Radiotherapy planning using MRI

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Future of Team-based Basic and Translational Science in Radiation Oncology

To further optimize radiotherapy, a more personalized treatment towards individual patient's risk profiles, dissecting both patient-specific tumor and normal tissue response to multimodality treatments is needed. Novel developments in radiobiology, using in vitro patient-specific complex tissue resembling 3D models and multiomics approaches at a spatial single-cell level, may provide unprecedented insight into the radiation responses of tumors and normal tissue. Here, we describe the necessary team effort, including all disciplines in radiation oncology, to integrate such data into clinical prediction models and link the relatively "big data" from the clinical practice, allowing accurate patient stratification for personalized treatment approaches.

Conceptual design of sandwich walls for shielding against secondary neutrons using MC simulations with FLUKA

FLUKA Monte-Carlo transport code was employed to evaluate the secondary neutron spectra emerging from spherical sandwich shielding configurations composed of concrete and soil, similar to that used at the particle therapy facility MedAustron. This study provides a comparative analysis of neutron spectra attenuated by a concrete-soil-concrete (CSC) sandwich wall shielding configuration versus a full concrete wall design (CCC). Furthermore, we enhanced the shielding performance of the CSC configuration by adding an additional concrete layer (CCSC) to achieve results comparable to the CCC shielding. Two scenarios were tested for shielding performance: (1) primary protons at 100 MeV, and (2) primary carbon ions (C-ions) at 190 MeV/u. Our simulations with primary protons of 100 MeV showed that adding additional internal concrete wall to the CSC configuration, therefore designing the CCSC configuration, the RP performance becomes slightly improved - the HE-peak drops from (1.43 ± 0.11)10-11 to (5.62 ± 0.3)10-12, about 2.5 times. Still, the HE-peak of the exiting neutron spectrum from CCC -(6.29 ± 1.87) 10-13 is about 9 times lower than that exiting CCSC - (5.62 ± 0.3) 10-12. Our simulations with primary C-ions showed that by placing an additional internal concrete wall to the CSC configuration (CCSC) the RP performance becomes slightly improved - the exiting HE peak can be further attenuated from (6.92 ± 0.40)10-9 for CSC to (3.79 ± 0.15)10-9, becoming comparable to the one exiting the CCC configuration, (0.92 ± 0.04)10-9, only 4 times higher. Future research should be focused on improvements of the RP performance of the CCSC, by increasing the soil layer thickness and taking into consideration the humidity (water content) in the soil and concrete and also improve the number of primaries to 109 or even 1010 for better statistical outcome.

Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI

OBJECTIVES

This study aimed to examine the equivalence of computed tomography (CT)-based synthetic T1-weighted imaging (T1WI) to conventional T1WI for the quantitative assessment of brain morphology.

MATERIALS AND METHODS

This prospective study examined 35 adult patients undergoing brain magnetic resonance imaging (MRI) and CT scans. An image synthesis method based on a deep learning model was used to generate synthetic T1WI (sT1WI) from CT data. Two senior radiologists used sT1WI and conventional T1WI on separate occasions to independently measure clinically relevant brain morphological parameters. The reliability and consistency between conventional and synthetic T1WI were assessed using statistical consistency checks, comprising intra-reader, inter-reader, and inter-method agreement.

RESULTS

The intra-reader, inter-reader, and inter-method reliability and variability mostly exhibited the desired performance, except for several poor agreements due to measurement differences between the radiologists. All the measurements of sT1WI were equivalent to that of T1WI at 5% equivalent intervals.

CONCLUSION

This study demonstrated the equivalence of CT-based sT1WI to conventional T1WI for quantitatively assessing brain morphology, thereby providing more information on imaging diagnosis with a single CT scan.

CLINICAL RELEVANCE STATEMENT

Real-time synthesis of MR images from CT scans reduces the time required to acquire MR signals, improving the efficiency of the treatment planning system and providing benefits in the clinical diagnosis of patients with contraindications such as presence of metal implants or claustrophobia.

KEY POINTS

• Deep learning-based image synthesis methods generate synthetic T1-weighted imaging from CT scans. • The equivalence of synthetic T1-weighted imaging and conventional MRI for quantitative brain assessment was investigated. • Synthetic T1-weighted imaging can provide more information per scan and be used in preoperative diagnosis and radiotherapy.

A Comprehensive Review on the Role of MRI in the Assessment of Supratentorial Neoplasms: Comparative Insights Into Adult and Pediatric Cases

Magnetic resonance imaging (MRI) is a critical diagnostic tool in assessing supratentorial neoplasms, offering unparalleled detail and specificity in brain imaging. Supratentorial neoplasms in the cerebral hemispheres, basal ganglia, thalamus, and other structures above the tentorium cerebelli present significant diagnostic and therapeutic challenges. These challenges vary notably between adult and pediatric populations due to differences in tumor types, biological behavior, and patient management strategies. This comprehensive review explores the role of MRI in diagnosing, planning treatment, monitoring response, and detecting recurrence in supratentorial neoplasms, providing comparative insights into adult and pediatric cases. The review begins with an overview of the epidemiology and pathophysiology of these tumors in different age groups, followed by a detailed examination of standard and advanced MRI techniques, including diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy (MRS). We discuss the specific imaging characteristics of various neoplasms and the importance of tailored approaches to optimize diagnostic accuracy and therapeutic efficacy. The review also addresses the technical and interpretative challenges unique to pediatric imaging and the implications for long-term patient outcomes. By highlighting the comparative utility of MRI in adult and pediatric cases, this review aims to enhance the understanding of its pivotal role in managing supratentorial neoplasms. It underscores the necessity of age-specific diagnostic and therapeutic strategies. Emerging MRI technologies and future research directions are also discussed, emphasizing the potential for advancements in personalized imaging approaches and improved patient care across all age groups.

A Complementary Fusion-Based Multimodal Non-Destructive Testing and Evaluation Using Phased-Array Ultrasonic and Pulsed Thermography on a Composite Structure

Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions. Subsequently, the aligned inspection images were merged using complementary techniques, including maximum, weighted-averaging, depth-driven combination (DDC), and wavelet decomposition. The results indicated that although individual inspections may have lower mean absolute error (MAE) ratings than fused images, the use of complementary fusion improved defect identification in the total number of detections across numerous layers of the structure. Detection errors are analyzed, and a tendency to overestimate defect sizes is revealed with individual inspection methods. This study concludes that complementary fusion provides a more comprehensive understanding of overall defect detection throughout the thickness, highlighting the importance of leveraging multiple modalities for improved inspection outcomes in structural analysis.

Comparative MRI analysis of the forebrain of three sauropsida models

The study of the brain by magnetic resonance imaging (MRI) allows to obtain detailed anatomical images, useful to describe specific encephalic structures and to analyze possible variabilities. It is widely used in clinical practice and is becoming increasingly used in veterinary medicine, even in exotic animals; however, despite its potential, its use in comparative neuroanatomy studies is still incipient. It is a technology that in recent years has significantly improved anatomical resolution, together with the fact that it is non-invasive and allows for systematic comparative analysis. All this makes it particularly interesting and useful in evolutionary neuroscience studies, since it allows for the analysis and comparison of brains of rare or otherwise inaccessible species. In the present study, we have analyzed the prosencephalon of three representative sauropsid species, the turtle Trachemys scripta (order Testudine), the lizard Pogona vitticeps (order Squamata) and the snake Python regius (order Squamata) by MRI. In addition, we used MRI sections to analyze the total brain volume and ventricular system of these species, employing volumetric and chemometric analyses together. The raw MRI data of the sauropsida models analyzed in the present study are available for viewing and downloading and have allowed us to produce an atlas of the forebrain of each of the species analyzed, with the main brain regions. In addition, our volumetric data showed that the three groups presented clear differences in terms of total and ventricular brain volumes, particularly the turtles, which in all cases presented distinctive characteristics compared to the lizards and snakes.

Interim 18F-FDG-PET based response-adaptive dose escalation of proton therapy for head and neck cancer: a treatment planning feasibility study

BACKGROUND

Image-driven dose escalation to tumor subvolumes has been proposed to improve treatment outcome in head and neck cancer (HNC). We used 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) acquired at baseline and into treatment (interim) to identify biologic target volumes (BTVs). We assessed the feasibility of interim dose escalation to the BTV with proton therapy by simulating the effects to organs at risk (OARs).

