4D-MRI driven MR-guided online adaptive radiotherapy for abdominal stereotactic body radiation therapy on a high field MR-Linac: Implementation and initial clinical experience

A patient-specific auto-planning method for MRI-guided adaptive radiotherapy in prostate cancer

BACKGROUND AND PURPOSE

Fast and automated generation of treatment plans is desirable for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART). This study proposed a novel patient-specific auto-planning method and validated its feasibility in improving the existing online planning workflow.

MATERIALS AND METHODS

Data from 40 patients with prostate cancer were collected retrospectively. A patient-specific auto-planning method was proposed to generate adaptive treatment plans. First, a population dose-prediction model (M0) was trained using data from previous patients. Second, a patient-specific model (Mps) was created for each new patient by fine-tuning M0 with the patient's data. Finally, an auto plan was optimized using the parameters derived from the predicted dose distribution by Mps. The auto plans were compared with manual plans in terms of plan quality, efficiency, dosimetric verification, and clinical evaluation.

RESULTS

The auto plans improved target coverage, reduced irradiation to the rectum, and provided comparable protection to other organs-at-risk. Target coverage for the planning target volume (+0.61 %, P = 0.023) and clinical target volume 4000 (+1.60 %, P < 0.001) increased. V2900cGy (-1.06 %, P = 0.004) and V1810cGy (-2.49 %, P < 0.001) to the rectal wall and V1810cGy (-2.82 %, P = 0.012) to the rectum were significantly reduced. The auto plans required less planning time (-3.92 min, P = 0.001), monitor units (-46.48, P = 0.003), and delivery time (-0.26 min, P = 0.004), and their gamma pass rates (3 %/2 mm) were higher (+0.47 %, P = 0.014).

CONCLUSION

The proposed patient-specific auto-planning method demonstrated a robust level of automation and was able to generate high-quality treatment plans in less time for MRIgART in prostate cancer.

Free-breathing time-resolved 4D MRI with improved T1-weighting contrast

This work proposes MP-Grasp4D (magnetization-prepared golden-angle radial sparse parallel 4D) MRI, a free-breathing, inversion recovery (IR)-prepared, time-resolved 4D MRI technique with improved T1-weighted contrast. MP-Grasp4D MRI acquisition incorporates IR preparation into a radial gradient echo sequence. MP-Grasp4D employs a golden-angle navi-stack-of-stars sampling scheme, where imaging data of rotating radial stacks and navigator stacks (acquired at a consistent rotation angle) are alternately acquired. The navigator stacks are used to estimate a temporal basis for low-rank subspace-constrained reconstruction. This allows for the simultaneous capture of both IR-induced contrast changes and respiratory motion. One temporal frame of the imaging volume in MP-Grasp4D MRI is reconstructed from a single stack and an adjacent navigator stack on average, resulting in a nominal temporal resolution of 0.16 seconds per volume. Images corresponding to the optimal inversion time (TI) can be retrospectively selected for providing the best image contrast. Reader studies were conducted to assess the performance of MP-Grasp4D MRI in liver imaging across 30 subjects in comparison with standard Grasp4D MRI without IR preparation. MP-Grasp4D MRI received significantly higher scores (P < 0.05) than Grasp4D in all assessment categories. There was a moderate to almost perfect agreement (kappa coefficient from 0.42 to 0.9) between the two readers for image quality assessment. When the scan time is reduced, MP-Grasp4D MRI preserves image contrast and quality, demonstrating additional acceleration capability. MP-Grasp4D MRI improves T1-weighted contrast for free-breathing time-resolved 4D MRI and eliminates the need for explicit motion compensation. This method is expected to be valuable in different MRI applications such as MR-guided radiotherapy.

Paired conditional generative adversarial network for highly accelerated liver 4D MRI

Purpose.4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampling k-space data is time-consuming. Accelerated acquisition with sparse samples is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) to shorten the 4D MRI reconstruction time while maintaining the reconstruction quality.Methods.Patients who underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3×, 6× and 10×) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer (RST), were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D +t) as temporal slices (2D +t). A total of 48 patients with 12 332 temporal slices were split into training (37 patients with 10 721 slices) and test (11 patients with 1611 slices). Compressed sensing (CS) reconstruction with spatiotemporal sparsity constraint was used as a benchmark. Reconstructed image quality was further evaluated with a liver gross tumor volume (GTV) localization task using Mask-RCNN trained from a separate 3D static liver MRI dataset (70 patients; 103 GTV contours).Results.Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15, 0.16, and 120 s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3× Re-Con-GAN 80.98%; 3× CS 80.74%; 3× UNet 79.88%) of unprocessed under-sampled images (3× 69.61%).Conclusion.A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.

Compliance of volunteers in a fully-enclosed patient rotation system for MR-guided radiation therapy: a prospective study

BACKGROUND

Particle therapy makes a noteworthy contribution in the treatment of tumor diseases. In order to be able to irradiate from different angles, usually expensive, complex and large gantries are used. Instead rotating the beam via a gantry, the patient itself might be rotated. Here we present tolerance and compliance of volunteers for a fully-enclosed patient rotation system in a clinical magnetic resonance (MR)-scanner for potential use in MR-guided radiotherapy, conducted within a prospective evaluation study.

METHODS

A patient rotation system was used to simulate and perform magnetic resonance imaging (MRI)-examinations with 50 volunteers without an oncological question. For 20 participants, the MR-examination within the bore was simulated by introducing realistic MRI noise, whereas 30 participants received an examination with image acquisition. Initially, body parameters and claustrophobia were assessed. The subjects were then rotated to different angles for simulation (0°, 45°, 90°, 180°) and imaging (0°, 70°, 90°, 110°). At each angle, anxiety and motion sickness were assessed using a 6-item State-Trait-Anxiety-Inventory (STAI-6) and a modified Motion Sickness Assessment Questionnaire (MSAQ). In addition, general areas of discomfort were evaluated.

RESULTS

Out of 50 subjects, three (6%) subjects terminated the study prematurely. One subject dropped out during simulation due to nausea while rotating to 45°. During imaging, further two subjects dropped out due to shoulder pain from positioning at 90° and 110°, respectively. The average result for claustrophobia (0 = no claustrophobia to 4 = extreme claustrophobia) was none to light claustrophobia (average score: simulation 0.64 ± 0.33, imaging 0.51 ± 0.39). The mean anxiety scores (0% = no anxiety to 100% = maximal anxiety) were 11.04% (simulation) and 15.82% (imaging). Mean motion sickness scores (0% = no motion sickness to 100% = maximal motion sickness) of 3.5% (simulation) and 6.76% (imaging) were obtained across all participants.

CONCLUSION

Our study proves the feasibility of horizontal rotation in a fully-enclosed rotation system within an MR-scanner. Anxiety scores were low and motion sickness was only a minor influence. Both anxiety and motion sickness showed no angular dependency. Further optimizations with regard to immobilization in the rotation device may increase subject comfort.

Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy

PURPOSE

We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties.

MATERIALS AND METHODS

Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications.

RESULTS

For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62 ± 0.27, 2.39 ± 1.30, 3.07 ± 2.42, 1.04 ± 0.72 mm, and average DSCs are 0.94 ± 0.03, 0.80 ± 0.12, 0.77 ± 0.15, 0.90 ± 0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC.

CONCLUSIONS

Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.

Development of shielding evaluation and management program for O-ring type linear accelerators

The shielding parameters can vary depending on the geometrical structure of the linear accelerators (LINAC), treatment techniques, and beam energies. Recently, the introduction of O-ring type linear accelerators is increasing. The objective of this study is to evaluate the shielding parameters of new type of linac using a dedicated program developed by us named ORSE (O-ring type Radiation therapy equipment Shielding Evaluation). The shielding evaluation was conducted for a total of four treatment rooms including Elekta Unity, Varian Halcyon, and Accuray Tomotherapy. The developed program possesses the capability to calculate transmitted dose, maximum treatable patient capacity, and shielding wall thickness based on patient data. The doses were measured for five days using glass dosimeters to compare with the results of program. The IMRT factors and use factors obtained from patient data showed differences of up to 65.0% and 33.8%, respectively, compared to safety management report. The shielding evaluation conducted in each treatment room showed that the transmitted dose at every location was below 1% of the dose limit. The results of program and measurements showed a maximum difference of 0.003 mSv/week in transmitted dose. The ORSE program allows for the shielding evaluation results to the clinical environment of each institution based on patient data.

