MRI-guidance for motion management in external beam radiotherapy: current status and future challenges

Deep-learning synthetized 4DCT from 4DMRI of the abdominal site in carbon-ion radiotherapy

PURPOSE

To investigate the feasibility of deep-learning-based synthetic 4DCT (4D-sCT) generation from 4DMRI data of abdominal patients undergoing Carbon Ion Radiotherapy (CIRT).

MATERIAL AND METHODS

A 3-channel conditional Generative Adversarial Network (cGAN) was trained and tested on twenty-six patients, using paired T1-weighted 4DMRI and 4DCT volumes. 4D-sCT data were generated via the cGAN following a 3-channels segmentation approach (air, bone, soft tissue) in two scenarios: (a) 4DCT-based approach (i.e. segmentation relying on 4DCT) and (b) 4DMRI-based approach (i.e. manual segmentation on 4DMRI, to simulate a 4DMRI-only scenario). The network was first validated on a 4D computational phantom, where a ground truth dataset was available. Subsequently, the network was tested on 6 independent held-out-of-training patients. Generated volumes were evaluated with respect to the original 4DCT based on motion analysis, similarity metrics (e.g. Mean Absolute Error (MAE), Normalized Cross Coefficient (NCC)) and dosimetric criteria, by means of recalculating clinically optimized CIRT plans on the 4D-sCT.

RESULTS

For the phantom, similarity metrics were in line with literature results, while dose volume histogram values were below 0.9 %. 4DCT-based patient results demonstrated an accurate representation with respect to the original 4DCT images (MAE: 50.64-51.29 HU), while 4DMRI-only-based results yielded higher values (MAE: 81.15-90.22 HU). Gamma pass rates (3 %/3mm) were ∼ 97 % for the 4DCT-based scenario, showing dosimetric consistency between the compared 4DCT and 4D-sCT dose distributions. D95% values on GTV/CTV were within clinical tolerances for the 4DMRI-only scenario.

CONCLUSION

Deep learning-based 4D-sCT generation shows potential to support treatment planning in abdominal tumors treated with CIRT.

Evaluation of motion mitigation strategies for carbon ion therapy of abdominal tumors based on non-periodic imaging data

Objective.To identify suitable combination strategies for treatment planning and beam delivery in scanned carbon ion therapy of moving tumors.Approach. Carbon ion treatment plans for five abdominal tumors were optimized on four-dimensional (4D) computed tomography (CT) data using the following approaches. 4DITV across all phases and within a gating window, single phase uniform dose, and an innovative 4D tracking internal target volume (ITV) strategy. Delivered single-fraction doses were calculated on time-resolved virtual CT images reconstructed from 2D cine-magnetic resonance imaging series, using a deformable image registration pipeline. Treatment plans were combined with various beam delivery techniques: three-dimensional (no motion mitigation), rescanning, gating, beam tracking, and multi-phase 4D delivery with and without residual tracking (MP4D and MP4DRT) to form in total 11 treatment modalities. Single fraction doses were accumulated to simulate a fractionated treatment.Main results. Breath-sampled treatments using the MP4D and MP4DRT delivery techniques were the only to achieveD95> 95% for hypofractionated treatments, with little dependence on the number of fractions. A combination of MP4DRT with the new 4D tracking ITV approach resulting in conformal dose distributions and demonstrated the greatest robustness against irregular motion and anatomical changes.Significance. This study demonstrates, that real-time adaptive beam delivery strategies can deliver conformal doses within single fractions, thereby enabling hypofractionated treatment schemes that are not feasible with conventional strategies.

MR-linac: role of artificial intelligence and automation

The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanced consistency, accuracy, and efficiency in clinical practice. Magnetic resonance (MR)-guided linear accelerators (MR-linacs) have greatly improved treatment accuracy and real-time plan adaptation, particularly for tumors near radiosensitive organs. Despite these improvements, MR-guided radiotherapy (MRgRT) remains labor intensive and time consuming, highlighting the need for AI to streamline workflows and support rapid decision-making. Synthetic CTs from MR images and automated contouring and treatment planning will reduce manual processes, thus optimizing treatment times and expanding access to MR-linac technology. AI-driven quality assurance will ensure patient safety by predicting machine errors and validating treatment delivery. Advances in intrafractional motion management will increase the accuracy of treatment, and the integration of imaging biomarkers for outcome prediction and early toxicity assessment will enable more precise and effective treatment strategies.

Feasibility study of using next-generation reservoir computing (NG-RC) model to estimate liver tumor motion from external breathing signals

BACKGROUND

Respiratory motion is a challenge for accurate radiotherapy that may be mitigated by real-time tracking. Commercial tracking systems utilize a hybrid external-internal correlation model (ECM), integrating continuous external breathing monitoring with sparse X-ray imaging of the internal tumor position.

PURPOSE

This study investigates the feasibility of using the next generation reservoir computing (NG-RC) model as a hybrid ECM to transform measured external motions into estimated 3D internal motions.

METHODS

The NG-RC model utilizes the nonlinear vector autoregressive (NVAR) machine to account for the hysteresis or phase differences between external and internal motions. The datasets used to evaluate the efficacy of the NG-RC model include 57 motion traces from the CyberKnife system. The datasets were divided into three regions (central, lower, and upper livers) and three motion patterns. These patterns include linear and nonlinear motion patterns (Group A), hysteresis motion patterns (Group B), and all motion patterns (Group C). Moreover, various updating techniques were examined, such as continuously updating the NG-RC model using the first-in-first-out (FIFO) approach and sampling the internal tumor position every 0 s (strategy A), 60 s (strategy B), 30 s (strategy C), and 50 s (strategy D).

RESULTS

The NG-RC model combined with strategy C resulted in better estimation accuracy than the reported CyberKnife cases (Wilcoxon signed rank p < 0.05). For linear and nonlinear motion patterns, the 3D radial estimation accuracy (mean ± SD) using the NG-RC model combined with strategy C and the CyberKnife system was 1.20 ± 0.78 and 1.1 ± 0.20 mm in the central liver, 0.66 ± 0.25 and 1.49 ± 0.50 mm in the lower liver, and 1.73 ± 0.86 and 1.61 ± 0.42 mm in the upper liver. For hysteresis motion patterns, the corresponding values were 1.13 ± 0.37 and 1.45 ± 0.33 mm, 1.43 ± 1.30 and 1.67 ± 0.42 mm, and 1.20 ± 0.68 and 1.46 ± 0.54 mm in the central, lower, and upper livers, respectively.

CONCLUSION

This study proposed a new hybrid correlation model for real-time tumor tracking, which can be used to account for both linear and nonlinear motion patterns, as well as hysteresis motion patterns. Additionally, the NG-RC model required shorter training data sets (15 s) during pre-treatment and short internal motion sampling (every 30 s) during treatment compared to other ECMs.

Artificial intelligence for treatment delivery: image-guided radiotherapy

Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.

Imaging error reduction in radial cine-MRI with deep learning-based intra-frame motion compensation

Objective.Radial cine-MRI allows for sliding window reconstruction at nearly arbitrary frame rate, promising high-speed imaging for intra-fractional motion monitoring in magnetic resonance guided radiotherapy. However, motion within the reconstruction window may determine the location of the reconstructed target to deviate from the true real-time position (target positioning errors), particularly in cases of fast breathing or for anatomical structures affected by the heartbeat. In this work, we present a proof-of-concept study aiming to enhance radial cine-MR imaging by implementing deep-learning-based intra-frame motion compensation techniques.Approach.A novel network (TransSin-UNet) was proposed to continuously estimate the final-position image of the target, corresponding to end of the frame acquisition. Within the radial k-space reconstruction window, the spatial-temporal dependencies among the sinogram representation of the spokes were modeled by a transformer encoder subnetwork, followed by a UNet subnetwork operating in the spatial domain for pixel-level fine-tuning. By simulating motion-dependent radial sampling with (tiny) golden angles, we generated datasets from 25 4D digital anthropomorphic lung cancer phantoms. The network was then trained and extensively evaluated across datasets characterized by varying azimuthal radial profile increments.Main Results.The method required additional 4.8 ms per frame over the conventional approach involving direct image reconstruction with motion-corrupted spokes. TransSin-UNet outperformed architectures relying solely on transformer encoders or UNets across all the comparative evaluations, leading to a noticeable enhancement in image quality and target positioning accuracy. The normalized root mean-squared error decreased by 50% from the initial value of 0.188 on average, whereas the mean Dice similarity coefficient of the gross tumor volume increased from 85.1% to 96.2% in the investigated cases. Furthermore, the final-positions of anatomical structures undergoing substantial intra-frame deformations were precisely derived.Significance.The proposed approach enables an effective intra-frame motion compensation, offering an opportunity to reduce errors in radial cine-MR imaging for real-time motion management.

The Use of Magnetic Resonance Imaging in Radiation Therapy Treatment Simulation and Planning

Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 5.