METHODS

We used the semiautomated just-enough-interaction (JEI) method to identify BTVs in 18F-FDG-PET images from nine HNC patients. Between baseline and interim FDG-PET, patients received photon radiotherapy. BTV was identified assuming that high standardized uptake value (SUV) at interim reflected tumor radioresistance. Using Eclipse (Varian Medical Systems), we simulated a 10% (6.8 Gy(RBE1.1)) and 20% (13.6 Gy(RBE1.1)) dose escalation to the BTV with protons and compared results with proton plans without dose escalation.

RESULTS

At interim 18F-FDG-PET, radiotherapy resulted in reduced SUV compared to baseline. However, spatial overlap between high-SUV regions at baseline and interim allowed for BTV identification. Proton therapy planning demonstrated that dose escalation to the BTV was feasible, and except for some 20% dose escalation plans, OAR doses did not significantly increase.

CONCLUSION

Our in silico analysis demonstrated the potential for interim 18F-FDG-PET response-adaptive dose escalation to the BTV with proton therapy. This approach may give more efficient treatment to HNC with radioresistant tumor subvolumes without increasing normal tissue toxicity. Studies in larger cohorts are required to determine the full potential for interim 18F-FDG-PET-guided dose escalation of proton therapy in HNC.

Linac- and CyberKnife-based MRI-only treatment planning of prostate SBRT using an optimized synthetic CT calibration curve

PURPOSE

CT Hounsfield Units (HUs) are converted to electron density using a calibration curve obtained from physical measurements of an electron density phantom. HU values assigned to an MRI-derived synthetic computed tomography (sCT) may present a different relationship with electron density compared to CT HU. Correct assignment of sCT HU values is critical for accurate dose calculation and delivery. The goals of this work were to develop a sCT calibration curve using patient data acquired on a clinically commissioned CT scanner and assess for CyberKnife- and volumetric modulated arc therapy (VMAT)-based MR-only treatment planning of prostate SBRT.

METHODS

Same-day CT and MRI simulation in the treatment position were performed on 10 patients treated with SBRT to the prostate. Dixon in-phase and out-of-phase MRIs were acquired on a 3T scanner using a 3D T1-weighted gradient-echo sequence to generate sCTs using a commercial sCT algorithm. CT and sCT datasets were co-registered and HU values compared using mean absolute error (MAE). An optimized HU-to-density calibration curve was created based on average HU values across an institutional patient database for each of the four sCT tissue types. Clinical CyberKnife and VMAT treatment plans were generated on each patient CT and recomputed onto corresponding sCTs. Dose distributions computed using CT and sCT were compared using gamma criteria and dose-volume-histograms.

RESULTS

For the optimized calibration curve, HU values were -96, 37, 204, and 1170 and relative electron densities were 0.95, 1.04, 1.1, and 1.7 for adipose, soft tissue, inner bone, and outer bone, respectively. The proposed sCT protocol produced total MAE of 94 ± 20HU. Gamma values mean ± std (min-max) were 98.9% ± 0.9% (97.1%-100%) and 97.7% ± 1.3% (95.3%-99.3%) for VMAT and CyberKnife plans, respectively.

CONCLUSION

MRI-derived sCT using the proposed approach shows excellent dosimetric agreement with conventional CT simulation, demonstrating the feasibility of MRI-derived sCT for prostate SBRT treatment planning.

MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM)

BACKGROUND

High-resolution magnetic resonance imaging (MRI) with excellent soft-tissue contrast is a valuable tool utilized for diagnosis and prognosis. However, MRI sequences with long acquisition time are susceptible to motion artifacts, which can adversely affect the accuracy of post-processing algorithms.

PURPOSE

This study proposes a novel retrospective motion correction method named "motion artifact reduction using conditional diffusion probabilistic model" (MAR-CDPM). The MAR-CDPM aimed to remove motion artifacts from multicenter three-dimensional contrast-enhanced T1 magnetization-prepared rapid acquisition gradient echo (3D ceT1 MPRAGE) brain dataset with different brain tumor types.

MATERIALS AND METHODS

This study employed two publicly accessible MRI datasets: one containing 3D ceT1 MPRAGE and 2D T2-fluid attenuated inversion recovery (FLAIR) images from 230 patients with diverse brain tumors, and the other comprising 3D T1-weighted (T1W) MRI images of 148 healthy volunteers, which included real motion artifacts. The former was used to train and evaluate the model using the in silico data, and the latter was used to evaluate the model performance to remove real motion artifacts. A motion simulation was performed in k-space domain to generate an in silico dataset with minor, moderate, and heavy distortion levels. The diffusion process of the MAR-CDPM was then implemented in k-space to convert structure data into Gaussian noise by gradually increasing motion artifact levels. A conditional network with a Unet backbone was trained to reverse the diffusion process to convert the distorted images to structured data. The MAR-CDPM was trained in two scenarios: one conditioning on the time step t $t$ of the diffusion process, and the other conditioning on both t $t$ and T2-FLAIR images. The MAR-CDPM was quantitatively and qualitatively compared with supervised Unet, Unet conditioned on T2-FLAIR, CycleGAN, Pix2pix, and Pix2pix conditioned on T2-FLAIR models. To quantify the spatial distortions and the level of remaining motion artifacts after applying the models, quantitative metrics were reported including normalized mean squared error (NMSE), structural similarity index (SSIM), multiscale structural similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multiscale gradient magnitude similarity deviation (MS-GMSD). Tukey's Honestly Significant Difference multiple comparison test was employed to quantify the difference between the models where p-value < 0.05 $ < 0.05$ was considered statistically significant.

RESULTS

Qualitatively, MAR-CDPM outperformed these methods in preserving soft-tissue contrast and different brain regions. It also successfully preserved tumor boundaries for heavy motion artifacts, like the supervised method. Our MAR-CDPM recovered motion-free in silico images with the highest PSNR and VIF for all distortion levels where the differences were statistically significant (p-values< 0.05 $< 0.05$ ). In addition, our method conditioned on t and T2-FLAIR outperformed (p-values< 0.05 $< 0.05$ ) other methods to remove motion artifacts from the in silico dataset in terms of NMSE, MS-SSIM, SSIM, and MS-GMSD. Moreover, our method conditioned on only t outperformed generative models (p-values< 0.05 $< 0.05$ ) and had comparable performances compared with the supervised model (p-values> 0.05 $> 0.05$ ) to remove real motion artifacts.

CONCLUSIONS

The MAR-CDPM could successfully remove motion artifacts from 3D ceT1 MPRAGE. It is particularly beneficial for elderly who may experience involuntary movements during high-resolution MRI imaging with long acquisition times.

Biocompatible and bioactivable terpolymer-lipid-MnO2 Nanoparticle-based MRI contrast agent for improving tumor detection and delineation

Early and precise detection of solid tumor cancers is critical for improving therapeutic outcomes. In this regard, magnetic resonance imaging (MRI) has become a useful tool for tumor diagnosis and image-guided therapy. However, its effectiveness is limited by the shortcomings of clinically available gadolinium-based contrast agents (GBCAs), i.e. poor tumor penetration and retention, and safety concerns. Thus, we have developed a novel nanoparticulate contrast agent using a biocompatible terpolymer and lipids to encapsulate manganese dioxide nanoparticles (TPL-MDNP). The TPL-MDNP accumulated in tumor tissue and produced paramagnetic Mn2+ ions, enhancing T1-weight MRI contrast via the reaction with H2O2 rich in the acidic tumor microenvironment. Compared to the clinically used GBCA, Gadovist®1.0, TPL-MDNP generated stronger T1-weighted MR signals by over 2.0-fold at 30 % less of the recommended clinical dose with well-defined tumor delineation in preclinical orthotopic tumor models of brain, breast, prostate, and pancreas. Importantly, the MRI signals were retained for 60 min by TPL-MDNP, much longer than Gadovist®1.0. Biocompatibility of TPL-MDNP was evaluated and found to be safe up to 4-fold of the dose used for MRI. A robust large-scale manufacturing process was developed with batch-to-batch consistency. A lyophilization formulation was designed to maintain the nanostructure and storage stability of the new contrast agent.

