Accuracy of automatic deformable structure propagation for high-field MRI guided prostate radiotherapy

An operator-independent quality assurance system for automatically generated structure sets

Objective. This study describes geometry-based and intensity-based tools for quality assurance (QA) of automatically generated structures for online adaptive radiotherapy, and designs an operator-independent traffic light system that identifies erroneous structure sets.Approach.A cohort of eight head and neck (HN) patients with daily CBCTs was selected for test development. Radiotherapy contours were propagated from planning computed tomography (CT) to daily cone beam CT (CBCT) using deformable image registration. These propagated structures were visually verified for acceptability. For each CBCT, several error scenarios were used to generate what were judged unacceptable structures. Ten additional HN patients with daily CBCTs and different error scenarios were selected for validation. A suite of tests based on image intensity, intensity gradient, and structure geometry was developed using acceptable and unacceptable HN planning structures. Combinations of one test applied to one structure, referred to as structure-test combinations, were selected for inclusion in the QA system based on their discriminatory power. A traffic light system was used to aggregate the structure-test combinations, and the system was evaluated on all fractions of the ten validation HN patients.Results.The QA system distinguished between acceptable and unacceptable fractions with high accuracy, labeling 294/324 acceptable fractions as green or yellow and 19/20 unacceptable fractions as yellow or red.Significance.This study demonstrates a system to supplement manual review of radiotherapy planning structures. Automated QA is performed by aggregating results from multiple intensity- and geometry-based tests.

Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy

BACKGROUND AND PURPOSE

MR-guided radiotherapy (MRgRT) online plan adaptation accounts for tumor volume changes, interfraction motion and thus allows daily sparing of relevant organs at risk. Due to the high interfraction variability of bladder and rectum, patients with tumors in the pelvic region may strongly benefit from adaptive MRgRT. Currently, fast automatic annotation of anatomical structures is not available within the online MRgRT workflow. Therefore, the aim of this study was to train and validate a fast, accurate deep learning model for automatic MRI segmentation at the MR-Linac for future implementation in a clinical MRgRT workflow.

MATERIALS AND METHODS

For a total of 47 patients, T2w MRI data were acquired on a 1.5 T MR-Linac (Unity, Elekta) on five different days. Prostate, seminal vesicles, rectum, anal canal, bladder, penile bulb, body and bony structures were manually annotated. These training data consisting of 232 data sets in total was used for the generation of a deep learning based autocontouring model and validated on 20 unseen T2w-MRIs. For quantitative evaluation the validation set was contoured by a radiation oncologist as gold standard contours (GSC) and compared in MATLAB to the automatic contours (AIC). For the evaluation, dice similarity coefficients (DSC), and 95% Hausdorff distances (95% HD), added path length (APL) and surface DSC (sDSC) were calculated in a caudal-cranial window of ± 4 cm with respect to the prostate ends. For qualitative evaluation, five radiation oncologists scored the AIC on the possible usage within an online adaptive workflow as follows: (1) no modifications needed, (2) minor adjustments needed, (3) major adjustments/ multiple minor adjustments needed, (4) not usable.

RESULTS

The quantitative evaluation revealed a maximum median 95% HD of 6.9 mm for the rectum and minimum median 95% HD of 2.7 mm for the bladder. Maximal and minimal median DSC were detected for bladder with 0.97 and for penile bulb with 0.73, respectively. Using a tolerance level of 3 mm, the highest and lowest sDSC were determined for rectum (0.94) and anal canal (0.68), respectively. Qualitative evaluation resulted in a mean score of 1.2 for AICs over all organs and patients across all expert ratings. For the different autocontoured structures, the highest mean score of 1.0 was observed for anal canal, sacrum, femur left and right, and pelvis left, whereas for prostate the lowest mean score of 2.0 was detected. In total, 80% of the contours were rated be clinically acceptable, 16% to require minor and 4% major adjustments for online adaptive MRgRT.

CONCLUSION

In this study, an AI-based autocontouring was successfully trained for online adaptive MR-guided radiotherapy on the 1.5 T MR-Linac system. The developed model can automatically generate contours accepted by physicians (80%) or only with the need of minor corrections (16%) for the irradiation of primary prostate on the clinically employed sequences.