Accuracy of four models and update strategies to estimate liver tumor motion from external respiratory motion

Background

This study investigates different strategies for estimating internal liver tumor motion during radiotherapy based on continuous monitoring of external respiratory motion combined with sparse internal imaging.

Methods

Fifteen patients underwent three-fraction stereotactic liver radiotherapy. The 3D internal tumor motion (INT) was monitored by electromagnetic transponders while a camera monitored the external marker block motion (EXT). The ability of four external-internal correlation models (ECM) to estimate INT as function of EXT was investigated: a simple linear model (ECM1), an augmented linear model (ECM2), an augmented quadratic model (ECM3), and an extended quadratic model (ECM4). Each ECM was constructed by fitting INT and EXT during the first 60s of each fraction. The fit accuracy was calculated as the root-mean-square error (RMSE) between ECM-estimated and actual tumor motion. Next, the RMSE of the ECM-estimated tumor motion throughout the fractions was calculated for four simulated ECM update strategies: (A) no update, 0.33Hz internal sampling with continuous update of either (B) all ECM parameters based on the last 2 minutes samples or (C) only the baseline term based on the last 5 samples, (D) full ECM update every minute using 20s continuous internal sampling.

Results

The augmented quadratic ECM3 had best fit accuracy with mean (± SD)) RMSEs of 0.32 ± 0.11mm (left-right, LR), 0.79 ± 0.30mm (cranio-caudal, CC) and 0.56 ± 0.31mm (anterior-posterior, AP). However, the simpler augmented linear ECM2 combined with frequent baseline updates (update strategy C) gave best motion estimations with mean RMSEs of 0.41 ± 0.14mm (LR), 1.02 ± 0.33mm (CC) and 0.78 ± 0.48mm (AP). This was significantly better than all other ECM-update strategy combinations for CC motion (Wilcoxon signed rank p<0.05).

Conclusion

The augmented linear ECM2 combined with frequent baseline updates provided the best compromise between fit accuracy and robustness towards irregular motion. It allows accurate internal motion monitoring by combining external motioning with sparse 0.33Hz kV imaging, which is available at conventional linacs.

A systematic review of 4D magnetic resonance imaging techniques for abdominal radiotherapy treatment planning

Background and purpose

Four-dimensional magnetic resonance imaging (4DMRI) has gained interest as an alternative to the current standard for motion management four-dimensional tomography (4DCT) in abdominal radiotherapy treatment planning (RTP). This review aims to assess the 4DMRI literature in abdomen, focusing on technical considerations and the validity of using 4DMRI for patients within radiotherapy protocols.

Materials and methods

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was performed across the Medline, Embase, Scopus, and Web of Science databases, covering all years up to December 31, 2023. The studies were grouped into two categories: 4DMRI reconstructed from 3DMRI acquisition; and 4DMRI reconstructed from multi-slice 2DMRI acquisition.

Results

A total of 39 studies met the inclusion criteria and were analysed to provide key findings. Key findings were 4DMRI had the potential to improve abdominal RTP for patients by providing accurate tumour definition and motion assessment compared to 4DCT. 4DMRI reconstructed from 3DMRI acquisition showed promise as a feasible approach for motion management in abdominal RTP regarding spatial resolution. Currently,the slice thickness achieved on 4DMRI reconstructed from multi-slice 2DMRI acquisitions was unsuitable for clinical purposes. Lastly, the current barriers for clinical implementation of 4DMRI were the limited availability of validated commercial solutions and the lack of larger cohort comparative studies to 4DCT for target delineation and plan optimisation.

Conclusion

4DMRI showed potential improvements in abdominal RTP, but standards and guidelines for the use of 4DMRI in radiotherapy were required to demonstrate clinical benefits.

Respiratory motion modelling for MR-guided lung cancer radiotherapy: model development and geometric accuracy evaluation

Objective.Respiratory motion of lung tumours and adjacent structures is challenging for radiotherapy. Online MR-imaging cannot currently provide real-time volumetric information of the moving patient anatomy, therefore limiting precise dose delivery, delivered dose reconstruction, and downstream adaptation methods.Approach.We tailor a respiratory motion modelling framework towards an MR-Linac workflow to estimate the time-resolved 4D motion from real-time data. We develop a multi-slice acquisition scheme which acquires thick, overlapping 2D motion-slices in different locations and orientations, interleaved with 2D surrogate-slices from a fixed location. The framework fits a motion model directly to the input data without the need for sorting or binning to account for inter- and intra-cycle variation of the breathing motion. The framework alternates between model fitting and motion-compensated super-resolution image reconstruction to recover a high-quality motion-free image and a motion model. The fitted model can then estimate the 4D motion from 2D surrogate-slices. The framework is applied to four simulated anthropomorphic datasets and evaluated against known ground truth anatomy and motion. Clinical applicability is demonstrated by applying our framework to eight datasets acquired on an MR-Linac from four lung cancer patients.Main results.The framework accurately reconstructs high-quality motion-compensated 3D images with 2 mm3isotropic voxels. For the simulated case with the largest target motion, the motion model achieved a mean deformation field error of 1.13 mm. For the patient cases residual error registrations estimate the model error to be 1.07 mm (1.64 mm), 0.91 mm (1.32 mm), and 0.88 mm (1.33 mm) in superior-inferior, anterior-posterior, and left-right directions respectively for the building (application) data.Significance.The motion modelling framework estimates the patient motion with high accuracy and accurately reconstructs the anatomy. The image acquisition scheme can be flexibly integrated into an MR-Linac workflow whilst maintaining the capability of online motion-management strategies based on cine imaging such as target tracking and/or gating.

A review of the clinical introduction of 4D particle therapy research concepts

Background and purpose

Many 4D particle therapy research concepts have been recently translated into clinics, however, remaining substantial differences depend on the indication and institute-related aspects. This work aims to summarise current state-of-the-art 4D particle therapy technology and outline a roadmap for future research and developments.

Material and methods

This review focused on the clinical implementation of 4D approaches for imaging, treatment planning, delivery and evaluation based on the 2021 and 2022 4D Treatment Workshops for Particle Therapy as well as a review of the most recent surveys, guidelines and scientific papers dedicated to this topic.

Results

Available technological capabilities for motion surveillance and compensation determined the course of each 4D particle treatment. 4D motion management, delivery techniques and strategies including imaging were diverse and depended on many factors. These included aspects of motion amplitude, tumour location, as well as accelerator technology driving the necessity of centre-specific dosimetric validation. Novel methodologies for X-ray based image processing and MRI for real-time tumour tracking and motion management were shown to have a large potential for online and offline adaptation schemes compensating for potential anatomical changes over the treatment course. The latest research developments were dominated by particle imaging, artificial intelligence methods and FLASH adding another level of complexity but also opportunities in the context of 4D treatments.

Conclusion

This review showed that the rapid technological advances in radiation oncology together with the available intrafractional motion management and adaptive strategies paved the way towards clinical implementation.

Retrospective reconstruction of four-dimensional magnetic resonance from interleaved cine imaging - A comparative study with four-dimensional computed tomography in the lung

Background and purpose

Imaging of respiration-induced anatomical changes is essential to ensure high accuracy in radiotherapy of lung cancer. We expanded here on methods for retrospective reconstruction of time-resolved volumetric magnetic resonance (4DMR) of the thoracic region and benchmarked the results against 4D computed tomography (4DCT).

Materials and method

MR data of six lung cancer patients were collected by interleaving cine-navigator images with 2D data frame images, acquired across the thorax. The data frame images have been stacked in volumes based on a similarity metric that considers the anatomical deformation of lungs, while addressing ambiguities in respiratory phase detection and interpolation of missing data. The resulting images were validated against cine-navigator images and compared to paired 4DCTs in terms of amplitude and period of motion, assessing differences in internal target volume (ITV) margin definition.

Results

4DMR-based motion amplitude was on average within 1.8 mm of that measured in the corresponding 2D cine-navigator images. In our dataset, the 4DCT motion and the 4DMR median amplitude were always within 3.8 mm. The median period was generally close to CT references, although deviations up to 24 % have been observed. These changes were reflected in the ITV, which was generally larger for MRI than for 4DCT (up to 39.7 %).

Conclusions

The proposed algorithm for retrospective reconstruction of time-resolved volumetric MR provided quality anatomical images with high temporal resolution for motion modelling and treatment planning. The potential for imaging organ motion variability makes 4DMR a valuable complement to standard 4DCT imaging.

