Counterpoint: Opportunities and Challenges of a Magnetic Resonance Imaging–Only Radiotherapy Work Flow

Clinical implementation of patient-specific quality assurance for synthetic computed tomography

Background and purpose

In a magnetic resonance (MR) only planning workflow, MR image is the sole dataset acquired. In order to calculate the dose deposition, a synthetic CT (sCT) is generated to substitute the planning computed tomography (CT). This study aimed to establish acceptance criteria for the clinical implementation of patient-specific quality assurance (PSQA) for sCT.

Materials and methods

A retrospective study was conducted on 60. 30 patients underwent a CT scan in treatment position and an MR in diagnostic position. 30 patients had both CT and MR images acquired in treatment position. For the latter group, a sCT for dose calculation was generated and compared against three PSQA methods: recalculation on (A) water override of the body, (B) tissue classes with bulk density overrides and (C) planning CT. The relative dose differences (ΔD [%]) between the sCT and the PSQA methos were evaluated.

Results

ΔD for PTV Dmean for method (A) were within 3% for pelvis and 4% for brain cohorts, with standard deviations below 1%. Methods (B) and (C) remained within 2% and 1%, respectively, with deviations up to 1%.

Conclusion

The present study proposes a robust PSQA method for MR-only planning. Method (A) is a valuable tool for identifying potential large outliers for Dmean deviations (> 5 %) and it is proposed as the routine PSQA. Method (B) can be used for pelvis cases to improve detection to the 2 % level if method (A) fails. If both (A) and (B) fail, method (C) can be used as a fall-back.

Does dose calculation algorithm affect the dosimetric accuracy of synthetic CT for MR-only radiotherapy planning in brain tumors?

PURPOSE

This study compares the dosimetric accuracy of deep-learning-based MR synthetic CT (sCT) in brain radiotherapy between the Analytical Anisotropic Algorithm (AAA) and AcurosXB (AXB). Additionally, it proposes a novel metric to predict the dosimetric accuracy of sCT for individual post-surgical brain cases.

MATERIALS AND METHODS

A retrospective analysis was conducted on 20 post-surgical brain tumor patients treated with Volumetric Modulated Arc Therapy (VMAT). sCT and planning CT images were obtained for each patient. Treatment plans were optimized on sCT and recalculated on planning CT using both AAA and AXB. Dosimetric parameters and 3D global gamma analysis between sCT and planning CT were recorded. The bone volume ratio, a novel metric, was calculated for each patient and tested its correlation with gamma passing rates.

RESULTS

For AAA, the mean differences in Dmean and Dmax of PTV between sCT and planning CT were 0.2% and -0.2%, respectively, with no significant difference in PTV (p > 0.05). For AXB, mean differences in Dmean and Dmax of PTV were 0.3% and 0.2%, respectively, with significant differences in Dmean (p = 0.016). Mean gamma passing rates for AXB were generally lower than AAA, with the most significant drop being 9.3% using 1%/1 mm analyzed in PTV. The bone volume ratio showed significant correlation with gamma passing rates.

CONCLUSIONS

Compared to AAA, AXB reveals larger dosimetric differences between sCT and planning CT in brain photon radiotherapy. For future dosimetric evaluation of sCT, it is recommended to employ AXB or Monte Carlo algorithms to achieve a more accurate assessment of sCT performance. The bone volume ratio can be used as an indicator to predict the suitability of sCT on a case-by-case basis.

A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy

The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.

Compensation cycle consistent generative adversarial networks (Comp-GAN) for synthetic CT generation from MR scans with truncated anatomy

BACKGROUND

MR scans used in radiotherapy can be partially truncated due to the limited field of view (FOV), affecting dose calculation accuracy in MR-based radiation treatment planning.

PURPOSE

We proposed a novel Compensation-cycleGAN (Comp-cycleGAN) by modifying the cycle-consistent generative adversarial network (cycleGAN), to simultaneously create synthetic CT (sCT) images and compensate the missing anatomy from the truncated MR images.

METHODS

Computed tomography (CT) and T1 MR images with complete anatomy of 79 head-and-neck patients were used for this study. The original MR images were manually cropped 10-25 mm off at the posterior head to simulate clinically truncated MR images. Fifteen patients were randomly chosen for testing and the rest of the patients were used for model training and validation. Both the truncated and original MR images were used in the Comp-cycleGAN training stage, which enables the model to compensate for the missing anatomy by learning the relationship between the truncation and known structures. After the model was trained, sCT images with complete anatomy can be generated by feeding only the truncated MR images into the model. In addition, the external body contours acquired from the CT images with full anatomy could be an optional input for the proposed method to leverage the additional information of the actual body shape for each test patient. The mean absolute error (MAE) of Hounsfield units (HU), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between sCT and real CT images to quantify the overall sCT performance. To further evaluate the shape accuracy, we generated the external body contours for sCT and original MR images with full anatomy. The Dice similarity coefficient (DSC) and mean surface distance (MSD) were calculated between the body contours of sCT and original MR images for the truncation region to assess the anatomy compensation accuracy.

RESULTS

The average MAE, PSNR, and SSIM calculated over test patients were 93.1 HU/91.3 HU, 26.5 dB/27.4 dB, and 0.94/0.94 for the proposed Comp-cycleGAN models trained without/with body-contour information, respectively. These results were comparable with those obtained from the cycleGAN model which is trained and tested on full-anatomy MR images, indicating the high quality of the sCT generated from truncated MR images by the proposed method. Within the truncated region, the mean DSC and MSD were 0.85/0.89 and 1.3/0.7 mm for the proposed Comp-cycleGAN models trained without/with body contour information, demonstrating good performance in compensating the truncated anatomy.

CONCLUSIONS

We developed a novel Comp-cycleGAN model that can effectively create sCT with complete anatomy compensation from truncated MR images, which could potentially benefit the MRI-based treatment planning.

