Audiovisual biofeedback improves the correlation between internal/external surrogate motion and lung tumor motion

Super-resolution neural networks improve the spatiotemporal resolution of adaptive MRI-guided radiation therapy

BACKGROUND

Magnetic resonance imaging (MRI) offers superb non-invasive, soft tissue imaging of the human body. However, extensive data sampling requirements severely restrict the spatiotemporal resolution achievable with MRI. This limits the modality's utility in real-time guidance applications, particularly for the rapidly growing MRI-guided radiation therapy approach to cancer treatment. Recent advances in artificial intelligence (AI) could reduce the trade-off between the spatial and the temporal resolution of MRI, thus increasing the clinical utility of the imaging modality.

METHODS

We trained deep learning-based super-resolution neural networks to increase the spatial resolution of real-time MRI. We developed a framework to integrate neural networks directly onto a 1.0 T MRI-linac enabling real-time super-resolution imaging. We integrated this framework with the targeting system of the MRI-linac to demonstrate real-time beam adaptation with super-resolution-based imaging. We tested the integrated system using large publicly available datasets, healthy volunteer imaging, phantom imaging, and beam tracking experiments using bicubic interpolation as a baseline comparison.

RESULTS

Deep learning-based super-resolution increases the spatial resolution of real-time MRI across a variety of experiments, offering measured performance benefits compared to bicubic interpolation. The temporal resolution is not compromised as measured by a real-time adaptation latency experiment. These two effects, an increase in the spatial resolution with a negligible decrease in the temporal resolution, leads to a net increase in the spatiotemporal resolution.

CONCLUSIONS

Deployed super-resolution neural networks can increase the spatiotemporal resolution of real-time MRI. This has applications to domains such as MRI-guided radiation therapy and interventional procedures.

Feasibility of surface-guidance combined with CBCT for intra-fractional breath-hold motion management during Ethos RT

PURPOSE

High-quality CBCT and AI-enhanced adaptive planning techniques allow CBCT-guided stereotactic adaptive radiotherapy (CT-STAR) to account for inter-fractional anatomic changes. Studies of intra-fractional respiratory motion management with a surface imaging solution for CT-STAR have not been fully conducted. We investigated intra-fractional motion management in breath-hold Ethos-based CT-STAR and CT-SBRT (stereotactic body non-adaptive radiotherapy) using optical surface imaging combined with onboard CBCTs.

METHODS

Ten cancer patients with mobile lower lung or upper abdominal malignancies participated in an IRB-approved clinical trial (Phase I) of optical surface image-guided Ethos CT-STAR/SBRT. In the clinical trial, a pre-configured gating window (± 2 mm in AP direction) on optical surface imaging was used for manually triggering intra-fractional CBCT acquisition and treatment beam irradiation during breath-hold (seven patients for the end of exhalation and three patients for the end of inhalation). Two inter-fractional CBCTs at the ends of exhalation and inhalation in each fraction were acquired to verify the primary direction and range of the tumor/imaging-surrogate (donut-shaped fiducial) motion. Intra-fractional CBCTs were used to quantify the residual motion of the tumor/imaging-surrogate within the pre-configured breath-hold window in the AP direction. Fifty fractions of Ethos RT were delivered under surface image-guidance: Thirty-two fractions with CT-STAR (adaptive RT) and 18 fractions with CT-SBRT (non-adaptive RT). The residual motion of the tumor was quantified by determining variations in the tumor centroid position. The dosimetric impact on target coverage was calculated based on the residual motion.