Sorting lung tumor volumes from 4D-MRI data using an automatic tumor-based signal reduces stitching artifacts

PURPOSE

To investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D-magnetic resonance (4D-MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes.

METHODS

(4D-MRI) scans were collected for 10 lung cancer patients using a 2D T2-weighted single-shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor-motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test.

RESULTS

The TumorPC1 signal was most strongly correlated with superior-inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior-inferior tumor motion (p < 0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p = 0.02-0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one.

CONCLUSION

Tumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.

Feasibility and safety of contrast-enhanced magnetic resonance-guided adaptive radiotherapy for upper abdominal tumors: A preliminary exploration

This study investigates the use of contrast-enhanced magnetic resonance (MR) in MR-guided adaptive radiotherapy (MRgART) for upper abdominal tumors. Contrast-enhanced T1-weighted MR (cT1w MR) using half doses of gadoterate was used to guide daily adaptive radiotherapy for tumors poorly visualized without contrast. The use of gadoterate was found to be feasible and safe in 5-fraction MRgART and could improve the contrast-to-noise ratio of MR images. And the use of cT1w MR could reduce the interobserver variation of adaptive tumor delineation compared to plain T1w MR (4.41 vs. 6.58, p < 0.001) and T2w MR (4.41 vs. 7.42, p < 0.001).

Stereotactic body radiotherapy of central lung tumours using a 1.5 T MR-linac: First clinical experiences

Background

MRI-guidance may aid better discrimination between Organs at Risk (OARs) and target volumes in proximity of the mediastinum. We report the first clinical experiences with Stereotactic Body Radiotherapy (SBRT) of (ultra)central lung tumours on a 1.5 T MR-linac.

Materials and Methods

Patients with an (ultra)central lung tumour were selected for MR-linac based SBRT treatment. A T2-weighted 3D sequence MRI acquired during free breathing was used for daily plan adaption. Prior to each fraction, contours of Internal Target Volume (ITV) and OARs were deformably propagated and amended by a radiation oncologist. Inter-fractional changes in volumes and coverage of target volumes as well as doses in OARs were evaluated in offline and online treatment plans.

Results

Ten patients were treated and completed 60 Gy in 8 or 12 fractions. In total 104 fractions were delivered. The median time in the treatment room was 41 min with a median beam-on time of 8.9 min. No grade ≥3 acute toxicity was observed. In two patients, the ITV significantly decreased during treatment (58 % and 37 %, respectively) due to tumour shrinkage. In the other patients, 81 % of online ITVs were within ±15 % of the volume of fraction 1. Comparison with the pre-treatment plan showed that ITV coverage of the online plan was similar in 52 % and improved in 34 % of cases. Adaptation to meet OAR constraints, led to decreased ITV coverage in 14 %.

Conclusions

We describe the workflow for MR-guided Radiotherapy and the feasibility of using 1.5 T MR-linac for SBRT of (ultra) central lung tumours.

Is it necessary to perform measurement-based patient-specific quality assurance for online adaptive radiotherapy with Elekta Unity MR-Linac?

This study aimed to investigate the necessity of measurement-based patient-specific quality assurance (PSQA) for online adaptive radiotherapy by analyzing measurement-based PSQA results and calculation-based 3D independent dose verification results with Elekta Unity MR-Linac. There are two workflows for Elekta Unity enabled in the treatment planning system: adapt to position (ATP) and adapt to shape (ATS). ATP plans are those which have relatively slighter shifts from reference plans by adjusting beam shapes or weights, whereas ATS plans are the new plans optimized from the beginning with probable re-contouring targets and organs-at-risk. PSQA gamma passing rates were measured using an MR-compatible ArcCHECK diode array for 78 reference plans and corresponding 208 adaptive plans (129 ATP plans and 79 ATS plans) of Elekta Unity. Subsequently, the relationships between ATP, or ATS plans and reference plans were evaluated separately. The Pearson's r correlation coefficients between ATP or ATS adaptive plans and corresponding reference plans were also characterized using regression analysis. Moreover, the Bland-Altman plot method was used to describe the agreement of PSQA results between ATP or ATS adaptive plans and reference plans. Additionally, Monte Carlo-based independent dose verification software ArcherQA was used to perform secondary dose check for adaptive plans. For ArcCHECK measurements, the average gamma passing rates (ArcCHECK vs. TPS) of PSQA (3%/2 mm criterion) were 99.51% ± 0.88% and 99.43% ± 0.54% for ATP and ATS plans, respectively, which were higher than the corresponding reference plans 99.34% ± 1.04% (p < 0.05) and 99.20% ± 0.71% (p < 0.05), respectively. The Pearson's r correlation coefficients were 0.720 between ATP and reference plans and 0.300 between ATS and reference plans with ArcCHECK, respectively. Furthermore, >95% of data points of differences between both ATP and ATS plans and reference plans were within ±2σ (standard deviation) of the mean difference between adaptive and reference plans with ArcCHECK measurements. With ArcherQA calculation, the average gamma passing rates (ArcherQA vs. TPS) were 98.23% ± 1.64% and 98.15% ± 1.07% for ATP and ATS adaptive plans, separately. It might be unnecessary to perform measurement-based PSQA for both ATP and ATS adaptive plans for Unity if the gamma passing rates of both measurements of corresponding reference plans and independent dose verification of adaptive plans have high gamma passing rates. Periodic machine QA and verification of adaptive plans were recommended to ensure treatment safety.

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

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

Full daily re-optimization improves plan quality during online adaptive radiotherapy

Background and purpose

Daily online treatment plan adaptation requires a fast workflow and planning process. Current online planning consists of adaptation of a predefined reference plan, which might be suboptimal in cases of large anatomic changes. The aim of this study was to investigate plan quality differences between the current online re-planning approach and a complete re-optimization.

Material and methods

Magnetic resonance linear accelerator reference plans for ten prostate cancer patients were automatically generated using particle swarm optimization (PSO). Adapted plans were created for each fraction using (1) the current re-planning approach and (2) full PSO re-optimization and evaluated overall compliance with institutional dose-volume criteria compared to (3) clinically delivered fractions. Relative volume differences between reference and daily anatomy were assessed for planning target volumes (PTV60, PTV57.6), rectum and bladder and correlated with dose-volume results.

Results

The PSO approach showed significantly higher adherence to dose-volume criteria than the reference approach and clinical fractions (p < 0.001). In 74 % of PSO plans at most one criterion failed compared to 56 % in the reference approach and 41 % in clinical plans. A fair correlation between PTV60 D98% and relative bladder volume change was observed for the reference approach. Bladder volume reductions larger than 50 % compared to the reference plan recurrently decreased PTV60 D98% below 56 Gy.

Conclusion

Complete re-optimization maintained target coverage and organs at risk sparing even after large anatomic variations. Re-planning based on daily magnetic resonance imaging was sufficient for small variations, while large variations led to decreasing target coverage and organ-at-risk sparing.

A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs

Purpose

The purpose of this work was to investigate the use of a segmentation approach that could potentially improve the speed and reproducibility of contouring during magnetic resonance-guided adaptive radiation therapy.

Methods and Materials

The segmentation algorithm was based on a hybrid deep neural network and graph optimization approach that also allows rapid user intervention (Deep layered optimal graph image segmentation of multiple objects and surfaces [LOGISMOS] + just enough interaction [JEI]). A total of 115 magnetic resonance-data sets were used for training and quantitative assessment. Expert segmentations were used as the independent standard for the prostate, seminal vesicles, bladder, rectum, and femoral heads for all 115 data sets. In addition, 3 independent radiation oncologists contoured the prostate, seminal vesicles, and rectum for a subset of patients such that the interobserver variability could be quantified. Consensus contours were then generated from these independent contours using a simultaneous truth and performance level estimation approach, and the deviation of Deep LOGISMOS + JEI contours to the consensus contours was evaluated and compared with the interobserver variability.