CAN3D: Fast 3D medical image segmentation via compact context aggregation

Direct automatic segmentation of objects in 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying multiple individual structures with complex geometries within a large volume under investigation. Most deep learning approaches address these challenges by enhancing their learning capability through a substantial increase in trainable parameters within their models. An increased model complexity will incur high computational costs and large memory requirements unsuitable for real-time implementation on standard clinical workstations, as clinical imaging systems typically have low-end computer hardware with limited memory and CPU resources only. This paper presents a compact convolutional neural network (CAN3D) designed specifically for clinical workstations and allows the segmentation of large 3D Magnetic Resonance (MR) images in real-time. The proposed CAN3D has a shallow memory footprint to reduce the number of model parameters and computer memory required for state-of-the-art performance and maintain data integrity by directly processing large full-size 3D image input volumes with no patches required. The proposed architecture significantly reduces computational costs, especially for inference using the CPU. We also develop a novel loss function with extra shape constraints to improve segmentation accuracy for imbalanced classes in 3D MR images. Compared to state-of-the-art approaches (U-Net3D, improved U-Net3D and V-Net), CAN3D reduced the number of parameters up to two orders of magnitude and achieved much faster inference, up to 5 times when predicting with a standard commercial CPU (instead of GPU). For the open-access OAI-ZIB knee MR dataset, in comparison with manual segmentation, CAN3D achieved Dice coefficient values of (mean = 0.87 ± 0.02 and 0.85 ± 0.04) with mean surface distance errors (mean = 0.36 ± 0.32 mm and 0.29 ± 0.10 mm) for imbalanced classes such as (femoral and tibial) cartilage volumes respectively when training volume-wise under only 12G video memory. Similarly, CAN3D demonstrated high accuracy and efficiency on a pelvis 3D MR imaging dataset for prostate cancer consisting of 211 examinations with expert manual semantic labels (bladder, body, bone, rectum, prostate) now released publicly for scientific use as part of this work.

Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

Simple Summary

MRI-only simulation in radiation therapy (RT) planning has received attention because the CT scan can be omitted. For MRI-only simulation, synthetic CT (sCT) is necessary for the dose calculation. Various methodologies have been suggested for the generation of sCT and, recently, methods using the deep learning approaches are actively investigated. GAN and cycle-consistent GAN (CycGAN) have been mainly tested, however, very limited studies compared the qualities of sCTs generated from these methods or suggested other models for sCT generation. We have compared GAN, CycGAN, and, reference-guided GAN (RgGAN), a new model of deep learning method. We found that the performance in the HU conservation for soft tissue was poorest for GAN. All methods could generate sCTs feasible for VMAT planning with the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D_98% and D_95% than sCTs from other methodologies.

Abstract

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (p = 0.03). The mean relative dose differences for planning target volumes or organs at risk were within 2% among the sCTs from the three deep-learning approaches. The differences in dosimetric parameters for D_98% and D_95% from original CT were lowest in sCT from RgGAN. In conclusion, HU conservation for soft tissue was poorest for GAN. There was the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D_98% and D_95% than sCTs from other methodologies.

Design and construction of a customizable phantom for the characterization of the three-dimensional magnetic resonance imaging geometric distortion

One of the main challenges to using magnetic resonance imaging (MRI) in radiotherapy is the existence of system‐related geometric inaccuracies caused mainly by the inhomogeneity in the main magnetic field and the nonlinearities of the gradient coils.

Several physical phantoms, with fixed configuration, have been developed and commercialized for the assessment of the MRI geometric distortion. In this study, we propose a new design of a customizable phantom that can fit any type of radio frequency (RF) coil. It is composed of 3D printed plastic blocks containing holes that can hold glass tubes which can be filled with any liquid. The blocks can be assembled to construct phantoms with any dimension.

The feasibility of this design has been demonstrated by assembling four phantoms with high robustness allowing the assessment of the geometric distortion for the GE split head coil, the head and neck array coil, the anterior array coil, and the body coil. Phantom reproducibility was evaluated by analyzing the geometric distortion on CT acquisition of five independent assemblages of the phantom.

This solution meets all expectations in terms of having a robust, lightweight, modular, and practical tool for measuring distortion in three dimensions. Mean error in the position of the tubes was less than 0.2 mm. For the geometric distortion, our results showed that for all typical MRI sequences used for radiotherapy, the mean geometric distortion was less than 1 mm and less than 2.5 mm over radial distances of 150 mm and 250 mm, respectively.

These tools will be part of a quality assurance program aimed at monitoring the image quality of MRI scanners used to guide radiation therapy.

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.

CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy

Magnetic Resonance Imaging (MRI) guided Radiation Therapy is a hot topic in the current studies of radiotherapy planning, which requires using MRI to generate synthetic Computed Tomography (sCT). Despite recent progress in image-to-image translation, it remains challenging to apply such techniques to generate high-quality medical images. This paper proposes a novel framework named Multi-Cycle GAN, which uses the Pseudo-Cycle Consistent module to control the consistency of generation and the domain control module to provide additional identical constraints. Besides, we design a new generator named Z-Net to improve the accuracy of anatomy details. Extensive experiments show that Multi-Cycle GAN outperforms state-of-the-art CT synthesis methods such as Cycle GAN, which improves MAE to 0.0416, ME to 0.0340, PSNR to 39.1053.