Assessing the impact of distortion correction on Gamma Knife radiosurgery for multiple metastasis: Volumetric and dosimetric analysis

Introduction

Magnetic resonance imaging (MRI) is a robust neuroimaging technique and is the preferred method for stereotactic radiosurgery (SRS) planning. However, MRI data always contain distortions caused by hardware and patient factors.

Research question

Can these distortions potentially compromise the effectiveness and safety of SRS treatments?

Material and methods

Twenty-six MR datasets with multiple metastatic brain tumors (METs) used for Gamma Knife radiosurgery (GKRS) were retrospectively evaluated. A commercially available software was used for distortion correction. Geometrical agreement between corrected and uncorrected tumor volumes was evaluated using MacDonald criteria, Euclidian distance, and Dice similarity coefficient (DSC). SRS plans were generated using uncorrected tumor volumes, which were assessed to determine their coverage of the corrected tumor volumes.

Results

The median target volume was 0.38 cm3 (range,0.01-12.38 cm3). A maximum displacement of METs of up to 2.87 mm and a median displacement of 0.55 mm (range,0.1-2.87 mm) were noted. The median DSC between uncorrected and corrected MRI was 0.92, and the most concerning case had a DSC of 0.46. Although all plans met the optimization criterion of at least 98% of the uncorrected tumor volume (median 99.55%, range 98.1-100%) receiving at least 100% of the prescription dose, the percent of the corrected tumor volume receiving the total prescription dose was a median of 95.45% (range,23.1-99.5%).

Discussion and conclusion

MRI distortion, though visually subtle, has significant implications for SRS planning. Regular utilization of corrected MRI is recommended for SRS planning as distortion is sometimes enough to cause a volumetric miss of SRS targets.

Channel-wise attention enhanced and structural similarity constrained cycleGAN for effective synthetic CT generation from head and neck MRI images

BACKGROUND

Magnetic resonance imaging (MRI) plays an increasingly important role in radiotherapy, enhancing the accuracy of target and organs at risk delineation, but the absence of electron density information limits its further clinical application. Therefore, the aim of this study is to develop and evaluate a novel unsupervised network (cycleSimulationGAN) for unpaired MR-to-CT synthesis.

METHODS

The proposed cycleSimulationGAN in this work integrates contour consistency loss function and channel-wise attention mechanism to synthesize high-quality CT-like images. Specially, the proposed cycleSimulationGAN constrains the structural similarity between the synthetic and input images for better structural retention characteristics. Additionally, we propose to equip a novel channel-wise attention mechanism based on the traditional generator of GAN to enhance the feature representation capability of deep network and extract more effective features. The mean absolute error (MAE) of Hounsfield Units (HU), peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE) and structural similarity index (SSIM) were calculated between synthetic CT (sCT) and ground truth (GT) CT images to quantify the overall sCT performance.

RESULTS

One hundred and sixty nasopharyngeal carcinoma (NPC) patients who underwent volumetric-modulated arc radiotherapy (VMAT) were enrolled in this study. The generated sCT of our method were more consistent with the GT compared with other methods in terms of visual inspection. The average MAE, RMSE, PSNR, and SSIM calculated over twenty patients were 61.88 ± 1.42, 116.85 ± 3.42, 36.23 ± 0.52 and 0.985 ± 0.002 for the proposed method. The four image quality assessment metrics were significantly improved by our approach compared to conventional cycleGAN, the proposed cycleSimulationGAN produces significantly better synthetic results except for SSIM in bone.

CONCLUSIONS

We developed a novel cycleSimulationGAN model that can effectively create sCT images, making them comparable to GT images, which could potentially benefit the MRI-based treatment planning.

Generation of tissues outside the field of view (FOV) of radiation therapy simulation imaging based on machine learning and patient body outline (PBO)

BACKGROUND

It is not unusual to see some parts of tissues are excluded in the field of view of CT simulation images. A typical mitigation is to avoid beams entering the missing body parts at the cost of sub-optimal planning.

METHODS

This study is to solve the problem by developing 3 methods, (1) deep learning (DL) mechanism for missing tissue generation, (2) using patient body outline (PBO) based on surface imaging, and (3) hybrid method combining DL and PBO. The DL model was built upon a Globally and Locally Consistent Image Completion to learn features by Convolutional Neural Networks-based inpainting, based on Generative Adversarial Network. The database used comprised 10,005 CT training slices of 322 lung cancer patients and 166 CT evaluation test slices of 15 patients. CT images were from the publicly available database of the Cancer Imaging Archive. Since existing data were used PBOs were acquired from the CT images. For evaluation, Structural Similarity Index Metric (SSIM), Root Mean Square Error (RMSE) and Peak signal-to-noise ratio (PSNR) were evaluated. For dosimetric validation, dynamic conformal arc plans were made with the ground truth images and images generated by the proposed method. Gamma analysis was conducted at relatively strict criteria of 1%/1 mm (dose difference/distance to agreement) and 2%/2 mm under three dose thresholds of 1%, 10% and 50% of the maximum dose in the plans made on the ground truth image sets.

RESULTS

The average SSIM in generation part only was 0.06 at epoch 100 but reached 0.86 at epoch 1500. Accordingly, the average SSIM in the whole image also improved from 0.86 to 0.97. At epoch 1500, the average values of RMSE and PSNR in the whole image were 7.4 and 30.9, respectively. Gamma analysis showed excellent agreement with the hybrid method (equal to or higher than 96.6% of the mean of pass rates for all scenarios).

CONCLUSIONS

It was first demonstrated that missing tissues in simulation imaging could be generated with high similarity, and dosimetric limitation could be overcome. The benefit of this study can be significantly enlarged when MR-only simulation is considered.

Quality requirements for MRI simulation in cranial stereotactic radiotherapy: a guideline from the German Taskforce "Imaging in Stereotactic Radiotherapy"

Accurate Magnetic Resonance Imaging (MRI) simulation is fundamental for high-precision stereotactic radiosurgery and fractionated stereotactic radiotherapy, collectively referred to as stereotactic radiotherapy (SRT), to deliver doses of high biological effectiveness to well-defined cranial targets. Multiple MRI hardware related factors as well as scanner configuration and sequence protocol parameters can affect the imaging accuracy and need to be optimized for the special purpose of radiotherapy treatment planning. MRI simulation for SRT is possible for different organizational environments including patient referral for imaging as well as dedicated MRI simulation in the radiotherapy department but require radiotherapy-optimized MRI protocols and defined quality standards to ensure geometrically accurate images that form an impeccable foundation for treatment planning. For this guideline, an interdisciplinary panel including experts from the working group for radiosurgery and stereotactic radiotherapy of the German Society for Radiation Oncology (DEGRO), the working group for physics and technology in stereotactic radiotherapy of the German Society for Medical Physics (DGMP), the German Society of Neurosurgery (DGNC), the German Society of Neuroradiology (DGNR) and the German Chapter of the International Society for Magnetic Resonance in Medicine (DS-ISMRM) have defined minimum MRI quality requirements as well as advanced MRI simulation options for cranial SRT.

Camouflaged Nanoreactors Mediated Radiotherapy-Adjuvant Chemodynamic Synergistic Therapy

Chemodynamic therapy based on the Fenton-like catalysis ability of Fe3O4 has the advantages of no involvement of chemical drugs and minimal adverse effects as well as the limitation of depletable efficacy. Radiotherapy based on high-energy radiation offers the convenience of treatment and cost-effectiveness but lacks precision and cellular adaptation of tumor cells. Approaching such dilemmas from a nanoscale materials perspective, we aim to bridge the weaknesses of both treatment methods by combining the principles of two therapeutics reciprocally. We have designed a camouflaged Fe3O4@HfO2 composite nanoreactor (FHCM), which combines a chemodynamic therapeutic agent Fe3O4 and a radiosensitizer HfO2 that both has passed clinical trials and was inspired by a cell membrane biomimetic technique. FHCM is employed as conceived radiotherapy-adjuvant chemodynamic synergistic therapy of malignant tumors, which has undergone dual scrutiny from both the physical and biological aspects. Experimental results obtained at different levels, including theory, material characterizations, and in vitro and in vivo verifications, suggest that FHCM effectively impaired tumor cells through physical and molecular biological mechanisms involving a HfO2-Fe3O4 photoelectron-electron transfer chain and DNA damage-ferroptosis-immunity chain. It is worth noting that compared to single therapies such as only chemodynamic therapy or radiotherapy, FHCM-mediated radiotherapy-adjuvant chemodynamic synergistic therapy exhibits stronger tumor inhibition efficacy. It significantly addresses the inherent limitations of chemodynamic therapy and radiotherapy and underscores the feasibility and importance of using existing clinical weapons, such as radiotherapy, as auxiliary strategies to overcome certain flaws of emerging antitumor therapeutics like chemodynamic therapy.

