Tumor Tracking Method Based on a Deformable 4D CT Breathing Motion Model Driven by an External Surface Surrogate

4D-Precise: Learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections

BACKGROUND AND OBJECTIVE

In radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts and erroneously characterizes the averaged breathing motion. Furthermore, conventional 4D-CBCT can only be generated post-hoc using the full sequence of kV projections after the treatment is complete, limiting its utility. Hence, our purpose is to develop a deep-learning motion model for estimating 3D+t CT images from treatment kV projection series.

METHODS

We propose an end-to-end learning-based 3D motion modelling and 4DCT reconstruction model named 4D-Precise, abbreviated from Probabilistic reconstruction of image sequences from CBCT kV projections. The model estimates voxel-wise motion fields and simultaneously reconstructs a 3DCT volume at any arbitrary time point of the input projections by transforming a reference CT volume. Developing a Torch-DRR module, it enables end-to-end training by computing Digitally Reconstructed Radiographs (DRRs) in PyTorch. During training, DRRs with matching projection angles to the input kVs are automatically extracted from reconstructed volumes and their structural dissimilarity to inputs is penalised. We introduced a novel loss function to regulate spatio-temporal motion field variations across the CT scan, leveraging planning 4DCT for prior motion distribution estimation.

RESULTS

The model is trained patient-specifically using three kV scan series, each including over 1200 angular/temporal projections, and tested on three other scan series. Imaging data from five patients are analysed here. Also, the model is validated on a simulated paired 4DCT-DRR dataset created using the Surrogate Parametrised Respiratory Motion Modelling (SuPReMo). The results demonstrate that the reconstructed volumes by 4D-Precise closely resemble the ground-truth volumes in terms of Dice, volume similarity, mean contour distance, and Hausdorff distance, whereas 4D-Precise achieves smoother deformations and fewer negative Jacobian determinants compared to SuPReMo.

CONCLUSIONS

Unlike conventional 4DCT reconstruction techniques that ignore breath inter-cycle motion variations, the proposed model computes both intra-cycle and inter-cycle motions. It represents motion over an extended timeframe, covering several minutes of kV scan series.

MRI-Guided Adaptive Radiation Therapy

Magnetic resonance imaging-guided radiation therapy (MRIgRT) has improved soft tissue contrast over computed tomography (CT) based image-guided RT. Superior visualization of the target and surrounding radiosensitive structures has the potential to improve oncological outcomes partly due to safer dose-escalation and adaptive planning. In this review, we highlight the workflow of adaptive MRIgRT planning, which includes simulation imaging, daily MRI, identifying isocenter shifts, contouring, plan optimization, quality control, and delivery. Increased utilization of MRIgRT will depend on addressing technical limitations of this technology, while addressing treatment efficacy, cost-effectiveness, and workflow training.

A Patient-Specific correspondence model to track tumor location in thorax during radiation therapy

PURPOSE

We present a patient-specific model to estimate tumor location in the thorax during radiation therapy using chest surface displacement as the surrogate signal.

METHODS

Two types of data are used for model construction: Four-dimensional computed tomography (4D-CT) images of the patient and the displacement of two points on the patient's skin on the thoracic area. Principal component analysis is used to fit the correspondence model. This model incorporates the recorded surrogate signals during radiation delivery as input and delivers the 3D trajectory of the tumor as output. We evaluated the accuracy of the proposed model on a respiratory phantom and five lung cancer patients.

RESULTS

For the respiratory phantom, the location of the center of the sphere during treatment was calculated in three directions: Left-Right (LR), Anterior-Posterior (AP) and, Superior-Inferior (SI). The error of localization was less than 1 mm in the LR and AP directions and less than 2 mm in the SI direction. The location of the tumor center for two of the patients, and the location of the apex of the diaphragm for the other three, was calculated in three directions. For all patients, the localization error in the LR and AP directions was less than 1.1 mm for two fractions and the maximum localization error in the SI direction was 6.4 mm.

CONCLUSIONS

This work presents a feasibility study of utilizing surface displacement data to locate the tumor in the thorax during radiation treatment. Future work will validate the model on a larger patient population.

Estimating follow-up CTs from geometric deformations of catheter implants in interstitial breast brachytherapy: A feasibility study using electromagnetic tracking

BACKGROUND

Electromagnetic tracking (EMT) systems have been shown to provide valuable information on the geometry of catheter implants in breast cancer patients undergoing interstitial brachytherapy (iBT). In the context of an extended patient-specific, pre-treatment verification, EMT can play a key role in determining the potential need and, if applicable, the appropriate time for treatment adaptation. To detect dosimetric shortcomings the relative position between catheters, and target volume and critical structures must be known. Since EMT cannot provide the anatomical context and standard imaging techniques such as cone-beam CT are not yet available in most brachytherapy suites, it is not possible to detect anatomic changes on a daily or fraction basis, so the need for adaptive planning cannot be identified.

PURPOSE

The aim of this feasibility study is to develop and evaluate a technique capable of estimating follow-up CTs at any time based on the initial treatment planning CT (PCT) and surrogate information about changes of the implant geometry from an EMT system.

METHODS

A deformation vector field is calculated from two different implant reconstructions acquired in treatment position through EMT, the first immediately after the PCT and the second at another time point during the course of treatment. The calculation is based on discrete displacement vectors of pairs of control and target points. These are extrapolated by means of different radial basis functions in order to cover the entire CT volume. The adequate parameters for the calculation of the deformation field were identified. By warping the PCT according to the deformation field, one obtains an estimated CT (ECT) that reflects the geometric changes. For the proof of concept, ECTs were computed for the time point of the clinical follow-up CT (FCT) that is embedded in the treatment workflow after the fourth fraction.

RESULTS

ECT and clinical FCTs of 20 patients were compared to each other quantitatively in terms of absolute Hounsfield unit differences in the planning target volume (PTV) and in a convex hull (CH) enclosing the catheters. The median differences were 31.2  and 29.5 HU for the CH and the PTV, respectively.

CONCLUSION

The proposed ECT approach was able to approximate the "anatomy of the day" and therefore, in principle, allows a dosimetric appraisal of the treatment plan quality before each fraction. In this way, it can contribute to a more detailed patient-specific quality assurance in iBT of the breast and help to identify the timing for a potential treatment adaptation.

Effect of subject motion and gantry rotation speed on image quality and dose delivery in CT-guided radiotherapy

PURPOSE

To investigate the effects of subject motion and gantry rotation speed on computed tomography (CT) image quality over a range of image acquisition speeds for fan-beam (FB) and cone-beam (CB) CT scanners, and quantify the geometric and dosimetric errors introduced by FB and CB sampling in the context of adaptive radiotherapy.

METHODS

Images of motion phantoms were acquired using four CT scanners with gantry rotation speeds of 0.5 s/rotation (denoted FB-0.5), 1.9 s/rotation (FB-1.9), 16.6 s/rotation (CB-16.6), and 60.0 s/rotation (CB-60.0). A phantom presenting various tissue densities undergoing motion with 4-s period and ranging in amplitude from ±0.5 to ±10.0 mm was used to characterize motion artifacts (streaks), motion blur (edge-spread function, ESF), and geometric inaccuracy (excursion of insert centroids and distortion of known shape). An anthropomorphic abdomen phantom undergoing ±2.5-mm motion with 4-s period was used to simulate an adaptive radiotherapy workflow, and relative geometric and dosimetric errors were compared between scanners.