Effects of MR imaging time reduction on substitute CT generation for prostate MRI-only treatment planning

The introduction of MRI linear accelerators (MR-linacs) and the increased use of MR imaging in radiotherapy, requires improved approaches to MRI-only radiotherapy. MRI provides excellent soft tissue visualisation but does not provide any electron density information required for radiotherapy dose calculation, instead MRI is registered to CT images to enable dose calculations. MRI-only radiotherapy eliminates registration errors and reduces patient discomfort, workload and cost. Electron density requirements may be addressed in different ways, from manually applying bulk density corrections, to more computationally intensive methods to produce substitute CT datasets (sCT), requiring additional sequences, increasing overall imaging time. Reducing MR imaging time would reduce potential artefacts from intrafraction motion and patient discomfort. The aim of this study was to assess the impact of reducing MR imaging time on a hybrid atlas-voxel sCT conversion for prostate MRI-only treatment planning, considering both anatomical and dosimetric parameters. 10 volunteers were scanned on a Siemens Skyra 3T MRI. Sequences included the 3D T2-weighted (T2-w) SPACE sequence used for sCT conversion as previously validated against CT, along with variations to this sequence in repetition time (TR), turbo factor, and combinations of these to reduce the imaging time. All scans were converted to sCT and were compared to the sCT from the original SPACE sequence, evaluating for anatomical changes and dosimetric differences for a standard prostate VMAT plan. Compared to the previously validated T2-w SPACE sequence, scan times were reduced by up to 80%. The external volume and bony anatomy were compared, with all but one sequence meeting a DICE coefficient of 0.9 or better, with the largest variations occurring at the edges of the external body volume. The generated sCT agreed with the gold standard sCT within an isocentre dose of 1% and a gamma pass rate of 99% for a 1%/1 mm gamma tolerance for all but one sequence. This study demonstrates that the MR imaging sequence time was able to be reduced by approximately 80% with similar dosimetric results.

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region

PURPOSE

In the past years, many different neural network-based conversion techniques for synthesizing computed tomographys (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, for example, for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated.

METHODS

41 patients with tumors in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method ( C B C T RS ) and a population-based dose calculation method ( C B C T Pop ) were employed for CBCT-based dose calculation. The dose distributions were analysed using 3D global gamma analysis, applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs-at-risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs.

RESULTS

The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60 HU for MAE. Using the 2%/2 mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0  ±  0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0  ±  0.8% and 99.1  ±  0.8% for the C B C T RS and C B C T Pop , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4  ±  1.6% and 99.2  ±  0.6% for C B C T RS and C B C T Pop , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT-based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%.

CONCLUSION

The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed up. A systematic shift of approximately 1% should be taken into account when using the C B C T RS approach in an MR only workflow.

Improving generalization in MR-to-CT synthesis in radiotherapy by using an augmented cycle generative adversarial network with unpaired data

PURPOSE

MR-to-CT synthesis is one of the first steps in the establishment of an MRI-only workflow in radiotherapy. Current MR-to-CT synthesis methods in deep learning use unpaired MR and CT training images with a cycle generative adversarial network (CycleGAN) to minimize the effect of misalignment between paired images. However, this approach critically assumes that the underlying interdomain mapping is approximately deterministic and one-to-one. In the current study, we use an Augmented CycleGAN (AugCGAN) model to create a robust model that can be applied to different scanners and sequences using unpaired data.

MATERIALS AND METHODS

This study included T2-weighted MR and CT pelvic images of 38 patients in treatment position from five different centers. The AugCGAN was trained on 2D transverse slices of 19 patients from three different sites. The network was then used to generate synthetic CT (sCT) images of 19 patients from the two other sites. Mean absolute errors (MAEs) for each patient were evaluated between real and synthetic CT images. Original treatment plans of nine patients were recalculated using sCT images to assess the dose distribution in terms of voxel-wise dose difference, gamma, and dose-volume histogram analysis.

RESULTS

The mean MAEs were 59.8 Hounsfield units ( HU ) and 65.8 HU for the first and second test sites, respectively. The maximum dose difference to the target was 1.2 % with a gamma pass rate using the 3%, 3 mm criteria above 99%. The average time required to generate a complete sCT image for a patient on our GPU was 8.5 s.

CONCLUSION

This study suggests that our unpaired approach achieves good performance in generalization with respect to sCT image generation.

[Magnetic-resonance-guided radiotherapy : The beginning of a new era in radiation oncology?]

CLINICAL ISSUE

Image-guided radiotherapy (IGRT) using X‑rays and cone-beam computed tomography (CT) has fostered precision radiotherapy. However, inter- and intrafractional variations of target volume position and organs at risk still limit target volume dose and sparing of radiosensitive organs at risk.

METHODOLOGICAL INNOVATIONS

Hybrid machines directly combining linear accelerators and magnetic resonance (MR) imaging allow for live imaging during radiotherapy.

PERFORMANCE

Besides highly improved soft tissue contrast, MR-linacs enable online, on-table adaptive radiotherapy. Thus, adaptation of the treatment plan to the anatomy of the day, dose escalation and superior sparing of organs at risk become possible.

ACHIEVEMENTS

This article summarizes the underlying intention for the development of MR-guided radiotherapy, technical innovations and challenges as well as the current state-of-the-art. Potential clinical benefits and future developments are discussed.

PRACTICAL RECOMMENDATIONS

Increasing availability of MR imaging at linear accelerators calls for the ability to review and interpret MR images. Therefore, close collaborations of diagnostic radiologists and radiation oncologists are mandatory to foster this fascinating technique.

Dosimetric impact of geometric distortions in an MRI-only proton therapy workflow for lung, liver and pancreas

In a radiation therapy workflow based on Magnetic Resonance Imaging (MRI), dosimetric errors may arise due to geometric distortions introduced by MRI. The aim of this study was to quantify the dosimetric effect of system-dependent geometric distortions in an MRI-only workflow for proton therapy applied at extra-cranial sites. An approach was developed, in which computed tomography (CT) images were distorted using an MRI displacement map, which represented the MR distortions in a spoiled gradient-echo sequence due to gradient nonlinearities and static magnetic field inhomogeneities. A retrospective study was conducted on 4DCT/MRI digital phantoms and 18 4DCT clinical datasets of the thoraco-abdominal site. The treatment plans were designed and separately optimized for each beam in a beam specific Planning Target Volume on the distorted CT, and the final dose distribution was obtained as the average. The dose was then recalculated in undistorted CT using the same beam geometry and beam weights. The analysis was performed in terms of Dose Volume Histogram (DVH) parameters. No clinically relevant dosimetric impact was observed on organs at risk, whereas in the target structure, geometric distortions caused statistically significant variations in the planned dose DVH parameters and dose homogeneity index (DHI). The dosimetric variations in the target structure were smaller in abdominal cases (ΔD_2%, ΔD_98%, and ΔD_mean all below 0.1% and ΔDHI below 0.003) compared to the lung cases. Indeed, lung patients with tumors isolated inside lung parenchyma exhibited higher dosimetric variations (ΔD_2%≥0.3%, ΔD_98%≥15.9%, ΔD_mean≥3.3% and ΔDHI≥0.102) than lung patients with tumor close to soft tissue (ΔD_2%≤0.4%, ΔD_98%≤5.6%, ΔD_mean≤0.9% and ΔDHI≤0.027) potentially due to higher density variations along the beam path. Results suggest the potential applicability of MRI-only proton therapy, provided that specific analysis is applied for isolated lung tumors.