Results

The absolute accuracy of Deep LOGISMOS + JEI generated contours was evaluated using median absolute surface-to-surface distance which ranged from a minimum of 0.20 mm for the bladder to a maximum of 0.93 mm for the prostate compared with the independent standard across all data sets. The median relative surface-to-surface distance was less than 0.17 mm for all organs, indicating that the Deep LOGISMOS + JEI algorithm did not exhibit a systematic under- or oversegmentation. Interobserver variability testing yielded a mean absolute surface-to-surface distance of 0.93, 1.04, and 0.81 mm for the prostate, seminal vesicles, and rectum, respectively. In comparison, the deviation of Deep LOGISMOS + JEI from consensus simultaneous truth and performance level estimation contours was 0.57, 0.64, and 0.55 mm for the same organs. On average, the Deep LOGISMOS algorithm took less than 26 seconds for contour segmentation.

Conclusions

Deep LOGISMOS + JEI segmentation efficiently generated clinically acceptable prostate and normal tissue contours, potentially limiting the need for time intensive manual contouring with each fraction.

Effectiveness of visual biofeedback-guided respiratory-correlated 4D-MRI for radiotherapy guidance on the MR-linac

PURPOSE

Respiratory-correlated 4D-MRI may provide motion characteristics for radiotherapy but is susceptible to irregular breathing. This study investigated the effectiveness of visual biofeedback (VBF) guidance for breathing regularization during 4D-MRI acquisitions on an MR-linac.

METHODS

A simultaneous multislice-accelerated 4D-MRI sequence was interleaved with a one-dimensional respiratory navigator (1D-RNAV) in 10 healthy volunteers on a 1.5T Unity MR-linac (Elekta AB, Stockholm, Sweden). Volunteer-specific breathing amplitudes and periods were derived from the 1D-RNAV signal obtained during unguided 4D-MRI acquisitions. These were used for the guidance waveform, while the 1D-RNAV positions were overlayed as VBF. VBF effectiveness was quantified by calculating the change in coefficient of variation (CV diff $$ {\mathrm{CV}}^{\mathrm{diff}} $$ ) for the breathing amplitude and period, the position SD of end-exhale, end-inhale and midposition locations, and the agreement between the 1D-RNAV signals and guidance waveforms. The 4D-MRI quality was assessed by quantifying amounts of missing data.

RESULTS

VBF had an average latency of 520 ± 2 ms. VBF reduced median breathing variations by 18% to 35% (amplitude) and 29% to 57% (period). Median position SD reductions ranged from -3% to 35% (end-exhale), 29% to 38% (end-inhale), and 25% to 37% (midposition). Average differences between guidance waveforms and 1D-RNAV signals were 0.0 s (period) and +1.7 mm (amplitude). VBF also decreased the median amount of missing data by 11% and 29%.

CONCLUSION

A VBF system was successfully implemented, and all volunteers were able to adapt to the guidance waveform. VBF during 4D-MRI acquisitions drastically reduced breathing variability but had limited effect on missing data in respiratory-correlated 4D-MRI.

Empirical analysis of a plan-of-the-day strategy to approximate daily online reoptimization for prostate CBCT-guided adaptive radiotherapy

PURPOSE

Adaptive radiotherapy (ART) can improve the dose delivered to the patient in the presence of anatomic variations. However, the required time, effort, and clinical resources are intensive. This work analyzed a plan-of-the-day (POD) approach on clinical patients treated with online ART to explore implementations that balance dosimetric benefit and clinical resource cost.

METHODS

Eight patients treated to the prostate and proximal seminal vesicles with 26 fractions of CBCT-guided, daily online ART were retrospectively analyzed. With a plan library composed of daily adaptive plans from the initial week of treatment and the original plan, the effect of a POD approach starting the following week was investigated by simulating use of these previously generated plans under 3- and 6-degree-of-freedom patient alignment. The plan selected for each treatment was that from the library that maximized the Dice similarity coefficient of the clinical target volume with that of the current treatment fraction. The resulting distribution of several target coverage and organ-at-risk dose metrics are described relative to those achieved with the daily online reoptimized adaptive technique.

RESULTS

The values of target coverage and organ-at-risk dose metrics varied across patients and metrics. The POD schemas closely approximated the reference values from a fully reoptimized adaptive plan yet required less than 20% of the reoptimization effort. The POD schemas also had a much greater effect on target coverage metrics than 6-degree-of-freedom registration did. Organ-at-risk dose metrics also varied considerably across patients but did not exhibit a consistent dependence on the particular schema.

CONCLUSIONS

POD schemas were able to achieve the vast majority of the dosimetric benefit of daily online ART with a small fraction of the online reoptimization effort. Strategies like this might allow for more practical and strategic implementation of ART so as to benefit a greater number of patients.

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

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

Biomedical advances and future prospects of high-precision three-dimensional radiotherapy and four-dimensional radiotherapy

Biomedical advances of external-beam radiotherapy (EBRT) with improvements in physical accuracy are reviewed. High-precision (±1 mm) three-dimensional radiotherapy (3DRT) can utilize respective therapeutic open doors in the tumor control probability curve and in the normal tissue complication probability curve instead of the one single therapeutic window in two-dimensional EBRT. High-precision 3DRT achieved higher tumor control and probable survival rates for patients with small peripheral lung and liver cancers. Four-dimensional radiotherapy (4DRT), which can reduce uncertainties in 3DRT due to organ motion by real-time (every 0.1-1 s) tumor-tracking and immediate (0.1-1 s) irradiation, have achieved reduced adverse effects for prostate and pancreatic tumors near the digestive tract and with similar or better tumor control. Particle beam therapy improved tumor control and probable survival for patients with large liver tumors. The clinical outcomes of locally advanced or multiple tumors located near serial-type organs can theoretically be improved further by integrating the 4DRT concept with particle beams.

A new workflow of the on-line 1.5-T MR-guided adaptive radiation therapy

PURPOSE

The aim of this study was to develop a new workflow for 1.5-T magnetic resonance (MR)-guided on-line adaptive radiation therapy (MRgART) and assess its feasibility in achieving dose constraints.

MATERIALS AND METHODS

We retrospectively evaluated the clinical data of patients who underwent on-line adaptive radiation therapy using a 1.5-T MR linear accelerator (MR-Linac). The workflow in MRgART was established by reviewing the disease site, number of fractions, and re-planning procedures. Five cases of prostate cancer were selected to evaluate the feasibility of the new workflow with respect to achieving dose constraints.

RESULTS

Between December 2021 and September 2022, 50 consecutive patients underwent MRgART using a 1.5-T MR-Linac. Of these, 20 had prostate cancer, 10 had hepatocellular carcinoma, 6 had pancreatic cancer, 5 had lymph node oligo-metastasis, 3 had renal cancer, 3 had bone metastasis, 2 had liver metastasis from colon cancer, and 1 had a mediastinal tumor. Among a total of 247 fractions, 235 (95%) were adapt-to-shape (ATS)-based re-planning. The median ATS re-planning time in all 50 cases was 17 min. In the feasibility study, all dose constraint sets were met in all 5 patients by ATS re-planning. Conversely, a total of 14 dose constraints in 5 patients could not be achieved by virtual plan without using adaptive re-planning. These dose constraints included the minimum dose received by the highest irradiated volume of 1 cc in the planning target volume and the maximum dose of the rectal/bladder wall.

CONCLUSION

A new workflow of 1.5-T MRgART was established and found to be feasible. Our evaluation of the dose constraint achievement demonstrated the effectiveness of the workflow.

Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system

BACKGROUND

MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac). Free-breathing 4D-MRI is desirable in respiratory motion management for therapy. Moreover, high-quality 3D-MRIs without motion artifacts are needed to delineate lesions. Existing MRI methods require multiple scans with lengthy acquisition times or are limited by low spatial resolution, contrast, and signal-to-noise ratio.

PURPOSE

We developed a novel method to obtain motion-resolved 4D-MRIs and motion-integrated 3D-MRI reconstruction using a single rapid (35-45 s scan on a 0.35 T MRI-Linac.