The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance

Although magnetic resonance imaging (MRI) has become standard diagnostic workup for head and neck malignancies and is currently recommended by most radiological societies for pharyngeal and oral carcinomas, its utilization in radiotherapy has been heterogeneous during the last decades. However, few would argue that implementing MRI for annotation of target volumes and organs at risk provides several advantages, so that implementation of the modality for this purpose is widely accepted. Today, the term MR-guidance has received a much broader meaning, including MRI for adaptive treatments, MR-gating and tracking during radiotherapy application, MR-features as biomarkers and finally MR-only workflows. First studies on treatment of head and neck cancer on commercially available dedicated hybrid-platforms (MR-linacs), with distinct common features but also differences amongst them, have also been recently reported, as well as "biological adaptation" based on evaluation of early treatment response via functional MRI-sequences such as diffusion weighted ones. Yet, all of these approaches towards head and neck treatment remain at their infancy, especially when compared to other radiotherapy indications. Moreover, the lack of standardization for reporting MR-guided radiotherapy is a major obstacle both to further progress in the field and to conduct and compare clinical trials. Goals of this article is to present and explain all different aspects of MR-guidance for radiotherapy of head and neck cancer, summarize evidence, as well as possible advantages and challenges of the method and finally provide a comprehensive reporting guidance for use in clinical routine and trials.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Prostate volume analysis in image registration for prostate cancer care: a verification study

Combined magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) may enhance diagnosis, aid surgical planning and intra-operative orientation for prostate biopsy and radical prostatectomy. Although PET-MRI may provide these benefits, PET-MRI machines are not widely available. Image fusion of Prostate specific membrane antigen PET/CT and MRI acquired separately may be a suitable clinical alternative. This study compares CT-MR registration algorithms for urological prostate cancer care. Paired whole-pelvis MR and CT scan data were used (n = 20). A manual prostate CTV contour was performed independently on each patients MR and CT image. A semi-automated rigid-, automated rigid- and automated non-rigid registration technique was applied to align the MR and CT data. Dice Similarity Index (DSI), 95% Hausdorff distance (95%HD) and average surface distance (ASD) measures were used to assess the closeness of the manual and registered contours. The automated non-rigid approach had a significantly improved performance compared to the automated rigid- and semi-automated rigid-registration, having better average scores and decreased spread for the DSI, 95%HD and ASD (all p < 0.001). Additionally, the automated rigid approach had similar significantly improved performance compared to the semi-automated rigid registration across all accuracy metrics observed (all p < 0.001). Overall, all registration techniques studied here demonstrated sufficient accuracy for exploring their clinical use. While the fully automated non-rigid registration algorithm in the present study provided the most accurate registration, the semi-automated rigid registration is a quick, feasible, and accessible method to perform image registration for prostate cancer care by urologists and radiation oncologists now.

An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy

Background

Adaptive MRI-guided radiotherapy (MRIgRT) requires accurate and efficient segmentation of organs and targets on MRI scans. Manual segmentation is time-consuming and variable, while deformable image registration (DIR)-based contour propagation may not account for large anatomical changes. Therefore, we developed and evaluated an automatic segmentation method using the nnU-net framework.

Methods

The network was trained on 38 patients (76 scans) with localized prostate cancer and tested on 30 patients (60 scans) with localized prostate, metastatic prostate, or bladder cancer treated at a 1.5 T MRI-linac at our institution. The performance of the network was compared with the current clinical workflow based on DIR. The segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) metrics.

Results

The trained network successfully segmented all 600 structures in the test set. High similarity was obtained for most structures, with 90% of the contours having a DSC above 0.9 and 86% having an MSD below 1 mm. The largest discrepancies were found in the sigmoid and colon structures. Stratified analysis on cancer type showed that the best performance was seen in the same type of patients that the model was trained on (localized prostate). Especially in patients with bladder cancer, the performance was lower for the bladder and the surrounding organs. A complete automatic delineation workflow took approximately 1 minute. Compared with contour transfer based on the clinically used DIR algorithm, the nnU-net performed statistically better across all organs, with the most significant gain in using the nnU-net seen for organs subject to more considerable volumetric changes due to variation in the filling of the rectum, bladder, bowel, and sigmoid.