Automatic lung segmentation of magnetic resonance images: A new approach applied to healthy volunteers undergoing enhanced Deep-Inspiration-Breath-Hold for motion-mitigated 4D proton therapy of lung tumors

Background and purpose

Respiratory suppression techniques represent an effective motion mitigation strategy for 4D-irradiation of lung tumors with protons. A magnetic resonance imaging (MRI)-based study applied and analyzed methods for this purpose, including enhanced Deep-Inspiration-Breath-Hold (eDIBH). Twenty-one healthy volunteers (41-58 years) underwent thoracic MR scans in four imaging sessions containing two eDIBH-guided MRIs per session to simulate motion-dependent irradiation conditions. The automated MRI segmentation algorithm presented here was critical in determining the lung volumes (LVs) achieved during eDIBH.

Materials and methods

The study included 168 MRIs acquired under eDIBH conditions. The lung segmentation algorithm consisted of four analysis steps: (i) image preprocessing, (ii) MRI histogram analysis with thresholding, (iii) automatic segmentation, (iv) 3D-clustering. To validate the algorithm, 46 eDIBH-MRIs were manually contoured. Sørensen-Dice similarity coefficients (DSCs) and relative deviations of LVs were determined as similarity measures. Assessment of intrasessional and intersessional LV variations and their differences provided estimates of statistical and systematic errors.

Results

Lung segmentation time for 100 2D-MRI planes was ∼ 10 s. Compared to manual lung contouring, the median DSC was 0.94 with a lower 95 % confidence level (CL) of 0.92. The relative volume deviations yielded a median value of 0.059 and 95 % CLs of -0.013 and 0.13. Artifact-based volume errors, mainly of the trachea, were estimated. Estimated statistical and systematic errors ranged between 6 and 8 %.

Conclusions

The presented analytical algorithm is fast, precise, and readily available. The results are comparable to time-consuming, manual segmentations and other automatic segmentation approaches. Post-processing to remove image artifacts is under development.

The Future of MR-Guided Radiation Therapy

Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality. This article summarizes limitations of current available integrated MRIgRT systems and gives an outlook about scientific developments to further expand the use of MRIgRT.

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects

MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.

Assessment of interfraction dose variation in pancreas SBRT using daily simulation MR images

Pancreatic Cancer is associated with poor treatment outcomes compared to other cancers. High local control rates have been achieved by using hypofractionated stereotactic body radiotherapy (SBRT) to treat pancreatic cancer. Challenges in delivering SBRT include close proximity of several organs at risk (OARs) and target volume inter and intra fraction positional variations. Magnetic resonance image (MRI) guided radiotherapy has shown potential for online adaptive radiotherapy for pancreatic cancer, with superior soft tissue contrast compared to CT. The aim of this study was to investigate the variability of target and OAR volumes for different treatment approaches for pancreatic cancer, and to assess the suitability of utilizing a treatment-day MRI for treatment planning purposes. Ten healthy volunteers were scanned on a Siemens Skyra 3 T MRI scanner over two sessions (approximately 3 h apart), per day over 5 days to simulate an SBRT daily simulation scan for treatment planning. A pretreatment scan was also done to simulate patient setup and treatment. A 4D MRI scan was taken at each session for internal target volume (ITV) generation and assessment. For each volunteer a treatment plan was generated in the Raystation treatment planning system (TPS) following departmental protocols on the day one, first session dataset (D1S1), with bulk density overrides applied to enable dose calculation. This treatment plan was propagated through other imaging sessions, and the dose calculated. An additional treatment plan was generated on each first session of each day (S1) to simulate a daily replan process, with this plan propagated to the second session of the day. These accumulated mock treatment doses were assessed against the original treatment plan through DVH comparison of the PTV and OAR volumes. The generated ITV showed large variations when compared to both the first session ITV and daily ITV, with an average magnitude of 22.44% ± 13.28% and 25.83% ± 37.48% respectively. The PTV D95 was reduced by approximately 23.3% for both plan comparisons considered. Surrounding OARs had large variations in dose, with the small bowel V30 increasing by 128.87% when compared to the D1S1 plan, and 43.11% when compared to each daily S1 plan. Daily online adaptive radiotherapy is required for accurate dose delivery for pancreas cancer in the absence of additional motion management and tumour tracking techniques.

Continuous time-resolved estimated synthetic 4D-CTs for dose reconstruction of lung tumor treatments at a 0.35 T MR-linac

Objective.To experimentally validate a method to create continuous time-resolved estimated synthetic 4D-computed tomography datasets (tresCTs) based on orthogonal cine MRI data for lung cancer treatments at a magnetic resonance imaging (MRI) guided linear accelerator (MR-linac).Approach.A breathing porcine lung phantom was scanned at a CT scanner and 0.35 T MR-linac. Orthogonal cine MRI series (sagittal/coronal orientation) at 7.3 Hz, intersecting tumor-mimicking gelatin nodules, were deformably registered to mid-exhale 3D-CT and 3D-MRI datasets. The time-resolved deformation vector fields were extrapolated to 3D and applied to a reference synthetic 3D-CT image (sCTref), while accounting for breathing phase-dependent lung density variations, to create 82 s long tresCTs at 3.65 Hz. Ten tresCTs were created for ten tracked nodules with different motion patterns in two lungs. For each dataset, a treatment plan was created on the mid-exhale phase of a measured ground truth (GT) respiratory-correlated 4D-CT dataset with the tracked nodule as gross tumor volume (GTV). Each plan was recalculated on the GT 4D-CT, randomly sampled tresCT, and static sCTrefimages. Dose distributions for corresponding breathing phases were compared in gamma (2%/2 mm) and dose-volume histogram (DVH) parameter analyses.Main results.The mean gamma pass rate between all tresCT and GT 4D-CT dose distributions was 98.6%. The mean absolute relative deviations of the tresCT with respect to GT DVH parameters were 1.9%, 1.0%, and 1.4% for the GTVD98%,D50%, andD2%, respectively, 1.0% for the remaining nodulesD50%, and 1.5% for the lungV20Gy. The gamma pass rate for the tresCTs was significantly larger (p< 0.01), and the GTVD50%deviations with respect to the GT were significantly smaller (p< 0.01) than for the sCTref.Significance.The results suggest that tresCTs could be valuable for time-resolved reconstruction and intrafractional accumulation of the dose to the GTV for lung cancer patients treated at MR-linacs in the future.

Dosimetry impact of distinct gating strategies in cine MR image-guided breath-hold pancreatic cancer radiotherapy

PURPOSE

To investigate the dosimetry effects of different gating strategies in cine magnetic resonance imaging (MRI)-guided breath-hold pancreatic cancer radiotherapy.

METHODS

Two cine MRI-based gating strategies were investigated： a tumor contour-based gating strategy at a gating threshold of 0-5% and a tumor displacement-based gating strategy at a gating threshold of 3-5 mm. The cine MRI videos were obtained from 17 pancreatic cancer patients who received MRI-guided radiation therapy. We calculated the tumor displacement in each cine MR frame that satisfied the gating threshold and obtained the proportion of frames with different displacements. We generated IMRT and VMAT plans using a 33 Gy prescription, and motion plans were generated by adding up all isocenter-shift plans corresponding to different tumor displacements. The dose parameters of GTV, PTV, and organs at risk (OAR) were compared between the original and motion plans.

RESULTS

In both gating strategies, the difference was significant in PTV coverage but not in GTV coverage between the original and motion plans. OAR dose parameters deteriorate with increasing gating threshold. The beam duty cycle increased from 19.5±14.3% (median 18.0%) to 60.8±15.6% (61.1%) for gating thresholds from 0% to 5% in tumor contour-based gating and from 51.7±11.5% (49.7%) to 67.3±12.4% (67.1%) for gating thresholds from 3 to 5 mm in tumor displacement-based gating.

CONCLUSION

In tumor contour-based gating strategy, the dose delivery accuracy deteriorates while the dose delivery efficiency improves with increasing gating thresholds. To ensure treatment efficiency, the gating threshold might be no less than 3%. A threshold up to 5% may be acceptable in terms of the GTV coverage. The displacement-based gating strategy may serve as a potential alternative to the tumor contour based gating strategy, in which the gating threshold of approximately 4 mm might be a good choice for reasonably balancing the dose delivery accuracy and efficiency.

Multi-parametric MRI for radiotherapy simulation

Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.

Clinical application of MR-Linac in tumor radiotherapy: a systematic review

Recent years have seen both a fresh knowledge of cancer and impressive advancements in its treatment. However, the clinical treatment paradigm of cancer is still difficult to implement in the twenty-first century due to the rise in its prevalence. Radiotherapy (RT) is a crucial component of cancer treatment that is helpful for almost all cancer types. The accuracy of RT dosage delivery is increasing as a result of the quick development of computer and imaging technology. The use of image-guided radiation (IGRT) has improved cancer outcomes and decreased toxicity. Online adaptive radiotherapy will be made possible by magnetic resonance imaging-guided radiotherapy (MRgRT) using a magnetic resonance linear accelerator (MR-Linac), which will enhance the visibility of malignancies. This review's objectives are to examine the benefits of MR-Linac as a treatment approach from the perspective of various cancer patients' prognoses and to suggest prospective development areas for additional study.

