealand College of Radiologists.)

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

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 V 95% (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 D 95% 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., (© 2019 American Association of Physicists in Medicine.)

Magnetic-resonance linear-accelerator (MR-LINAC) systems integrating in-room magnetic-resonance-imaging (MRI) guidance are a currently emerging technology. Such systems address the need to provide frequent imaging at optimal soft-tissue contrast for treatment guidance. However, the use of MRI-guidance in radiotherapy should address imaging-related spatial distortions, which may hinder accurate geometrical characterization of the treatment site. Since spatial encoding relies on well-defined magnetic fields, accurate modeling of the magnetic field alterations due to [Formula: see text]-inhomogeneities, gradient nonlinearities, and susceptibilities is needed. In this work, the modeling of susceptibility induced distortions is considered. Dedicated susceptibility measurements are reported, aiming at extending the characterization of different tissues for MRI-guided extra-cranial radiotherapy applications. A digital 4D anthropomorphic phantom, providing time-resolved anatomical changes due to breathing, is exploited as reference anatomy to quantify spatial distortions due to variations in tissue susceptibility. Sub-millimeter values can be attributed to susceptibility-induced distortions, with maximum values up to 2.3 mm at a gradient strength of 5 mT m -1 . Improvements in susceptibility simulation for extra-cranial sites are shown when including specifically the contributions from lung, liver and muscular tissues.

Purpose: In retrospective 4-Dimensional Magnetic Resonance Imaging (4D MRI) sorting, respiratory surrogate selection affects the image quality of reconstructed volumes. We propose a method for retrospective 4D MRI sorting based on clustering, which allowed us to compare the performance of single or multiple internal surrogates vs. a conventional external signal., Methods: A k-medoids clustering algorithm was exploited for sorting 2D MRI into 4D MRI, relying on (A) multiple or (B) single automatically tracked internal landmarks or (C) respiratory belt signal. 4D MRI reconstructions for seven liver cancer patients were compared to those of the state-of-the-art mutual information (MI) approach. Sorting artifacts were measured by the root mean square error (RMSE) between the diaphragm profile and a fitted second order curve. Diaphragm and tumor motions were evaluated., Results: The median RMSEs ranged 0.97-1.66 mm, 1.24-1.89 mm, 1.43-2.27 mm, 1.74-3.72 mm for the MI, (A), (B) and (C) methods, respectively. Significant differences (Friedman, α = 5%) were found between (C) and all other methods, and between (B) and MI approaches. The discrepancies between (A) and MI approaches ranged 1.1-6.2 mm and 0.7-5.3 mm respectively in diaphragm and tumor motions. Methods (A) and (B) showed similar ranges of motion., Conclusion: With multiple internal points, our method yielded the description of a higher range of motion and similar image quality with respect to the MI approach. The single point method led to more artifacts, suggesting the superior suitability of multiple internal surrogates for retrospective 4D MRI sorting. Considering internal rather than external information favored superior performance., (Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

High precision conformal radiotherapy requires sophisticated imaging techniques to aid in target localisation for planning and treatment, particularly when organ motion due to respiration is involved. X-ray based imaging is a well-established standard for radiotherapy treatments. Over the last few years, the ability of magnetic resonance imaging (MRI) to provide radiation-free images with high-resolution and superb soft tissue contrast has highlighted the potential of this imaging modality for radiotherapy treatment planning and motion management. In addition, these advantageous properties motivated several recent developments towards combined MRI radiation therapy treatment units, enabling in-room MRI-guidance and treatment adaptation. The aim of this review is to provide an overview of the state-of-the-art in MRI-based image guidance for organ motion management in external beam radiotherapy. Methodological aspects of MRI for organ motion management are reviewed and their application in treatment planning, in-room guidance and adaptive radiotherapy described. Finally, a roadmap for an optimal use of MRI-guidance is highlighted and future challenges are discussed.