METHODS

Golden-angle radial stack-of-stars MRI scans were acquired from a respiratory motion phantom and 12 healthy volunteers (n = 12) on a 0.35 T MRI-Linac. A self-navigated method was employed to detect respiratory motion using 2000 (acquisition time = 5-7 min) and the first 200 spokes (acquisition time = 35-45 s). Multi-coil non-uniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and deep-learning Phase2Phase (P2P) methods were employed to reconstruct motion-resolved 4D-MRI using 2000 spokes (MCNUFFT2000) and 200 spokes (CS200 and P2P200). Deformable motion vector fields (MVFs) were computed from the 4D-MRIs and used to reconstruct motion-corrected 3D-MRIs with the MOtion Transformation Integrated forward-Fourier (MOTIF) method. Image quality was evaluated quantitatively using the structural similarity index measure (SSIM) and the root mean square error (RMSE), and qualitatively in a blinded radiological review.

RESULTS

Evaluation using the respiratory motion phantom experiment showed that the proposed method reversed the effects of motion blurring and restored edge sharpness. In the human study, P2P200 had smaller inaccuracy in MVFs estimation than CS200. P2P200 had significantly greater SSIMs (p < 0.0001) and smaller RMSEs (p < 0.001) than CS200 in motion-resolved 4D-MRI and motion-corrected 3D-MRI. The radiological review found that MOTIF 3D-MRIs using MCNUFFT2000 exhibited the highest image quality (scoring > 8 out of 10), followed by P2P200 (scoring > 5 out of 10), and then motion-uncorrected (scoring < 3 out of 10) in sharpness, contrast, and artifact-freeness.

CONCLUSIONS

We have successfully demonstrated a method for respiratory motion management for MRI-guided RT. The method integrated self-navigated respiratory motion detection, deep-learning P2P 4D-MRI reconstruction, and a motion integrated reconstruction (MOTIF) for 3D-MRI using a single rapid MRI scan (35-45 s) on a 0.35 T MRI-Linac system.

Live-view 4D GRASP MRI: A framework for robust real-time respiratory motion tracking with a sub-second imaging latency

PURPOSE

To propose a framework called live-view golden-angle radial sparse parallel (GRASP) MRI for low-latency and high-fidelity real-time volumetric MRI.

METHODS

Live-view GRASP MRI has two stages. The first one is called an off-view stage and the second one is called a live-view stage. In the off-view stage, 3D k-space data and 2D navigators are acquired alternatively using a new navi-stack-of-stars sampling scheme. A 4D motion database is then generated that contains time-resolved MR images at a sub-second temporal resolution, and each image is linked to a 2D navigator. In the live-view stage, only 2D navigators are acquired. At each time point, a live-view 2D navigator is matched to all the off-view 2D navigators. A 3D image that is linked to the best-matched off-view 2D navigator is then selected for this time point. This framework places the typical acquisition and reconstruction burden of MRI in the off-view stage, enabling low-latency real-time 3D imaging in the live-view stage. The accuracy of live-view GRASP MRI and the robustness of 2D navigators for characterizing respiratory variations and/or body movements were assessed.

RESULTS

Live-view GRASP MRI can efficiently generate real-time volumetric images that match well with the ground-truth references, with an imaging latency below 500 ms. Compared to 1D navigators, 2D navigators enable more reliable characterization of respiratory variations and/or body movements that may occur throughout the two imaging stages.

CONCLUSION

Live-view GRASP MRI represents a novel, accurate, and robust framework for real-time volumetric imaging, which can potentially be applied for motion adaptive radiotherapy on MRI-Linac.

Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks

BACKGROUND

Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. However, obtaining high-quality 4D-MRI suffers from long acquisition and reconstruction times.

PURPOSE

To develop a deep learning architecture to quickly acquire and reconstruct high-quality 4D-MRI, enabling accurate motion quantification for MRI-guided radiotherapy (MRIgRT).

METHODS

A small convolutional neural network called MODEST is proposed to reconstruct 4D-MRI by performing a spatial and temporal decomposition, omitting the need for 4D convolutions to use all the spatio-temporal information present in 4D-MRI. This network is trained on undersampled 4D-MRI after respiratory binning to reconstruct high-quality 4D-MRI obtained by compressed sensing reconstruction. The network is trained, validated, and tested on 4D-MRI of 28 lung cancer patients acquired with a T1-weighted golden-angle radial stack-of-stars (GA-SOS) sequence. The 4D-MRI of 18, 5, and 5 patients were used for training, validation, and testing. Network performances are evaluated on image quality measured by the structural similarity index (SSIM) and motion consistency by comparing the position of the lung-liver interface on undersampled 4D-MRI before and after respiratory binning. The network is compared to conventional architectures such as a U-Net, which has 30 times more trainable parameters.

RESULTS

MODEST can reconstruct high-quality 4D-MRI with higher image quality than a U-Net, despite a thirty-fold reduction in trainable parameters. High-quality 4D-MRI can be obtained using MODEST in approximately 2.5 min, including acquisition, processing, and reconstruction.

CONCLUSION

High-quality accelerated 4D-MRI can be obtained using MODEST, which is particularly interesting for MRIgRT.

Evaluation of non-vendor magnetic resonance imaging sequences for use in bladder cancer magnetic resonance image guided radiotherapy

Hybrid systems that combine Magnetic Resonance Imaging (MRI) and linear accelerators are available clinically to guide and adapt radiotherapy. Vendor-approved MRI sequences are provided, however alternative sequences may offer advantages. The aim of this study was to develop a systematic approach for non-vendor sequence evaluation, to determine safety, accuracy and overall clinical application of two potential sequences for bladder cancer MRI guided radiotherapy. Non-vendor sequences underwent and passed clinical image qualitative review, phantom quality assurance, and radiotherapy planning assessments. Volunteer workflow tests showed the potential for one sequence to reduce workflow time by 27% compared to the standard vendor sequence.

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.

X-ray source arrays for volumetric imaging during radiotherapy treatment

This work presents a novel hardware configuration for radiotherapy systems to enable fast 3D X-ray imaging before and during treatment delivery. Standard external beam radiotherapy linear accelerators (linacs) have a single X-ray source and detector located at ± 90° from the treatment beam respectively. The entire system can be rotated around the patient acquiring multiple 2D X-ray images to create a 3D cone-beam Computed Tomography (CBCT) image before treatment delivery to ensure the tumour and surrounding organs align with the treatment plan. Scanning with a single source is slow relative to patient respiration or breath holds and cannot be performed during treatment delivery, limiting treatment delivery accuracy in the presence of patient motion and excluding some patients from concentrated treatment plans that would be otherwise expected to have improved outcomes. This simulation study investigated whether recent advances in carbon nanotube (CNT) field emission source arrays, high frame rate (60 Hz) flat panel detectors and compressed sensing reconstruction algorithms could circumvent imaging limitations of current linacs. We investigated a novel hardware configuration incorporating source arrays and high frame rate detectors into an otherwise standard linac. We investigated four potential pre-treatment scan protocols that could be achieved in a 17 s breath hold or 2-10 1 s breath holds. Finally, we demonstrated for the first time volumetric X-ray imaging during treatment delivery by using source arrays, high frame rate detectors and compressed sensing. Image quality was assessed quantitatively over the CBCT geometric field of view as well as across each axis through the tumour centroid. Our results demonstrate that source array imaging enables larger volumes to be imaged with acquisitions as short as 1 s albeit with reduced image quality arising from lower photon flux and shorter imaging arcs.