Multi-scale contextual semantic enhancement network for 3D medical image segmentation

Objective. Accurate and automatic segmentation of medical images is crucial for improving the efficiency of disease diagnosis and making treatment plans. Although methods based on convolutional neural networks have achieved excellent results in numerous segmentation tasks of medical images, they still suffer from challenges including drastic scale variations of lesions, blurred boundaries of lesions and class imbalance. Our objective is to design a segmentation framework named multi-scale contextual semantic enhancement network (3D MCSE-Net) to address the above problems.Approach. The 3D MCSE-Net mainly consists of a multi-scale context pyramid fusion module (MCPFM), a triple feature adaptive enhancement module (TFAEM), and an asymmetric class correction loss (ACCL) function. Specifically, the MCPFM resolves the problem of unreliable predictions due to variable morphology and drastic scale variations of lesions by capturing the multi-scale global context of feature maps. Subsequently, the TFAEM overcomes the problem of blurred boundaries of lesions caused by the infiltrating growth and complex context of lesions by adaptively recalibrating and enhancing the multi-dimensional feature representation of suspicious regions. Moreover, the ACCL alleviates class imbalances by adjusting asy mmetric correction coefficient and weighting factor.Main results. Our method is evaluated on the nasopharyngeal cancer tumor segmentation (NPCTS) dataset, the public dataset of the MICCAI 2017 liver tumor segmentation (LiTS) challenge and the 3D image reconstruction for comparison of algorithm and DataBase (3Dircadb) dataset to verify its effectiveness and generalizability. The experimental results show the proposed components all have unique strengths and exhibit mutually reinforcing properties. More importantly, the proposed 3D MCSE-Net outperforms previous state-of-the-art methods for tumor segmentation on the NPCTS, LiTS and 3Dircadb dataset.Significance. Our method addresses the effects of drastic scale variations of lesions, blurred boundaries of lesions and class imbalance, and improves tumors segmentation accuracy, which facilitates clinical medical diagnosis and treatment planning.

Reduction of inter-observer variability using MRI and CT fusion in delineating of primary tumor for radiotherapy in lung cancer with atelectasis

Purpose

To compare the difference between magnetic resonance imaging (MRI) and computed tomography (CT) in delineating the target area of lung cancer with atelectasis.

Method

A retrospective analysis was performed on 15 patients with lung cancer accompanied by atelectasis. All positioning images were transferred to Eclipse treatment planning systems (TPSs). Six MRI sequences (T1WI, T1WI+C, T1WI+C Delay, T1WI+C 10 minutes, T2WI, DWI) were registered with positioning CT. Five radiation oncologists delineated the tumor boundary to obtain the gross tumor volume (GTV). Conformity index (CI) and dice coefficient (DC) were used to measure differences among observers.

Results

The differences in delineation mean volumes, CI, and DC among CT and MRIs were significant. Multiple comparisons were made between MRI sequences and CT. Among them, DWI, T2WI, and T1WI+C 10 minutes sequences were statistically significant with CT in mean volumes, DC, and CI. The mean volume of DWI, T2WI, and T1WI+C 10 minutes sequence in the target area is significantly smaller than that on the CT sequence, but the consistency is higher than that of CT sequences.

Conclusions

The recognition of atelectasis by MRI was better than that by CT, which could reduce interobserver variability of primary tumor delineation in lung cancer with atelectasis. Among them, DWI, T2WI, T1WI+C 10 minutes may be a better choice to improve the GTV delineation of lung cancer patients with atelectasis.

MR-guided beam gating: Residual motion, gating efficiency and dose reconstruction for stereotactic treatments of the liver and lung

PURPOSE

This study aims to investigate the efficiency and the geometric as well as the dosimetric benefit of magnetic-resonance guided beam gating for stereotactic treatments in moving organs.

METHOD

Patients treated with MR-guided (MRIdian system) SBRT for lung (n = 10) and liver (n = 10) targets were analyzed. Breath-hold gating was performed based on lesion tracking in sagittal cine MRI images. The target offset from the geometric center of the gating window with and without gating was evaluated. A dose reconstruction workflow based on convolution of these 2D position-probability maps and the daily 3D dose distribution was used to estimate the daily delivered dose including motion. The dose to the clinical target volume (CTV) and to a 2-cm ring structure around the planning target volume were evaluated.

RESULTS

The applied gating protocol resulted in a mean (±standard deviation) gating efficiency of 55%±16%. Over all patients, the mean target offset (2D-root-mean-square error) was 8.3 ± 4.3 mm, which reduced to 2.4 ± 0.6 mm during gating. The dose reconstruction showed a mean deviation in CTV coverage (D95) from the static plans of -1.7%±1.8% with gating and -12.0%±8.4% if no gating would have been used. The mean dose (Dmean) in the ring structure, with respect to the static plans, showed mean deviations of -0.1%±0.3% with gating and -1.6%±1.8% without gating.

CONCLUSION

The MRIdian system enables gating based on the inner anatomy and the implemented dose reconstruction workflow demonstrated geometric robust delivery of the planned radiation doses.

Integrated MRI-guided radiotherapy - opportunities and challenges

MRI can help to categorize tissues as malignant or non-malignant both anatomically and functionally, with a high level of spatial and temporal resolution. This non-invasive imaging modality has been integrated with radiotherapy in devices that can differentially target the most aggressive and resistant regions of tumours. The past decade has seen the clinical deployment of treatment devices that combine imaging with targeted irradiation, making the aspiration of integrated MRI-guided radiotherapy (MRIgRT) a reality. The two main clinical drivers for the adoption of MRIgRT are the ability to image anatomical changes that occur before and during treatment in order to adapt the treatment approach, and to image and target the biological features of each tumour. Using motion management and biological targeting, the radiation dose delivered to the tumour can be adjusted during treatment to improve the probability of tumour control, while simultaneously reducing the radiation delivered to non-malignant tissues, thereby reducing the risk of treatment-related toxicities. The benefits of this approach are expected to increase survival and quality of life. In this Review, we describe the current state of MRIgRT, and the opportunities and challenges of this new radiotherapy approach.

Effect of intrafraction adaptation on PTV margins for MRI guided online adaptive radiotherapy for rectal cancer

Purpose

To determine PTV margins for intrafraction motion in MRI-guided online adaptive radiotherapy for rectal cancer and the potential benefit of performing a 2nd adaptation prior to irradiation.

Methods

Thirty patients with rectal cancer received radiotherapy on a 1.5 T MR-Linac. On T2-weighted images for adaptation (MRI_adapt), verification prior to (MRI_ver) and after irradiation (MRI_post) of 5 treatment fractions per patient, the primary tumor GTV (GTV_prim) and mesorectum CTV (CTV_meso) were delineated. The structures on MRI_adapt were expanded to corresponding PTVs. We determined the required expansion margins such that on average over 5 fractions, 98% of CTV_meso and 95% of GTV_prim on MRI_post was covered in 90% of the patients. Furthermore, we studied the benefit of an additional adaptation, just prior to irradiation, by evaluating the coverage between the structures on MRI_ver and MRI_post. A threshold to assess the need for a secondary adaptation was determined by considering the overlap between MRI_adapt and MRI_ver.

Results

PTV margins for intrafraction motion without 2nd adaptation were 6.4 mm in the anterior direction and 4.0 mm in all other directions for CTV_meso and 5.0 mm isotropically for GTV_prim. A 2nd adaptation, applied for all fractions where the motion between MRI_adapt and MRI_ver exceeded 1 mm (36% of the fractions) would result in a reduction of the PTV_meso margin to 3.2 mm/2.0 mm. For PTV_prim a margin reduction to 3.5 mm is feasible when a 2nd adaptation is performed in fractions where the motion exceeded 4 mm (17% of the fractions).

Conclusion

We studied the potential benefit of intrafraction motion monitoring and a 2nd adaptation to reduce PTV margins in online adaptive MRIgRT in rectal cancer. Performing 2nd adaptations immediately after online replanning when motion exceeded 1 mm and 4 mm for CTV_meso and GTV_prim respectively, could result in a 30–50% margin reduction with limited reduction of dose to the bowel.

A hybrid 2D/4D-MRI methodology using simultaneous multislice imaging for radiotherapy guidance

Purpose

Respiratory motion management is important in abdominothoracic radiotherapy. Fast imaging of the tumor can facilitate multileaf collimator (MLC) tracking that allows for smaller treatment margins, while repeatedly imaging the full field‐of‐view is necessary for 4D dose accumulation. This study introduces a hybrid 2D/4D‐MRI methodology that can be used for simultaneous MLC tracking and dose accumulation on a 1.5 T Unity MR‐linac (Elekta AB, Stockholm, Sweden).