Conclusion

We successfully trained and tested a network for automatically segmenting organs and targets for MRIgRT in the male pelvis region. Good test results were seen for the trained nnU-net, with test results outperforming the current clinical practice using DIR-based contour propagation at the 1.5 T MRI-linac. The trained network is sufficiently fast and accurate for clinical use in an online setting for MRIgRT. The model is provided as open-source.

Efficient segmentation using domain adaptation for MRI-guided and CBCT-guided online adaptive radiotherapy

BACKGROUND

Delineation of regions of interest (ROIs) is important for adaptive radiotherapy (ART) but it is also time consuming and labor intensive.

AIM

This study aims to develop efficient segmentation methods for magnetic resonance imaging-guided ART (MRIgART) and cone-beam computed tomography-guided ART (CBCTgART).

MATERIALS AND METHODS

MRIgART and CBCTgART studies enrolled 242 prostate cancer patients and 530 nasopharyngeal carcinoma patients, respectively. A public dataset of CBCT from 35 pancreatic cancer patients was adopted to test the framework. We designed two domain adaption methods to learn and adapt the features from planning computed tomography (pCT) to MRI or CBCT modalities. The pCT was transformed to synthetic MRI (sMRI) for MRIgART, while CBCT was transformed to synthetic CT (sCT) for CBCTgART. Generalized segmentation models were trained with large popular data in which the inputs were sMRI for MRIgART and pCT for CBCTgART. Finally, the personalized models for each patient were established by fine-tuning the generalized model with the contours on pCT of that patient. The proposed method was compared with deformable image registration (DIR), a regular deep learning (DL) model trained on the same modality (DL-regular), and a generalized model in our framework (DL-generalized).

RESULTS

The proposed method achieved better or comparable performance. For MRIgART of the prostate cancer patients, the mean dice similarity coefficient (DSC) of four ROIs was 87.2%, 83.75%, 85.36%, and 92.20% for the DIR, DL-regular, DL-generalized, and proposed method, respectively. For CBCTgART of the nasopharyngeal carcinoma patients, the mean DSC of two target volumes were 90.81% and 91.18%, 75.17% and 58.30%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively. For CBCTgART of the pancreatic cancer patients, the mean DSC of two ROIs were 61.94% and 61.44%, 63.94% and 81.56%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively.

CONCLUSION

The proposed method utilizing personalized modeling improved the segmentation accuracy of ART.

An integrated and fast imaging quality assurance phantom for a 0.35 T magnetic resonance imaging linear accelerator

Purpose

Periodic imaging quality assurance (QA) of magnetic resonance imaging linear accelerator (MRL) is critical. The feasibility of a new MRL imaging phantom used for QA in the low field was evaluated with automated image analysis of various parameters for accuracy and reproducibility.

Methods and materials

The new MRL imaging phantom was scanned across every 30 degrees of the gantry, having the on/off state of the linac in a low-field MRL system using three magnetic resonance imaging sequences: true fast imaging with steady-state precession (TrueFISP), T1 weighted (T1W), and T2 weighted (T2W). The DICOM files were used to calculate the imaging parameters: geometric distortion, uniformity, resolution, signal-to-noise ratio (SNR), and laser alignment. The point spread function (PSF) and edge spread function (ESF) were also calculated for resolution analysis.

Results

The phantom data showed a small standard deviation - and high consistency for each imaging parameter. The highest variability in data was observed with the true fast imaging sequence at the calibration angle, which was expected because of low resolution and short scan time (25 sec). The mean magnitude of the largest distortion measured within 200 mm diameter with TrueFISP was 0.31 ± 0.05 mm. The PSF, ESF, signal uniformity, and SNR measurements remained consistent. Laser alignment traditional offsets and angular deviation remained consistent.

Conclusions

The new MRL imaging phantom is reliable, reproducible, time effective, and easy to use for a 0.35 T MRL system. The results promise a more streamlined, time-saving, and error-free QA process for low-field MRL adapted in our clinical setting.

Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation

MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.

Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy

BACKGROUND AND PURPOSE

Deep Learning (DL) technique has shown great potential but still has limited success in online contouring for MR-guided adaptive radiotherapy (MRgART). This study proposed a patient-specific DL auto-segmentation (DLAS) strategy using the patient's previous images and contours to update the model and improve segmentation accuracy and efficiency for MRgART.