RESULTS

We used 46 fractions for the analysis of intra-fractional residual motion and 43 fractions for the inter-fractional motion analysis due to study constraints. Using the image registration method, 43 pairs of inter-fractional CBCTs and 100 intra-fractional CBCTs attached to dose maps were analyzed. In the motion range study (image registration) from the inter-fractional CBCTs, the primary motion (mean ± std) was 16.6 ± 9.2 mm in the SI direction (magnitude: 26.4 ± 11.3 mm) for the tumors and 15.5 ± 7.3 mm in the AP direction (magnitude: 20.4 ± 7.0 mm) for the imaging-surrogate, respectively. The residual motion of the tumor (image registration) from intra-fractional breath-hold CBCTs was 2.2 ± 2.0 mm for SI, 1.4 ± 1.4 mm for RL, and 1.3 ± 1.3 mm for AP directions (magnitude: 3.5 ± 2.1 mm). The ratio of the actual dose coverage to 99%, 90%, and 50% of the target volume decreased by 0.95 ± 0.11, 0.96 ± 0.10, 0.99 ± 0.05, respectively. The mean percentage of the target volume covered by the prescribed dose decreased by 2.8 ± 4.4%.

CONCLUSION

We demonstrated the intra-fractional motion-managed treatment strategy in breath-hold Ethos CT-STAR/SBRT using optical surface imaging and CBCT. While the controlled residual tumor motion measured at 3.5 mm exceeded the predetermined setup value of 2 mm, it is important to note that this motion still fell within the clinically acceptable range defined by the PTV margin of 5 mm. Nonetheless, additional caution is needed with intra-fractional motion management in breath-hold Ethos CT-STAR/SBRT using optical surface imaging and CBCT.

Neural signals-based respiratory motion tracking: a proof-of-concept study

Objective.Respiratory motion tracking techniques can provide optimal treatment accuracy for thoracoabdominal radiotherapy and robotic surgery. However, conventional imaging-based respiratory motion tracking techniques are time-lagged owing to the system latency of medical linear accelerators and surgical robots. This study aims to investigate the precursor time of respiratory-related neural signals and analyze the potential of neural signals-based respiratory motion tracking.Approach.The neural signals and respiratory motion from eighteen healthy volunteers were acquired simultaneously using a 256-channel scalp electroencephalography (EEG) system. The neural signals were preprocessed using the MNE python package to extract respiratory-related EEG neural signals. Cross-correlation analysis was performed to assess the precursor time and cross-correlation coefficient between respiratory-related EEG neural signals and respiratory motion.Main results.Respiratory-related neural signals that precede the emergence of respiratory motion are detectable via non-invasive EEG. On average, the precursor time of respiratory-related EEG neural signals was 0.68 s. The representative cross-correlation coefficients between EEG neural signals and respiratory motion of the eighteen healthy subjects varied from 0.22 to 0.87.Significance.Our findings suggest that neural signals have the potential to compensate for the system latency of medical linear accelerators and surgical robots. This indicates that neural signals-based respiratory motion tracking is a potential promising solution to respiratory motion and could be useful in thoracoabdominal radiotherapy and robotic surgery.

Assessing target localization accuracy across different soft-tissue matching protocols using end-exhalation breath-hold cone-beam computed tomography in patients with pancreatic cancer

The purpose of this study was to retrospectively assess target localization accuracy across different soft-tissue matching protocols using cone-beam computed tomography (CBCT) in a large sample of patients with pancreatic cancer and to estimate the optimal margin size for each protocol. Fifty-four consecutive patients with pancreatic cancer who underwent 15-fraction volumetric modulated arc therapy under the end-exhalation breath-hold condition were enrolled. Two soft-tissue matching protocols were used according to the resectability classification, including gross tumor volume (GTV) matching for potentially resectable tumors and planning target volume (PTV) matching for borderline resectable or unresectable tumors. The tolerance of the target localization error in both matching protocols was set to 5 mm in any direction. The optimal margin size for each soft-tissue matching protocol was calculated from the systematic and random errors of the inter- and intrafraction positional variations using the van Herk formula. The inter- and intrafraction positional variations of PTV matching were smaller than those of GTV matching. The percentage of target localization errors exceeding 5 mm in the first CBCT scan of each fraction in the superior-inferior direction was 12.6 and 4.8% for GTV and PTV matching, respectively. The optimal margin sizes for GTV and PTV matching were 3.7 and 2.7, 5.4 and 4.1 and 3.9 and 3.0 mm in the anterior-posterior, superior-inferior and left-right directions, respectively. Target localization accuracy in PTV matching was higher than that in GTV matching. By setting the tolerance of the target localization error, treatment can be successful within the planned margin size.