RESULTS

At ±2.5-mm motion, phantom measurements demonstrated mean ± SD ESF widths of 0.6 ± 0.0, 1.3 ± 0.4, 2.0 ± 1.1, and 2.9 ± 2.0 mm and geometric inaccuracy (excursion) of 2.7 ± 0.4, 4.1 ± 1.2, 2.6 ± 0.7, and 2.0 ± 0.5 mm for the FB-0.5, FB-1.9, CB-16.6, and CB-60.0 scanners, respectively. The results demonstrated nonmonotonic trends with scanner speed for FB and CB geometries. Geometric and dosimetric errors in adaptive radiotherapy plans were largest for the slowest (CB-60.0) scanner and similar for the three faster systems (CB-16.6, FB-1.9, and FB-0.5).

CONCLUSIONS

Clinically standard CB-60.0 demonstrates strong image quality degradation in the presence of subject motion, which is mitigated through faster CBCT or FBCT. Although motion blur is minimized for FB-0.5 and FB-1.9, such systems suffer from increased geometric distortion compared to CB-16.6. Each system reflects tradeoffs in image artifacts and geometric inaccuracies that affect treatment delivery/dosimetric error and should be considered in the design of next-generation CT-guided radiotherapy systems.

Evidence-based region of interest (ROI) definition for surface-guided radiotherapy (SGRT) of abdominal cancers using deep-inspiration breath-hold (DIBH)

To define and evaluate the appropriate abdominal region of interest (ROI) as a surrogate of diaphragm positioning in deep-inspiration breath-hold (DIBH) for surface-guided radiotherapy (SGRT) of abdominal cancers using 3D optical surface imaging (OSI). Six potential abdominal ROIs were evaluated to calculate their correlations with the diaphragm position using 4DCT images of 20 abdominal patients. Twelve points of interest (POIs) were defined (six on the central soft tissue and six on the bilateral ribs) at three superior-inferior levels, and different sub-groups represented different ROIs. ROI-1 was the largest, containing all 12 POIs from the xiphoid to the umbilicus and between the lateral body midlines while ROI-2 had only eight inferior POIs, ROI-3 had six lateral POIs, and ROI-4 had four superior-lateral POIs over the ribs, ROI-5 contained six central and two most inferior-lateral POIs and ROI-6 contained six central and four inferior-lateral POIs. Internally, the right diaphragm dome was used to represent its positions in 4DCT (0% and 50% within the cycle). The Pearson correlation coefficients were calculated between the diaphragm dome and all 12 external POIs individually or grouped as six ROIs. The quality of the abdominal ROIs was evaluated as potential internal surrogates and, therefore, potential ROIs for SGRT DIBH setup. The four most inferior POIs show the highest mean correlation (r = 0.75) with diaphragmatic motion, and the correlation decreases as POIs move superiorly. The mean correlations are the highest for ROIs with little or no rib support: r = 0.67 for ROI-2, r = 0.64 for ROI-5, and r = 0.63 for ROI-6, while lower for ROIs with rib support: ROI-1 has r = 0.60, ROI-3 has r = 0.50, and ROI-4 has only r = 0.28. This study demonstrates that the rectangular/triangular soft-tissue ROI (with little rib support) is an optimal surrogate for body positioning and diaphragmatic motion, even when treating tumors under the rib cage. This evidence-based ROI definition should be utilized when treating abdominal cancers with free-breathing (FB) and/or DIBH setup.

Intrafractional accuracy and efficiency of a surface imaging system for deep inspiration breath hold during ablative gastrointestinal cancer treatment

PURPOSE

Beam gating with deep inspiration breath hold (DIBH) usually depends on some external surrogate to infer internal target movement, and the exact internal movement is unknown. In this study, we tracked internal targets and characterized residual motion during DIBH treatment, guided by a surface imaging system, for gastrointestinal cancer. We also report statistics on treatment time.

METHODS AND MATERIALS

We included 14 gastrointestinal cancer patients treated with surface imaging-guided DIBH volumetrically modulated arc therapy, each with at least one radiopaque marker implanted near or within the target. They were treated in 25, 15, or 10 fractions. Thirteen patients received treatment for pancreatic cancer, and one underwent separate treatments for two liver metastases. The surface imaging system monitored a three-dimensional surface with ± 3 mm translation and ± 3° rotation threshold. During delivery, a kilovolt image was automatically taken every 20° or 40° gantry rotation, and the internal marker was identified from the image. The displacement and residual motion of the markers were calculated. To analyze the treatment efficiency, the treatment time of each fraction was obtained from the imaging and treatment timestamps in the record and verify system.

RESULTS

Although the external surface was monitored and limited to ± 3 mm and ± 3°, significant residual internal target movement was observed in some patients. The range of residual motion was 3-21 mm. The average displacement for this cohort was 0-3 mm. In 19% of the analyzed images, the magnitude of the instantaneous displacement was > 5 mm. The mean treatment time was 17 min with a standard deviation of 4 min.

CONCLUSIONS

Precaution is needed when applying surface image guidance for gastrointestinal cancer treatment. Using it as a solo DIBH technique is discouraged when the correlation between internal anatomy and patient surface is limited. Real-time radiographic verification is critical for safe treatments.

Towards Accurate and Precise Image-Guided Radiotherapy: Clinical Applications of the MR-Linac

Advances in image-guided radiotherapy have brought about improved oncologic outcomes and reduced toxicity. The next generation of image guidance in the form of magnetic resonance imaging (MRI) will improve visualization of tumors and make radiation treatment adaptation possible. In this review, we discuss the role that MRI plays in radiotherapy, with a focus on the integration of MRI with the linear accelerator. The MR linear accelerator (MR-Linac) will provide real-time imaging, help assess motion management, and provide online adaptive therapy. Potential advantages and the current state of these MR-Linacs are highlighted, with a discussion of six different clinical scenarios, leading into a discussion on the future role of these machines in clinical workflows.

Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling

Objective. Real-time imaging is highly desirable in image-guided radiotherapy, as it provides instantaneous knowledge of patients’ anatomy and motion during treatments and enables online treatment adaptation to achieve the highest tumor targeting accuracy. Due to extremely limited acquisition time, only one or few x-ray projections can be acquired for real-time imaging, which poses a substantial challenge to localize the tumor from the scarce projections. For liver radiotherapy, such a challenge is further exacerbated by the diminished contrast between the tumor and the surrounding normal liver tissues. Here, we propose a framework combining graph neural network-based deep learning and biomechanical modeling to track liver tumor in real-time from a single onboard x-ray projection. Approach. Liver tumor tracking is achieved in two steps. First, a deep learning network is developed to predict the liver surface deformation using image features learned from the x-ray projection. Second, the intra-liver deformation is estimated through biomechanical modeling, using the liver surface deformation as the boundary condition to solve tumor motion by finite element analysis. The accuracy of the proposed framework was evaluated using a dataset of 10 patients with liver cancer. Main results. The results show accurate liver surface registration from the graph neural network-based deep learning model, which translates into accurate, fiducial-less liver tumor localization after biomechanical modeling (<1.2 (±1.2) mm average localization error). Significance. The method demonstrates its potentiality towards intra-treatment and real-time 3D liver tumor monitoring and localization. It could be applied to facilitate 4D dose accumulation, multi-leaf collimator tracking and real-time plan adaptation. The method can be adapted to other anatomical sites as well.

Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study

A method for generating fluoroscopic (time-varying) volumetric images using patient-specific motion models derived from four-dimensional cone-beam CT (4D-CBCT) images was developed. 4D-CBCT images acquired immediately prior to treatment have the potential to accurately represent patient anatomy and respiration during treatment. Fluoroscopic 3D image estimation is performed in two steps: (1) deriving motion models and (2) optimization. To derive motion models, every phase in a 4D-CBCT set is registered to a reference phase chosen from the same set using deformable image registration (DIR). Principal components analysis (PCA) is used to reduce the dimensionality of the displacement vector fields (DVFs) resulting from DIR into a few vectors representing organ motion found in the DVFs. The PCA motion models are optimized iteratively by comparing a cone-beam CT (CBCT) projection to a simulated projection computed from both the motion model and a reference 4D-CBCT phase, resulting in a sequence of fluoroscopic 3D images. Patient datasets were used to evaluate the method by estimating the tumor location in the generated images compared to manually defined ground truth positions. Experimental results showed that the average tumor mean absolute error (MAE) along the superior–inferior (SI) direction and the 95th percentile in two patient datasets were 2.29 and 5.79 mm for patient 1, and 1.89 and 4.82 mm for patient 2. This study demonstrated the feasibility of deriving 4D-CBCT-based PCA motion models that have the potential to account for the 3D non-rigid patient motion and localize tumors and other patient anatomical structures on the day of treatment.

Stability and Reliability of Enhanced External-Internal Motion Correlation via Dynamic Phase-Shift Corrections Over 30-min Timeframe for Respiratory-Gated Radiotherapy

To assess the stability of patient-specific phase shifts between external- and internal-respiratory motion waveforms, the reliability of enhanced external–internal correlation with phase-shift correction, and the feasibility of guiding respiratory-gated radiotherapy (RGRT) over 30 min. In this clinical feasibility investigation, external bellows and internal-navigator waveforms were simultaneously and prospectively acquired along with two four-dimensional magnetic resonance imaging (4DMRI) scans (6–15 m each) with 15–20 m intervals in 10 volunteers. A bellows was placed 5 cm inferior to the xiphoid to monitor abdominal motion, and an MR navigator was used to track the diaphragmatic motion. The mean phase-domain (MPD) method was applied, which combines three individual phase-calculating methods: phase-space oval fitting, principal component analysis, and analytic signal analysis, weighted by the reciprocal of their residual errors (RE) excluding outliers (RE >2σ). The time-domain cross-correlation (TCC) analysis was applied for comparison. Dynamic phase-shift correction was performed based on the phase shift detected on the fly within two 10 s moving datasets. Simulating bellows-triggered gating, the median and 95% confidence interval for the navigator's position at beam-on/beam-off and %harm (percentage of beam-on time outside the safety margin) were calculated. Averaged across all subjects, the mean phase shifts are found indistinguishable (p > .05) between scan 1 (55˚ ± 9˚) and scan 2 (59˚ ± 11˚). Using the MPD method the averaged correlation increases from 0.56 ± 0.22 to 0.85 ± 0.11 for scan 1 and from 0.47 ± 0.30 to 0.84 ± 0.08 for scan 2. The TCC correction results in similar results. After phase-shift correction, the number of cases that were suitable for amplitude gating (with <10%harm) increased from 2 to 17 out of 20 cases. A patient-specific, stable phase-shift between the external and internal motions was observed and corrected using the MPD and TCC methods, producing long-lasting enhanced motion correlation over 30m. Phase-shift correction offers a feasible strategy for improving the accuracy of tumor-motion prediction during RGRT.

Automatic liver tumor localization using deep learning-based liver boundary motion estimation and biomechanical modeling (DL-Bio)

PURPOSE

Recently, two-dimensional-to-three-dimensional (2D-3D) deformable registration has been applied to deform liver tumor contours from prior reference images onto estimated cone-beam computed tomography (CBCT) target images to automate on-board tumor localizations. Biomechanical modeling has also been introduced to fine-tune the intra-liver deformation-vector-fields (DVFs) solved by 2D-3D deformable registration, especially at low-contrast regions, using tissue elasticity information and liver boundary DVFs. However, the caudal liver boundary shows low contrast from surrounding tissues in the cone-beam projections, which degrades the accuracy of the intensity-based 2D-3D deformable registration there and results in less accurate boundary conditions for biomechanical modeling. We developed a deep-learning (DL)-based method to optimize the liver boundary DVFs after 2D-3D deformable registration to further improve the accuracy of subsequent biomechanical modeling and liver tumor localization.

METHODS

The DL-based network was built based on the U-Net architecture. The network was trained in a supervised fashion to learn motion correlation between cranial and caudal liver boundaries to optimize the liver boundary DVFs. Inputs of the network had three channels, and each channel featured the 3D DVFs estimated by the 2D-3D deformable registration along one Cartesian direction (x, y, z). To incorporate patient-specific liver boundary information into the DVFs, the DVFs were masked by a liver boundary ring structure generated from the liver contour of the prior reference image. The network outputs were the optimized DVFs along the liver boundary with higher accuracy. From these optimized DVFs, boundary conditions were extracted for biomechanical modeling to further optimize the solution of intra-liver tumor motion. We evaluated the method using 34 liver cancer patient cases, with 24 for training and 10 for testing. We evaluated and compared the performance of three methods: 2D-3D deformable registration, 2D-3D-Bio (2D-3D deformable registration with biomechanical modeling), and DL-Bio (DL model prediction with biomechanical modeling). The tumor localization errors were quantified through calculating the center-of-mass-errors (COMEs), DICE coefficients, and Hausdorff distance between deformed liver tumor contours and manually segmented "gold-standard" contours.

RESULTS

The predicted DVFs by the DL model showed improved accuracy at the liver boundary, which translated into more accurate liver tumor localizations through biomechanical modeling. On a total of 90 evaluated images and tumor contours, the average (± sd) liver tumor COMEs of the 2D-3D, 2D-3D-Bio, and DL-Bio techniques were 4.7 ± 1.9 mm, 2.9 ± 1.0 mm, and 1.7 ± 0.4 mm. The corresponding average (± sd) DICE coefficients were 0.60 ± 0.12, 0.71 ± 0.07, and 0.78 ± 0.03; and the average (± sd) Hausdorff distances were 7.0 ± 2.6 mm, 5.4 ± 1.5 mm, and 4.5 ± 1.3 mm, respectively.

CONCLUSION

DL-Bio solves a general correlation model to improve the accuracy of the DVFs at the liver boundary. With improved boundary conditions, the accuracy of biomechanical modeling can be further increased for accurate intra-liver low-contrast tumor localization.