Purpose: Several image-based retrospective sorting methods of 4D magnetic resonance imaging (4D MRI) have been proposed for respiratory motion reconstruction in external beam radiotherapy. However, the optimal strategy for providing accurate and artifact-free 4D MRI, ideally corresponding to an average breathing cycle, is not yet defined. This study presents a proactive comparison of three published image-based sorting methods, to define a groundwork for benchmarking in 4D MRI., Methods: Three published 4D MRI methods were selected for image retrospective sorting: body area, mutual information, and navigator slice. The three image-based methods were compared against a conventional retrospective sorting method based on an external surrogate. Comparisons were performed by means of an MRI digital phantom, derived from the XCAT CT phantom generated with different patient-derived signals, for a total of 12 cases. Specific multislice MRI acquisitions were simulated for slice sorting and sagittal, coronal, and axial orientations were tested. An average 4D cycle was generated as ground truth., Results: Individual and grouped patient analyses showed better performance of the navigator slice and mutual information in amplitude binning with respect to the body area strategy. Binning artifacts were reduced on the diaphragm with the slice navigator method due to the acquired internal information. Tumor motion description accurately matched the ground truth in the mutual information strategy with amplitude binning. The body area method followed the performance of the external surrogate and presented larger errors, since was not correlated with the internal anatomy. Sagittal and coronal orientations reported lower errors than axial slicing. Individual analysis showed the need of a patient-specific evaluation for the selection of the best method., Conclusions: A comparison between three different image-based retrospective sorting methods for 4D MRI is proposed, providing guidelines for benchmark definition in MRI-guided radiotherapy., (© 2018 American Association of Physicists in Medicine.)

Objective: MRI-guided radiotherapy (MRIgRT) is an emerging treatment technique where anatomical and pathological structures are imaged through integrated MR-radiotherapy units. This work aims 1) at assessing the accuracy of optical-flow-based motion tracking in liver cine-MRI sequences; and 2) at testing a MRIgRT workflow combining similarity-based image matching with image registration., Methods: After an initialization stage, a set of template images is collected and registered to the first frame of the cine-MRI sequence. Subsequent incoming frames are either matched to the most similar template image or registered to the first frame when the similarity index is lower than a given threshold. The tracking accuracy was evaluated by considering ground-truth liver landmarks trajectories, as obtained through the scale-invariant features transform (SIFT)., Results: Results on a population of 30 liver subjects show that the median difference between SIFT- and optical flow-based landmarks trajectories is 1.0 mm, i.e., lower than the cine-MRI pixel size (1.28 mm). The computational time of the motion tracking workflow (<50 ms) is suitable for real-time motion compensation in MRIgRT. Such time could be further reduced to ≍30 ms with limited loss of accuracy by the combined image matching/registration approach., Conclusion: The reported workflow allows us to track liver motion with accuracy comparable to robust feature matching. Its computational time is suitable for online motion monitoring., Significance: Real-time feedback on the patient anatomy is a crucial requirement for the treatment of mobile tumors using advanced motion mitigation strategies.

Purpose: The integration of CT and multiparametric MRI (mpMRI) is a challenging task in high-precision radiotherapy for prostate cancer. A simple methodology for multimodal deformable image registration (DIR) of prostate cancer patients is presented., Methods: CT and mpMRI of 10 patients were considered. Organs at risk and prostate were contoured on both scans. The dominant intraprostatic lesion was additionally delineated on MRI. After a preliminary rigid image registration, the voxel intensity of all the segmented structures in both scans except the prostate was increased by a specific amount (a constant additional value, A), in order to enhance the contrast of the main organs influencing its position and shape. 70 couples of scans were obtained by varying A from 0 to 800 and they were subsequently non-rigidly registered. Quantities derived from image analysis and contour statistics were considered for the tuning of the best performing A., Results: A = 200 resulted the minimum enhancement value required to obtain statistically significant superior registration results. Mean centre of mass distance between corresponding structures decreases from 7.4 mm in rigid registration to 5.3 mm in DIR without enhancement (DIR-0) and to 2.7 mm in DIR with A = 200 (DIR-200). Mean contour distance was 2.5, 1.9 and 0.67 mm in rigid registration, DIR-0 and DIR-200, respectively. In DIR-200 mean contours overlap increases of +13 and +24% with respect to DIR-0 and rigid registration, respectively., Conclusion: Contour propagation according to the vector field resulting from DIR-200 allows the delineation of dominant intraprostatic lesion on CT scan and its use for high-precision radiotherapy treatment planning. Advances in knowledge: We investigated the application of a B-spline, mutual information-based multimodal DIR coupled with a simple, patient-unspecific but efficient contrast enhancement procedure in the pelvic body area, thus obtaining a robust and accurate methodology to transfer the functional information deriving from mpMRI onto a planning CT reference volume.