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.

Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor

Objectives: The complexity and specificity of lung tumor motion render it necessary to determine the external and internal correlation individually before applying indirect tumor tracking. However, the correlation cannot be determined from patient respiratory and tumor clinical characteristics before treatment. The purpose of this study is to present a machine learning model for an external/internal correlation prediction that is based on computed tomography (CT) radiomic features. Methods: 4-dimensional computed tomography (4DCT) images of 67 patients were collected retrospectively, and the external/internal correlation of lung tumor was calculated based on Spearman's rank correlation coefficient. Radiomic features were extracted from average intensity projection and the light gradient boosting machine (LightGBM)-based cross-validation (the recursive elimination method) was used for feature selection. The LightGBM framework forecasting models with classification thresholds 0.7, 0.8, and 0.9 are established using stratified 5-fold cross-validation. Model performance was assessed using receiver operating characteristics, sensitivity, and specificity. Results: There were 16, 18, and 13 features selected for models 0.7, 0.8, and 0.9, respectively. Texture features are of great importance in external/internal correlation prediction compared to other features in all models. The sensitivities of the predictions in models 0.7, 0.8, and 0.9 were 0.800 ± 0.126, 0.829 ± 0.140, and 0.864 ± 0.086, respectively. The specificities were 0.771 ± 0.114, 0.936 ± 0.0581, and 0.839 ± 0.101, whereas the area under the curve (AUC) was 0.837, 0.946, and 0.877, respectively. Conclusions: Our findings indicate that radiomics is an effective tool for respiratory motion correlation prediction, which can extract tumor motion characteristics. We proposed a machine learning framework for correlation prediction in the motion management strategy for lung tumor patients.

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.

Application of Continuous Positive Airway Pressure for Thoracic Respiratory Motion Management: An Assessment in a Magnetic Resonance Imaging–Guided Radiation Therapy Environment

Purpose

Patient tolerability of magnetic resonance (MR)–guided radiation treatment delivery is limited by the need for repeated deep inspiratory breath holds (DIBHs). This volunteer study assessed the feasibility of continuous positive airway pressure (CPAP) with and without DIBH for respiratory motion management during radiation treatment with an MR-linear accelerator (MR-linac).

Methods and Materials

MR imaging safety was first addressed by placing the CPAP device in an MR-safe closet and configuring a tube circuit via waveguide to the magnet bore. Reproducibility and linearity of the final configuration were assessed. Six healthy volunteers underwent thoracic imaging in a 0.35T MR-linac, with one free breathing (FB) and 2 DIBH acquisitions being obtained at 5 pressures from 0 to 15 cm-H₂O. Lung and heart volumes and positions were recorded; repeatability was assessed by comparing 2 consecutive DIBH scans. Blinded reviewers graded images for motion artifact using a 3-point grading scale. Participants completed comfort and perception surveys before and after imaging sessions.

Results

Compared with FB alone, FB-10, FB-12, and FB-15 cm H₂O significantly increased lung volumes (+23%, +34%, +44%; all P <.05) and inferiorly displaced the heart (0.86 cm, 0.96 cm, 1.18 cm; all P < . 05). Lung volumes were significantly greater with DIBH-0 cm H₂O compared with FB-15 cm H₂O (+105% vs +44%, P = .01), and DIBH-15 cm H₂O yielded additional volume increase (+131% vs +105%, P = .01). Adding CPAP to DIBH decreased lung volume differences between consecutive breath holds (correlation coefficient 0.97 at 15 cm H₂O vs 0.00 at 0 cm H₂O). The addition of 15 cm H₂O CPAP reduced artifact scores (P = .03) compared with FB; all DIBH images (0-15 cm H₂O) had less artifact (P < .01).

Conclusions

This work demonstrates the feasibility of integrating CPAP in an MR-linac environment in healthy volunteers. Extending this work to a larger patient cohort is warranted to further establish the role of CPAP as an alternative and concurrent approach to DIBH in MR-guided radiation therapy.