Rigid and non-rigid motion artifact reduction in X-ray CT using attention module

Motion artifacts are a major factor that can degrade the diagnostic performance of computed tomography (CT) images. In particular, the motion artifacts become considerably more severe when an imaging system requires a long scan time such as in dental CT or cone-beam CT (CBCT) applications, where patients generate rigid and non-rigid motions. To address this problem, we proposed a new real-time technique for motion artifacts reduction that utilizes a deep residual network with an attention module. Our attention module was designed to increase the model capacity by amplifying or attenuating the residual features according to their importance. We trained and evaluated the network by creating four benchmark datasets with rigid motions or with both rigid and non-rigid motions under a step-and-shoot fan-beam CT (FBCT) or a CBCT. Each dataset provided a set of motion-corrupted CT images and their ground-truth CT image pairs. The strong modeling power of the proposed network model allowed us to successfully handle motion artifacts from the two CT systems under various motion scenarios in real-time. As a result, the proposed model demonstrated clear performance benefits. In addition, we compared our model with Wasserstein generative adversarial network (WGAN)-based models and a deep residual network (DRN)-based model, which are one of the most powerful techniques for CT denoising and natural RGB image deblurring, respectively. Based on the extensive analysis and comparisons using four benchmark datasets, we confirmed that our model outperformed the aforementioned competitors. Our benchmark datasets and implementation code are available at https://github.com/youngjun-ko/ct_mar_attention.

New method for measurement of chest surface motion in lung cancer patients: Quantification using a technique of deformable image registration

The purpose of this study was to measure the motion of the chest surface during breath-holding treatment for lung cancer using deformable image registration (DIR). Forty non-small-cell lung cancer patients treated with breath-holding stereotactic body radiation therapy were retrospectively examined. First, intensity-based DIR between 2 breath-holding computed tomography (CT) images was performed. Subsequently, deformation vector field (DVF) for all dimensions (left-right, anterior-posterior, and superior-inferior) was calculated from the result. For the analysis of chest surface, the DVF value of the only chest surface area was extracted after the chest surface was divided into 12 regions of interest (ROI) based on anatomy. Additionally, for the analysis of the correlation with the internal tumor motion, the median value of DVF for each surface ROI and the motion of the center of gravity of the tumor volume were used. It was possible to calculate the motion of chest surface without any outliers for all patients. For the average of 12 surface ROIs, the motion of 3D chest surface was within 2 mm (30 cases), 3 mm (8 cases), and 4 mm (2 cases). There was no correlation between the motion of the chest surface and that of the tumor for all 12 surface ROIs. We proposed a technique to evaluate the surface motion using DIR between multiple CT images. It could be a useful tool to calculate the motion of chest surface.

Recent advanced in Surface Guided Radiation Therapy

The growing acceptance and recognition of Surface Guided Radiation Therapy (SGRT) as a promising imaging technique has supported its recent spread in a large number of radiation oncology facilities. Although this technology is not new, many aspects of it have only recently been exploited. This review focuses on the latest SGRT developments, both in the field of general clinical applications and special techniques.SGRT has a wide range of applications, including patient positioning with real-time feedback, patient monitoring throughout the treatment fraction, and motion management (as beam-gating in free-breathing or deep-inspiration breath-hold). Special radiotherapy modalities such as accelerated partial breast irradiation, particle radiotherapy, and pediatrics are the most recent SGRT developments.The fact that SGRT is nowadays used at various body sites has resulted in the need to adapt SGRT workflows to each body site. Current SGRT applications range from traditional breast irradiation, to thoracic, abdominal, or pelvic tumor sites, and include intracranial localizations.Following the latest SGRT applications and their specifications/requirements, a stricter quality assurance program needs to be ensured. Recent publications highlight the need to adapt quality assurance to the radiotherapy equipment type, SGRT technology, anatomic treatment sites, and clinical workflows, which results in a complex and extensive set of tests.Moreover, this review gives an outlook on the leading research trends. In particular, the potential to use deformable surfaces as motion surrogates, to use SGRT to detect anatomical variations along the treatment course, and to help in the establishment of personalized patient treatment (optimized margins and motion management strategies) are increasingly important research topics. SGRT is also emerging in the field of patient safety and integrates measures to reduce common radiotherapeutic risk events (e.g. facial and treatment accessories recognition).This review covers the latest clinical practices of SGRT and provides an outlook on potential applications of this imaging technique. It is intended to provide guidance for new users during the implementation, while triggering experienced users to further explore SGRT applications.

Quantifying day-to-day variations in 4DCBCT-based PCA motion models

The aim of this paper is to quantify the day-to-day variations of motion models derived from pre-treatment 4-dimensional cone beam CT (4DCBCT) fractions for lung cancer stereotactic body radiotherapy (SBRT) patients. Motion models are built by (1) applying deformable image registration (DIR) on each 4DCBCT image with respect to a reference image from that day, resulting in a set of displacement vector fields (DVFs), and (2) applying principal component analysis (PCA) on the DVFs to obtain principal components representing a motion model. Variations were quantified by comparing the PCA eigenvectors of the motion model built from the first day of treatment to the corresponding eigenvectors of the other motion models built from each successive day of treatment. Three metrics were used to quantify the variations: root mean squared (RMS) difference in the vectors, directional similarity, and an introduced metric called the Euclidean Model Norm (EMN). EMN quantifies the degree to which a motion model derived from the first fraction can represent the motion models of subsequent fractions. Twenty-one 4DCBCT scans from five SBRT patient treatments were used in this retrospective study. Experimental results demonstrated that the first two eigenvectors of motion models across all fractions have smaller RMS (0.00017), larger directional similarity (0.528), and larger EMN (0.678) than the last three eigenvectors (RMS: 0.00025, directional similarity: 0.041, and EMN: 0.212). The study concluded that, while the motion model eigenvectors varied from fraction to fraction, the first few eigenvectors were shown to be more stable across treatment fractions than others. This supports the notion that a pre-treatment motion model built from the first few PCA eigenvectors may remain valid throughout a treatment course. Future work is necessary to quantify how day-to-day variations in these models will affect motion reconstruction accuracy for specific clinical tasks.

Anthropomorphic phantom for deformable lung and liver CT and MR imaging for radiotherapy

In this study, a functioning and ventilated anthropomorphic phantom was further enhanced for the purpose of CT and MR imaging of the lung and liver. A deformable lung, including respiratory tract was 3D printed. Within the lung's inner structures is a solid region shaped from a patient's lung tumour and six nitro-glycerine capsules as reference landmarks. The full internal mesh was coated, and the tumour filled, with polyorganosiloxane based gel. A moulded liver was created with an external casing of silicon filled with polyorganosiloxane gel and flexible plastic internal structures. The liver, fitted to the inferior portion of the right lung, moves along with the lung's ventilation. In the contralateral side, a cavity is designed to host a dosimeter, whose motion is correlated to the lung pressure. A 4DCT of the phantom was performed along with static and 4D T1 weighted MR images. The CT Hounsfield units (HU) for the flexible 3D printed material were -600-100 HU (lung and liver structures), for the polyorganosiloxane gel 30-120 HU (lung coating and liver filling) and for the silicon 650-800 HU (liver casing). The MR image intensity units were 0-40, 210-280 and 80-130, respectively. The maximum range of motion in the 4D imaging for the superior lung was 1-3.5 mm and 3.5-8 mm in the inferior portion. The liver motion was 5.5-8.0 mm at the tip and 5.7-10.0 mm at the dome. No measurable drift in motion was observed over a 2 h session and motion was reproducible over three different sessions for sin²(t), sin⁴(t) and a patient-like breathing curve with the interquartile range of amplitudes for all breathing cycles within 0.5 mm. The addition of features within the lung and of a deformable liver will allow the phantom to be used for imaging studies such as validation of 4DMRI and pseudo CT methods.