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.

Characterization of hardware-related spatial distortions for IR-PETRA pulse sequence using a brain specific phantom

OBJECTIVE

Inversion recovery-pointwise encoding time reduction with radial acquisition (IR-PETRA) is an effective magnetic resonance (MR) pulse sequence in generating pseudo-CTs. The hardware-related spatial-distortion (HRSD) in MR images potentially deteriorates the accuracy of pseudo-CTs. Thus, we aimed at characterizing HRSD for IR-PETRA.

MATERIALS AND METHODS

gross-HRSD_overall (Euclidean-sum of gross-HRSD_i (i = x, y, z)) for IR-PETRA was assessed using a brain-specific phantom for two MR scanners (1.5 T-Aera and 3.0 T-Prisma). Moreover, hardware imperfections were analyzed by determining gradient-nonlinearity spatial-distortion (GNSD) and B₀-inhomogeneity spatial-distortion (B₀ISD) for magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) which has well-known distortion characteristics.

RESULTS

In 3.0 T, maximum of gross-GNSD_overall (Euclidean-sum of gross-GNSD_i) and gross-B₀ISD for MP-RAGE was 2.77 mm and 0.57 mm, respectively. For this scanner, the mean and maximum of gross-HRSD_overall for IR-PETRA were 0.63 ± 0.38 mm and 1.91 mm, respectively. In 1.5 T, maximum of gross-GNSD_overall and gross-B₀ISD for MP-RAGE was 3.41 mm and 0.78 mm, respectively. The mean and maximum of gross-HRSD_overall for IR-PETRA were 1.02 ± 0.50 mm and 3.12 mm, respectively.

DISCUSSION

The spatial accuracy of MR images, besides being impacted by hardware performance, scanner capabilities, and imaging parameters, is mainly affected by its imaging strategy and data acquisition scheme. In 3.0 T, even without applying vendor correction algorithms, spatial accuracy of IR-PETRA image is sufficient for generating pseudo-CTs. In 1.5 T, distortion-correction is required to provide this accuracy.

The rationale for MR-only treatment planning for external radiotherapy

•MR-only treatment planning could improve the spatial accuracy of radiotherapy.•The benefit compared to a mixed MR-CT workflow will vary between patient groups.•Further development of QA tools is needed before the procedure will save resources.

MRI basics for radiation oncologists

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Issues of MRI that are relevant for radiation oncologists are addressed.

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Radiation oncology requires dedicated scan protocols.

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Use of diagnostic protocols is not recommended for radiotherapy.

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MR images must be made in treatment position with the standard positioning devices.

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Safety screening prior to entering the MRI room is crucial.

MRI is increasingly used in radiation oncology to facilitate tumor and organ-at-risk delineation and image guidance. In this review, we address issues of MRI that are relevant for radiation oncologists when interpreting MR images offered for radiotherapy. Whether MRI is used in combination with CT or in an MRI-only workflow, it is generally necessary to ensure that MR images are acquired in treatment position, using the positioning and fixation devices that are commonly applied in radiotherapy. For target delineation, often a series of separate image sets are used with distinct image contrasts, acquired within a single exam. MR images can suffer from image distortions. While this can be avoided with dedicated scan protocols, in a diagnostic setting geometrical fidelity is less relevant and is therefore less accounted for. Since geometrical fidelity is of utmost importance in radiation oncology, it requires dedicated scan protocols. The strong magnetic field of an MRI scanner and the use of radiofrequency radiation can cause safety hazards if not properly addressed. Safety screening is crucial for every patient and every operator prior to entering the MRI room.

Realizing the potential of magnetic resonance image guided radiotherapy in gynaecological and rectal cancer

CT-based radiotherapy workflow is limited by poor soft tissue definition in the pelvis and reliance on rigid registration methods. Current image-guided radiotherapy and adaptive radiotherapy models therefore have limited ability to improve clinical outcomes. The advent of MRI-guided radiotherapy solutions provides the opportunity to overcome these limitations with the potential to deliver online real-time MRI-based plan adaptation on a daily basis, a true "plan of the day." This review describes the application of MRI guided radiotherapy in two pelvic tumour sites likely to benefit from this approach.

Improving fiducial and prostate capsule visualization for radiotherapy planning using MRI

BACKGROUND AND PURPOSE

Intraprostatic fiducial markers (FM) improve the accuracy of radiotherapy (RT) delivery. Here we assess geometric integrity and contouring consistency using a T2*-weighted (T2*W) sequence alone, which allows visualization of the FM.

MATERIAL AND METHODS

Ten patients scanned within the Prostate Advances in Comparative Evidence (PACE) trial (NCT01584258) had prostate images acquired with computed tomography (CT) and Magnetic Resonance (MR) Imaging: T2-weighted (T2W) and T2*W sequences. The prostate was contoured independently on each imaging dataset by three clinicians. Interobserver variability was assessed using comparison indices with Monaco ADMIRE (research version 2.0, Elekta AB) and examined for statistical differences between imaging sets. CT and MR images of two test objects were acquired to assess geometric distortion and accuracy of marker positioning. The first was a linear test object comprising straight tubes in three orthogonal directions, the second was a smaller test object with markers suspended in gel.