METHODS AND MATERIALS

A prototype model was trained for each patient using the first set of MRI and corresponding contours as inputs. The patient-specific model was updated after each fraction with all the available fractional MRIs/contours, and then used to predict the segmentation for the next fraction. During model training, a variant was fitted under consistency constraints, limiting the differences in the volume, length and centroid between the predictions for the latest MRI within a reasonable range. The model performance was evaluated for both organ-at-risks and tumors auto-segmentation for a total of 6 abdominal/pelvic cases (each with at least 8 sets of MRIs/contours) underwent MRgART through Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD95), and was compared with deformable image registration (DIR) and frozen DL model (no updating after pre-training). The contouring time was also recorded and analyzed.

RESULTS

The proposed model achieved superior performance with higher mean DSC (0.90, 95 % CI: 0.88-0.95), as compared to DIR (0.63, 95 %CI: 0.59-0.68) and frozen DL models (0.74, 95 % CI: 0.71-0.79). As for tumors, the proposed method yielded a median DSC of 0.95, 95 % CI: 0.94-0.97, and a median HD95 of 1.63 mm, 95 % CI: 1.22 mm-2.06 mm. The contouring time was reduced significantly (p < 0.05) using the proposed method (73.4 ± 6.5 secs) compared to the manual process (12 ∼ 22 mins). The online ART time was reduced to 1650 ± 274 seconds with the proposed method, as compared to 3251.8 ± 447 seconds using the original workflow.

CONCLUSION

The proposed patient-specific DLAS method can significantly improve the segmentation accuracy and efficiency for longitudinal MRIs, thereby facilitating the routine practice of MRgART.

Dosimetric comparison of automatically propagated prostate contours with manually drawn contours in MRI-guided radiotherapy: A step towards a contouring free workflow?

Background

The prostate demonstrates inter- and intra- fractional changes and thus adaptive radiotherapy would be required to ensure optimal coverage. Daily adaptive radiotherapy for MRI-guided radiotherapy can be both time and resource intensive when structure delineation is completed manually. Contours can be auto-generated on the MR-Linac via a deformable image registration (DIR) based mapping process from the reference image. This study evaluates the performance of automatically generated target structure contours against manually delineated contours by radiation oncologists for prostate radiotherapy on the Elekta Unity MR-Linac.

Methods

Plans were generated from prostate contours propagated by DIR and rigid image registration (RIR) for forty fractions from ten patients. A two-dose level SIB (simultaneous integrated boost) IMRT plan is used to treat localised prostate cancer; 6000 cGy to the prostate and 4860 cGy to the seminal vesicles. The dose coverage of the PTV 6000 and PTV 4860 created from the manually drawn target structures was evaluated with each plan. If the dose objectives were met, the plan was considered successful in covering the gold standard (clinician-delineated) volume.

Results

The mandatory PTV 6000 dose objective (D98% > 5580 cGy) was met in 81 % of DIR plans and 45 % of RIR plans. The SV were mapped by DIR only and for all the plans, the PTV 4860 dose objective met the optimal target (D98% > 4617 cGy). The plans created by RIR led to under-coverage of the clinician-delineated prostate, predominantly at the apex or the bladder-prostate interface.

Conclusion

Plans created from DIR propagation of prostate contours outperform those created from RIR propagation. In approximately 1 in 5 DIR plans, dosimetric coverage of the gold standard PTV was not clinically acceptable. Thus, at our institution, we use a combination of DIR propagation of contours alongside manual editing of contours where deemed necessary for online treatments.

Plan quality in radiotherapy treatment planning - Review of the factors and challenges

A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.

Inter- and intrafraction motion assessment and accumulated dose quantification of upper gastrointestinal organs during magnetic resonance-guided ablative radiation therapy of pancreas patients

Background and purpose

Stereotactic body radiation therapy (SBRT) of locally advanced pancreatic cancer (LAPC) is challenging due to significant motion of gastrointestinal (GI) organs. The goal of our study was to quantify inter and intrafraction deformations and dose accumulation of upper GI organs in LAPC patients.