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors

Objective. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overridden by air density for dose calculation. Auto-delineation of these regions is desirable for speed purposes, but poses a challenge, since unlike computed tomography, they do not occupy all dark regions on the image. The purpose of this study is to develop an automated method to segment the air regions on MRI-guided adaptive radiation therapy (MRgART) of abdominal tumors.Approach. A modified ResUNet3D deep learning (DL)-based auto air delineation model was trained using 102 patients' MR images. The MR images were acquired by a dedicated in-house sequence named 'Air-Scan', which is designed to generate air regions that are especially dark and accentuated. The air volumes generated by the newly developed DL model were compared with the manual air contours using geometric similarity (Dice Similarity Coefficient (DSC)), and dosimetric equivalence using Gamma index and dose-volume parameters.Main results. The average DSC agreement between the DL generated and manual air contours is 99% ± 1%. The gamma index between the dose calculations with overriding the DL versus manual air volumes with density of 0.01 is 97% ± 2% for a local gamma calculation with a tolerance of 2% and 2 mm. The dosimetric parameters from planning target volume-PTV and organs at risk-OARs were all within 1% between when DL versus manual contours were overridden by air density. The model runs in less than five seconds on a PC with 28 Core processor and NVIDIA Quadro®P2000 GPU.Significance: a DL based automated segmentation method was developed to generate air volumes on specialized abdominal MR images and generate results that are practically equivalent to the manual contouring of air volumes.

Real-time radial reconstruction with domain transform manifold learning for MRI-guided radiotherapy

BACKGROUND

MRI-guidance techniques that dynamically adapt radiation beams to follow tumor motion in real time will lead to more accurate cancer treatments and reduced collateral healthy tissue damage. The gold-standard for reconstruction of undersampled MR data is compressed sensing (CS) which is computationally slow and limits the rate that images can be available for real-time adaptation.

PURPOSE

Once trained, neural networks can be used to accurately reconstruct raw MRI data with minimal latency. Here, we test the suitability of deep-learning-based image reconstruction for real-time tracking applications on MRI-Linacs.

METHODS

We use automated transform by manifold approximation (AUTOMAP), a generalized framework that maps raw MR signal to the target image domain, to rapidly reconstruct images from undersampled radial k-space data. The AUTOMAP neural network was trained to reconstruct images from a golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on lung cancer patient data and generic images from ImageNet. Model training was subsequently augmented with motion-encoded k-space data derived from videos in the YouTube-8M dataset to encourage motion robust reconstruction.

RESULTS

AUTOMAP models fine-tuned on retrospectively acquired lung cancer patient data reconstructed radial k-space with equivalent accuracy to CS but with much shorter processing times. Validation of motion-trained models with a virtual dynamic lung tumor phantom showed that the generalized motion properties learned from YouTube lead to improved target tracking accuracy.

CONCLUSION

AUTOMAP can achieve real-time, accurate reconstruction of radial data. These findings imply that neural-network-based reconstruction is potentially superior to alternative approaches for real-time image guidance applications.

Feasibility of online radial magnetic resonance imaging for adaptive radiotherapy of pancreatic tumors

Background and purpose

Online adaptive magnetic resonance (MR)-guided treatment planning for pancreatic tumors on 1.5T systems typically employs Cartesian 3D T ₂w magnetic resonance imaging (MRI). The main disadvantage of this sequence is that respiratory motion results in substantial blurring in the abdomen, which can hamper delineation accuracy. This study investigated the use of two motion-robust radial MRI sequences as main delineation scan for pancreatic MR-guided radiotherapy.

Materials and methods

Twelve patients with pancreatic tumors were imaged with a 3D T ₂w scan, a Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) scan (partially overlapping strips), and a 3D Vane scan (stack-of-stars), on a 1.5T MR-Linac under abdominal compression. The scans were assessed by three radiation oncologists for their suitability for online adaptive delineation. A quantitative comparison was made for gradient entropy and the effect of motion on apparent target position.

Results

The PROPELLER scans were selected as first preference in 56% of the cases, the 3D T ₂w in 42% and the 3D Vane in 3%. PROPELLER scans sometimes contained a large interslice variation which would have compromised delineation. Gradient entropy was significantly higher in 3D T ₂w patient scans. The apparent target position was more sensitive to motion amplitude in the PROPELLER scans, but substantial offsets did not occur under 10 mm peak-to-peak.

Conclusion

PROPELLER MRI may be a superior imaging sequence for pancreatic MRgRT compared to standard Cartesian sequences. The large interslice variation should be mitigated through further sequence optimization before PROPELLER can be adopted for online treatment adaptation.

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.

Deep learning based automatic contour refinement for inaccurate auto-segmentation in MR-guided adaptive radiotherapy

Objective.Fast and accurate auto-segmentation is essential for magnetic resonance-guided adaptive radiation therapy (MRgART). Deep learning auto-segmentation (DLAS) is not always clinically acceptable, particularly for complex abdominal organs. We previously reported an automatic contour refinement (ACR) solution of using an active contour model (ACM) to partially correct the DLAS contours. This study aims to develop a DL-based ACR model to work in conjunction with ACM-ACR to further improve the contour accuracy.Approach.The DL-ACR model was trained and tested using bowel contours created by an in-house DLAS system from 160 MR sets (76 from MR-simulation and 84 from MR-Linac). The contours were classified into acceptable, minor-error and major-error groups using two approaches of contour quality classification (CQC), based on the AAPM TG-132 recommendation and an in-house classification model, respectively. For the major-error group, DL-ACR was applied subsequently after ACM-ACR to further refine the contours. For the minor-error group, contours were directly corrected by DL-ACR without applying an initial ACM-ACR. The ACR workflow was performed separately for the two CQC methods and was evaluated using contours from 25 image sets as independent testing data.Main results.The best ACR performance was observed in the MR-simulation testing set using CQC by TG-132: (1) for the major-error group, 44% (177/401) were improved to minor-error group and 5% (22/401) became acceptable by applying ACM-ACR; among these 177 contours that shifted from major-error to minor-error with ACM-ACR, DL-ACR further refined 49% (87/177) to acceptable; and overall, 36% (145/401) were improved to minor-error contours, and 30% (119/401) became acceptable after sequentially applying ACM-ACR and DL-ACR; (2) for the minor-error group, 43% (320/750) were improved to acceptable contours using DL-ACR.Significance.The obtained ACR workflow substantially improves the accuracy of DLAS bowel contours, minimizing the manual editing time and accelerating the segmentation process of MRgART.

Parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) for accelerating 4D-MRI

Dynamic magnetic resonance imaging (MRI) acquisitions are relatively slow due to physical and physiological limitations. The spatial-temporal dictionary learning (DL) approach accelerates dynamic MRI by learning spatial-temporal correlations, but the regularization parameters need to be manually adjusted, the performance at high acceleration rate is limited, and the reconstruction can be time-consuming. Deep learning techniques have shown good performance in accelerating MRI due to the powerful representational capabilities of neural networks. In this work, we propose a parallel non-Cartesian spatial-temporal dictionary learning neural networks (stDLNN) framework that combines dictionary learning with deep learning algorithms and utilizes the spatial-temporal prior information of dynamic MRI data to achieve better reconstruction quality and efficiency. The coefficient estimation modules (CEM) are designed in the framework to adaptively adjust the regularization coefficients. Experimental results show that combining dictionary learning with deep neural networks and using spatial-temporal dictionaries can obviously improve the image quality and computational efficiency compared with the state-of-the-art non-Cartesian imaging methods for accelerating the 4D-MRI especially at high acceleration rate.

4D lung MRI with high-isotropic-resolution using half-spoke (UTE) and full-spoke 3D radial acquisition and temporal compressed sensing reconstruction

Objective. To develop a respiratory motion-resolved four-dimensional (4D) magnetic resonance imaging (MRI) technique with high-isotropic-resolution (1.1 mm) using 3D radial sampling, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for lung cancer imaging.Approach. Free-breathing half- and full-spoke 3D golden-angle radial acquisitions were performed on eight healthy volunteers and eight patients with lung tumors of varying size. A back-and-forth k-space ordering between consecutive interleaves of the 3D radial acquisition was performed to minimize eddy current-related artifacts. Data were sorted into respiratory motion states using camera-based motion navigation and 4D images were reconstructed using temporal compressed sensing to reduce scan time. Normalized sharpness indices of the diaphragm, apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (CNR) of the lung tumor (patients only), liver, and aortic arch were compared between half- and full-spoke 4D MRI images to evaluate the impact of respiratory motion and image contrast on 4D MRI image quality. Respiration-induced changes in lung volumes and center of mass shifts were compared between half- and full-spoke 4D MRI measurements. In addition, the motion measurements from 4D MRI and the same-day 4D CT were presented in one of the lung tumor patients.Main results. Half-spoke 4D MRI provides better visualization of the lung parenchyma, while full-spoke 4D MRI presents sharper diaphragm images and higher aSNR and CNR in the lung tumor, liver, and aortic arch. Lung volume changes and center of mass shifts measured by half- and full-spoke 4D MRI were not statistically different. For the patient with 4D MRI and same-day 4D CT, lung volume changes and center of mass shifts were generally comparable.Significance. This work demonstrates the feasibility of a motion-resolved 4D MRI technique with high-isotropic-resolution using 3D radial acquisition, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for treatment planning and motion monitoring in radiotherapy of lung cancer.