Bony structure enhanced synthetic CT generation using Dixon sequences for pelvis MR-only radiotherapy

BACKGROUND

MRI-only radiotherapy planning (MROP) is beneficial to patients by avoiding MRI/CT registration errors, simplifying the radiation treatment simulation workflow and reducing exposure to ionizing radiation. MRI is the primary imaging modality for soft tissue delineation. Treatment planning CTs (i.e., CT simulation scan) are redundant if a synthetic CT (sCT) can be generated from the MRI to provide the patient positioning and electron density information. Unsupervised deep learning (DL) models like CycleGAN are widely used in MR-to-sCT conversion, when paired patient CT and MR image datasets are not available for model training. However, compared to supervised DL models, they cannot guarantee anatomic consistency, especially around bone.

PURPOSE

The purpose of this work was to improve the sCT accuracy generated from MRI around bone for MROP.

METHODS

To generate more reliable bony structures on sCT images, we proposed to add bony structure constraints in the unsupervised CycleGAN model's loss function and leverage Dixon constructed fat and in-phase (IP) MR images. Dixon images provide better bone contrast than T2-weighted images as inputs to a modified multi-channel CycleGAN. A private dataset with a total of 31 prostate cancer patients were used for training (20) and testing (11).

RESULTS

We compared model performance with and without bony structure constraints using single- and multi-channel inputs. Among all the models, multi-channel CycleGAN with bony structure constraints had the lowest mean absolute error, both inside the bone and whole body (50.7 and 145.2 HU). This approach also resulted in the highest Dice similarity coefficient (0.88) of all bony structures compared with the planning CT.

CONCLUSION

Modified multi-channel CycleGAN with bony structure constraints, taking Dixon-constructed fat and IP images as inputs, can generate clinically suitable sCT images in both bone and soft tissue. The generated sCT images have the potential to be used for accurate dose calculation and patient positioning in MROP radiation therapy.

Implementation of a retrofit MRI simulator for radiation therapy planning

Magnetic resonance imaging (MRI) is being integrated into routine radiation therapy (RT) planning workflows. To reap the benefits of this imaging modality, patient positioning, image acquisition parameters and a quality assurance programme must be considered for accurate use. This paper will report on the implementation of a retrofit MRI Simulator for RT planning, demonstrating an economical, resource efficient solution to improve the accuracy of MRI in this setting.

Comprehensive MR imaging QA of 0.35 T MR-Linac using a multi-purpose large FOV phantom: A single-institution experience

PURPOSE

Magnetic resonance-guided radiotherapy (MRgRT) is desired for the treatment of diseases in the abdominothoracic region, which has a broad imaging area and continuous motion. To ensure accurate treatment delivery, an effective image quality assurance (QA) program, with a phantom that covers the field of view (FOV) similar to a human torso, is required. However, routine image QA for a large FOV is not readily available at many MRgRT centers. In this work, we present the clinical experience of the large FOV MRgRT Insight phantom for periodic daily and monthly comprehensive magnetic resonance imaging (MRI)-QA and its feasibility compared to the existing institutional routine MRI-QA procedures in 0.35 T MRgRT.

METHODS

Three phantoms; ViewRay cylindrical water phantom, Fluke 76-907 uniformity and linearity phantom, and Modus QA large FOV MRgRT Insight phantom, were imaged on the 0.35 T MR-Linac. The measurements were made in MRI mode with the true fast imaging with steady-state free precession (TRUFI) sequence. The ViewRay cylindrical water phantom was imaged in a single-position setup whereas the Fluke phantom and Insight phantom were imaged in three different orientations: axial, sagittal, and coronal. Additionally, the phased array coil QA was performed using the horizontal base plate of the Insight phantom by placing the desired coil around the base section which was compared to an in-house built Polyurethane foam phantom for reference.

RESULT

The Insight phantom captured image artifacts across the entire planar field of view, up to 400 mm, in a single image acquisition, which is beyond the FOV of the conventional phantoms. The geometric distortion test showed a similar distortion of 0.45 ± 0.01  and 0.41 ± 0.01 mm near the isocenter, that is, within 300 mm lengths for Fluke and Insight phantoms, respectively, but showed higher geometric distortion of 0.8 ± 0.4 mm in the peripheral region between 300 and 400 mm of the imaging slice for the Insight phantom. The Insight phantom with multiple image quality features and its accompanying software utilized the modulation transform function (MTF) to evaluate the image spatial resolution. The average MTF values were 0.35 ± 0.01, 0.35 ± 0.01, and 0.34 ± 0.03 for axial, coronal, and sagittal images, respectively. The plane alignment and spatial accuracy of the ViewRay water phantom were measured manually. The phased array coil test for both the Insight phantom and the Polyurethane foam phantoms ensured the proper functionality of each coil element.

CONCLUSION

The multifunctional large FOV Insight phantom helps in tracking MR imaging quality of the system to a larger extent compared to the routine daily and monthly QA phantoms currently used in our institute. Also, the Insight phantom is found to be more feasible for routine QA with easy setup.

Clinical feasibility of a commercially available MRI-only method for radiotherapy treatment planning of the brain

BACKGROUND

Advancements in deep-learning based synthetic computed tomography (sCT) image conversion methods have enabled the development of magnetic resonance imaging (MRI)-only based radiotherapy treatment planning (RTP) of the brain.

PURPOSE

This study evaluates the clinical feasibility of a commercial, deep-learning based MRI-only RTP method with respect to dose calculation and patient positioning verification performance in RTP of the brain.

METHODS

Clinical validation of dose calculation accuracy was performed by a retrospective evaluation for 25 glioma and 25 brain metastasis patients. Dosimetric and image quality of the studied MRI-only RTP method was evaluated by a direct comparison of the sCT-based and computed tomography (CT)-based external beam radiation therapy (EBRT) images and treatment plans. Patient positioning verification accuracy of sCT images was evaluated retrospectively for 10 glioma and 10 brain metastasis patients based on clinical cone-beam computed tomography (CBCT) imaging.

RESULTS

An average mean dose difference of Dmean = 0.1% for planning target volume (PTV) and 0.6% for normal tissue (NT) structures were obtained for glioma patients. Respective results for brain metastasis patients were Dmean = 0.5% for PTVs and Dmean =1.0% for NTs. Global three-dimensional (3D) gamma pass rates using 2%/2 mm dose difference and distance-to-agreement (DTA) criterion were 98.0% for the glioma subgroup, and 95.2% for the brain metastasis subgroup using 1%/1 mm criterion. Mean distance differences of <1.0 mm were observed in all Cartesian directions between CT-based and sCT-based CBCT patient positioning in both subgroups.

CONCLUSIONS

In terms of dose calculation and patient positioning accuracy, the studied MRI-only method demonstrated its clinical feasibility for RTP of the brain. The results encourage the use of the studied method as part of a routine clinical workflow.

A transformer-based hierarchical registration framework for multimodality deformable image registration

Deformable image registration (DIR) between daily and reference images is fundamentally important for adaptive radiotherapy. In the last decade, deep learning-based image registration methods have been developed with faster computation time and improved robustness compared to traditional methods. However, the registration performance is often degraded in extra-cranial sites with large volume containing multiple anatomic regions, such as Computed Tomography (CT)/Magnetic Resonance (MR) images used in head and neck (HN) radiotherapy. In this study, we developed a hierarchical deformable image registration (DIR) framework, Patch-based Registration Network (Patch-RegNet), to improve the accuracy and speed of CT-MR and MR-MR registration for head-and-neck MR-Linac treatments. Patch-RegNet includes three steps: a whole volume global registration, a patch-based local registration, and a patch-based deformable registration. Following a whole-volume rigid registration, the input images were divided into overlapping patches. Then a patch-based rigid registration was applied to achieve accurate local alignment for subsequent DIR. We developed a ViT-Morph model, a combination of a convolutional neural network (CNN) and the Vision Transformer (ViT), for the patch-based DIR. A modality independent neighborhood descriptor was adopted in our model as the similarity metric to account for both inter-modality and intra-modality registration. The CT-MR and MR-MR DIR models were trained with 242 CT-MR and 213 MR-MR image pairs from 36 patients, respectively, and both tested with 24 image pairs (CT-MR and MR-MR) from 6 other patients. The registration performance was evaluated with 7 manually contoured organs (brainstem, spinal cord, mandible, left/right parotids, left/right submandibular glands) by comparing with the traditional registration methods in Monaco treatment planning system and the popular deep learning-based DIR framework, Voxelmorph. Evaluation results show that our method outperformed VoxelMorph by 6 % for CT-MR registration, and 4 % for MR-MR registration based on DSC measurements. Our hierarchical registration framework has been demonstrated achieving significantly improved DIR accuracy of both CT-MR and MR-MR registration for head-and-neck MR-guided adaptive radiotherapy.

Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy

Objective.Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, enhancing quality and improving efficiency in radiotherapy. No commercial models exist for OAR contouring based on brain magnetic resonance imaging (MRI). We trained and evaluated computed tomography (CT) and MRI OAR autosegmentation models in RayStation. To ascertain clinical usability, we investigated the geometric impact of contour editing before training on model quality.Approach.Retrospective glioma cases were randomly selected for training (n= 32, 47) and validation (n= 9, 10) for MRI and CT, respectively. Clinical contours were edited using international consensus (gold standard) based on MRI and CT. MRI models were trained (i) using the original clinical contours based on planning CT and rigidly registered T1-weighted gadolinium-enhanced MRI (MRIu), (ii) as (i), further edited based on CT anatomy, to meet international consensus guidelines (MRIeCT), and (iii) as (i), further edited based on MRI anatomy (MRIeMRI). CT models were trained using: (iv) original clinical contours (CTu) and (v) clinical contours edited based on CT anatomy (CTeCT). Auto-contours were geometrically compared to gold standard validation contours (CTeCT or MRIeMRI) using Dice Similarity Coefficient, sensitivity, and mean distance to agreement. Models' performances were compared using paired Student's t-testing.Main results.The edited autosegmentation models successfully generated more segmentations than the unedited models. Paired t-testing showed editing pituitary, orbits, optic nerves, lenses, and optic chiasm on MRI before training significantly improved at least one geometry metric. MRI-based DL-AC performed worse than CT-based in delineating the lacrimal gland, whereas the CT-based performed worse in delineating the optic chiasm. No significant differences were found between the CTeCT and CTu except for optic chiasm.Significance.T1w-MRI DL-AC could segment all brain OARs except the lacrimal glands, which cannot be easily visualized on T1w-MRI. Editing contours on MRI before model training improved geometric performance. MRI DL-AC in RT may improve consistency, quality and efficiency but requires careful editing of training contours.

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis

BACKGROUND

Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation.

PURPOSE

investigating domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation.

METHODS

CT and corresponding T1-weighted MRI with/without contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were collected, considering FLAIR the unseen sequence where to investigate generalisation. A "Baseline" generative adversarial network was trained with/without the FLAIR sequence to test how a model performs without DR. Image similarity and accuracy of sCT-based dose plans were assessed against CT to select the best-performing DR approach against the Baseline.

RESULTS

The Baseline model had the poorest performance on FLAIR, with mean absolute error (MAE) = 106 ± 20.7 HU (mean ±σ). Performance on FLAIR significantly improved for the DR model with MAE = 99.0 ± 14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE = 72.6 ± 10.1 HU). Similarly, an improvement in γ-pass rate was obtained for DR vs Baseline.

CONCLUSION

DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI. DR makes the model more robust, reducing the need for re-training when applying a model on sequences unseen and unavailable for retraining.

Urethral identification using three-dimensional magnetic resonance imaging and interfraction urethral motion evaluation for prostate stereotactic body radiotherapy

Prostatic urethra identification is crucial in prostate stereotactic body radiotherapy (SBRT) to reduce the risk of urinary toxicity. Although computed tomography (CT) with a catheter is commonly employed, it is invasive, and catheter placement may displace the urethral position, resulting in possible planning inaccuracies. However, magnetic resonance imaging (MRI) can overcome these weaknesses. Accurate urethral identification and minimal daily variation could ensure a highly accurate SBRT. In this study, we investigated the usefulness of a three-dimensional (3D) T2-weighted (T2W) sequence for urethral identification, and the interfractional motion of the prostatic urethra on CT with a catheter and MRI without a catheter for implementing noninvasive SBRT. Thirty-two patients were divided into three groups. The first group underwent MRI without a catheter to evaluate urethral identification by two-dimensional (2D)- and 3D-T2W sequences using mean slice-wise Hausdorff distance (MSHD) and Dice similarity coefficient (DSC) of the contouring by two operators and using visual assessment. The second group provided 3-day MRI data without a catheter using 3D-T2W, and the third provided 3-day CT data with a catheter to evaluate the interfractional motion using MSHD, DSC, and displacement distance (Dd). The MSHD and DSC for the interoperator variability in urethral identification and visual assessment were superior in 3D-T2W than in 2D-T2W. Regarding interfractional motion, the Dd value for prostatic urethra was smaller in MRI than in CT. These findings indicate that the 3D-T2W yielded adequate prostatic urethral identification, and catheter-free MRI resulted in less interfractional motion, suggesting that 3D-T2W MRI without a catheter is a feasible noninvasive approach to performing prostate SBRT.

Accelerating 4D image reconstruction for magnetic resonance-guided radiotherapy

Background and purpose

Physiological motion impacts the dose delivered to tumours and vital organs in external beam radiotherapy and particularly in particle therapy. The excellent soft-tissue demarcation of 4D magnetic resonance imaging (4D-MRI) could inform on intra-fractional motion, but long image reconstruction times hinder its use in online treatment adaptation. Here we employ techniques from high-performance computing to reduce 4D-MRI reconstruction times below two minutes to facilitate their use in MR-guided radiotherapy.

Material and methods

Four patients with pancreatic adenocarcinoma were scanned with a radial stack-of-stars gradient echo sequence on a 1.5T MR-Linac. Fast parallelised open-source implementations of the extra-dimensional golden-angle radial sparse parallel algorithm were developed for central processing unit (CPU) and graphics processing unit (GPU) architectures. We assessed the impact of architecture, oversampling and respiratory binning strategy on 4D-MRI reconstruction time and compared images using the structural similarity (SSIM) index against a MATLAB reference implementation. Scaling and bottlenecks for the different architectures were studied using multi-GPU systems.

Results

All reconstructed 4D-MRI were identical to the reference implementation (SSIM > 0.99). Images reconstructed with overlapping respiratory bins were sharper at the cost of longer reconstruction times. The CPU  + GPU implementation was over 17 times faster than the reference implementation, reconstructing images in 60 ± 1 s and hyper-scaled using multiple GPUs.

Conclusion

Respiratory-resolved 4D-MRI reconstruction times can be reduced using high-performance computing methods for online workflows in MR-guided radiotherapy with potential applications in particle therapy.

On the effect of training database size for MR-based synthetic CT generation in the head

Generation of computed tomography (CT) images from magnetic resonance (MR) images using deep learning methods has recently demonstrated promise in improving MR-guided radiotherapy and PET/MR imaging.

PURPOSE

To investigate the performance of unsupervised training using a large number of unpaired data sets as well as the potential gain in performance after fine-tuning with supervised training using spatially registered data sets in generation of synthetic computed tomography (sCT) from magnetic resonance (MR) images.

MATERIALS AND METHODS

A cycleGAN method consisting of two generators (residual U-Net) and two discriminators (patchGAN) was used for unsupervised training. Unsupervised training utilized unpaired T1-weighted MR and CT images (2061 sets for each modality). Five supervised models were then fine-tuned starting with the generator of the unsupervised model for 1, 10, 25, 50, and 100 pairs of spatially registered MR and CT images. Four supervised training models were also trained from scratch for 10, 25, 50, and 100 pairs of spatially registered MR and CT images using only the residual U-Net generator. All models were evaluated on a holdout test set of spatially registered images from 253 patients, including 30 with significant pathology. sCT images were compared against the acquired CT images using mean absolute error (MAE), Dice coefficient, and structural similarity index (SSIM). sCT images from 60 test subjects generated by the unsupervised, and most accurate of the fine-tuned and supervised models were qualitatively evaluated by a radiologist.