Assessment of visual feedback system for reproducibility of voluntary deep inspiration breath hold in left-sided breast radiotherapy

INTRODUCTION

Deep inspiration breath-hold (DIBH) for left-sided breast cancer radiotherapy reduces the dose exposure of the heart and left anterior descending coronary artery. DIBH requires the patient to maintain an adequate breath-hold position during their daily radiotherapy fraction. This study aimed to assess the reproducibility of the breath-hold position by implementing DIBH with visual feedback (VF) system.

METHODS

Forty-three patients who underwent left-sided radiotherapy with DIBH were reviewed. Data from 35 patients who underwent DIBH with VF (VF-DIBH) were compared with data from 8 patients who underwent DIBH with audio coaching (AC-DIBH). Reproducibility during radiotherapy was evaluated using the portal images obtained. Images were acquired daily during the tangential field treatment with DIBH. The distances between the field edge and chest wall at the central beam axis were manually measured on the portal image and digital reconstruction radiograph (DRR). The displacements of the chest wall during radiotherapy were assessed by subtracting the measurement made on the portal image from the DRR measurement. The overall average distances for the VF-DIBH and AC-DIBH cohorts were compared to assess reproducibility. The statistical analysis was performed using Mann-Whitney U tests (p < 0.05).

RESULTS

The mean chest wall displacement (±2 SD) was 0.59 ± 3.64 mm for VF-DIBH and 2.09 ± 4.96 mm for AC-DIBH, respectively. This value differed significantly between the VF-DIBH and AC-DIBH cohorts (p < 0.001).

CONCLUSION

In DIBH radiotherapy, the implementation of VF was confirmed to improve breath-hold position reproducibility, and the utility of VF for DIBH was demonstrated.

Motion management with variable cycle-based respiratory guidance method for carbon-ion pencil beam scanning treatment

PURPOSE

A novel variable cycle-based respiratory guidance method was proposed to synchronize the patterns between patients' breathing and the magnetic excitation of synchrotron under the mode of full-energy depth scanning beam delivery, in order to improve the treatment precision and efficiency for carbon ion therapy.

METHODS

Audio-visual biofeedback system with variable cycle-based respiratory guidance method was developed. We enrolled 6 healthy volunteers and a simulation study of the fixed cycle-based and variable cycle-based respiratory guidance with three treatment fractions was performed. A total of 72 breathing curves were collected for 4D dose calculations with three 4DCT datasets of lung tumor cases. Target dose coverage (D95: the percent dose covering 95% of the target), dose homogeneity (D5-D95), and treatment time were analyzed. The Wilcoxon signed-rank test was used for statistical difference analysis, and p < 0.05 was considered significant.

RESULTS

With the variable cycle-based respiratory guidance method, the breath hold phase of breathing curve could be synchronized with the synchrotron flat-top phase over time. The dose homogeneity was improved by factors of 1.94-2.92 compared to the fixed cycle-based respiratory guidance maneuvers alone or in combination with gating technique. Moreover, the treatment efficiency increased by 11-23%, depending on the duty cycle settings of the gating window.

CONCLUSIONS

The proposed variable cycle-based respiratory guidance method could improve both the treatment efficiency and precision under the mode of the full-energy depth scanning beam delivery for synchrotron-based carbon ion therapy.

Potential of a probabilistic framework for target prediction from surrogate respiratory motion during lung radiotherapy