Deep learning-based prediction of deliverable adaptive plans for MR-guided adaptive radiotherapy: A feasibility study

Purpose

Fast and automated plan generation is desirable in radiation therapy (RT), in particular, for MR-guided online adaptive RT (MRgOART) or real-time (intrafractional) adaptive RT (MRgRART), to reduce replanning time. The purpose of this study is to investigate the feasibility of using deep learning to quickly predict deliverable adaptive plans based on a target dose distribution for MRgOART/MRgRART.

Methods

A conditional generative adversarial network (cGAN) was trained to predict the MLC leaf sequence corresponding to a target dose distribution based on reference plan created prior to MRgOART using a 1.5T MR-Linac. The training dataset included 50 ground truth dose distributions and corresponding beam parameters (aperture shapes and weights) created during MRgOART for 10 pancreatic cancer patients (each with five fractions). The model input was the dose distribution from each individual beam and the output was the predicted corresponding field segments with specific shape and weight. Patient-based leave-one-out-cross-validation was employed and for each model trained, four (44 training beams) out of five fractionated plans of the left-out patient were set aside for testing purposes. We deliberately kept a single fractionated plan in the training dataset so that the model could learn to replan the patient based on a prior plan. The model performance was evaluated by calculating the gamma passing rate of the ground truth dose vs. the dose from the predicted adaptive plan and calculating max and mean dose metrics.

Results

The average gamma passing rate (95%, 3mm/3%) among 10 test cases was 88%. In general, we observed 95% of the prescription dose to PTV achieved with an average 7.6% increase of max and mean dose, respectively, to OARs for predicted replans. Complete adaptive plans were predicted in ≤20 s using a GTX 1660TI GPU.

Conclusion

We have proposed and demonstrated a deep learning method to generate adaptive plans automatically and rapidly for MRgOART. With further developments using large datasets and the inclusion of patient contours, the method may be implemented to accelerate MRgOART process or even to facilitate MRgRART.

Intra-fraction motion of pelvic oligometastases and feasibility of PTV margin reduction using MRI guided adaptive radiotherapy

Purpose

This study assesses the impact of intra-fraction motion and PTV margin size on target coverage for patients undergoing radiation treatment of pelvic oligometastases. Dosimetric sparing of the bowel as a function of the PTV margin is also evaluated.

Materials and methods

Seven patients with pelvic oligometastases previously treated on our MR-linac (35 Gy in 5 fractions) were included in this study. Retrospective adaptive plans were created for each fraction on the daily MRI datasets using PTV margins of 5 mm, 3 mm, and 2 mm. Dosimetric constraint violations and GTV coverage were measured as a function of PTV margin size. The impact of intra-fraction motion on GTV coverage was assessed by tracking the GTV position on the cine MR images acquired during treatment delivery and creating an intra-fraction dose distribution for each IMRT beam. The intra-fraction dose was accumulated for each fraction to determine the total dose delivered to the target for each PTV size.

Results

All OAR constraints were achieved in 85.7%, 94.3%, and 100.0% of fractions when using 5 mm, 3 mm, and 2 mm PTV margins while scaling to 95% PTV coverage. Compared to plans with a 5 mm PTV margin, there was a 27.4 ± 12.3% (4.0 ± 2.2 Gy) and an 18.5 ± 7.3% (2.7 ± 1.4 Gy) reduction in the bowel D_0.5cc dose for 2 mm and 3 mm PTV margins, respectively. The target dose (GTV V_{35 Gy}) was on average 100.0 ± 0.1% (99.6 - 100%), 99.6 ± 1.0% (97.2 - 100%), and 99.0 ± 1.4% (95.0 - 100%), among all fractions for the 5 mm, 3 mm, and 2 mm PTV margins on the adaptive plans when accounting for intra-fraction motion, respectively.

Conclusion

A 2 mm PTV margin achieved a minimum of 95% GTV coverage while reducing the dose to the bowel for all patients.

Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier

PURPOSE

MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor-intensive and time-consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set.

METHODS

A total of 109 daily MRI sets acquired on a 1.5T MR-Linac during MRgART for 22 pancreatic cancer patients each treated with five fractions were retrospectively analyzed. For each daily MRI set, OLAR and reposition (No-OLAR) plans were created and the superior plan with the daily fraction determined per clinical dose-volume criteria. SSIM and entropy maps were extracted from each daily MRI set, with respect to its reference (e.g., dry-run) MRI in the region enclosed by 50-100% isodose surfaces. A total of six common features were extracted from SSIM maps. Pearson's rank correlation coefficient was utilized to rule out redundant SSIM features. A t-test was used to determine significant SSIM features which were combined with the change in entropy to develop anensemble machine classifier with fivefold cross validation. The performance of the classifier was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

A machine learning classifier model using two SSIM features (mean and full width at half maximum) and change in entropy was determined to be able to significantly discriminate between No-OLAR and OLAR groups. The obtained machine learning ensemble classifier can predict OLAR necessity with a cross validated AUC of 0.93. Misclassification was found primarily for No-OLAR cases with dosimetric plan quality closely comparable to the corresponding OLAR plans, thus, are not a major practical concern.

CONCLUSION

A machine learning classifier based on simple first-order image features, that is, SSIM features and change in entropy, was developed to determine when OLAR is necessary for a daily MRI set with practical acceptable prediction accuracy. This classifier may be implemented in the MRgART process to automatically and objectively determine if OLAR is required following daily MRI.

Robustness assessment of clinical adaptive proton and photon radiotherapy for oesophageal cancer in the model-based approach

PURPOSE

In the Netherlands, oesophageal cancer (EC) patients are selected for intensity modulated proton therapy (IMPT) using the expected normal tissue complication probability reduction (ΔNTCP) when treating with IMPT compared to volumetric modulated arc therapy (VMAT). In this study, we evaluate the robustness of the first EC patients treated with IMPT in our clinic in terms of target and organs-at-risk (OAR) dose with corresponding NTCP, as compared to VMAT.

MATERIALS AND METHODS

For 20 consecutive EC patients, clinical IMPT and VMAT plans were created on the average planning 4DCT. Both plans were robustly evaluated on weekly repeated 4DCTs and if target coverage degraded, replanning was performed. Target coverage was evaluated for complete treatment trajectories with and without replanning. The planned and accumulated mean lung dose (MLD) and mean heart dose (MHD) were additionally evaluated and translated into NTCP.

RESULTS

Replanning in the clinic was performed more often for IMPT (15x) than would have been needed for VMAT (8x) (p = 0.11). Both adaptive treatments would have resulted in adequate accumulated target dose coverage. Replanning in the first week of treatment had most clinical impact, as anatomical changes resulting in insufficient accumulated target coverage were already observed at this stage. No differences were found in MLD between the planned dose and the accumulated dose. Accumulated MHD differed from the planned dose (p < 0.001), but since these differences were similar for VMAT and IMPT (1.0 and 1.5 Gy, respectively), the ΔNTCP remained unchanged.

CONCLUSION

Following an adaptive clinical workflow, adequate target dose coverage and stable OAR doses with corresponding NTCPs was assured for both IMPT and VMAT.

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.