RESULTS

While unsupervised training produced realistic-appearing sCT images, addition of even one set of registered images improved quantitative metrics. Addition of more paired data sets to the training further improved image quality, with the best results obtained using the highest number of paired data sets (n=100). Supervised training was found to be superior to unsupervised training, while fine-tuned training showed no clear benefit over supervised learning, regardless of the training sample size.

CONCLUSION

Supervised learning (using either fine tuning or full supervision) leads to significantly higher quantitative accuracy in the generation of sCT from MR images. However, fine-tuned training using both a large number of unpaired image sets was generally no better than supervised learning using registered image sets alone, suggesting the importance of well registered paired data set for training compared to a large set of unpaired data.

Image-Guided Proton Therapy: A Comprehensive Review

Image guidance for radiation therapy can improve the accuracy of the delivery of radiation, leading to an improved therapeutic ratio. Proton radiation is able to deliver a highly conformal dose to a target due to its advantageous dosimetric properties, including the Bragg peak. Proton therapy established the standard for daily image guidance as a means of minimizing uncertainties associated with proton treatment. With the increasing adoption of the use of proton therapy over time, image guidance systems for this modality have been changing. The unique properties of proton radiation present a number of differences in image guidance from photon therapy. This paper describes CT and MRI-based simulation and methods of daily image guidance. Developments in dose-guided radiation, upright treatment, and FLASH RT are discussed as well.

Geometric impact and dose estimation of on-patient placement of a lightweight receiver coil in a clinical magnetic resonance imaging-only radiotherapy workflow for prostate cancer

Background and Purpose

For pelvic magnetic resonance imaging (MRI)-only radiotherapy the use of receiver coil bridges (CB) is recommended to avoid deformation of the patient. Development in coil technology has enabled lightweight, flexible coils. In this work we evaluate the effects of a lightweight coil in a pelvic MRI-only radiotherapy workflow.

Materials and Methods

Twenty-one patients, referred to prostate MRI-only radiotherapy, were included. Images were acquired with and without CB. Anatomical deformation from the on-patient coil placement was measured in the anterior-posterior (AP) and left-right (LR) direction. The change in signal-to-noise ratio (SNR) was measured in phantom and in vivo.The clinical treatment plan, created on the image with CB, was transferred and recalculated on the image without the CB. Dose metrics for the targets (planning- and clinical target volume) and organs at risks (OAR) were analyzed.

Results

There was a statistically significant increase in SNR in-vivo (median 21 %, p = 0.002) when removing the CB. Anatomical differences after removing the CB in patients were -1.5 mm in AP (median change) and + 2.5 mm in LR direction. Dosimetric differences for the target structures were clinically negligible, but statistically significant. The difference in target mean doses were 0.2 % (both p = 0.004) of the prescribed dose. No dosimetric differences were observed for the OAR, except for the penile bulb.

Conclusions

We concluded that anatomical change and dosimetric differences, originating from scanning without CB were minor. The CB can thereby be removed from the workflow, enabling easier patient positioning and increased SNR when using lightweight coils.

Real-time motion monitoring using orthogonal cine MRI during MR-guided adaptive radiation therapy for abdominal tumors on 1.5T MR-Linac

BACKGROUND

Real-time motion monitoring (RTMM) is necessary for accurate motion management of intrafraction motions during radiation therapy (RT).

PURPOSE

Building upon a previous study, this work develops and tests an improved RTMM technique based on real-time orthogonal cine magnetic resonance imaging (MRI) acquired during magnetic resonance-guided adaptive RT (MRgART) for abdominal tumors on MR-Linac.

METHODS

A motion monitoring research package (MMRP) was developed and tested for RTMM based on template rigid registration between beam-on real-time orthogonal cine MRI and pre-beam daily reference 3D-MRI (baseline). The MRI data acquired under free-breathing during the routine MRgART on a 1.5T MR-Linac for 18 patients with abdominal malignancies of 8 liver, 4 adrenal glands (renal fossa), and 6 pancreas cases were used to evaluate the MMRP package. For each patient, a 3D mid-position image derived from an in-house daily 4D-MRI was used to define a target mask or a surrogate sub-region encompassing the target. Additionally, an exploratory case reviewed for an MRI dataset of a healthy volunteer acquired under both free-breathing and deep inspiration breath-hold (DIBH) was used to test how effectively the RTMM using the MMRP can address through-plane motion (TPM). For all cases, the 2D T2/T1-weighted cine MRIs were captured with a temporal resolution of 200 ms interleaved between coronal and sagittal orientations. Manually delineated contours on the cine frames were used as the ground-truth motion. Common visible vessels and segments of target boundaries in proximity to the target were used as anatomical landmarks for reproducible delineations on both the 3D and the cine MRI images. Standard deviation of the error (SDE) between the ground-truth and the measured target motion from the MMRP package were analyzed to evaluate the RTMM accuracy. The maximum target motion (MTM) was measured on the 4D-MRI for all cases during free-breathing.

RESULTS

The mean (range) centroid motions for the 13 abdominal tumor cases were 7.69 (4.71-11.15), 1.73 (0.81-3.05), and 2.71 (1.45-3.93) mm with an overall accuracy of <2 mm in the superior-inferior (SI), the left-right (LR), and the anterior-posterior (AP) directions, respectively. The mean (range) of the MTM from the 4D-MRI was 7.38 (2-11) mm in the SI direction, smaller than the monitored motion of centroid, demonstrating the importance of the real-time motion capture. For the remaining patient cases, the ground-truth delineation was challenging under free-breathing due to the target deformation and the large TPM in the AP direction, the implant-induced image artifacts, and/or the suboptimal image plane selection. These cases were evaluated based on visual assessment. For the healthy volunteer, the TPM of the target was significant under free-breathing which degraded the RTMM accuracy. RTMM accuracy of <2 mm was achieved under DIBH, indicating DIBH is an effective method to address large TPM.

CONCLUSIONS

We have successfully developed and tested the use of a template-based registration method for an accurate RTMM of abdominal targets during MRgART on a 1.5T MR-Linac without using injected contrast agents or radio-opaque implants. DIBH may be used to effectively reduce or eliminate TPM of abdominal targets during RTMM.

The relationship of apparent diffusion coefficient values of renal cell carcinoma before and after cryotherapy ablation

INTRODUCTION

The diagnosis of renal cell carcinoma (RCC) is increasing due to incidental findings with more frequent use of cross-sectional imaging. Therefore improvements to diagnostic and follow up imaging techniques is necessary. MRI diffusion weighted imaging (DWI) is a recognised method of measuring the diffusion of water within lesions using the apparent diffusion coefficient (ADC), and may have a role in monitoring the efficacy of cryotherapy ablation of RCC.

METHODS

A retrospective cohort study of 50 patients was approved to investigate if the ADC value can determine the success of cryotherapy ablation treatment for RCC. DWI was performed at a single centre using 1.5 T MRI before and after cryotherapy ablation to the RCC. The control group was considered as the unaffected kidney. The ADC value of the RCC tumour and normal kidney tissue prior to and after cryotherapy ablation was measured, and compared to the result of the MRI.

RESULTS

A statistically significant change in the ADC values was observed, pre ablation (1.562 × 10¯mm²/sec) to the post ablation (1.126 × 10¯³mm²/sec), p < 0.0005. There was no statistical significance in any of the other outcomes measured.

CONCLUSION

Although a change of ADC value occurred this is likely due to cryotherapy ablation causing coagulative necrosis at the site, and does not determine the success of the cryotherapy ablation. This can be considered a feasibility study for future research.

IMPLICATIONS FOR PRACTICE

DWI is a quick addition to routine protocols, does not require intravenous gadolinium based contrast agent, and provides qualitative and quantitative data. Further research is required to establish the role of ADC for treatment monitoring.

Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration

Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2  ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.