3D Measurement of Human Chest and Abdomen Surface Based on 3D Fourier Transform and Time Phase Unwrapping

Monitoring respiratory movements is an effective way to improve radiotherapy treatments of thoracic and abdominal tumors, but the current approach is limited to measuring specific points in the chest and abdomen. In this paper, a dynamic three-dimensional (3D) measurement approach of the human chest and abdomen surface is proposed, which can infer tumor movement more accurately, so the radiotherapy damage to the human body can be reduced. Firstly, color stripe patterns in the RGB color model are projected, then after color correction, the collected stripe image sequences are separated into the three RGB primary color stripe image sequences. Secondly, a fringe projection approach is used to extract the folded phase combined 3D Fourier transform with 3D Gaussian filtering. By the relationship between adjacent fringe images in the time sequence, Gaussian filter parameters with individual characteristics are designed and optimized to improve the accuracy of wrapped phase extraction. In addition, based on the difference between the fractional parts of the folded phase error, one remainder equation can be determined, which is used for time-phase unwrapping. The simulation model and human experiments show that the proposed approach can obtain the 3D image sequences of the chest and abdomen surface in respiratory motion effectively and accurately with strong anti-interference ability.

Tumor motion tracking based on a four-dimensional computed tomography respiratory motion model driven by an ultrasound tracking technique

Background

An ultrasound image tracking algorithm (UITA) was combined with four-dimensional computed tomography (4DCT) to create a real-time tumor motion-conversion model. The real-time position of a lung tumor phantom based on the real-time diaphragm motion trajectories detected by ultrasound imaging in the superior-inferior (SI) and medial-lateral (ML) directions were obtained.

Methods

Three different tumor motion-conversion models were created using a respiratory motion simulation system (RMSS) combined with 4DCT. The tumor tracking error was verified using cone-beam computed tomography (CBCT). The tumor motion-conversion model was produced by using the UITA to monitor the motion trajectories of the diaphragm phantom in the SI direction, and using 4DCT to monitor the motion trajectories of the tumor phantom in the SI and ML directions over the same time period, to obtain parameters for the motion-conversion model such as the tumor center position and the amplitude and phase ratios.

Results

The tumor movement was monitored for 90 s using CBCT to determine the real motion trajectories of the tumor phantom and using ultrasound imaging to simultaneously record the diaphragm movement. The absolute error of the motion trajectories of the real and estimated tumor varied between 0.5 and 2.1 mm in the two directions.

Conclusions

This study has demonstrated the feasibility of using ultrasound imaging to track diaphragmatic motion combined with a 4DCT tumor motion-conversion model to track tumor motion in the SI and ML directions. The proposed method makes tracking a lung tumor feasible in real time, including under different breathing conditions.

Method for a motion model based automated 4D dose calculation

The Vero system can treat intra-fractionally moving tumors with gimbaled dynamic tumor tracking (DTT) by rotating the treatment beam so that it follows the motion of the tumor. However, the changes in the beam geometry and the constant breathing motion of the patient influence the dose applied to the patient. This study aims to perform a full 4D dose reconstruction for thirteen patients treated with DTT at the Vero system at the Universitätsklinikum Erlangen and investigates the temporal resolution required to perform an accurate 4D dose reconstruction. For all patients, a 4DCT was used to train a 4D motion model, which is able to calculate pseudo-CT images for arbitrary breathing phases. A new CT image was calculated for every 100 ms of treatment and a dose calculation was performed according to the current beam geometry (i.e. the rotation of the treatment beam at this moment in time) by rotating according to the momentary beam rotation, which is extracted from log-files. The resulting dose distributions were accumulated on the planning CT and characteristic parameters were extracted and compared. [Formula: see text]-evaluations of dose accumulations with different spatial-temporal resolutions were performed to determine the minimal required resolution. In total 173 700 dose calculations were performed. The accumulated 4D dose distributions show a reduced mean GTV dose of 0.77% compared to the static treatment plan. For some patients larger deviations were observed, especially in the presence of a poor 4DCT quality. The [Formula: see text]-evaluation showed that a temporal resolution of 500 ms is sufficient for an accurate dose reconstruction. If the tumor motion is regarded as well, a spatial-temporal sampling of 1400 ms and 2 mm yields accurate results, which reduces the workload by 84%.

Respiratory motion compensation in interventional liver SPECT using simultaneous fluoroscopic and nuclear imaging

PURPOSE

Quantitative accuracy of the single photon emission computed tomography (SPECT) reconstruction of the pretreatment procedure of liver radioembolization is crucial for dosimetry; visual quality is important for detecting doses deposited outside the planned treatment volume. Quantitative accuracy is limited by respiratory motion. Conventional gating eliminates motion by count rejection but increases noise, which degrades the visual reconstruction quality. Motion compensation using all counts can be performed if the motion signal and motion vector field over time are known. The measurement of the motion signal of a patient currently requires a device (such as a respiratory belt) attached to the patient, which complicates the acquisition. The motion vector field is generally extracted from a previously acquired four-dimensional scan and can differ from the motion in the scan performed during the intervention. The simultaneous acquisition of fluoroscopic and nuclear projections can be used to obtain both the motion vector field and the projections of the corresponding (moving) activity distribution. This eliminates the need for devices attached to the patient and provides an accurate motion vector field for SPECT reconstruction. Our approach to motion compensation would primarily be beneficial for interventional SPECT because the time-critical setting requires fast scans and no inconvenience of an external apparatus. The purpose of this work is to evaluate the performance of the motion compensation approach for interventional liver SPECT by means of simulations.

METHODS

Nuclear and fluoroscopic projections of a realistic digital human phantom with respiratory motion were generated using fast Monte Carlo simulators. Fluoroscopic projections were sampled at 1-5 Hz. Nuclear data were acquired continuously in list mode. The motion signal was extracted from the fluoroscopic projections by calculating the center-of-mass, which was then used to assign each photon to a corresponding motion bin. The fluoroscopic projections were reconstructed per bin and coregistered, resulting in a motion vector field that was used in the SPECT reconstruction. The influence of breathing patterns, fluoroscopic imaging dose, sampling rate, number of bins, and scanning time was studied. In addition, the motion compensation method was compared with conventional gating to evaluate the detectability of spheres with varying uptake ratios.

RESULTS

The liver motion signal was accurately extracted from the fluoroscopic projections, provided the motion was stable in amplitude and the sampling rate was greater than 2 Hz. The minimum total fluoroscopic dose for the proposed method to function in a 5-min scan was 10 µGy. Although conventional gating improved the quantitative reconstruction accuracy, substantial background noise was observed in the short scans because of the limited counts available. The proposed method similarly improved the quantitative accuracy, but generated reconstructions with higher visual quality. The proposed method provided better visualization of low-contrast features than when using gating.

CONCLUSION

The proposed motion compensation method has the potential to improve SPECT reconstruction quality. The method eliminates the need for external devices to measure the motion signal and generates an accurate motion vector field for reconstruction. A minimal increase in the fluoroscopic dose is required to substantially improve the results, paving the way for clinical use.

Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate

We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 s and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internal-external correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.

Reconstruction of a high-quality volumetric image and a respiratory motion model from patient CBCT projections

PURPOSE

To develop and evaluate a method of reconstructing a patient- and treatment day- specific volumetric image and motion model from free-breathing cone-beam projections and respiratory surrogate measurements. This Motion-Compensated Simultaneous Algebraic Reconstruction Technique (MC-SART) generates and uses a motion model derived directly from the cone-beam projections, without requiring prior motion measurements from 4DCT, and can compensate for both inter- and intrabin deformations. The motion model can be used to generate images at arbitrary breathing points, which can be used for estimating volumetric images during treatment delivery.

METHODS

The MC-SART was formulated using simultaneous image reconstruction and motion model estimation. For image reconstruction, projections were first binned according to external surrogate measurements. Projections in each bin were used to reconstruct a set of volumetric images using MC-SART. The motion model was estimated based on deformable image registration between the reconstructed bins, and least squares fitting to model parameters. The model was used to compensate for motion in both projection and backprojection operations in the subsequent image reconstruction iterations. These updated images were then used to update the motion model, and the two steps were alternated between. The final output is a volumetric reference image and a motion model that can be used to generate images at any other time point from surrogate measurements.

RESULTS

A retrospective patient dataset consisting of eight lung cancer patients was used to evaluate the method. The absolute intensity differences in the lung regions compared to ground truth were 50.8 ± 43.9 HU in peak exhale phases (reference) and 80.8 ± 74.0 in peak inhale phases (generated). The 50th percentile of voxel registration error of all voxels in the lung regions with >5 mm amplitude was 1.3 mm. The MC-SART was also applied to measured patient cone-beam projections acquired with a linac-mounted CBCT system. Results from this patient data demonstrate the feasibility of MC-SART and showed qualitative image quality improvements compared to other state-of-the-art algorithms.

CONCLUSION

We have developed a simultaneous image reconstruction and motion model estimation method that uses Cone-beam computed tomography (CBCT) projections and respiratory surrogate measurements to reconstruct a high-quality reference image and motion model of a patient in treatment position. The method provided superior performance in both HU accuracy and positional accuracy compared to other existing methods. The resultant reference image and motion model can be combined with respiratory surrogate measurements to generate volumetric images representing patient anatomy at arbitrary time points.

ELPHA: Dynamically deformable liver phantom for real-time motion-adaptive radiotherapy treatments

PURPOSE

Real-time motion-adaptive radiotherapy of intrahepatic tumors needs to account for motion and deformations of the liver and the target location within. Phantoms representative of anatomical deformations are required to investigate and improve dynamic treatments. A deformable phantom capable of testing motion detection and motion mitigation techniques is presented here.

METHODS

The dynamically dEformable Liver PHAntom (ELPHA) was designed to fulfill three main constraints: First, a reproducibly deformable anatomy is required. Second, the phantom should provide multimodality imaging contrast for motion detection. Third, a time-resolved dosimetry system to measure temporal effects should be provided. An artificial liver with vasculature was casted from soft silicone mixtures. The silicones allow for deformation and radiographic image contrast, while added cellulose provides ultrasonic contrast. An actuator was used for compressing the liver in the inferior direction according to a prescribed respiratory motion trace. Electromagnetic (EM) transponders integrated in ELPHA help provide ground truth motion traces. They were used to quantify the motion reproducibility of the phantom and to validate motion detection based on ultrasound imaging. A two-dimensional ultrasound probe was used to follow the position of the vessels with a template-matching algorithm. This detected vessel motion was compared to the EM transponder signal by calculating the root-mean-square error (RMSE). ELPHA was then used to investigate the dose deposition of dynamic treatment deliveries. Two dosimetry systems, radio-chromic film and plastic scintillation dosimeters (PSD), were integrated in ELPHA. The PSD allow for time-resolved measurement of the delivered dose, which was compared to a time-resolved dose of the treatment planning system. Film and PSD were used to investigate dose delivery to the deforming phantom without motion compensation and with treatment-couch tracking for motion compensation.

RESULTS

ELPHA showed densities of 66 and 45 HU in the liver and the surrounding tissues. A high motion reproducibility with a submillimeter RMSE (<0.32 mm) was measured. The motion of the vasculature detected with ultrasound agreed well with the EM transponder position (RMSE < 1 mm). A time-resolved dosimetry system with a 1 Hz time resolution was achieved with the PSD. The agreement of the planned and measured dose to the PSD decreased with increasing motion amplitude: A dosimetric RMSE of 1.2, 2.1, and 2.7 cGy/s was measured for motion amplitudes of 8, 16, and 24 mm, respectively. With couch tracking as motion compensation, these values decreased to 1.1, 1.4, and 1.4 cGy/s. This is closer to the static situation with 0.7 cGy/s. Film measurements showed that couch tracking was able to compensate for motion with a mean target dose within 5% of the static situation (-5% to +1%), which was higher than in the uncompensated cases (-41% to -1%).

CONCLUSIONS

ELPHA is a deformable liver phantom with high motion reproducibility. It was demonstrated to be suitable for the verification of motion detection and motion mitigation modalities. Based on the multimodality image contrast, a high accuracy of ultrasound based motion detection was shown. With the time-resolved dosimetry system, ELPHA is suitable for performance assessment of real-time motion-adaptive radiotherapy, as was shown exemplary with couch tracking.

Automated fiducial marker detection and fiducial point localization in CT images for lung biopsy image-guided surgery systems

In the lung biopsy image-guided surgery systems, the fiducial markers are used for point-based registration of the patient space to the CT image space. Fiducial marker detection and fiducial point localization in CT images have great influence on the accuracy of registration and guidance. This study proposes a fiducial marker detection approach based on the features of marker image slice sequences and a fiducial point localization approach according to marker projection images, without depending on the priori-knowledge of the marker default parameters provided by the manufacturers. The accuracy of our method was validated based on a CT image dataset of 24 patients. The experimental results showed that all 144 markers of 24 patients were correctly detected, and the fiducial points were localized with the average error of 0.35 mm. In addition, the localization accuracy of the proposed method was improved by an average of 12.5% compared with the accuracy of the previous method using the marker default parameters provided by the manufacturers. Thus, the study demonstrated that the proposed detection and localization methods are accurate and robust, which is quite encouraging to meet the requirement of future clinical applications in the image guided lung biopsy and surgery systems.

Clinical implementations of 4D pencil beam scanned particle therapy: Report on the 4D treatment planning workshop 2016 and 2017

In 2016 and 2017, the 8th and 9th 4D treatment planning workshop took place in Groningen (the Netherlands) and Vienna (Austria), respectively. This annual workshop brings together international experts to discuss research, advances in clinical implementation as well as problems and challenges in 4D treatment planning, mainly in spot scanned proton therapy. In the last two years several aspects like treatment planning, beam delivery, Monte Carlo simulations, motion modeling and monitoring, QA phantoms as well as 4D imaging were thoroughly discussed. This report provides an overview of discussed topics, recent findings and literature review from the last two years. Its main focus is to highlight translation of 4D research into clinical practice and to discuss remaining challenges and pitfalls that still need to be addressed and to be overcome.