Dynamic magnetic resonance imaging (MRI) is emerging as the elected image modality for organ motion quantification and management in image-guided radiotherapy. However, the lack of validation tools is an open issue for image guidance in the abdominal and thoracic organs affected by organ motion due to respiration. We therefore present an abdominal four-dimensional (4D) CT/MRI digital phantom, including the estimation of MR tissue parameters, simulation of dedicated abdominal MR sequences, modeling of radiofrequency coil response and noise, followed by k-space sampling and image reconstruction. The phantom allows the realistic simulation of images generated by MR pulse sequences with control of scan and tissue parameters, combined with co-registered CT images. In order to demonstrate the potential of the phantom in a clinical scenario, we describe the validation of a virtual T1-weighted 4D MRI strategy. Specifically, the motion extracted from a T2-weighted 4D MRI is used to warp a T1-weighted breath-hold acquisition, with the aim of overcoming trade-offs that limit T1-weighted acquisitions. Such an application shows the applicability of the digital CT/MRI phantom as a validation tool, which should be especially useful for cases unsuited to obtain real imaging data.

Particle therapy (PT) has shown positive therapeutic results in local control of locally advanced pancreatic lesions. PT effectiveness is highly influenced by target localization accuracy both in space, since the pancreas is located in proximity to radiosensitive vital organs, and in time as it is subject to substantial breathing-related motion. The purpose of this preliminary study was to quantify pancreas range of motion under typical PT treatment conditions. Three common immobilization devices (vacuum cushion, thermoplastic mask, and compressor belt) were evaluated on five male patients in prone and supine positions. Retrospective four-dimensional magnetic resonance imaging data were reconstructed for each condition and the pancreas was manually segmented on each of six breathing phases. A k-means algorithm was then applied on the manually segmented map in order to obtain clusters representative of the three pancreas segments: head, body, and tail. Centers of mass (COM) for the pancreas and its segments were computed, as well as their displacements with respect to a reference breathing phase (beginning exhalation). The median three-dimensional COM displacements were in the range of 3 mm. Latero-lateral and superior-inferior directions had a higher range of motion than the anterior-posterior direction. Motion analysis of the pancreas segments showed slightly lower COM displacements for the head cluster compared to the tail cluster, especially in prone position. Statistically significant differences were found within patients among the investigated setups. Hence a patient-specific approach, rather than a general strategy, is suggested to define the optimal treatment setup in the frame of a millimeter positioning accuracy., (© 2016 The Authors.)

Purpose: Four-dimensional magnetic resonance imaging (4DMRI) is an emerging technique in radiotherapy treatment planning for organ motion quantification. In this paper, the authors present a novel 4DMRI retrospective image-based sorting method, providing reduced motion artifacts than using a standard monodimensional external respiratory surrogate., Methods: Serial interleaved 2D multislice MRI data were acquired from 24 liver cases (6 volunteers + 18 patients) to test the proposed 4DMRI sorting. Image similarity based on mutual information was applied to automatically identify a stable reference phase and sort the image sequence retrospectively, without the use of additional image or surrogate data to describe breathing motion., Results: The image-based 4DMRI provided a smoother liver profile than that obtained from standard resorting based on an external surrogate. Reduced motion artifacts were observed in image-based 4DMRI datasets with a fitting error of the liver profile measuring 1.2 ± 0.9 mm (median ± interquartile range) vs 2.1 ± 1.7 mm of the standard method., Conclusions: The authors present a novel methodology to derive a patient-specific 4DMRI model to describe organ motion due to breathing, with improved image quality in 4D reconstruction.

Purpose: This study applied automatic feature detection on cine-magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy., Methods and Materials: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each feature was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error., Results: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57 mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance., Conclusions: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies., (Copyright © 2015 Elsevier Inc. All rights reserved.)

We propose the use of Scale Invariant Feature Transform (SIFT) as a method able to extract stable landmarks from 4D images and to quantify internal motion. We present a preliminary validation of the SIFT method relying on expert user identification of landmarks and then apply it to 4D lung CT and liver MRI data. Results demonstrate SIFT capabilities as an operator-independent feature tracking method.

