Geometric distortion characterization and correction for the 1.0 T Australian MRI‐linac system using an inverse electromagnetic method

Characterization of real-time cine MR imaging distortion on 0.35 T MRgRT with concentric cine imaging QA phantom

Objective. Real-time MRgRT uses 2D-cine imaging for target tracking and motion evaluation. Rotation of gantry inducedB0off-resonance, resulting in image artifacts and imaging isocenter-shift precluding MR-guided arc therapy. Standard MRI phantoms designed for higher resolution images face challenges when low-resolution cine imaging is needed to achieve high frame rates. This work aimed to examine the spatial accuracy including geometric distortion and isocenter shift in real-time during gantry rotation on a 0.35 T MR-Linac using the concentric Cine imaging quality assurance (QA) phantom and its associated image analysis software.Approach. The Cine imaging QA phantom consists of two concentric shells of low-T1mineral oil and a central alignment structure. The phantom was scanned on three different MRI systems; 0.55 T Siemens Free.Max, 1.5 T Philips Ingenia, and 0.35 T ViewRay MRIdian MR-Linac using 2D balanced steady-state free precession (bSSFP) imaging sequence. In addition, bSSFP cine MRI with the banding artifact correction was tested on 0.35 T ViewRay MR-Linac. Images from the MR-Linac were acquired with the Linac gantry stationary and rotating from gantry 300°→ 0° and vice versa. Three orthogonal image planes were scanned excluding the 1.5 T Philips Ingenia, where only the axial plane was scanned. The image analysis software calculated the distortion values as well as the isocenter position for each cine frame.Main results. The geometric distortion of cine imaging on MRIs and MR-Linac at gantry stationary are within 1 mm while the substantial geometric distortion of 2 and 2.2 mm were observed on 0.35 T MR-Linac while rotating the gantry clockwise (300°→ 0°) and counterclockwise 0°→ 300° respectively. The average imaging isocenter shift was 0.1 mm for both MRIs and the static gantry and imaging isocenter shift of ≤1.5 mm was observed during the gantry rotation. The imaging isocenter shift decreased by 1 ± 0.2 mm clockwise and counterclockwise withB0compensation.Significance. The concentric Cine imaging QA phantom and its associated software effectively demonstrate the image distortion on real-time cine imaging on regular MRIs and 0.35 T MR-Linac. The results of significant geometric distortion with a rotating gantry in the MR-Linac system require further investigation to alleviate the extent of the image distortion.

Distortion-corrected image reconstruction with deep learning on an MRI-Linac

PURPOSE

MRI is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for MR-guided radiotherapy applications.

METHODS

We leverage recent advances in interpretable unrolling networks to develop a Distortion-Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regularizations and nonuniform fast Fourier transforms for GNL-encoding. DCReconNet was trained on a public MR brain dataset from 11 healthy volunteers for fully sampled and accelerated techniques, including parallel imaging (PI) and compressed sensing (CS). The performance of DCReconNet was tested on phantom, brain, pelvis, and lung images acquired on a 1.0T MRI-Linac. The DCReconNet, CS-, PI-and UNet-based reconstructed image quality was measured by structural similarity (SSIM) and RMS error (RMSE) for numerical comparisons. The computation time and residual distortion for each method were also reported.

RESULTS

Imaging results demonstrated that DCReconNet better preserves image structures compared to CS- and PI-based reconstruction methods. DCReconNet resulted in the highest SSIM (0.95 median value) and lowest RMSE (<0.04) on simulated brain images with four times acceleration. DCReconNet is over 10-times faster than iterative, regularized reconstruction methods.

CONCLUSIONS

DCReconNet provides fast and geometrically accurate image reconstruction and has the potential for MRI-guided radiotherapy applications.

Rapid distortion correction enables accurate magnetic resonance imaging-guided real-time adaptive radiotherapy

Background and purpose

Magnetic resonance imaging (MRI)-Linac systems combine simultaneous MRI with radiation delivery, allowing treatments to be guided by anatomically detailed, real-time images. However, MRI can be degraded by geometric distortions that cause uncertainty between imaged and actual anatomy. In this work, we develop and integrate a real-time distortion correction method that enables accurate real-time adaptive radiotherapy.

Materials and methods

The method was based on the pre-treatment calculation of distortion and the rapid correction of intrafraction images. A motion phantom was set up in an MRI-Linac at isocentre (P₀ ), the edge (P ₁) and just outside (P ₂) the imaging volume. The target was irradiated and tracked during real-time adaptive radiotherapy with and without the distortion correction. The geometric tracking error and latency were derived from the measurements of the beam and target positions in the EPID images.