RESULTS

Interobserver variability for prostate contouring was lower for both T2W and T2*W compared to CT, this was statistically significant when comparing CT and T2*W images. All markers are visible in T2*W images with 29/30 correctly identified, only 3/30 are visible in T2W images. Assessment of geometric distortion revealed in-plane displacements were under 0.375 mm in MRI, and through plane displacements could not be detected. The signal loss in the MR images is symmetric in relation to the true marker position shown in CT images.

CONCLUSION

Prostate T2*W images are geometrically accurate, and yield consistent prostate contours. This single sequence can be used to identify FM and for prostate delineation in a mixed MR-CT workflow.

Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy

Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over computed tomographies (CTs), without the ionizing radiation exposure. However, it requires the generation of a synthetic CT (SCT) from MRIs for patient setup and dose calculation. In this study, we aim to investigate the accuracy of dose calculation in prostate cancer radiotherapy using SCTs generated from MRIs using our learning-based method. We retrospectively investigated a total of 17 treatment plans from 10 patients, each having both planning CTs (pCT) and MRIs acquired before treatment. The SCT was registered to the pCT for generating SCT-based treatment plans. The original pCT-based plans served as ground truth. Clinically-relevant dose volume histogram (DVH) metrics were extracted from both ground truth and SCT-based plans for comparison and evaluation. Gamma analysis was performed for the comparison of absorbed dose distributions between SCT- and pCT-based plans of each patient. Gamma analysis of dose distribution on pCT and SCT within 1%/1 mm at 10% dose threshold showed greater than 99% pass rate. The average differences in DVH metrics for planning target volumes (PTVs) were less than 1%, and similar metrics for organs at risk (OAR) were not statistically different. The SCT images created from MR images using our proposed machine learning method are accurate for dose calculation in prostate cancer radiation treatment planning. This study also demonstrates the great potential for MRI to completely replace CT scans in the process of simulation and treatment planning. However, MR images are needed to further analyze geometric distortion effects. Digitally reconstructed radiograph (DRR) can be generated within our method, and their accuracy in patient setup needs further analysis.

Assessment of positional reproducibility in the head and neck on a 1.5-T MR simulator for an offline MR-guided radiotherapy solution

Background

Recently, a shuttle-based offline magnetic resonance-guided radiotherapy (MRgRT) approach was proposed. This study aims to evaluate the positional reproducibility in the immobilized head and neck using a 1.5-T MR-simulator (MR-sim) on healthy volunteers.

Methods

A total of 159 scans of 14 healthy volunteers were conducted on a 1.5-T MR-sim with thermoplastic mask immobilization. MR images with isotropic 1.05³ mm³ voxel size were rigidly registered to the first scan based on fiducial, anatomical and gross positions. Mean and standard deviation of positional displacements in translation and rotation were assessed. Systematic error and random errors of positioning in the head and neck on the MR-sim were determined in the translation of, and in the rotation of roll, pitch and yaw.

Results

The systematic error (Σ) of translation in left-right (LR), anterior-posterior (AP) and superior-inferior (SI) direction was 0.57, 0.22 and 0.26 mm for fiducial displacement, 0.28, 0.10 and 0.52 mm for anatomical displacement, and 0.53, 0.22 and 0.49 mm for gross displacement, respectively. The random error (σ) in corresponding translation direction was 2.07, 0.54 and 1.32 mm for fiducial displacement, 1.34, 0.73 and 2.04 mm for anatomical displacement, and 2.24, 0.86 and 2.61 mm for gross displacement. The systematic error and random error of rotation were generally smaller than 1°.

Conclusions

Our results suggested that high gross positional reproducibility (<1 mm translational and <1° rotational systematic error) could be achieved on an MR-sim for the proposed offline MRgRT.

Dosimetric Impact of MRI Distortions: A Study on Head and Neck Cancers

PURPOSE

To evaluate the effect of magnetic resonance (MR) imaging (MRI) geometric distortions on head and neck radiation therapy treatment planning (RTP) for an MRI-only RTP. We also assessed the potential benefits of patient-specific shimming to reduce the magnitude of MR distortions for a 3-T scanner.

METHODS AND MATERIALS

Using an in-house Matlab algorithm, shimming within entire imaging volumes and user-defined regions of interest were simulated. We deformed 21 patient computed tomography (CT) images with MR distortion fields (gradient nonlinearity and patient-induced susceptibility effects) to create distorted CT (dCT) images using bandwidths of 122 and 488 Hz/mm at 3 T. Field parameters from volumetric modulated arc therapy plans initially optimized on dCT data sets were transferred to CT data to compute a new plan. Both plans were compared to determine the impact of distortions on dose distributions.

RESULTS

Shimming across entire patient volumes decreased the percentage of voxels with distortions of more than 2 mm from 15.4% to 2.0%. Using the user-defined region of interest (ROI) shimming strategy, (here the Planning target volume (PTV) was the chosen ROI volume) led to increased geometric for volumes outside the PTV, as such voxels within the spinal cord with geometric shifts above 2 mm increased from 11.5% to 32.3%. The worst phantom-measured residual system distortions after 3-dimensional gradient nonlinearity correction within a radial distance of 200 mm from the isocenter was 2.17 mm. For all patients, voxels with distortion shifts of more than 2 mm resulting from patient-induced susceptibility effects were 15.4% and 0.0% using bandwidths of 122 Hz/mm and 488 Hz/mm at 3 T. Dose differences between dCT and CT treatment plans in D₅₀ at the planning target volume were 0.4% ± 0.6% and 0.3% ± 0.5% at 122 and 488 Hz/mm, respectively.

CONCLUSIONS

The overall effect of MRI geometric distortions on data used for RTP was minimal. Shimming over entire imaging volumes decreased distortions, but user-defined subvolume shimming introduced significant errors in nearby organs and should probably be avoided.