Materials and methods

Five LAPC patients undergoing five-fraction magnetic resonance-guided radiation therapy (MRgRT) using abdominal compression and daily online plan adaptation to 50 Gy were analyzed. A pre-treatment, verification, and post-treatment MR imaging (MRI) for each of the five fractions (75 total) were used to calculate intra and interfraction motion. The MRIs were registered using Large Deformation Diffeomorphic Metric Mapping (LDDMM) deformable image registration (DIR) method and total dose delivered to stomach_duodenum, small bowel (SB) and large bowel (LB) were accumulated. Deformations were quantified using gradient magnitude and Jacobian integral of the Deformation Vector Fields (DVF). Registration DVFs were geometrically assessed using Dice and 95th percentile Hausdorff distance (HD95) between the deformed and physician’s contours. Accumulated doses were then calculated from the DVFs.

Results

Median Dice and HD95 were: Stomach_duodenum (0.9, 1.0 mm), SB (0.9, 3.6 mm), and LB (0.9, 2.0 mm). Median (max) interfraction deformation for stomach_duodenum, SB and LB was 6.4 (25.8) mm, 7.9 (40.5) mm and 7.6 (35.9) mm. Median intrafraction deformation was 5.5 (22.6) mm, 8.2 (37.8) mm and 7.2 (26.5) mm. Accumulated doses for two patients exceeded institutional constraints for stomach_duodenum, one of whom experienced Grade1 acute and late abdominal toxicity.

Conclusion

LDDMM method indicates feasibility to measure large GI motion and accumulate dose. Further validation on larger cohort will allow quantitative dose accumulation to more reliably optimize online MRgRT.

Online adaptive radiotherapy potentially reduces toxicity for high-risk prostate cancer treatment

BACKGROUND AND PURPOSE

With daily, MR-guided online adapted radiotherapy (MRgART) it may be possible to reduce the PTV in pelvic RT. This study investigated the potential reduction in normal tissue complication probability (NTCP) of MRgART compared to standard radiotherapy for high-risk prostate cancer.

MATERIALS AND METHODS

Twenty patients treated with 78 Gy to the prostate and 56 Gy to elective pelvic lymph nodes were included. VMAT plans were generated with standard clinical PTV margins. Additionally to the planning MR, patients had three MRI scans during treatment to simulate an MRgART. A reference plan with PTV margins determined for MRgART was created per patient and adapted to each of the following MRs. Adapted plans were warped to the planning MR for dose accumulation. The standard plan was rigidly registered to each adaptation MR before it was warped to the planning MR for dose accumulation. Dosimetric impact was compared by DVH analysis and potential clinical effects were assessed by NTCP modeling.

RESULTS

MRgART yielded statistically significant lower doses for the bladder wall, rectum and peritoneal cavity, compared to the standard RT, which translated into reduced median risks of urine incontinence (ΔNTCP 2.8%), urine voiding pain (ΔNTCP 2.8%) and acute gastrointestinal toxicity (ΔNTCP 17.4%). Mean population accumulated doses were as good or better for all investigated OAR when planned for MRgART as standard RT.

CONCLUSION

Online adapted radiotherapy may reduce the dose to organs at risk in high-risk prostate cancer patients, due to reduced PTV margins. This potentially translates to significant reductions in the risks of acute and late adverse effects.

Accuracy of automatic structure propagation for daily magnetic resonance image-guided head and neck radiotherapy

BACKGROUND AND PURPOSE

Deformable image registration (DIR) and contour propagation are used in daily online adaptation for hybrid MRI linac (MRL) treatments. The accuracy of the propagated contours may vary depending on the chosen workflow (WF), affecting the amount of required manual corrections. This study investigated the impact of three different WFs of contour propagations produced by a clinical treatment planning system for a high-field MRL on head and neck cancer patients.

METHODS

Seventeen patients referred for curative radiotherapy for oropharyngeal cancer underwent standard CT-based dose planning and MR scans in the treatment position for planning (pMR), and at the 10th (MR10), 20th (MR20) and 30th (MR30) fraction (±2). The primary tumour, a metastatic lymph node and 8 organs at risk were manually delineated on each set of T2 weighted images. Delineations were repeated one month later on the pMR by the same observer to determine the intra-observer variation (IOV). Three WFs were used to deform images in the treatment planning system for the high-field MRL: In WF1, only the planning image and contours were used as a reference for DIR and propagation to MR_10,20,30. The most recently acquired image set prior to the daily images was deformed and uncorrected (WF2) versus manually corrected (WF3) structures propagated to the session image. Dice similarity coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD) were calculated for each structure in each model.