XFCT-MRI hybrid multimodal contrast agents for complementary imaging

Multimodal contrast agents in biomedical imaging enable the collection of more comprehensive diagnostic information. In the present work, we design hybrid ruthenium-decorated superparamagnetic iron oxide nanoparticles (NPs) as the contrast agents for both magnetic resonance imaging (MRI) and X-ray fluorescence computed tomography (XFCT). The NPs are synthesized via a one-pot polyol hot injection route, in diethylene glycol. In vivo preclinical studies demonstrate the possibility of correlative bioimaging with these contrast agents. The complementarity allows accurate localization, provided by the high contrast of the soft tissues in MRI combined with the elemental selectivity of XFCT, leading to NP detection with high specificity and resolution. We envision that this multimodal imaging could find future applications for early tumor diagnosis, improved long-term treatment monitoring, and enhanced radiotherapy planning.

MRI-LINAC: A transformative technology in radiation oncology

Advances in radiotherapy technologies have enabled more precise target guidance, improved treatment verification, and greater control and versatility in radiation delivery. Amongst the recent novel technologies, Magnetic Resonance Imaging (MRI) guided radiotherapy (MRgRT) may hold the greatest potential to improve the therapeutic gains of image-guided delivery of radiation dose. The ability of the MRI linear accelerator (LINAC) to image tumors and organs with on-table MRI, to manage organ motion and dose delivery in real-time, and to adapt the radiotherapy plan on the day of treatment while the patient is on the table are major advances relative to current conventional radiation treatments. These advanced techniques demand efficient coordination and communication between members of the treatment team. MRgRT could fundamentally transform the radiotherapy delivery process within radiation oncology centers through the reorganization of the patient and treatment team workflow process. However, the MRgRT technology currently is limited by accessibility due to the cost of capital investment and the time and personnel allocation needed for each fractional treatment and the unclear clinical benefit compared to conventional radiotherapy platforms. As the technology evolves and becomes more widely available, we present the case that MRgRT has the potential to become a widely utilized treatment platform and transform the radiation oncology treatment process just as earlier disruptive radiation therapy technologies have done.

Usability of magnetic resonance images acquired at a novel low-field 0.55 T scanner for brain radiotherapy treatment planning

Background and Purpose

Low-field magnetic resonance imaging (MRI) may offer specific advantages over high-field MRI, e.g. lower susceptibility-dependent distortions and simpler installation. The study aim was to evaluate if a novel 0.55 T MRI scanner provides sufficient image accuracy and quality for radiotherapy (RT) treatment planning.

Material and methods

The geometric accuracy of images acquired at a low-field MRI scanner was evaluated in phantom measurements regarding gradient non-linearity-related distortions. Patient-induced B0-susceptibility changes were investigated via B0-field-mapping in ten volunteers. Patients were positioned in RT-setup using a 3D-printed insert for the head/neck-coil that was tested for sufficient signal-to-noise-ratio (SNR). The suitability of the MRI-system for detection of metastases was evaluated in eleven patients. In comparison to diagnostic images, acquired at ≥1.5 T, three physicians evaluated the detectability of metastases by counting them in low- and high-field-images, respectively.

Results

The phantom measurements showed a high imaging fidelity after 3D-distortion-correction with (1.2 ± 0.9) mm geometric distortion in 10 cm radius from isocentre. At the edges remaining distortions were greater than at 1.5 T. The mean susceptibility-induced distortions in the head were (0.05 ± 0.05) mm and maximum 0.69 mm. SNR analysis showed that optimised positioning of RT-patients without signal loss in the head/neck-coil was possible with the RT-insert. No significant differences (p = 0.48) in detectability of metastases were found.

Conclusion

The 0.55 T MRI system provided sufficiently geometrically accurate and high-resolution images that can be used for RT-planning for brain metastases. Hence, modern low-field MRI may contribute to simply access MRI for RT-planning after further investigations.

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.

Evaluation of a New, Highly Flexible Radiofrequency Coil for MR Simulation of Patients Undergoing External Beam Radiation Therapy

PURPOSE

To evaluate the performance of a new, highly flexible radiofrequency (RF) coil system for imaging patients undergoing MR simulation.

METHODS

Volumetric phantom and in vivo images were acquired with a commercially available and prototype RF coil set. Phantom evaluation was performed using a silicone-filled humanoid phantom of the head and shoulders. In vivo assessment was performed in five healthy and six patient subjects. Phantom data included T1-weighted volumetric imaging, while in vivo acquisitions included both T1- and T2-weighted volumetric imaging. Signal to noise ratio (SNR) and uniformity metrics were calculated in the phantom data, while SNR values were calculated in vivo. Statistical significance was tested by means of a non-parametric analysis of variance test.

RESULTS

At a threshold of p = 0.05, differences in measured SNR distributions within the entire phantom volume were statistically different in two of the three paired coil set comparisons. Differences in per slice average SNR between the two coil sets were all statistically significant, as well as differences in per slice image uniformity. For patients, SNRs within the entire imaging volume were statistically significantly different in four of the nine comparisons and seven of the nine comparisons performed on the per slice average SNR values. For healthy subjects, SNRs within the entire imaging volume were statistically significantly different in seven of the nine comparisons and eight of the nine comparisons when per slice average SNR was tested.

CONCLUSIONS

Phantom and in vivo results demonstrate that image quality obtained from the novel flexible RF coil set was similar or improved over the conventional coil system. The results also demonstrate that image quality is impacted by the specific coil configurations used for imaging and should be matched appropriately to the anatomic site imaged to ensure optimal and reproducible image quality.

To see or not to see: Evaluation of magnetic resonance imaging sequences for use in MR Linac-based radiotherapy treatment

BACKGROUND/PURPOSE

This work evaluated the suitability of MR derived sequences for use in online adaptive RT workflows on a 1.5 Tesla (T) MR-Linear Accelerator (MR Linac).

MATERIALS/METHODS

Non-patient volunteers were recruited to an ethics approved MR Linac imaging study. Participants attended 1-3 imaging sessions in which a combination of DIXON, 2D and 3D volumetric T1 and T2 weighted images were acquired axially, with volunteers positioned using immobilisation devices typical for radiotherapy to the anatomical region being scanned. Images from each session were appraised by three independent reviewers to determine optimal sequences over six anatomical regions: head and neck, female and male pelvis, thorax (lung), thorax (breast/chest wall) and abdomen. Site specific anatomical structures were graded by the perceived ability to accurately contour a typical organ at risk. Each structure was independently graded on a 4-point Likert scale as 'Very Clear', 'Clear', 'Unclear' or 'Not visible' by observers, consisting of radiographers (therapeutic and diagnostic) and clinicians.

RESULTS

From July 2019 to September 2019, 18 non-patient volunteers underwent 24 imaging sessions in the following anatomical regions: head and neck (n=3), male pelvis (n=4), female pelvis (n=5), lung/oesophagus (n=5) abdomen (n=4) and chest wall/breast (n=3). T2 sequences were the most preferred for perceived ability to contour anatomy in both male and female pelvis. For all other sites T1 weighted DIXON sequences were most favourable.

CONCLUSION

This study has determined the preferential sequence selection for organ visualisation, as a pre-requisite to our institution adopting MR-guided radiotherapy for a more diverse range of disease sites.

Structurally-constrained optical-flow-guided adversarial generation of synthetic CT for MR-only radiotherapy treatment planning

The rapid progress in image-to-image translation methods using deep neural networks has led to advancements in the generation of synthetic CT (sCT) in MR-only radiotherapy workflow. Replacement of CT with MR reduces unnecessary radiation exposure, financial cost and enables more accurate delineation of organs at risk. Previous generative adversarial networks (GANs) have been oriented towards MR to sCT generation. In this work, we have implemented multiple augmented cycle consistent GANs. The augmentation involves structural information constraint (StructCGAN), optical flow consistency constraint (FlowCGAN) and the combination of both the conditions (SFCGAN). The networks were trained and tested on a publicly available Gold Atlas project dataset, consisting of T2-weighted MR and CT volumes of 19 subjects from 3 different sites. The network was tested on 8 volumes acquired from the third site with a different scanner to assess the generalizability of the network on multicenter data. The results indicate that all the networks are robust to scanner variations. The best model, SFCGAN achieved an average ME of 0.9   5.9 HU, an average MAE of 40.4   4.7 HU and 57.2   1.4 dB PSNR outperforming previous research works. Moreover, the optical flow constraint between consecutive frames preserves the consistency across all views compared to 2D image-to-image translation methods. SFCGAN exploits the features of both StructCGAN and FlowCGAN by delivering structurally robust and 3D consistent sCT images. The research work serves as a benchmark for further research in MR-only radiotherapy.