The characterization of a large multi-axis ionization chamber array in a 1.5 T MRI linac

By combining magnetic resonance imaging (MRI) scanners and radiotherapy treatment units the need arises for new radiation measurement equipment that can be used in the magnetic field of the MRI. This study describes the investigation of the influence of the 1.5 T magnetic field from an MRI linac on the STARCHECK^{MAXI MR}, a large 2D ionization chamber detector panel. Measurements were performed on an MRI linac and a conventional linac to investigate the behaviour of the detector panel with and without the 1.5 T magnetic field. We measured reproducibility, linearity, warm-up effect, saturation/recombination and chamber orientation. A comparison with gafchromic film was performed and the effect of motion of the panel during measurements inside a magnetic field was investigated. The reproducibility, linearity, warm-up effect, saturation/recombination show no significant deviations with or without magnetic field. An absolute difference in reading of 2.1% was found between off-axis chambers on different axes. The comparison with film shows good agreement. Spurious readings are induced while the panel is undergoing a motion in the magnetic field during measurements. The STARCHECK^{MAXI MR} is suited for use in a 1.5 T MRI linac. Care must be taken when comparing un-normalized profiles from different axes of the detector panel and when the panel is undergoing motion during measurements.

Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region

PURPOSE

Magnetic resonance imaging (MRI)-guided radiation therapy (RT) treatment planning is limited by the fact that the electron density distribution required for dose calculation is not readily provided by MR imaging. We compare a selection of novel synthetic CT generation algorithms recently reported in the literature, including segmentation-based, atlas-based and machine learning techniques, using the same cohort of patients and quantitative evaluation metrics.

METHODS

Six MRI-guided synthetic CT generation algorithms were evaluated: one segmentation technique into a single tissue class (water-only), four atlas-based techniques, namely, median value of atlas images (ALMedian), atlas-based local weighted voting (ALWV), bone enhanced atlas-based local weighted voting (ALWV-Bone), iterative atlas-based local weighted voting (ALWV-Iter), and a machine learning technique using deep convolution neural network (DCNN).

RESULTS

Organ auto-contouring from MR images was evaluated for bladder, rectum, bones, and body boundary. Overall, DCNN exhibited higher segmentation accuracy resulting in Dice indices (DSC) of 0.93 ± 0.17, 0.90 ± 0.04, and 0.93 ± 0.02 for bladder, rectum, and bones, respectively. On the other hand, ALMedian showed the lowest accuracy with DSC of 0.82 ± 0.20, 0.81 ± 0.08, and 0.88 ± 0.04, respectively. DCNN reached the best performance in terms of accurate derivation of synthetic CT values within each organ, with a mean absolute error within the body contour of 32.7 ± 7.9 HU, followed by the advanced atlas-based methods (ALWV: 40.5 ± 8.2 HU, ALWV-Iter: 42.4 ± 8.1 HU, ALWV-Bone: 44.0 ± 8.9 HU). ALMedian led to the highest error (52.1 ± 11.1 HU). Considering the dosimetric evaluation results, ALWV-Iter, ALWV, DCNN and ALWV-Bone led to similar mean dose estimation within each organ at risk and target volume with less than 1% dose discrepancy. However, the two-dimensional gamma analysis demonstrated higher pass rates for ALWV-Bone, DCNN, ALMedian and ALWV-Iter at 1%/1 mm criterion with 94.99 ± 5.15%, 94.59 ± 5.65%, 93.68 ± 5.53% and 93.10 ± 5.99% success, respectively, while ALWV and water-only resulted in 86.91 ± 13.50% and 80.77 ± 12.10%, respectively.

CONCLUSIONS

Overall, machine learning and advanced atlas-based methods exhibited promising performance by achieving reliable organ segmentation and synthetic CT generation. DCNN appears to have slightly better performance by achieving accurate automated organ segmentation and relatively small dosimetric errors (followed closely by advanced atlas-based methods, which in some cases achieved similar performance). However, the DCNN approach showed higher vulnerability to anatomical variation, where a greater number of outliers was observed with this method. Considering the dosimetric results obtained from the evaluated methods, the challenge of electron density estimation from MR images can be resolved with a clinically tolerable error.

Chaotic Sensing

We propose a sparse imaging methodology called Chaotic Sensing (ChaoS) that enables the use of limited yet deterministic linear measurements through fractal sampling. A novel fractal in the discrete Fourier transform is introduced that always results in the artefacts being turbulent in nature. These chaotic artefacts have characteristics that are image independent, facilitating their removal through dampening (via image denoising) and obtaining the maximum likelihood solution. In contrast with existing methods, such as compressed sensing, the fractal sampling is based on digital periodic lines that form the basis of discrete projected views of the image without requiring additional transform domains. This allows the creation of finite iterative reconstruction schemes in recovering an image from its fractal sampling that is also new to discrete tomography. As a result, ChaoS supports linear measurement and optimisation strategies, while remaining capable of recovering a theoretically exact representation of the image. We apply the method to simulated and experimental limited magnetic resonance (MR) imaging data, where restrictions imposed by MR physics typically favour linear measurements for reducing acquisition time.

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region

Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R2  <  0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region

Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R²  <  0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.

MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network

PURPOSE

This work aims to facilitate a fast magnetic resonance (MR)-only workflow for radiation therapy of intracranial tumors. Here, we evaluate whether synthetic computed tomography (sCT) images generated with a dilated convolutional neural network (CNN) enable accurate MR-based dose calculations in the brain.

METHODS AND MATERIALS

We conducted a retrospective study of 52 patients with brain tumors who underwent both computed tomography (CT) and MR imaging for radiation therapy treatment planning. To generate the sCTs, a T1-weighted gradient echo MR sequence was selected from the clinical protocol for multiple types of brain tumors. sCTs were created for all 52 patients with a dilated CNN using 2-fold cross validation; in each fold, 26 patients were used for training and the remaining 26 patients were used for evaluation. For each patient, the clinical CT-based treatment plan was recalculated on sCT. We calculated dose differences and gamma pass rates between CT- and sCT-based plans inside body and planning target volume. Geometric fidelity of the sCT and differences in beam depth and equivalent path length were assessed between both treatment plans.