Objective. Respiration negatively affects the outcome of a radiation therapy treatment, with potentially severe effects especially in particle therapy (PT). If compensation strategies are not applied, accuracy cannot be achieved. To support the clinical practice based on 4D computed tomography (CT), 4D magnetic resonance imaging (MRI) acquisitions can be exploited. The purpose of this study was to validate a method for virtual 4DCT generation from 4DMRI data for lung cancers on a porcine lung phantom, and to apply it to lung cancer patients in PT. Approach. Deformable image registration was used to register each respiratory phase of the 4DMRI to a reference phase. Then, a static 3DCT was registered to this reference MR image set, and the virtual 4DCT was generated by warping the registered CT according to previously obtained deformation fields. The method was validated on a physical phantom for which a ground truth 4DCT was available and tested on lung tumor patients, treated with gated PT at end-exhale, by comparing the virtual 4DCT with a re-evaluation 4DCT. The geometric and dosimetric evaluation was performed for both proton and carbon ion treatment plans. Main results. The phantom validation exhibited a geometrical accuracy within the maximum resolution of the MRI and mean dose deviations, with respect to the prescription dose, up to 3.2% for target D 95%, with a mean gamma pass rate of 98%. For patients, the virtual and re-evaluation 4DCTs showed good correspondence, with errors on target D 95% up to 2% within the gating window. For one patient, dose variations up to 10% at end-exhale were observed due to relevant inter-fraction anatomo-pathological changes that occurred between the planning and re-evaluation CTs. Significance. Results obtained on phantom data showed that the virtual 4DCT method was accurate, allowing its application on patient data for testing within a clinical scenario. [ABSTRACT FROM AUTHOR]

Purpose: Spatial distortions in magnetic resonance imaging (MRI) are mainly caused by inhomogeneities of the static magnetic field, nonlinearities in the applied gradients, and tissue‐specific magnetic susceptibility variations. These factors may significantly alter the geometrical accuracy of the reconstructed MR image, thus questioning the reliability of MRI for guidance in image‐guided radiation therapy. In this work, we quantified MRI spatial distortions and created a quantitative model where different sources of distortions can be separated. The generated model was then integrated into a four‐dimensional (4D) computational phantom for simulation studies in MRI‐guided radiation therapy at extra‐cranial sites. Methods: A geometrical spatial distortion phantom was designed in four modules embedding laser‐cut PMMA grids, providing 3520 landmarks in a field of view of (345 × 260 × 480) mm3. The construction accuracy of the phantom was verified experimentally. Two fast MRI sequences for extra‐cranial imaging at 1.5 T were investigated, considering axial slices acquired with online distortion correction, in order to mimic practical use in MRI‐guided radiotherapy. Distortions were separated into their sources by acquisition of images with gradient polarity reversal and dedicated susceptibility calculations. Such a separation yielded a quantitative spatial distortion model to be used for MR imaging simulations. Finally, the obtained spatial distortion model was embedded into an anthropomorphic 4D computational phantom, providing registered virtual CT/MR images where spatial distortions in MRI acquisition can be simulated. Results: The manufacturing accuracy of the geometrical distortion phantom was quantified to be within 0.2 mm in the grid planes and 0.5 mm in depth, including thickness variations and bending effects of individual grids. Residual spatial distortions after MRI distortion correction were strongly influenced by the applied correction mode, with larger effects in the trans‐axial direction. In the axial plane, gradient nonlinearities caused the main distortions, with values up to 3 mm in a 1.5 T magnet, whereas static field and susceptibility effects were below 1 mm. The integration in the 4D anthropomorphic computational phantom highlighted that deformations can be severe in the region of the thoracic diaphragm, especially when using axial imaging with 2D distortion correction. Adaptation of the phantom based on patient‐specific measurements was also verified, aiming at increased realism in the simulation. Conclusions: The implemented framework provides an integrated approach for MRI spatial distortion modeling, where different sources of distortion can be quantified in time‐dependent geometries. The computational phantom represents a valuable platform to study motion management strategies in extra‐cranial MRI‐guided radiotherapy, where the effects of spatial distortions can be modeled on synthetic images in a virtual environment. [ABSTRACT FROM AUTHOR]