RESULTS

Population median DSC, MSD and HD for WF1 and WF3 were similar and slightly better than for WF2. WF3 provided higher accuracy than WF1 for structures that are likely to shrink. All DIR workflows were less accurate than the IOV.

CONCLUSIONS

WF1 and WF3 provide higher accuracy in structure propagation than WF2. Manual revision and correction of propagated structures are required for all evaluated workflows.

End-to-end validation of the geometric dose delivery performance of MR linac adaptive radiotherapy

The clinical introduction of hybrid magnetic resonance (MR) guided radiotherapy (RT) delivery systems has led to the need to validate the end-to-end dose delivery performance on such machines. In the current study, an MR visible phantom was developed and used to test the spatial deviation between planned and delivered dose at two 1.5 T MR linear accelerator (MR linac) systems, including pre-treatment imaging, dose planning, online imaging, image registration, plan adaptation, and dose delivery. The phantom consisted of 3D printed plastic and MR visible silicone rubber. It was designed to minimise air gaps close to the radiochromic film used as a dosimeter. Furthermore, the phantom was designed to allow submillimetre, reproducible positioning of the film in the phantom. At both MR linac systems, 54 complete adaptive, MR guided RT workflow sessions were performed. To test the dose delivery performance of the MR linac systems in various adaptive RT (ART) scenarios, the sessions comprised a range of systematic positional shifts of the phantom and imaging or plan adaptation conditions. In each workflow session, the positional translation between the film and the adaptive planned dose was determined. The results showed that the accuracy of the MR linac systems was between 0.1 and 0.9 mm depending on direction. The highest mean deviance observed was in the posterior-anterior direction, and the direction of the error was consistent between centres. The precision of the systems was related to whether the workflow utilized the internal image registration algorithm of the MR linac. Workflows using the internal registration algorithm led to a worse precision (0.2-0.7 mm) compared to workflows where the algorithm was decoupled (0.2 mm). In summary, the spatial deviation between planned and delivered dose of MR-guided ART at the two MR linac systems was well below 1 mm and thus acceptable for clinical use.

Online adaptive planning for prostate stereotactic body radiotherapy using a 1.5 Tesla magnetic resonance imaging-guided linear accelerator

Recent advances in integrating 1.5 Tesla magnetic resonance (MR) imaging with a linear accelerator (MR-Linac) allow MR-guided stereotactic body radiotherapy (SBRT) for prostate cancer. Choosing an optimal strategy for daily online plan adaptation is particularly important for MR-guided radiotherapy. We analyzed deformable dose accumulation on scans from four patients and found that daily anatomy changes had little impact on the delivered dose, with the dose to the prostate within 0.5% and dose to the rectum/bladder mostly less than 0.5 Gy. These findings could help in the choice of an optimal strategy for online plan adaptation for MR-guided prostate SBRT.

Targeted Radiosensitizers for MR-Guided Radiation Therapy of Prostate Cancer

Adjuvant radiotherapy is frequently prescribed to treat cancer. To minimize radiation-related damage to healthy tissue, it requires high precision in tumor localization and radiation dose delivery. This can be achieved by MR guidance and targeted amplification of radiation dose selectively to tumors by using radiosensitizers. Here, we demonstrate prostate cancer-targeted gold nanoparticles (AuNPs) for MR-guided radiotherapy to improve the targeting precision and efficacy. By conjugating Gd(III) complexes and prostate-specific membrane antigen (PSMA) targeting ligands to AuNP surfaces, we found enhanced uptake of AuNPs by PSMA-expressing cancer cells with excellent MR contrast and radiation therapy outcome in vitro and in vivo. The AuNPs binding affinity and r₁ relaxivity were dramatically improved and the combination of Au and Gd(III)provided better tumor suppression after radiation. The precise tumor localization by MR and selective tumor targeting of the PSMA-1-targeted AuNPs could enable precise radiotherapy, reduction in irradiating dose, and minimization of healthy tissue damage.