Purpose.Respiration-induced motion introduces significant positioning uncertainties in radiotherapy treatments for thoracic sites. Accounting for this motion is a non-trivial task commonly addressed with surrogate-based strategies and latency compensating techniques. This study investigates the potential of a new unified probabilistic framework to predict both future target motion in real-time from a surrogate signal and associated uncertainty.Method.A Bayesian approach is developed, based on a Kalman filter theory adapted specifically for surrogate measurements. Breathing motions are collected simultaneously from a lung target, two external surrogates (abdominal and thoracic markers) and an internal surrogate (liver structure) for 9 volunteers during 4 min, in which severe breathing changes occur to assess the robustness of the method. A comparison with an artificial non-linear neural network (NN) is performed, although no confidence interval prediction is provided. A static worst-case scenario and a simple static design are investigated.Results.Although the NN can reduce the prediction errors from thoracic surrogate in some cases, the Bayesian framework outperforms in most cases the NN when using the other surrogates: bias on predictions is reduced by 38% and 16% on average when using respectively the liver and the abdomen for the simple scenario, and by respectively 40% and 31% for the worst-case scenario. The standard deviation of residuals is reduced on average by up to 42%. The Bayesian method is also found to be more robust to increasing latencies. The thoracic marker appears to be less reliable to predict the target position, while the liver shows to be a better surrogate. A statistical test confirms the significance of both observations.Conclusion.The proposed framework predicts both the future target position and the associated uncertainty, which can be valuably used to further assist motion management decisions. Further investigation is required to improve the predictions by using an adaptive version of the proposed framework.

Visually guided inspiration breath-hold facilitated with nasal high flow therapy in locally advanced lung cancer

BACKGROUND AND PURPOSE

Reducing breathing motion in radiotherapy (RT) is an attractive strategy to reduce margins and better spare normal tissues. The objective of this prospective study (NCT03729661) was to investigate the feasibility of irradiation of non-small cell lung cancer (NSCLC) with visually guided moderate deep inspiration breath-hold (IBH) using nasal high-flow therapy (NHFT).

MATERIAL AND METHODS

Locally advanced NSCLC patients undergoing photon RT were given NHFT with heated humidified air (flow: 40 L/min with 80% oxygen) through a nasal cannula. IBH was monitored by optical surface tracking (OST) with visual feedback. At a training session, patients had to hold their breath as long as possible, without and with NHFT. For the daily cone beam CT (CBCT) and RT treatment in IBH, patients were instructed to keep their BH as long as it felt comfortable. OST was used to analyze stability and reproducibility of the BH, and CBCT to analyze daily tumor position. Subjective tolerance was measured with a questionnaire at 3 time points.

RESULTS

Of 10 included patients, 9 were treated with RT. Seven (78%) completed the treatment with NHFT as planned. At the training session, the mean BH length without NHFT was 39 s (range 15-86 s), and with NHFT 78 s (range 29-223 s) (p = .005). NHFT prolonged the BH duration by a mean factor of 2.1 (range 1.1-3.9s). The mean overall stability and reproducibility were within 1 mm. Subjective tolerance was very good with the majority of patients having no or minor discomfort caused by the devices. The mean inter-fraction tumor position variability was 1.8 mm (-1.1-8.1 mm;SD 2.4 mm).

CONCLUSION

NHFT for RT treatment of NSCLC in BH is feasible, well tolerated and significantly increases the breath-hold duration. Visually guided BH with OST is stable and reproducible. We therefore consider this an attractive patient-friendly approach to treat lung cancer patients with RT in BH.

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Effects of age-related breathing characteristics on the performance of four-dimensional magnetic resonance imaging reconstructed by prospective gating for radiation therapy planning

Background and purpose

Four-dimensional magnetic resonance imaging (4D MRI) has advanced recently by incorporating prospective gating, but its performance on pediatric populations has not been investigated. This study aimed to determine the age-related performance of prospective gating, as compared with retrospective sorting.

Materials and methods

Prospectively gated 4D MRI scans were acquired on a motion phantom driven by real respiratory waveforms obtained from 23 pediatric and young adult patients (aged 5–24 years). The correlations between patient-specific breathing characteristics and the performance of 4D MRI were comparatively evaluated against retrospective sorting for the same scan time. For six patients who underwent both 4D MRI and 4D CT, the internal target volumes (ITVs) determined by the two modalities were compared.