Methods

We developed a hybrid 2D/4D‐MRI methodology that uses a simultaneous multislice (SMS) accelerated MRI sequence, which acquires two coronal slices simultaneously and repeatedly cycles through slice positions over the image volume. As a result, the fast 2D imaging can be used prospectively for MLC tracking and the SMS slices can be sorted retrospectively into respiratory‐correlated 4D‐MRIs for dose accumulation. Data were acquired in five healthy volunteers with an SMS‐bTFE and SMS‐TSE MRI sequence. For each sequence, a prebeam dataset and a beam‐on dataset were acquired simulating the two phases of MR‐linac treatments. Prebeam data were used to generate a 4D‐based motion model and a reference mid‐position volume, while beam‐on data were used for real‐time motion extraction and reconstruction of beam‐on 4D‐MRIs. In addition, an in‐silico computational phantom was used for validation of the hybrid 2D/4D‐MRI methodology. MLC tracking experiments were performed with the developed methodology, for which real‐time SMS data reconstruction was enabled on the scanner. A 15‐beam 8× 7.5 Gy intensity‐modulated radiotherapy plan for lung stereotactic body radiotherapy with isotropic 3 mm GTV‐to‐PTV margins was created. Dosimetry experiments were performed using a 4D motion phantom. The latency between target motion and updating the radiation beam was determined and compensated. Local gamma analyses were performed to quantify dose differences compared to a static reference delivery, and dose area histograms (DAHs) were used to quantify the GTV and PTV coverage.

Results

In‐vivo data acquisition and MLC tracking experiments were successfully performed with the developed hybrid 2D/4D‐MRI methodology. Real‐time liver–lung interface motion estimation had a Pearson's correlation of 0.996 (in‐vivo) and 0.998 (in‐silico). A median (5th–95th percentile) error of 0.0 (−0.9 to 0.7) mm and 0.0 (−0.2 to 0.2) mm was found for real‐time motion estimation for in‐vivo and in‐silico, respectively. Target motion prediction beyond the liver–lung interface had a median root mean square error of 1.6 mm (in‐vivo) and 0.5 mm (in‐silico). Beam‐on 4D MRI reconstruction required a median amount of data equal to an acquisition time of 2:21–3:17 min, which was 20% less data compared to the prebeam‐derived 4D‐MRI. System latency was reduced from 501 ± 12 ms to −1 ± 3 ms (SMS‐TSE) and from 398 ± 10 ms to −10 ± 4 ms (SMS‐bTFE) by a linear regression prediction filter. The local gamma analysis agreed within −3.8% to 3.3% (SMS‐bTFE) and −5.3% to 10% (SMS‐TSE) with a reference MRI sequence. The DAHs revealed a relative D98% GTV coverage between 97% and 100% (SMS‐bTFE) and 100% and 101% (SMS‐TSE) compared to the static reference.

Conclusions

The presented 2D/4D‐MRI methodology demonstrated the potential for accurately extracting real‐time motion for MLC tracking in abdominothoracic radiotherapy, while simultaneously reconstructing contiguous respiratory‐correlated 4D‐MRIs for dose accumulation.

Contrast-enhanced 4D-MRI for internal target volume generation in treatment planning for liver tumors

BACKGROUND

Liver tumors are often invisible on four-dimensional commuted tomography (4D-CT). Imperfect imaging surrogates are used to estimate the tumor motion. Here, we assessed multiple 4D magnetic resonance (MR) binning algorithms for directly visualizing liver tumor motion for radiotherapy planning.

METHODS

Patients were simulated using a 3 Tesla MR and CT scanner. Three prototype binning algorithms (phase, amplitude, and two-directional) were applied to the 4D-MRIs, and the image quality was assessed using a qualitative clarity score and quantitative sharpness score. Radiation plans were generated for internal target volumes (ITVs) derived using 4D-MRI and 4D-CT, and the dosimetry of targets were compared. Paired t-tests were used to compare sharpness scores and dosimetric data.

RESULTS

Twelve patients with 17 liver tumors were scanned between May and November 2021. Compared to phase binning, two-directional demonstrated equal or better clarity and sharpness scores (end-expiration: 0.33 vs. 0.38, p=0.018, end-inspiration: 0.28 vs. 0.31, p=0.010). Compared to amplitude binning, two-directional binning captured hysteresis of ≥3 mm in 35% of patients. Evaluation of dosimetry CT-optimized plans revealed that PTV coverage of MR-derived targets were significantly lower than CT-derived targets (PTV receiving 90% of prescription: 75.56% vs. 89.38%, p=0.002).

CONCLUSION

Using contrast-enhanced 4D-MRI is feasible for directly delineating liver tumors throughout the respiratory cycle. The current standard of using radiation plans optimized for 4D-CT-derived targets achieved lower coverage of directly visualized MRI targets, suggesting that adopting MRI for motion management may improve radiation treatment of liver lesions and reduce the risk of marginal misses.

Research on new treatment mode of radiotherapy based on pseudo-medical images

BACKGROUND AND OBJECTIVE

Multi-modal medical images with multiple feature information are beneficial for radiotherapy. A new radiotherapy treatment mode based on triangle generative adversarial network (TGAN) model was proposed to synthesize pseudo-medical images between multi-modal datasets.

METHODS

CBCT, MRI and CT images of 80 patients with nasopharyngeal carcinoma were selected. The TGAN model based on multi-scale discriminant network was used for data training between different image domains. The generator of the TGAN model refers to cGAN and CycleGAN, and only one generation network can establish the non-linear mapping relationship between multiple image domains. The discriminator used multi-scale discrimination network to guide the generator to synthesize pseudo-medical images that are similar to real images from both shallow and deep aspects. The accuracy of pseudo-medical images was verified in anatomy and dosimetry.

RESULTS

In the three synthetic directions, namely, CBCT → CT, CBCT → MRI, and MRI → CT, significant differences (p < 0.05) in the three-fold-cross validation results on PSNR and SSIM metrics between the pseudo-medical images obtained based on TGAN and the real images. In the testing stage, for TGAN, the MAE metric results in the three synthesis directions (CBCT → CT, CBCT → MRI, and MRI → CT) were presented as mean (standard deviation), which were 68.67 (5.83), 83.14 (8.48), and 79.96 (7.59), and the NMI metric results were 0.8643 (0.0253), 0.8051 (0.0268), and 0.8146 (0.0267) respectively. In terms of dose verification, the differences in dose distribution between the pseudo-CT obtained by TGAN and the real CT were minimal. The H values of the measurement results of dose uncertainty in PGTV, PGTVnd, PTV1, and PTV2 were 42.510, 43.121, 17.054, and 7.795, respectively (P < 0.05). The differences were statistically significant. The gamma pass rate (2%/2 mm) of pseudo-CT obtained by the new model was 94.94% (0.73%), and the numerical results were better than those of the three other comparison models.

CONCLUSIONS

The pseudo-medical images acquired based on TGAN were close to the real images in anatomy and dosimetry. The pseudo-medical images synthesized by the TGAN model have good application prospects in clinical adaptive radiotherapy.

Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy

Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for training and validation (70 patients, 13.1 h), and for testing (18 patients, 3.0 h). Three patients treated at Fondazione Policlinico Universitario Agostino Gemelli were used as a second testing set (1.5 h). The performance of the LSTMs in terms of root mean square error (RMSE) was compared to baseline linear regression (LR) models for forecasted time spans of 250 ms, 500 ms and 750 ms. Both the LSTM and the LR were trained with offline (offline LSTM and offline LR) and online schemes (offline+online LSTM and online LR), the latter to allow for continuous adaptation to recent respiratory patterns. Main results. We found the offline+online LSTM to perform best for all investigated forecasts. Specifically, when predicting 500 ms ahead it achieved a mean RMSE of 1.20 mm and 1.00 mm, while the best performing LR model achieved a mean RMSE of 1.42 mm and 1.22 mm for the LMU and Gemelli testing set, respectively. Significance. This indicates that LSTM networks have potential as respiratory motion predictors and that continuous online re-optimization can enhance their performance.

Management of Motion and Anatomical Variations in Charged Particle Therapy: Past, Present, and Into the Future

The major aim of radiation therapy is to provide curative or palliative treatment to cancerous malignancies while minimizing damage to healthy tissues. Charged particle radiotherapy utilizing carbon ions or protons is uniquely suited for this task due to its ability to achieve highly conformal dose distributions around the tumor volume. For these treatment modalities, uncertainties in the localization of patient anatomy due to inter- and intra-fractional motion present a heightened risk of undesired dose delivery. A diverse range of mitigation strategies have been developed and clinically implemented in various disease sites to monitor and correct for patient motion, but much work remains. This review provides an overview of current clinical practices for inter and intra-fractional motion management in charged particle therapy, including motion control, current imaging and motion tracking modalities, as well as treatment planning and delivery techniques. We also cover progress to date on emerging technologies including particle-based radiography imaging, novel treatment delivery methods such as tumor tracking and FLASH, and artificial intelligence and discuss their potential impact towards improving or increasing the challenge of motion mitigation in charged particle therapy.