News in magnetic resonance imaging use for radiation oncology

The purpose of this article is to give a summary of the progress of magnetic resonance imaging (MRI) in radiotherapy. MRI is an important imaging modality for treatment planning in radiotherapy. However, the registration step with the simulation scanner can be a source of errors, motivating the implementation of all-MRI simulation methods and new accelerators coupled with on-board MRI. First, practical MRI imaging for radiotherapy is detailed, but also the importance of a coherent imaging workflow incorporating all imaging modalities. Second, future evolutions and research domains such as quantitative imaging biomarkers, MRI-only pseudo computed tomography and radiomics are discussed. Finally, the application of MRI during radiotherapy treatment is reviewed: the use of MR-linear accelerators. MRI is increasingly integrated into radiotherapy. Advances in diagnostic imaging can thus benefit radiotherapy, but specific radiotherapy constraints lead to additional challenges and require close collaboration between radiologists, radiation oncologists, technologists and physicists. The integration of quantitative imaging biomarkers in the radiotherapy process will result in mutual benefit for diagnostic imaging and radiotherapy. MRI-guided radiotherapy has already been used for several years in clinical routine. Abdominopelvic neoplasias (pancreas, liver, prostate) are the preferred locations for treatment because of their favourable contrast in MRI, their movement during irradiation and their proximity to organs at risk of radiation exposure, making the tracking and daily adaptation of the plan essential. MRI has emerged as an increasingly necessary imaging modality for radiotherapy planning. Inclusion of patients in clinical trials evaluating new MRI-guided radiotherapy techniques and associated quantitative imaging biomarkers will be necessary to assess the benefits.

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.

State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression

Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.

TransDose: a transformer-based UNet model for fast and accurate dose calculation for MR-LINACs

Objective. To present a transformer-based UNet model (TransDose) for fast and accurate dose calculation for magnetic resonance-linear accelerators (MR-LINACs). Approach. A 2D fluence map from each beam was first projected into a 3D fluence volume and then fed into the TransDose model together with patient density volume and output predicted beam dose. The proposed TransDose model combined a 3D residual UNet with a transformer encoder, where convolutional layers extracted the volumetric spatial features, and the transformer encoder processed the long-range dependencies in a global space. Ninety-eight cases with four tumor sites (brain, nasopharynx, lung, and rectum) treated with fixed-beam intensity-modulated radiotherapy were included in the dataset; 78 cases were used for model training and validation; and 20 cases were used for testing. The ground-truth beam doses were calculated with Monte Carlo (MC) simulations within 1% statistical uncertainty and magnetic field strength B = 1.5 T in the superior and inferior direction. Beam angles from the training and validation datasets were rotated 2–5 times, and doses were recalculated to augment the datasets. Results. The dose-volume histograms and indices between the predicted and MC doses showed good consistency. The average 3D γ-passing rates (3%/2 mm, for dose regions above 10% of maximum dose) were 99.13 ± 0.89% (brain), 98.31 ± 1.92% (nasopharynx), 98.74 ± 0.70% (lung), and 99.28 ± 0.25% (rectum). The average dose calculation time, which included the fluence projection and model prediction, was less than 310 ms for each beam. Significance. We successfully developed a transformer-based UNet dose calculation model—TransDose in magnetic fields. Its accuracy and efficiency indicated its potential for use in online adaptive plan optimization for MR-LINACs.

Rates of MRI simulator utilisation in a tertiary cancer therapy centre

Magnetic resonance imaging (MRI) is increasingly being integrated into the radiation oncology workflow, due to its improved soft tissue contrast without additional exposure to ionising radiation. A review of MRI utilisation according to evidence based departmental guidelines was performed. Guideline utilisation rates were calculated to be 50% (true utilisation rate was 46%) of all new cancer patients treated with adjuvant or curative intent, excluding simple skin and breast cancer patients. Guideline utilisation rates were highest in the lower gastrointestinal and gynaecological subsites, with the lowest being in the upper gastrointestinal and thorax subsites. Head and neck (38% vs 45%) and CNS (46% vs 67%) cancers had the largest discrepancy between true and guideline utilisation rates due to unnamed reasons and non-contemporaneous diagnostic imaging respectively. This report outlines approximate MRI utilisation rates in a tertiary radiation oncology service and may help guide planning for future departments contemplating installation of an MRI simulator.

Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function

BACKGROUND

Magnetic resonance imaging (MRI) images synthesized from computed tomography (CT) data can provide more detailed information on pathological structures than that of CT data alone; thus, the synthesis of MRI has received increased attention especially in medical scenarios where only CT images are available. A novel convolutional neural network (CNN) combined with a contextual loss function was proposed for synthesis of T1- and T2-weighted images (T1WI and T2WI) from CT data.

METHODS

A total of 5,053 and 5,081 slices of T1WI and T2WI, respectively were selected for the dataset of CT and MRI image pairs. Affine registration, image denoising, and contrast enhancement were done on the aforementioned multi-modality medical image dataset comprising T1WI, T2WI, and CT images of the brain. A deep CNN was then proposed by modifying the ResNet structure to constitute the encoder and decoder of U-Net, called double ResNet-U-Net (DRUNet). Three different loss functions were utilized to optimize the parameters of the proposed models: mean squared error (MSE) loss, binary crossentropy (BCE) loss, and contextual loss. Statistical analysis of the independent-sample t-test was conducted by comparing DRUNets with different loss functions and different network layers.

RESULTS

DRUNet-101 with contextual loss yielded higher values of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Tenengrad function (i.e., 34.25±2.06, 0.97±0.03, and 17.03±2.75 for T1WI and 33.50±1.08, 0.98±0.05, and 19.76±3.54 for T2WI respectively). The results were statistically significant at P<0.001 with a narrow confidence interval of difference, indicating the superiority of DRUNet-101 with contextual loss. In addition, both image zooming and difference maps presented for the final synthetic MR images visually reflected the robustness of DRUNet-101 with contextual loss. The visualization of convolution filters and feature maps showed that the proposed model can generate synthetic MR images with high-frequency information.

CONCLUSIONS

The results demonstrated that DRUNet-101 with contextual loss function provided better high-frequency information in synthetic MR images compared with the other two functions. The proposed DRUNet model has a distinct advantage over previous models in terms of PSNR, SSIM, and Tenengrad score. Overall, DRUNet-101 with contextual loss is recommended for synthesizing MR images from CT scans.

Accuracy of MRI-CT registration in brain stereotactic radiotherapy: Impact of MRI acquisition setup and registration method

BACKGROUND

In MR-based radiotherapy (RT), MRI images are co-registered to the planning CT to leverage MR image information for RT planning. Especially in brain stereotactic RT, where typical CTV-PTV margins are 1-2 mm, high registration accuracy is critical. Several factors influence the registration accuracy, including the acquisition setup during MR simulation and the registration methods.

PURPOSE

In this work, the impact of the MRI acquisition setup and registration method was evaluated in the context of brain RT, both geometrically and dosimetrically.

METHODS AND MATERIALS

MRI of 20 brain radiotherapy patients was acquired in two MRI acquisition setups (RT and diagnostic). Three different automatic registration tools provided by three treatment planning systems were used to rigidly register both MRIs and CT in addition to the clinical registration. Segmentation-based evaluation using Hausdorff Distance (HD)/Dice Similarity Coefficient and landmark-based evaluation were used as evaluation metrics. Dose-volume-histograms were evaluated for target volumes and various organs at risks.

RESULTS

MRI acquisition in the RT setup provided a similar head extension as compared to the planning CT. The registration method had a more significant influence than the acquisition setup (Wilcoxon signed-rank test, p<0.05). When registering using a less optimal registration method, the RT setup improved the registration accuracy compared to the diagnostic setup (Difference: ΔMHD = 0.16 mm, ΔHD_P95 = 0.64 mm, mean Euclidean distance (ΔmEuD) = 2.65 mm). Different registration methods and acquisition setups lead to the variation of the clinical DVH. Acquiring MRI in the RT setup can improve PTV and GTV coverage compared to the diagnostic setup.

CONCLUSIONS

Both MRI acquisition setup and registration method influence the MRI-CT registration accuracy in brain RT patients geometrically and dosimetrically. MR-simulation in the RT setup assures optimal registration accuracy if automatic registration is impaired, and therefore recommended for brain RT.