Role of Functional MRI in Liver SBRT: Current Use and Future Directions

Stereotactic body radiation therapy (SBRT) is an emerging treatment for liver cancers whereby large doses of radiation can be delivered precisely to target lesions in 3-5 fractions. The target dose is limited by the dose that can be safely delivered to the non-tumour liver, which depends on the baseline liver functional reserve. Current liver SBRT guidelines assume uniform liver function in the non-tumour liver. However, the assumption of uniform liver function is false in liver disease due to the presence of cirrhosis, damage due to previous chemo- or ablative therapies or irradiation, and fatty liver disease. Anatomical information from magnetic resonance imaging (MRI) is increasingly being used for SBRT planning. While its current use is limited to the identification of target location and size, functional MRI techniques also offer the ability to quantify and spatially map liver tissue microstructure and function. This review summarises and discusses the advantages offered by functional MRI methods for SBRT treatment planning and the potential for adaptive SBRT workflows.

Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy

PURPOSE

Online adaptive replanning (OLAR) is generally labor-intensive and time-consuming during MRI-guided adaptive radiation therapy (MRgART). This work aims to develop a method to determine OLAR necessity during MRgART.

METHODS

A machine learning classifier was developed to predict OLAR necessity based on wavelet multiscale texture features extracted from daily MRIs and was trained and tested with data from 119 daily MRI datasets acquired during MRgART for 24 pancreatic cancer patients treated on a 1.5 T MR-Linac. Spearman correlations, interclass correlation (ICC), coefficient of variance (COV), t-test (p < 0.05), self-organized map (SOM) and maximum stable extremal region (MSER) algorithm were used to determine candidate features, which were used to build the prediction models using Bayesian classifiers. The model performance was judged using the AUC of the ROC curve.

RESULTS

Spearman correlation identified 123 features that were not redundant (r < 0.9). Of them 82 showed high ICC for repositioning > 0.6, 67 had a COV greater than 9% for OLAR. Among the 38 features passed the t-test, 25 passed the SOM and 12 passed the MSER. These final 12 features were used to build the classifier model. The combination of 2-3 features at a time was used to build the classifier models. The best performing model was a 3-feature combination, which can predict OLAR necessity with a CV-AUC of 0.98.

CONCLUSIONS

A machine learning classifier model based on the wavelet features extracted from daily MRI for pancreatic cancer was developed to automatically and objectively determine if OLAR is necessary for a treatment fraction avoiding unnecessary effort during MRgART.

Potential utility of cone-beam CT-guided adaptive radiotherapy under end-exhalation breath-hold conditions for pancreatic cancer

PURPOSE

The purpose of this study was to demonstrate the potential utility of cone-beam computed tomography (CBCT)-guided online adaptive radiotherapy (ART) under end-exhalation breath-hold (EE-BH) conditions for pancreatic cancer (PC).

METHODS

Eleven PC patients who underwent 15-fraction volumetric-modulated arc therapy under EE-BH conditions were included. Planning CT images and daily 165 CBCT images were imported into a dedicated treatment planning system. The prescription dose was set to 48 Gy in 15 fractions. The reference plan was automatically generated along with predefined clinical goals. After segmentation was completed on CBCT images, two different plans were generated: One was an adapted (ADP) plan in which re-optimization was performed on the anatomy of the day, and the other was a scheduled (SCH) plan, which was the same as the reference plan. The dose distributions calculated using the synthetic CT created from both planning CT and CBCT were compared between the two plans. Independent calculation-based quality assurance was also performed for the ADP plans, with a gamma passing rate of 3%/3 mm.

RESULTS

All clinical goals were successfully achieved during the reference plan generation. Of the 165 sessions, gross tumor volume D_98% and clinical target volume D_98% were higher in 100 (60.1%) and 122 (74.0%) ADP fractions. In each fraction, the V_3 Gy  < 1 cm³ of the stomach and duodenum was violated in 47 (28.5%) and 48 (29.1%), respectively, of the SCH fractions, whereas no violations were observed in the ADP fractions. There were statistically significant differences in the dose-volume indices between the SCH and ADP fractions (p < 0.05). The gamma passing rates were above 95% in all ADP fractions.

CONCLUSIONS

The CBCT-guided online ART under EE-BH conditions successfully reduced the dose to the stomach and duodenum while maintaining target coverage.

Stereotactic ablative radiation for pancreatic cancer on a 1.5 Telsa magnetic resonance-linac system

Purpose

Ablative radiation therapy (A-RT) appears to improve outcomes in locally advanced pancreatic cancer (LAPC) yet requires solutions for respiratory and digestive motion. We report outcomes of A-RT for pancreatic cancer using 1.5 T MR-adaptive treatment delivery.

Methods

Between March 2020 and July 2021, we treated 30 patients with pancreatic cancer with 50 Gy in 5 fractions (biologically effective dose [BED10] = 100 Gy10) using a novel compression belt workflow and remote planning on the Unity 1.5 T MR linac system. Cumulative incidence of progression was computed from A-RT initiation with death as a competing risk. Overall (OS) and progression-free survival (PFS) were calculated using Kaplan Meier methods.

Results

Of 30 patients, most (73 %) were locally advanced, 4 (13 %) were metastatic, 2 (7 %) were medically inoperable, and 2 (7 %) were locally recurrent. Most (73 %) received FOLFIRINOX prior to A-RT. Median follow-up times from diagnosis and A-RT were 17.6 (IQR 15.8-23.1) and 11.5 months (IQR 9.7-16.1), respectively. Cumulative incidences at 1-year of local and distant progression were 19.3 % (95 %CI 6.7-36.8 %) and 47.4 % (95 %CI 26.7-65.6 %), respectively. Median OS from diagnosis and A-RT were not reached. One-year OS from diagnosis and A-RT were 96.4 % (95 %CI 77.2-99.5 %) and 80.0 % (95 %CI 57.3-91.4 %), respectively. Median and 1-year PFS were 10.1 months (95 %CI 4.4-14.4) and 39.7 % (95 %CI 20.3-58.5 %), respectively. No grade 3 + toxicities were observed.

Conclusions

A-RT using the 1.5 T Unity MR Linac resulted in promising LC and OS with no severe toxicity in patients with LAPC despite radiosensitive organs adjacent to the target volumes. Longer follow-up is needed to assess long-term outcomes.

A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging

PURPOSE

Despite recent substantial improvement in autosegmentation using deep learning (DL) methods, labor-intensive and time-consuming slice-by-slice manual editing is often needed, particularly for complex anatomy (eg, abdominal organs). This work aimed to develop a fast, prior knowledge-guided DL semiautomatic segmentation (DL-SAS) method for complex structures on abdominal magnetic resonance imaging (MRI) scans.

METHODS AND MATERIALS

A novel application using contours on an adjacent slice as a prior knowledge informant in a 2-dimensional UNet DL model to guide autosegmentation for a subsequent slice was implemented for DL-SAS. A generalized, instead of organ-specific, DL-SAS model was trained and tested for abdominal organs on T2-weighted MRI scans collected from 75 patients (65 for training and 10 for testing). The DL-SAS model performance was compared with 3 common autocontouring methods (linear interpolation, rigid propagation, and a full 3-dimensional DL autosegmentation model trained with the same training data set) based on various quantitative metrics including the Dice similarity coefficient (DSC) and ratio of acceptable slices (ROA) using paired t tests.

RESULTS

For the 10 testing cases, the DL-SAS model performed best with the slice interval (SI) of 1, resulting in an average DSC of 0.93 ± 0.02, 0.92 ± 0.02, 0.91 ± 0.02, 0.88 ± 0.03, and 0.87 ± 0.02 for the large bowel, stomach, small bowel, duodenum, and pancreas, respectively. The performance decreased with increased SIs from the guidance slice. The DL-SAS method performed significantly better (P < .05) than the other 3 methods. The ROA values were in the range of 48% to 66% for all the organs with an SI of 1 for DL-SAS, higher than those for linear interpolation (31%-57% for an SI of 1) and DL auto-segmentation (16%-51%).

CONCLUSIONS

The developed DL-SAS model segmented complex abdominal structures on MRI with high accuracy and efficiency and may be implemented as an interactive manual contouring tool or a contour editing tool in conjunction with a full autosegmentation process, facilitating fast and accurate segmentation for MRI-guided online adaptive radiation therapy.