Dosimetry impact of gating latency in cine magnetic resonance image guided breath-hold pancreatic cancer radiotherapy

Objective.We investigated dosimetry effect of gating latency in cine magnetic resonance image (cine MRI) guided breath-hold pancreatic cancer radiotherapy.Approach.The gating latency was calculated based on cine MRI obtained from 17 patients who received MRI guided radiotherapy. Because of the cine MRI-related latency, beam overshoot occurs when beam remains on while the tracking target already moves out of the target boundary. The number of beam on/off events was calculated from the cine MRI data. We generated both IMRT and VMAT plans for all 17 patients using 33 Gy prescription, and created motion plans by applying isocenter shift that corresponds to motion-induced tumor displacement. The GTV and PTV coverage and dose to nearby critical structures were compared between the motion and original plan to evaluate the dosimetry change caused by cine MRI latency.Main results.The time ratio of cine MRI imaging latency over the treatment duration is 6.6 ± 3.1%, the mean and median percentage of beam-on events <4 s are 67.0 ± 14.3% and 66.6%. When a gating boundary of 4 mm and a target-out threshold of 5% is used, there is no significant difference for GTV V33Gy between the motion and original plan (p = 0.861 and 0.397 for IMRT and VMAT planning techniques, respectively). However, the PTV V33Gy and stomach Dmax for the motion plans are significantly lower; duodenum V12.5 Gy and V18Gy are significantly higher when compared with the original plans, for both IMRT and VMAT planning techniques.Significance.The cine MRI gating latency can significantly decrease the dose delivered to the PTV, and increase the dose to the nearby critical structures. However, no significant difference is observed for the GTV coverage. The dosimetry impact can be mitigated by implementing additional beam-on control techniques which reduces unnecessary beam on events and/or by using faster cine MRI sequences which reduces the latency period.

A narrative review of MRI acquisition for MR-guided-radiotherapy in prostate cancer

Magnetic resonance guided radiotherapy (MRgRT), enabled by the clinical introduction of the integrated MRI and linear accelerator (MR-LINAC), is a novel technique for prostate cancer (PCa) treatment, promising to further improve clinical outcome and reduce toxicity. The role of prostate MRI has been greatly expanded from the traditional PCa diagnosis to also PCa screening, treatment and surveillance. Diagnostic prostate MRI has been relatively familiar in the community, particularly with the development of Prostate Imaging - Reporting and Data System (PI-RADS). But, on the other hand, the use of MRI in the emerging clinical practice of PCa MRgRT, which is substantially different from that in PCa diagnosis, has been so far sparsely presented in the medical literature. This review attempts to give a comprehensive overview of MRI acquisition techniques currently used in the clinical workflows of PCa MRgRT, from treatment planning to online treatment guidance, in order to promote MRI practice and research for PCa MRgRT. In particular, the major differences in the MRI acquisition of PCa MRgRT from that of diagnostic prostate MRI are demonstrated and explained. Limitations in the current MRI acquisition for PCa MRgRT are analyzed. The future developments of MRI in the PCa MRgRT are also discussed.

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.

Feasibility of cardiac-synchronized quantitative T1 and T2 mapping on a hybrid 1.5 Tesla magnetic resonance imaging and linear accelerator system

Background and Purpose

Materials and methods

Results

Conclusions

Synthesizing high-resolution magnetic resonance imaging using parallel cycle-consistent generative adversarial networks for fast magnetic resonance imaging

PURPOSE

The common practice in acquiring the magnetic resonance (MR) images is to obtain two-dimensional (2D) slices at coarse locations while keeping the high in-plane resolution in order to ensure enough body coverage while shortening the MR scan time. The aim of this study is to propose a novel method to generate HR MR images from low-resolution MR images along the longitudinal direction. In order to address the difficulty of collecting paired low- and high-resolution MR images in clinical settings and to gain the advantage of parallel cycle consistent generative adversarial networks (CycleGANs) in synthesizing realistic medical images, we developed a parallel CycleGANs based method using a self-supervised strategy.

METHODS AND MATERIALS

The proposed workflow consists of two parallely trained CycleGANs to independently predict the HR MR images in the two planes along the directions that are orthogonal to the longitudinal MR scan direction. Then, the final synthetic HR MR images are generated by fusing the two predicted images. MR images, including T1-weighted (T1), contrast enhanced T1-weighted (T1CE), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR), of the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were processed to evaluate the proposed workflow along the cranial-caudal (CC), lateral, and anterior-posterior directions. Institutional collected MR images were also processed for evaluation of the proposed method. The performance of the proposed method was investigated via both qualitative and quantitative evaluations. Metrics of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), edge keeping index (EKI), structural similarity index measurement (SSIM), information fidelity criterion (IFC), and visual information fidelity in pixel domain (VIFP) were calculated.

RESULTS

It is shown that the proposed method can generate HR MR images visually indistinguishable from the ground truth in the investigations on the BraTS2020 dataset. In addition, the intensity profiles, difference images and SSIM maps can also confirm the feasibility of the proposed method for synthesizing HR MR images. Quantitative evaluations on the BraTS2020 dataset shows that the calculated metrics of synthetic HR MR images can all be enhanced for the T1, T1CE, T2, and FLAIR images. The enhancements in the numerical metrics over the low-resolution and bi-cubic interpolated MR images, as well as those genearted with a comparative deep learning method, are statistically significant. Qualitative evaluation of the synthetic HR MR images of the clinical collected dataset could also confirm the feasibility of the proposed method.

CONCLUSIONS

The proposed method is feasible to synthesize HR MR images using self-supervised parallel CycleGANs, which can be expected to shorten MR acquisition time in clinical practices.

Integrated MRI-guided proton therapy planning: accounting for the full MRI field in a perpendicular system

PURPOSE

To present a first study on the treatment planning feasibility in perpendicular field MRI-integrated proton therapy which considers the full transport of protons from the pencil beam scanning assembly to the patient inside the MRI scanner.

METHODS

A generic proton pencil beam scanning (PBS) gantry was modelled as being integrated with a realistic split-bore MRI system in the perpendicular orientation. MRI field strengths were modeled as 0.5 T, 1 T, and 1.5 T. The PBS beam delivery and dose calculation was modeled using the TOPAS Monte Carlo toolkit coupled with matRad as the optimizer engine. A water phantom, liver and prostate plans were evaluated and optimized in the presence of the full MRI field distribution. A simple combination of gantry angle offset and small PBS nozzle skew was used to direct the proton beams along a path that closely follows the reference planning scenario, i.e. without magnetic field.

RESULTS

All planning metrics could be successfully achieved with the inclusion of gantry angle offsets in the range of 8°-29° when coupled with a PBS nozzle skew of 1.6°-4.4°. These two hardware based corrections were selected to minimize the average Euclidean distance (AED) in the beam path enabling the proton beams to travel inside the patient in a path that is close to the original path (AED smaller than 3 mm at 1.5 T). Final dose optimization, performed through further changes in the pencil beam scanning delivery, was then shown to be feasible for our selection of plans studied yielding comparable plan quality metrics to reference conditions.

CONCLUSIONS

For the first time, we have shown a robust method to account for the full proton beam deflection in a perpendicular orientation MRI-integrated proton therapy. These results support the ongoing development of the current prototype systems. This article is protected by copyright. All rights reserved.

Probabilistic 4D predictive model from in-room surrogates using conditional generative networks for image-guided radiotherapy

Shape and location organ variability induced by respiration constitutes one of the main challenges during dose delivery in radiotherapy. Providing up-to-date volumetric information during treatment can improve tumor tracking, thereby increasing treatment efficiency and reducing damage to healthy tissue. We propose a novel probabilistic model to address the problem of volumetric estimation with scalable predictive horizon from image-based surrogates during radiotherapy treatments, thus enabling out-of-plane tracking of targets. This problem is formulated as a conditional learning task, where the predictive variables are the 2D surrogate images and a pre-operative static 3D volume. The model learns a distribution of realistic motion fields over a population dataset. Simultaneously, a seq-2-seq inspired temporal mechanism acts over the surrogate images yielding extrapolated-in-time representations. The phase-specific motion distributions are associated with the predicted temporal representations, allowing the recovery of dense organ deformation in multiple times. Due to its generative nature, this model enables uncertainty estimations by sampling the latent space multiple times. Furthermore, it can be readily personalized to a new subject via fine-tuning, and does not require inter-subject correspondences. The proposed model was evaluated on free-breathing 4D MRI and ultrasound datasets from 25 healthy volunteers, as well as on 11 cancer patients. A navigator-based data augmentation strategy was used during the slice reordering process to increase model robustness against inter-cycle variability. The patient data was used as a hold-out test set. Our approach yields volumetric prediction from image surrogates with a mean error of 1.67 ± 1.68 mm and 2.17 ± 0.82 mm in unseen cases of the patient MRI and US datasets, respectively. Moreover, model personalization yields a mean landmark error of 1.4 ± 1.1 mm compared to ground truth annotations in the volunteer MRI dataset, with statistically significant improvements over state-of-the-art.