RESULTS

sCT generation took 1 minute per patient. Over the patient population, the mean absolute error of the sCT within the intersection of body contours was 67 ± 11 HU (±1 standard deviation [SD], range: 51-117 HU), and the mean error was 13 ± 9 HU (±1 SD, range: -2 to 38 HU). Dosimetric analysis showed mean deviations of 0.00% ± 0.02% (±1 SD, range: -0.05 to 0.03) for dose within the body contours and -0.13% ± 0.39% (±1 SD, range: -1.43 to 0.80) inside the planning target volume. Mean γ_1mm/1% was 98.8% ± 2.2% for doses >50% of the prescribed dose.

CONCLUSIONS

The presented dilated CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate and can therefore be used for MR-only intracranial radiation therapy treatment planning.

MRI-only treatment planning: benefits and challenges

Over the past decade, the application of magnetic resonance imaging (MRI) has increased, and there is growing evidence to suggest that improvements in the accuracy of target delineation in MRI-guided radiation therapy may improve clinical outcomes in a variety of cancer types. However, some considerations should be recognized including patient motion during image acquisition and geometric accuracy of images. Moreover, MR-compatible immobilization devices need to be used when acquiring images in the treatment position while minimizing patient motion during the scan time. Finally, synthetic CT images (i.e. electron density maps) and digitally reconstructed radiograph images should be generated from MRI images for dose calculation and image guidance prior to treatment. A short review of the concepts and techniques that have been developed for implementation of MRI-only workflows in radiation therapy is provided in this document.

Magnetic Resonance Imaging-Guided Adaptive Radiation Therapy: A "Game Changer" for Prostate Treatment?

Radiation therapy to the prostate involves increasingly sophisticated delivery techniques and changing fractionation schedules. With a low estimated α/β ratio, a larger dose per fraction would be beneficial, with moderate fractionation schedules rapidly becoming a standard of care. The integration of a magnetic resonance imaging (MRI) scanner and linear accelerator allows for accurate soft tissue tracking with the capacity to replan for the anatomy of the day. Extreme hypofractionation schedules become a possibility using the potentially automated steps of autosegmentation, MRI-only workflow, and real-time adaptive planning. The present report reviews the steps involved in hypofractionated adaptive MRI-guided prostate radiation therapy and addresses the challenges for implementation.

Developing and characterizing MR/CT-visible materials used in QA phantoms for MRgRT systems

PURPOSE

Synthetic tissue equivalent (STE) materials currently used to simulate tumor and surrounding tissues for IROC-Houston's anthropomorphic head and thorax QA phantoms cannot be visualized using magnetic resonance (MR) imaging. The purpose of this study was to characterize dual MR/CT-visible STE materials that can be used in an end-to-end QA phantom for MR-guided radiotherapy (MRgRT) modalities.

METHODS

Over 80 materials' MR, CT, and dosimetric STE properties were investigated for use in MRgRT QA phantoms. The materials tested included homogeneous and heterogeneous materials to simulate soft tissue/tumor and lung tissues. Materials were scanned on a Siemens' Magnetom Espree 1.5 T using four sequences, which showed the materials visual contrast between T1- and T2-weighted images. Each material's Hounsfield number and electron density data was collected using a GE's CT Lightspeed Simulator. Dosimetric properties were examined by constructing a 10 × 10 × 20 cm³ phantom of the selected STE materials that was divided into three sections: anterior, middle, and posterior. Anterior and posterior pieces were composed of polystyrene, whereas the middle section was substituted with the selected STE materials. EBT3 film was inserted into the phantom's midline and was irradiated using an Elekta's Versa 6 MV beam with a prescription of 6 Gy at 1.5 cm and varying field size of: 10 × 10 cm² , 6 × 6 cm² , and 3 × 3 cm² . Measured film PDD curves were compared to planning system calculations and conventional STE materials' percent depth dose (PDD) curves.

RESULTS

The majority of the tested materials showed comparable CT attenuation properties to their respective organ site; however, most of the tested materials were not visible on either T1- or T2-weighted MR images. Silicone, hydrocarbon, synthetic gelatin, and liquid PVC plastic-based materials showed good MR image contrast. In-house lung equivalent materials made with either silicone- or hydrocarbon-based materials had HUs ranging from: -978 to -117 and -667 to -593, respectively. Synthetic gelatin and PVC plastic-based materials resembled soft tissue/tumor equivalent materials and had HUs of: -175 to -170 and -29 to 32, respectively. PDD curves of the selected MR/CT-visible materials were comparable to IROC-Houston's conventional phantom STE materials. The smallest field size showed the largest disagreements, where the average discrepancies between calculated and measured PDD curves were 1.8% and 5.9% for homogeneous and heterogeneous testing materials, respectively.

CONCLUSIONS

Gelatin, liquid plastic, and hydrocarbon-based materials were determined as alternative STE substitutes for MRgRT QA phantoms.

MRI-guided lung SBRT: Present and future developments

Stereotactic body radiotherapy (SBRT) is rapidly becoming an alternative to surgery for the treatment of early-stage non-small cell lung cancer patients. Lung SBRT is administered in a hypo-fractionated, conformal manner, delivering high doses to the target. To avoid normal-tissue toxicity, it is crucial to limit the exposure of nearby healthy organs-at-risk (OAR). Current image-guided radiotherapy strategies for lung SBRT are mostly based on X-ray imaging modalities. Although still in its infancy, magnetic resonance imaging (MRI) guidance for lung SBRT is not exposure-limited and MRI promises to improve crucial soft-tissue contrast. Looking beyond anatomical imaging, functional MRI is expected to inform treatment decisions and adaptations in the future. This review summarises and discusses how MRI could be advantageous to the different links of the radiotherapy treatment chain for lung SBRT: diagnosis and staging, tumour and OAR delineation, treatment planning, and inter- or intrafractional motion management. Special emphasis is placed on a new generation of hybrid MRI treatment devices and their potential for real-time adaptive radiotherapy.

Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imaging-Only Radiation Therapy

Magnetic resonance imaging (MRI) offers superior soft-tissue contrast as compared with computed tomography (CT), which is conventionally used for radiation therapy treatment planning (RTP) and patient positioning verification, resulting in improved target definition. The 2 modalities are co-registered for RTP; however, this introduces a systematic error. Implementing an MRI-only radiation therapy workflow would be advantageous because this error would be eliminated, the patient pathway simplified, and patient dose reduced. Unlike CT, in MRI there is no direct relationship between signal intensity and electron density; however, various methodologies for MRI-only RTP have been reported. A systematic review of these methods was undertaken. The PRISMA guidelines were followed. Embase and Medline databases were searched (1996 to March, 2017) for studies that generated synthetic CT scans (sCT)s for MRI-only radiation therapy. Sixty-one articles met the inclusion criteria. This review showed that MRI-only RTP techniques could be grouped into 3 categories: (1) bulk density override; (2) atlas-based; and (3) voxel-based techniques, which all produce an sCT scan from MR images. Bulk density override techniques either used a single homogeneous or multiple tissue override. The former produced large dosimetric errors (>2%) in some cases and the latter frequently required manual bone contouring. Atlas-based techniques used both single and multiple atlases and included methods incorporating pattern recognition techniques. Clinically acceptable sCTs were reported, but atypical anatomy led to erroneous results in some cases. Voxel-based techniques included methods using routine and specialized MRI sequences, namely ultra-short echo time imaging. High-quality sCTs were produced; however, use of multiple sequences led to long scanning times increasing the chances of patient movement. Using nonroutine sequences would currently be problematic in most radiation therapy centers. Atlas-based and voxel-based techniques were found to be the most clinically useful methods, with some studies reporting dosimetric differences of <1% between planning on the sCT and CT and <1-mm deviations when using sCTs for positional verification.

Evaluation of Dosimetric Robustness of Carbon Ion Boost Therapy for Anal Carcinoma

Purpose

The radiation therapy treatment outcome of human papillomavirus-negative anal carcinoma may be improved by the biological effectiveness of carbon ions. However, abdominal tissue motion can compromise the precision of carbon ion therapy. This work aims to evaluate the dosimetric feasibility of carbon ion boost (CIB) therapy for anal carcinoma.

Materials and Methods

An algorithm to generate computed tomographies based on daily magnetic resonance imaging data and deformable image registration was developed. By means of this algorithm, fractional computed tomography data for 54 treatment fractions for 3 different patients with anal carcinoma were derived. The dose for a sequential CIB (CIB_seq) treatment plan was recalculated on the fractional computed tomography data and accumulated over the number of fractions. The resulting dose distributions were compared to standard intensity-modulated radiation therapy treatment with an integrated photon boost.

Results

For the investigated patient cases, similar dosimetric results for CIB_seq treatment and for intensity-modulated radiation therapy with an integrated photon boost were found. For CIB_seq treatment, bladder-filling variation had the strongest influence on the dose distribution. However, the detrimental effects on the mean target dose remained below 1 Gy (RBE) as compared to photon therapy.

Conclusion

This study shows the dosimetric feasibility of CIB therapy for anal carcinoma for the first time and gives reason for clinical exploitation of the enhanced biological effect of carbon ions for patients with human papillomavirus-negative anal cancer.

The feasibility of semi-automatically generated red bone marrow segmentations based on MR-only for patients with gynecologic cancer

PURPOSE

For patients with cervical cancer the delivery of chemotherapy with radiotherapy improves survival compared with radiotherapy alone. However, high rates of acute hematologic toxicity occur when combining both therapies due to the damage of the red bone marrow (RBM). This study aimed to reduce the radiation damage to the RBM. A tool has been developed for semi-automatic delineation of the red bone marrow based on MR-only. This delineation can be included into the treatment planning process to reduce the volume of RBM irradiated in patients receiving pelvic radiation therapy.

METHODS

13 patients with cervical cancer were enrolled. All the patients underwent MR, CT and FDG-PET imaging. A tool for RBM determination from water and fat MR images was developed. Our MR-based RBM tool was optimized and validated with the FDG-PET scans of the patients.

RESULTS

Our tool identified RBM regions in the pelvic area. The mean total volume of these regions was 34% of the pelvic bone marrow. The corresponding SUV values based on the FDG-PET scans were above the reported threshold of active/red bone marrow.

CONCLUSION

This study shows that delineations of the RBM for the radiotherapy with RBM sparing can be generated semi-automatically using MR scans only.

The integration of MRI in radiation therapy: collaboration of radiographers and radiation therapists

The increased utilisation of magnetic resonance imaging (MRI) in radiation therapy (RT) has led to the implementation of MRI simulators for RT treatment planning and influenced the development of MRI-guided treatment systems. There is extensive literature on the advantages of MRI for tumour volume and organ-at-risk delineation compared to computed tomography. MRI provides both anatomical and functional information for RT treatment planning (RTP) as well as quantitative information to assess tumour response for adaptive treatment. Despite many advantages of MRI in RT, introducing an MRI simulator into a RT department is a challenge. Collaboration between radiographers and radiation therapists is paramount in making the best use of this technology. The cross-disciplinary training of radiographers and radiation therapists alike is an area rarely discussed; however, it is becoming an important requirement due to detailed imaging needs for advanced RT treatment techniques and with the emergence of hybrid treatment systems. This article will discuss the initial experiences of a radiation oncology department in implementing a dedicated MRI simulator for RTP, with a focus on the training required for both radiographer and RT staff. It will also address the future of MRI in RT and the implementation of MRI-guided treatment systems, such as MRI-Linacs, and the role of both radiation therapists and radiographers in this technology.

Fast automated segmentation of multiple objects via spatially weighted shape learning

Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice's similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12-15 min, nearly an order of magnitude faster than the multi-atlas approach.