Experience With Normal Breathhold Planning Scans for Radiosurgery of Moving Targets With Live Tracking

PURPOSE

Utilization of breathhold scans with live tracking has a long track record of good published outcomes for stereotactic body radiation therapy (SBRT) and is recommended by the manufacturer of the Synchrony tracking system. However, the popularity of four-dimensional computed tomography (4DCT) scans challenges the validity of the breathhold scan with live tracking technique. Although this study is not intended to prove the superiority of either method, we demonstrate the feasibility of using the breathhold scans with a phantom test and clinical examples.

METHODS

A 4DCT of a perfect sphere was scanned at 20 breaths per minute and compared to a 4DCT of a small lung tumor in one patient and a 4DCT of a larger renal tumor in another patient, as well as to fiducial matching in a patient with pancreatic cancer. Normal exhale and normal inhale breathhold CT scans were performed for the pancreatic cancer patient, combined with Synchrony tracking on CyberKnife (Sunnyvale, CA: Accuray) for treatment.

RESULTS

The 4DCT scan of the phantom exhibited considerable apparent deformation, which must be entirely due to imaging artifact since the perfect sphere in the phantom is known to be completely rigid. The 4DCT of the lung and renal tumors in patients had similar apparent deformation. Usually in patients, from 4DCT alone, it is difficult to determine how much was due to deformation and how much was due to artifact. Fiducial positions in the final normal exhale and normal inhale breathhold scans for Synchrony matched each other within 1mm for the pancreatic cancer patient.

CONCLUSION

We demonstrated the feasibility of breathhold scans with Synchrony live tracking, as recommended by the manufacturer. More studies will be needed to determine whether this method is better than using a 4DCT.

History of Technological Advancements towards MR-Linac: The Future of Image-Guided Radiotherapy

Image-guided radiotherapy (IGRT) enables optimal tumor targeting and sparing of organs-at-risk, which ultimately results in improved outcomes for patients. Magnetic resonance imaging (MRI) revolutionized diagnostic imaging with its superior soft tissue contrast, high spatiotemporal resolution, and freedom from ionizing radiation exposure. Over the past few years there has been burgeoning interest in MR-guided radiotherapy (MRgRT) to overcome current challenges in X-ray-based IGRT, including but not limited to, suboptimal soft tissue contrast, lack of efficient daily adaptation, and incremental exposure to ionizing radiation. In this review, we present an overview of the technologic advancements in IGRT that led to MRI-linear accelerator (MRL) integration. Our report is organized in three parts: (1) a historical timeline tracing the origins of radiotherapy and evolution of IGRT, (2) currently available MRL technology, and (3) future directions and aspirations for MRL applications.

Evaluation of an anthropomorphic ion chamber and 3D gel dosimetry head phantom at a 0.35 T MR-linac using separate 1.5 T MR-scanners for gel readout

PURPOSE

To date, no universally accepted technique for the evaluation of the overall dosimetric performance of hybrid integrated magnetic resonance imaging (MR) - linear accelerators (linacs) is available. We report on the suitability and reliability of a novel phantom with modular inserts for combined polymer gel (PG) and ionisation chamber (IC) measurements at a 0.35 T MR-linac.

METHODS

Three 3D-printed, modular head phantoms, based on real patient anatomy, were used for repeated (2 times) PG irradiations of cranial treatment plans on a 0.35 T MR-linac. The PG readout was performed on two 1.5 T diagnostic MR-scanners to reduce scanning time. The PG dose volumes were normalised to the IC dose (normalised dose N1) and to the median planning target volume dose (normalised dose N2). Linearity of the PG dose response was validated and dose profiles, centres of mass (COM) of the 95% isodoses and dose volume histograms (DVH) were compared between planned and measured dose distributions and a 3D gamma analysis was performed.

RESULTS

Dose linearity of the PG was good (R2> 0.99 for all linear fit functions). High agreement was found between planned and measured dose volumes in the dose profiles and DVHs. The largest dose deviation was found in the intermediate dose region (mean dose deviation 0.2Gy; 5.6%). A mean COM offset of 1.2mm indicated high spatial accuracy. Mean 3D gamma passing rates (2%, 2mm) of 83.3% for N1 and 91.6% for N2 dose distributions were determined. When comparing repeated PG measurements to each other, a mean gamma passing rate of 95.7% was found.

CONCLUSION

The new modular phantom was found practical for use at a 0.35 T MR-linac. In contrast to the high dose region, larger mean deviations were found in the mid dose range. The PG measurements showed high reproducibility. The MR-linac performed well in a non-adaptive setting in terms of spatial and dosimetric accuracy.

Development and implementation of an automatic air delineation technique for MRI-guided adaptive radiation therapy

Objective. Auto-delineation of air regions on daily MRI for MR-guided online adaptive radiotherapy (MRgOART) of abdominal tumors is challenging since the air packets occur randomly and their MR intensities can be similar to some other tissue types. This work reports a new method to auto-delineate air regions on MRI. Approach. The proposed method (named DIFF method) consists of (1) generating a combined volume V comb , which is a union of the air-containing organs on a reference MR image offline, (2) transferring V comb from the reference MR to a daily MR via DIR, (3) combining the transferred V comb with a region of high DIR inaccuracy, and (4) applying a threshold to the obtained final combined volume to generate the air volumes. The high DIR inaccuracy region was calculated from the absolute difference between the deformed daily and the reference images. This method was tested on 36 abdominal daily MRI sets acquired from 7 patients on a 1.5 T MR-Linac. The performance of DIFF was compared with alternative auto-air generation methods that (1) does not account for DIR inaccuracies, and (2) uses rigid registration instead of DIR. Main results. The results show that the proposed DIFF method can be fully automated and can be executed within 25 s. The Dice similarity coefficient of manual and DIFF auto-generated air contours was >92% for all cases, while it was 90% for the alternative auto-delineation methods. Dosimetrically, the auto-generated air regions using DIFF resulted in practically identical DVHs as those generated by using manual air contours. Significance. The DIFF method is robust and accurate and can be implemented to automatically consider the inter- and intra- fractional air volume variations during MRgOART for abdominal tumors. The use of DIFF method improves dosimetric accuracy as compared to other methods, especially beneficial for the patients with large daily abdominal air volume variations.

Integrated MRI-guided radiotherapy - opportunities and challenges

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

Estimation of secondary cancer projected risk after partial breast irradiation at the 1.5 T MR-linac

PURPOSE

For patients treated with partial breast irradiation (PBI), potential long-term treatment-related toxicities are important. The 1.5 T magnetic resonance guided linear accelerator (MRL) offers excellent tumor bed visualization and a daily treatment plan adaption possibility, but MRL-specific electron stream and return effects may cause increased dose deposition at air-tissue interfaces. In this study, we aimed to investigate the projected risk of radiation-induced secondary malignancies (RISM) in patients treated with PBI at the 1.5 T MRL.

METHODS

Projected excess absolute risk values (EARs) for the contralateral breast, lungs, thyroid and esophagus were estimated for 11 patients treated with PBI at the MRL and compared to 11 patients treated with PBI and 11 patients treated with whole breast irradiation (WBI) at the conventional linac (CTL). All patients received 40.05 Gy in 15 fractions. For patients treated at the CTL, additional dose due to daily cone beam computed tomography (CBCT) was simulated. The t‑test with Bonferroni correction was used for comparison.

RESULTS

The highest projected risk for a radiation-induced secondary cancer was found for the ipsilateral lung, without significant differences between the groups. A lower contralateral breast EAR was found for MRL-PBI (EAR = 0.89) compared to CTL-PBI (EAR = 1.41, p = 0.01), whereas a lower thyroid EAR for CTL-PBI (EAR = 0.17) compared to MRL-PBI (EAR = 0.33, p = 0.03) and CTL-WBI (EAR = 0.46, p = 0.002) was observed. Nevertheless, when adding the CBCT dose no difference between thyroid EAR for CTL-PBI compared to MRL-PBI was detected.

CONCLUSION

Better breast tissue visualization and the possibility for daily plan adaption make PBI at the 1.5 T MRL particularly attractive. Our simulations suggest that this treatment can be performed without additional projected risk of RISM.

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

Purpose

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

Methods

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

Results

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

Conclusions

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