Toward MR-integrated proton therapy: modeling the potential benefits for liver tumors

Magnetic resonance imaging (MRI)-integrated proton therapy (MRiPT) is envisioned to improve treatment quality for many cancer patients. However, given the availability of alternative image-guided strategies, its clinical need is yet to be justified. This study aims to compare the expected clinical outcomes of MRiPT with standard of practice cone-beam CT (CBCT)-guided PT, and other MR-guided methods, i.e. offline MR-guided PT and MR-linac, for treatment of liver tumors. Clinical outcomes were assessed by quantifying the dosimetric and biological impact of target margin reduction enabled by each image-guided approach. Planning target volume (PTV) margins were calculated using random and systematic setup, delineation and motion uncertainties, which were quantified by analyzing longitudinal MRI data for 10 patients with liver tumors. Proton treatment plans were created using appropriate PTV margins for each image-guided PT method. Photon plans with margins equivalent to MRiPT were generated to represent MR-linac. Normal tissue complication probabilities (NTCP) of the uninvolved liver were compared. We found that PTV margin can be reduced by 20% and 40% for offline MR-guided PT and MRiPT, respectively, compared with CBCT-guided PT. Furthermore, clinical target volume expansion could be largely alleviated when delineating on MRI rather than CT. Dosimetric implications included decreased equivalent mean dose of the uninvolved liver, i.e. up to 24.4 Gy and 27.3 Gy for offline MR-guided PT and MRiPT compared to CBCT-guided PT, respectively. Considering Child-Pugh score increase as endpoint, NTCP of the uninvolved liver was significantly decreased for MRiPT compared to CBCT-guided PT (up to 48.4%,p < 0.01), offline MR-guided PT (up to 12.9%,p < 0.01) and MR-linac (up to 30.8%,p < 0.05). Target underdose was possible in the absence of MRI-guidance (D90 reduction up to 4.2 Gy in 20% of cases). In conclusion, MRiPT has the potential to significantly reduce healthy liver toxicities in patients with liver tumors. It is superior to other image-guided techniques currently available.

Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging

Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Dosimetric Deviations of Bragg-Peak Position Shifts in Uniform Magnetic Fields for Magnetic Resonance Imaging-Guiding Proton Radiotherapy: A Monte Carlo Study

Objective: To investigate dosimetric deviations in scanning protons for Bragg-peak position shifts, which were caused by proton spiral tracks in an ideal uniform field of magnetic resonance (MRI) imaging-guided proton radiotherapy (MRI-IGPRT).

Methods: The FLUKA Monte-Carlo (MC) code was used to simulate the spiral tracks of protons penetrating water with initial energies of 70–270 MeV under the influence of field strength of 0.0–3.0 Tesla in commercial MRI systems. Two indexes, lateral shift (marked as WD) perpendicular to the field and a penetration-depth shift (marked as ΔDD) along the beam path, were employed for the Bragg-peak position of spiral proton track analysis. A comparison was performed between MC and classical analytical model to check the simulation results. The shape of the 2D/3D dose distribution of proton spots at the depth of Bragg-Peak was also investigated. The ratio of Gaussian-fit value between longitudinal and transverse major axes was used to indicate the asymmetric index. The skewness of asymmetry was evaluated at various dose levels by the radius ratio of circumscribed and inscribed circles by fitting a semi-ellipse circle of 2D distribution.

Results: The maximum of WD deflection is 2.82 cm while the maximum of shortening ΔDD is 0.44 cm for proton at 270 MeV/u under a magnetic field of 3.0 Tesla. The trend of WD and ΔDD from MC simulation was consistent with the analytical model, which means the reverse equation of the analytical model can be applied to determine the proper field strength of the magnet and the initial energy of the proton for the planned dose. The asymmetry of 2D/3D dose distribution under the influence of a magnetic field was increased with higher energy, and the skewness of asymmetry for one proton energy at various dose levels was also increased with a larger radius, i.e., a lower dose level.

Conclusions: The trend of the spiral proton track under a uniform magnetic field was obtained in this study using either MC simulation or the analytical model, which can provide an optimized and planned dose of the proton beam in the clinical application of MRI-IGPRT.

Deep learning based synthetic-CT generation in radiotherapy and PET: A review

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives

Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.

Validation of a 4D-MRI guided liver stereotactic body radiation therapy strategy for implementation on the MR-linac

Purpose. Accurate tumor localization for image-guided liver stereotactic body radiation therapy (SBRT) is challenging due to respiratory motion and poor tumor visibility on conventional x-ray based images. Novel integrated MRI and radiotherapy systems enable direct in-room tumor visualization, potentially increasing treatment accuracy. As these systems currently do not provide a 4D image-guided radiotherapy strategy, we developed a 4D-MRI guided liver SBRT workflow and validated all steps for implementation on the Unity MR-linac.Materials and Methods. The proposed workflow consists of five steps: (1) acquisition of a daily 4D-MRI scan, (2) 4D-MRI to mid-position planning-CT rigid tumor registration, (3) calculation of daily tumor midP misalignment, (4) plan adaptation using adapt-to-position (ATP) with segment-weights optimization and (5) adapted plan delivery. The workflow was first validated in a motion phantom, performing regular motion at different baselines (±5 to ±10 mm) and patient-derived respiratory signals with varying degrees of irregularity. 4D-MRI derived respiratory signals and 4D-MRI to planning CT registrations were compared to the phantom input, and gamma and dose-area-histogram analyses were performed on the delivered dose distributions on film. Additionally, 4D-MRI to CT registration performance was evaluated in patient images using the full-circle method (transitivity analysis). Plan adaption was further analyzedin-silicoby creating adapted treatment plans for 15 patients with oligometastatic liver disease.Results. Phantom trajectories could be reliably extracted from 4D-MRI scans and 4D-MRI to CT registration showed submillimeter accuracy. The DAH-analysis demonstrated excellent coverage of the dose evaluation structures GTV and GTV_TD. The median daily rigid 4D-MRI to midP-CT registration precision in patient images was <2 mm. The ATP strategy restored the target dose without increased exposure to the OARs and plan quality was independent from 3D shift distance in the range of 1-26 mm.Conclusions. The proposed 4D-MRI guided strategy showed excellent performance in all workflow tests in preparation of the clinical introduction on the Unity MR-linac.

Simultaneous multi-slice accelerated 4D-MRI for radiotherapy guidance

4D-MRI is becoming increasingly important for daily guidance of thoracic and abdominal radiotherapy. This study exploits the simultaneous multi-slice (SMS) technique to accelerate the acquisition of a balanced turbo field echo (bTFE) and a turbo spin echo (TSE) coronal 4D-MRI sequence performed on 1.5 T MRI scanners. SMS single-shot bTFE and TSE sequences were developed to acquire a stack of 52 coronal 2D images over 30 dynamics. Simultaneously excited slices were separated by half the field of view. Slices intersecting with the liver-lung interface were used as navigator slices. For each navigator slice location, an end-exhale dynamic was automatically identified, and used to derive the self-sorting signal by rigidly registering the remaining dynamics. Navigator slices were sorted into 10 amplitude bins, and the temporal relationship of simultaneously excited slices was used to generate sorted 4D-MRIs for 12 healthy volunteers. The self-sorting signal was validated using anin vivopeak-to-peak motion analysis. The smoothness of the liver-lung interface was quantified by comparing to sagittal cine images acquired directly after the SMS-4D-MRI sequence. To ensure compatibility with the MR-linac radiotherapy workflow, the 4D-MRIs were transformed into 3D mid-position (MidP) images using deformable image registration. Consistency of the deformable vector fields was quantified in terms of the distance discordance metric (DDM) in the body. The SMS-4D-TSE sequence was additionally acquired for 3 lung cancer patients to investigate tumor visibility. SMS-4D-MRI acquisition and processing took approximately 7 min. 4D-MRI reconstruction was possible for 26 out of 27 acquired datasets. Missing data in the sorted 4D-MRIs varied from 4%-26% for the volunteers and varied from 8%-24% for the patients. Peak-to-peak (SD) amplitudes analysis agreed within 1.8 (1.1) mm and 0.9 (0.4) mm between the sorted 4D-MRIs and the self-sorting signals of the volunteers and patients, respectively. Liver-lung interface smoothness was found to be in the range of 0.6-3.1 mm for volunteers. The percentage of DDM values smaller than 2 mm was in the range of 85%-89% and 86%-92% for the volunteers and patients, respectively. Lung tumors were clearly visibility in the SMS-4D-TSE images and MidP images. Two fast SMS-accelerated 4D-MRI sequences were developed resulting in T₂/T₁or T₂weighted contrast. The SMS-4D-MRIs and derived 3D MidP-MRIs yielded anatomically plausible images and good tumor visibility. SMS-4D-MRI is therefore a strong candidate to be used for treatment simulation and daily guidance of thoracic and abdominal MR-guided radiotherapy.