Open Source 3D Printed Lung Tumor Movement Simulator for Radiotherapy Quality Assurance

In OECD (Organization for Economic Co-operation and Development) countries, cancer is one of the main causes of death, lung cancer being one of the most aggressive. There are several techniques for the treatment of lung cancer, among which radiotherapy is one of the most effective and least invasive for the patient. However, it has associated difficulties due to the moving target tumor. It is possible to reduce the side effects of radiotherapy by effectively tracking a tumor and reducing target irradiation margins. This paper presents a custom electromechanical system that follows the movement of a lung tumor. For this purpose, a hysteresis loop of human lung movement during breathing was studied to obtain its characteristic movement equation. The system is controlled by an Arduino, steppers motors and a customized 3D printed mechanism to follow the characteristic human breathing, obtaining an accurate trajectory. The developed device helps the verification of individualized radiation treatment plans and permits the improvement of radiotherapy quality assurance procedures.

Optimization of training periods for the estimation model of three-dimensional target positions using an external respiratory surrogate

Background

During therapeutic beam irradiation, an unvisualized three-dimensional (3D) target position should be estimated using an external surrogate with an estimation model. Training periods for the developed model with no additional imaging during beam irradiation were optimized using clinical data.

Methods

Dual-source 4D-CBCT projection data for 20 lung cancer patients were used for validation. Each patient underwent one to three scans. The actual target positions of each scan were equally divided into two equal parts: one for the modeling and the other for the validating session. A quadratic target position estimation equation was constructed during the modeling session. Various training periods for the session—i.e., modeling periods (T_M)—were employed: T_M ∈ {5,10,15,25,35} [s]. First, the equation was used to estimate target positions in the validating session of the same scan (intra-scan estimations). Second, the equation was then used to estimate target positions in the validating session of another temporally different scan (inter-scan estimations). The baseline drift of the surrogate and target between scans was corrected. Various training periods for the baseline drift correction—i.e., correction periods (T_Cs)—were employed: T_C ∈ {5,10,15; T_C ≤ T_M} [s]. Evaluations were conducted with and without the correction. The difference between the actual and estimated target positions was evaluated by the root-mean-square error (RMSE).

Results

The range of mean respiratory period and 3D motion amplitude of the target was 2.4–13.0 s and 2.8–34.2 mm, respectively. On intra-scan estimation, the median 3D RMSE was within 1.5–2.1 mm, supported by previous studies. On inter-scan estimation, median elapsed time between scans was 10.1 min. All T_Ms exhibited 75th percentile 3D RMSEs of 5.0–6.4 mm due to baseline drift of the surrogate and the target. After the correction, those for each T_Ms fell by 1.4–2.3 mm. The median 3D RMSE for both the 10-s T_M and the T_C period was 2.4 mm, which plateaued when the two training periods exceeded 10 s.

Conclusions

A widely-applicable estimation model for the 3D target positions during beam irradiation was developed. The optimal T_M and T_C for the model were both 10 s, to allow for more than one respiratory cycle.

Trial registration

UMIN000014825. Registered: 11 August 2014.

Electronic supplementary material

The online version of this article (10.1186/s13014-018-1019-9) contains supplementary material, which is available to authorized users.

Internal Motion Estimation by Internal-external Motion Modeling for Lung Cancer Radiotherapy

The aim of this study is to develop an internal-external correlation model for internal motion estimation for lung cancer radiotherapy. Deformation vector fields that characterize the internal-external motion are obtained by respectively registering the internal organ meshes and external surface meshes from the 4DCT images via a recently developed local topology preserved non-rigid point matching algorithm. A composite matrix is constructed by combing the estimated internal phasic DVFs with external phasic and directional DVFs. Principle component analysis is then applied to the composite matrix to extract principal motion characteristics, and generate model parameters to correlate the internal-external motion. The proposed model is evaluated on a 4D NURBS-based cardiac-torso (NCAT) synthetic phantom and 4DCT images from five lung cancer patients. For tumor tracking, the center of mass errors of the tracked tumor are 0.8(±0.5)mm/0.8(±0.4)mm for synthetic data, and 1.3(±1.0)mm/1.2(±1.2)mm for patient data in the intra-fraction/inter-fraction tracking, respectively. For lung tracking, the percent errors of the tracked contours are 0.06(±0.02)/0.07(±0.03) for synthetic data, and 0.06(±0.02)/0.06(±0.02) for patient data in the intra-fraction/inter-fraction tracking, respectively. The extensive validations have demonstrated the effectiveness and reliability of the proposed model in motion tracking for both the tumor and the lung in lung cancer radiotherapy.

Examination of a deformable motion model for respiratory movements and 4D dose calculations using different driving surrogates

PURPOSE

The aim of this study was to evaluate a surrogate-driven motion model based on four-dimensional computed tomography that is able to predict CT volumes corresponding to arbitrary respiratory phases. Furthermore, the comparison of three different driving surrogates is examined and the feasibility of using the model for 4D dose re-calculation will be discussed.

METHODS

The study is based on repeated 4DCTs of twenty patients treated for bronchial carcinoma and metastasis. The motion model was estimated from the planning 4DCT through deformable image registration. To predict a certain phase of a follow-up 4DCT, the model considers inter-fractional variations (baseline correction) and intra-fractional respiratory parameters (amplitude and phase) derived from surrogates. The estimated volumes resulting from the model were compared to ground-truth clinical 4DCTs using absolute HU differences in the lung region and landmarks localized using the Scale Invariant Feature Transform. Finally, the γ-index was used to evaluate the dosimetric effects of the intensity differences measured between the estimated and the ground-truth CT volumes.

RESULTS

The results show absolute HU differences between estimated and ground-truth images with median value (± standard deviation) of (61.3 ± 16.7) HU. Median 3D distances, measured on about 400 matching landmarks in each volume, were (2.9 ± 3.0) mm. 3D errors up to 28.2 mm were found for CT images with artifacts or reduced quality. Pass rates for all surrogate approaches were above 98.9% with a γ-criterion of 2%/2 mm.

CONCLUSION

The results depend mainly on the image quality of the initial 4DCT and the deformable image registration. All investigated surrogates can be used to estimate follow-up 4DCT phases, however, uncertainties decrease for volumetric approaches. Application of the model for 4D dose calculations is feasible.

Motion management strategies and technical issues associated with stereotactic body radiotherapy of thoracic and upper abdominal tumors: A review from NRG oncology

The efficacy of stereotactic body radiotherapy (SBRT) has been well demonstrated. However, it presents unique challenges for accurate planning and delivery especially in the lungs and upper abdomen where respiratory motion can be significantly confounding accurate targeting and avoidance of normal tissues. In this paper, we review the current literature on SBRT for lung and upper abdominal tumors with particular emphasis on addressing respiratory motion and its affects. We provide recommendations on strategies to manage motion for different, patient-specific situations. Some of the recommendations will potentially be adopted to guide clinical trial protocols.

Development of a four-axis moving phantom for patient-specific QA of surrogate signal-based tracking IMRT

Purpose:

The purposes of this study were two-fold: first, to develop a four-axis moving phantom for patient-specific quality assurance (QA) in surrogate signal-based dynamic tumor-tracking intensity-modulated radiotherapy (DTT-IMRT), and second, to evaluate the accuracy of the moving phantom and perform patient-specific dosimetric QA of the surrogate signal-based DTT-IMRT.