Results

Longer scan time and greater sorting error were most highly correlated (P < 0.001) with breathing irregularity and extent of diaphragm motion, but age was not a strong covariate because of interindividual variation. Prospective gating was more accurate than retrospective sorting except for those patients with severe breathing irregularity (peak-to-peak coefficient of variation >30%). The ITVs of 4D MRI and 4D CT were comparable (Dice similarity: >90%) unless the breathing characteristics changed between the two imaging sessions.

Conclusions

For most patients analyzed in this study, prospective gating provided more accurate 4D MRI (95th percentile of deviation: <1.5 mm) than did retrospective sorting within a clinically feasible scan time (median: 5.9 min). The 4D MRI tended to take longer and to give larger sorting errors with deeper and irregular breathers.

Evaluation of the effects of motion mitigation strategies on respiration-induced motion in each pancreatic region using cine-magnetic resonance imaging

Purpose

This study aimed to quantify the respiration‐induced motion in each pancreatic region during motion mitigation strategies and to characterize the correlations between this motion and that of the surrogate signals in cine‐magnetic resonance imaging (MRI). We also aimed to evaluate the effects of these motion mitigation strategies in each pancreatic region.

Methods

Sagittal and coronal two‐dimensional cine‐MR images were obtained in 11 healthy volunteers, eight of whom also underwent imaging with abdominal compression (AC). For each pancreatic region, the magnitude of pancreatic motion with and without motion mitigation and the positional error between the actual and predicted pancreas motion based on surrogate signals were evaluated.

Results

The magnitude of pancreatic motion with and without AC in the left–right (LR) and superior–inferior (SI) directions varied depending on the pancreatic region. In respiratory gating (RG) assessments based on a surrogate signal, although the correlation was reasonable, the positional error was large in the pancreatic tail region. Furthermore, motion mitigation in the anterior‐posterior and SI directions with RG was more effective than was that with AC in the head region.

Conclusions

This study revealed pancreatic region‐dependent variations in respiration‐induced motion and their effects on motion mitigation outcomes during AC or RG. The magnitude of pancreatic motion with or without AC and the magnitude of the positional error with RG varied depending on the pancreatic region. Therefore, during radiation therapy for pancreatic cancer, it is important to consider that the effects of motion mitigation during AC or RG may differ depending on the pancreatic region.

Model-Interpolated Gating for Magnetic Resonance Image-Guided Radiation Therapy

PURPOSE

To develop and validate a technique for radiation therapy gating using slow (≤1 frame per second) magnetic resonance imaging (MRI) and a motion model. Proposed uses of the technique include radiation therapy gating using T2-weighted images and conducting additional imaging studies during gated treatments.

METHODS AND MATERIALS

The technique uses a physiologically guided breathing motion model to interpolate deformed target position between 2-dimensional (2D) MRI images acquired every 1 to 3 seconds. The model is parameterized by a 1-dimensional respiratory bellows surrogate and is continuously updated with the most recently acquired 2D images. A phantom and 8 volunteers were imaged with a 0.35T MRI-guided radiation therapy system. A balanced steady-state free precession sequence with a 2D frame rate of 3 frames per second was used to evaluate the technique. The accuracy and beam-on positive predictive value (PPV) of the model-based gating decisions were evaluated using the gating decisions derived from imaging as a ground truth. A T2-weighted gating offline proof-of-concept study using a half-Fourier, single-shot, turbo-spin echo sequence is reported.

RESULTS

Model-interpolated gating accuracy, beam-on PPV, and median absolute distances between model and image-tracked target centroids were, on average, 98.3%, 98.4%, and 0.33 mm, respectively, in the balanced steady-state free precession phantom studies and 93.7%, 92.1%, and 0.86 mm, respectively, in the volunteer studies. T2 model-interpolated gating in 6 volunteers yielded an average accuracy and PPV of 94.3% and 92.5%, respectively, and the mean absolute median distance between modeled and imaged target centroids was 0.86 mm.

CONCLUSIONS

This work demonstrates the concept of model-interpolated gating for MRI-guided radiation therapy. The technique was found to be potentially sufficiently accurate for clinical use. Further development is needed to accommodate out-of-plane motion and the use of an internal MR-based respiratory surrogate.