Results

Without distortion correction, the mean geometric tracking error was 1.3 mm at P ₁ and 3.1 mm at P ₂. When distortion correction was applied, the error was reduced to 1.0 mm at P ₁ and 1.1 mm at P ₂. The corrected error was similar to an error of 0.9 mm at P₀ where the target was unaffected by distortion indicating that this method has accurately accounted for distortion during tracking. The latency was 319 ± 12 ms without distortion correction and 335 ± 34 ms with distortion correction.

Conclusions

We have demonstrated a real-time distortion correction method that maintains accurate radiation delivery to the target, even at treatment locations with large distortion.

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

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

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.

Characterizing magnetically focused contamination electrons by off-axis irradiation on an inline MRI-Linac

Purpose

The aim of this study is to investigate off‐axis irradiation on the Australian MRI‐Linac using experiments and Monte Carlo simulations. Simulations are used to verify experimental measurements and to determine the minimum offset distance required to separate electron contamination from the photon field.

Methods

Dosimetric measurements were performed using a microDiamond detector, Gafchromic^® EBT3 film, and MOSkin^TM. Three field sizes were investigated including 1.9 × 1.9, 5.8 × 5.8, and 9.7 × 9.6 cm². Each field was offset a maximum distance, approximately 10 cm, from the central magnetic axis (isocenter). Percentage depth doses (PDDs) were collected at a source‐to‐surface distance (SSD) of 1.8 m for fields collimated centrally and off‐axis. PDD measurements were also acquired at isocenter for each off‐axis field to measure electron contamination. Monte Carlo simulations were used to verify experimental measurements, determine the minimum field offset distance, and demonstrate the use of a spoiler to absorb electron contamination.

Results

Off‐axis irradiation separates the majority of electron contamination from an x‐ray beam and was found to significantly reduce in‐field surface dose. For the 1.9 × 1.9, 5.8 × 5.8, and 9.7 × 9.6 cm² field, surface dose was reduced from 120.9% to 24.9%, 229.7% to 39.2%, and 355.3% to 47.3%, respectively. Monte Carlo simulations generally were within experimental error to MOSkin^TM and microDiamond, and used to determine the minimum offset distance, 2.1 cm, from the field edge to isocenter. A water spoiler 2 cm thick was shown to reduce electron contamination dose to near zero.

Conclusions

Experimental and simulation data were acquired for a range of field sizes to investigate off‐axis irradiation on an inline MRI‐Linac. The skin sparing effect was observed with off‐axis irradiation, a feature that cannot be achieved to the same extent with other methods, such as bolusing, for beams at isocenter.

ReUINet: A fast GNL distortion correction approach on a 1.0 T MRI-Linac scanner

PURPOSE

The hybrid system combining a magnetic resonance imaging (MRI) scanner with a linear accelerator (Linac) has become increasingly desirable for tumor treatment because of excellent soft tissue contrast and nonionizing radiation. However, image distortions caused by gradient nonlinearity (GNL) can have detrimental impacts on real-time radiotherapy using MRI-Linac systems, where accurate geometric information of tumors is essential.

METHODS

In this work, we proposed a deep convolutional neural network-based method to efficiently recover undistorted images (ReUINet) for real-time image guidance. The ReUINet, based on the encoder-decoder structure, was created to learn the relationship between the undistorted images and distorted images. The ReUINet was pretrained and tested on a publically available brain MR image dataset acquired from 23 volunteers. Then, transfer learning was adopted to implement the pretrained model (i.e., network with optimal weights) on the experimental three-dimensional (3D) grid phantom and in-vivo pelvis image datasets acquired from the 1.0 T Australian MRI-Linac system.

RESULTS

Evaluations on the phantom (768 slices) and pelvis data (88 slices) showed that the ReUINet achieved improvement over 15 times and 45 times on computational efficiency in comparison with standard interpolation and GNL-encoding methods, respectively. Moreover, qualitative and quantitative results demonstrated that the ReUINet provided better correction results than the standard interpolation method, and comparable performance compared to the GNL-encoding approach.

CONCLUSIONS

Validated by simulation and experimental results, the proposed ReUINet showed promise in obtaining accurate MR images for the implementation of real-time MRI-guided radiotherapy.

Imaging performance of a high-field in-line magnetic resonance imaging linear accelerator with a patient rotation system for fixed-gantry radiotherapy

This paper describes the imaging performance of a high-field in-line MRI linear accelerator with a patient rotation system in-situ. Signal quality was quantified using signal-to-noise ratio (SNR) and RF uniformity maps. B0-field inhomogeneity was assessed using magnetic field mapping. SNR was evaluated with various entries into the Faraday cage which were required for extended couch translations. SNR varied between 103 and 87 across PRS rotation angles. Maximum B0-field inhomogeneity corresponded to 0.7 mm of geometric distortion. A 45 × 55 cm² aperture allowed PRS translation with no reduction in SNR. Imaging performance with the PRS in-situ was found to be acceptable.