An MRI framework for respiratory motion modelling validation

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 mm³), 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.

Porcine lung phantom-based validation of estimated 4D-MRI using orthogonal cine imaging for low-field MR-Linacs

Real-time motion monitoring of lung tumors with low-field magnetic resonance imaging-guided linear accelerators (MR-Linacs) is currently limited to sagittal 2D cine magnetic resonance imaging (MRI). To provide input data for improved intrafractional and interfractional adaptive radiotherapy, the 4D anatomy has to be inferred from data with lower dimensionality. The purpose of this study was to experimentally validate a previously proposed propagation method that provides continuous time-resolved estimated 4D-MRI based on orthogonal cine MRI for a low-field MR-Linac. Ex vivo porcine lungs were injected with artificial nodules and mounted in a dedicated phantom that allows for the simulation of periodic and reproducible breathing motion. The phantom was scanned with a research version of a commercial 0.35 T MR-Linac. Respiratory-correlated 4D-MRI were reconstructed and served as ground truth images. Series of interleaved orthogonal slices in sagittal and coronal orientation, intersecting the injected targets, were acquired at 7.3 Hz. Estimated 4D-MRI at 3.65 Hz were created in post-processing using the propagation method and compared to the ground truth 4D-MRI. Eight datasets at different breathing frequencies and motion amplitudes were acquired for three porcine lungs. The overall median (95[Formula: see text] percentile) deviation between ground truth and estimated deformation vector fields was 2.3 mm (5.7 mm), corresponding to 0.7 (1.6) times the in-plane imaging resolution (3.5 × 3.5 mm²). Median (95[Formula: see text] percentile) estimated nodule position errors were 1.5 mm (3.8 mm) for nodules intersected by orthogonal slices and 2.1 mm (7.1 mm) for nodules located more than 2 cm away from either of the orthogonal slices. The estimation error depended on the breathing phase, the motion amplitude and the location of the estimated position with respect to the orthogonal slices. By using the propagation method, the 4D motion within the porcine lung phantom could be accurately and robustly estimated. The method could provide valuable information for treatment planning, real-time motion monitoring, treatment adaptation, and post-treatment evaluation of MR-guided radiotherapy treatments.

Feasibility of MR-guided radiotherapy using beam-eye-view 2D-cine with tumor-volume projection

PURPOSE

Current magnetic resonance imaging (MRI) guided radiotherapy (MRgRT) applies sagittal/coronal 2D-cine to monitor major tumor motions, however, the beam eye's view (BEV) with volumetric tumor projection would be the best measure for radiation beam conformality, independent of tumor through-plane motion. The goal is to assess the feasibility, accuracy, and performance of the BEV approach.

METHODS

Beam-specific BEV 2D-cine with volume-projected tumor contours were simulated to establish a 2D/3D tumor match against a tumor-motion library based on multi-breath time-resolved (TR) 4DMRI images. Two BEV-library-matching methods were developed: (1) fast screening with tumor center-of-mass (∆COM), in-plane area ratio, and DICE similarity, and finalizing with the highest DICE score and (2) DICE screening for top-3 candidates and finalizing with rigid registration. A 4D-XCAT digital phantom and 8 lung-cancer patients were used for assessment. For each patient, 3 sets of 40 s TR-4DMRI were acquired at 2 Hz and 6 representative BEV were created with the isocenter set at tumor COM in mid-respiration. One TR-4DMRI set (40 × 2 = 80-images) was used to simulate BEV 2D-cine and the other two (160-images) were used to create a library. The matching result was validated against the ground truth within the test set. Using a leave-one-out strategy, the success rate, accuracy, and speed of tumor matching were assessed for volume-projected tumors over 11520 time-points (=8patients•3sets•80images•6BEVs).

RESULTS

Volume-projected tumor contour area on the 6 BEVs varies by 60% ± 8% and [Formula: see text] (in-plane/volume-projected) varies by 82% ± 9%. The [Formula: see text] changes with tumor shape, orientation, and through-plane motion. Method-1 produces 96% matching success (ΔCOM = 0.7 ± 0.2 mm, [Formula: see text]=1.01 ± 0.02, Dice=0.92 ± 0.02) with the computational time of 15 ± 1 ms/match, while method-2 produces 94% ± 1% success (ΔCOM = 0.2 ± 0.1 mm, [Formula: see text]=1.00 ± 0.01, Dice = 0.94 ± 0.02) with 223 ± 13 ms/match.

CONCLUSION

This study has demonstrated the feasibility, accuracy, and benefits of BEV 2D-cine imaging with tumor-volume projection, allowing real-time tumor motion monitoring and beam conformality checking. Further clinical evaluation is necessary before MRgRT applications.

Development of phantom materials with independently adjustable CT- and MR-contrast at 0.35, 1.5 and 3 T

Quality assurance in magnetic resonance (MR)-guided radiotherapy lacks anthropomorphic phantoms that represent tissue-equivalent imaging contrast in both computed tomography (CT) and MR imaging. In this study, we developed phantom materials with individually adjustable CT value as well as [Formula: see text]- and [Formula: see text]-relaxation times in MR imaging at three different magnetic field strengths. Additionally, their experimental stopping power ratio (SPR) for carbon ions was compared with predictions based on single- and dual-energy CT. Ni-DTPA doped agarose gels were used for individual adjustment of [Formula: see text] and [Formula: see text] at [Formula: see text] and 3.0 T. The CT value was varied by adding potassium chloride (KCl). By multiple linear regression, equations for the determination of agarose, Ni-DTPA and KCl concentrations for given [Formula: see text] [Formula: see text] and CT values were derived and employed to produce nine specific soft tissue samples. Experimental [Formula: see text] [Formula: see text] and CT values of these soft tissue samples were compared with predictions and additionally, carbon ion SPR obtained by range measurements were compared with predictions based on single- and dual-energy CT. The measured CT value, [Formula: see text] and [Formula: see text] of the produced soft tissue samples agreed very well with predictions based on the derived equations with mean deviations of less than [Formula: see text] While single-energy CT overestimates the measured SPR of the soft tissue samples, the dual-energy CT-based predictions showed a mean SPR deviation of only [Formula: see text] To conclude, anthropomorphic phantom materials with independently adjustable CT values as well as [Formula: see text] and [Formula: see text] relaxation times at three different magnetic field strengths were developed. The derived equations describe the material specific relaxation times and the CT value in dependence on agarose, Ni-DTPA and KCl concentrations as well as the chemical composition of the materials based on given [Formula: see text] and CT value. Dual-energy CT allows accurate prediction of the carbon ion range in these materials.

Magnetic modeling of actively shielded rotating MRI magnets in the presence of environmental steel

Rotating MRI systems could enable novel integrated medical devices such as MRI-Linacs, MRI-xray-angiography systems, and MRI-proton therapy systems. This work aimed to investigate the feasibility of rotating actively shielded superconducting MRI magnets in the presence of environmental steel-in particular, construction steel in the floor of the installation site. Two magnets were investigated: a 1.0 T split bore magnet, and a 1.5 T closed bore magnet. Each magnet was scaled to emulate field strengths of 0.5, 1.0, and 1.5 T. Finite Element Modeling was used to simulate these magnets in the presence of a 3 × 4 m steel plate located 1250 mm or 1400 mm below the isocenter. There are two possible rotation directions: around the longitudinal (z) axis or around the transverse (x) axis. Each model was solved for rotation angles between 0 and 360° in 30° intervals around each of these axes. For each simulation, a 300 mm DSV was extracted and decomposed into spherical harmonics. For the closed-bore magnet, total induced perturbation for the zero degree rotation angle was 223, 432, and 562 μT peak-to-peak (pk-pk) for the 0.5, 1.0, and 1.5 T models respectively (steel at 1250 mm). For the split-bore magnet, the same numbers were 1477, 16747, and 1766 μT. The substantially higher perturbation for the split-bore magnet can be traced to its larger fringe field. For rotation around the z-axis, total perturbation does not change as a function of angle but is exchanged between different harmonics. For rotation around the x-axis, total perturbation is different at each rotation angle. For the closed bore magnet, maximum perturbations occurred for a 90° rotation around the transverse axis. For the split-bore magnet, the opposite was observed, with the same 90° rotation yielding total perturbation lower than the conventional position. In all cases, at least 95% of the total perturbation was composed of 1st and 2nd order harmonics. The presence of environmental steel poses a major challenge to the realization of an actively shielded rotating superconducting MRI system, requiring some novel form of shimming. Possible shimming strategies are discussed at length.