Methods:

The four-axis moving phantom comprised three orthogonal linear actuators for target motion and a fourth one for surrogate motion. The positional accuracy was verified using four laser displacement gauges under static conditions (±40 mm displacements along each axis) and moving conditions [eight regular sinusoidal and fourth-power-of-sinusoidal patterns with peak-to-peak motion ranges (H) of 10–80 mm and a breathing period (T) of 4 s, and three irregular respiratory patterns with H of 1.4–2.5 mm in the left–right, 7.7–11.6 mm in the superior-inferior, and 3.1–4.2 mm in the anterior–posterior directions for the target motion, and 4.8–14.5 mm in the anterior–posterior direction for the surrogate motion, and T of 3.9–4.9 s]. Furthermore, perpendicularity, defined as the vector angle between any two axes, was measured using an optical measurement system. The reproducibility of the uncertainties in DTT-IMRT was then evaluated. Respiratory motions from 20 patients acquired in advance were reproduced and compared three-dimensionally with the originals. Furthermore, patient-specific dosimetric QAs of DTT-IMRT were performed for ten pancreatic cancer patients. The doses delivered to Gafchromic films under tracking and moving conditions were compared with those delivered under static conditions without dose normalization.

Results:

Positional errors of the moving phantom under static and moving conditions were within 0.05 mm. The perpendicularity of the moving phantom was within 0.2° of 90°. The differences in prediction errors between the original and reproduced respiratory motions were −0.1 ± 0.1 mm for the lateral direction, −0.1 ± 0.2 mm for the superior-inferior direction, and −0.1 ± 0.1 mm for the anterior–posterior direction. The dosimetric accuracy showed significant improvements, of 92.9% ± 4.0% with tracking versus 69.8% ± 7.4% without tracking, in the passing rates of γ with the criterion of 3%/1 mm (p < 0.001). Although the dosimetric accuracy of IMRT without tracking showed a significant negative correlation with the 3D motion range of the target (r = − 0.59, p < 0.05), there was no significant correlation for DTT-IMRT (r = 0.03, p = 0.464).

Conclusions:

The developed four-axis moving phantom had sufficient accuracy to reproduce patient respiratory motions, allowing patient-specific QA of the surrogate signal-based DTT-IMRT under realistic conditions. Although IMRT without tracking decreased the dosimetric accuracy as the target motion increased, the DTT-IMRT achieved high dosimetric accuracy.

Particle therapy of moving targets-the strategies for tumour motion monitoring and moving targets irradiation

Particle therapy of moving targets is still a great challenge. The motion of organs situated in the thorax and abdomen strongly affects the precision of proton and carbon ion radiotherapy. The motion is responsible for not only the dislocation of the tumour but also the alterations in the internal density along the beam path, which influence the range of particle beams. Furthermore, in case of pencil beam scanning, there is an interference between the target movement and dynamic beam delivery. This review presents the strategies for tumour motion monitoring and moving target irradiation in the context of hadron therapy. Methods enabling the direct determination of tumour position (fluoroscopic imaging of implanted radio-opaque fiducial markers, electromagnetic detection of inserted transponders and ultrasonic tumour localization systems) are presented. Attention is also drawn to the techniques which use external surrogate motion for an indirect estimation of target displacement during irradiation. The role of respiratory-correlated CT [four-dimensional CT (4DCT)] in the determination of motion pattern prior to the particle treatment is also considered. An essential part of the article is the review of the main approaches to moving target irradiation in hadron therapy: gating, rescanning (repainting), gated rescanning and tumour tracking. The advantages, drawbacks and development trends of these methods are discussed. The new accelerators, called "cyclinacs", are presented, because their application to particle therapy will allow making a breakthrough in the 4D spot scanning treatment of moving organs.

Autoadaptive motion modelling for MR-based respiratory motion estimation

Respiratory motion poses significant challenges in image-guided interventions. In emerging treatments such as MR-guided HIFU or MR-guided radiotherapy, it may cause significant misalignments between interventional road maps obtained pre-procedure and the anatomy during the treatment, and may affect intra-procedural imaging such as MR-thermometry. Patient specific respiratory motion models provide a solution to this problem. They establish a correspondence between the patient motion and simpler surrogate data which can be acquired easily during the treatment. Patient motion can then be estimated during the treatment by acquiring only the simpler surrogate data. In the majority of classical motion modelling approaches once the correspondence between the surrogate data and the patient motion is established it cannot be changed unless the model is recalibrated. However, breathing patterns are known to significantly change in the time frame of MR-guided interventions. Thus, the classical motion modelling approach may yield inaccurate motion estimations when the relation between the motion and the surrogate data changes over the duration of the treatment and frequent recalibration may not be feasible. We propose a novel methodology for motion modelling which has the ability to automatically adapt to new breathing patterns. This is achieved by choosing the surrogate data in such a way that it can be used to estimate the current motion in 3D as well as to update the motion model. In particular, in this work, we use 2D MR slices from different slice positions to build as well as to apply the motion model. We implemented such an autoadaptive motion model by extending our previous work on manifold alignment. We demonstrate a proof-of-principle of the proposed technique on cardiac gated data of the thorax and evaluate its adaptive behaviour on realistic synthetic data containing two breathing types generated from 6 volunteers, and real data from 4 volunteers. On synthetic data the autoadaptive motion model yielded 21.45% more accurate motion estimations compared to a non-adaptive motion model 10 min after a change in breathing pattern. On real data we demonstrated the method's ability to maintain motion estimation accuracy despite a drift in the respiratory baseline. Due to the cardiac gating of the imaging data, the method is currently limited to one update per heart beat and the calibration requires approximately 12 min of scanning. Furthermore, the method has a prediction latency of 800 ms. These limitations may be overcome in future work by altering the acquisition protocol.

A Feasibility Study of Tumor Motion Estimate With Regional Deformable Registration Method for 4-Dimensional Radiation Therapy of Lung Cancer

This study aims to employ 4-dimensional computed tomography to quantify intrafractional tumor motion for patients with lung cancer to improve target localization in radiation therapy. A multistage regional deformable registration was implemented to calculate the excursion of gross tumor volume (GTV) during a breathing cycle. GTV was initially delineated on 0% phase of 4-dimensional computed tomography manually, and a subregion with 20 mm margin supplemented to GTV was generated with Eclipse treatment planning system (Varian Medical Systems, Palo Alto, California). The structures, together with the 4-dimensional computed tomography set, were exported into an in-house software, with which a 3-stage B-spline deformable registration was carried out to map the subregion and warp GTV contour to other breathing phases. The center of mass of the GTV was computed using the contours, and the tumor motion was appraised as the excursion of the center of mass between 0% phase and other phases. Application of the algorithm to the 10 patients showed that clinically satisfactory outcomes were achievable with a spatial accuracy around 2 mm for GTV contour propagation between adjacent phases and 3 mm between opposite phases. The tumor excursion was determined in the vast range of 1 mm through 1.6 cm, depending on the tumor location and tumor size. Compared to the traditional whole image-based registration, the regional method was found computationally a factor of 5 more efficient. The proposed technique has demonstrated its capability in extracting thoracic tumor motion and should find its application in 4-dimensional radiation therapy in the future to maximally utilize the available spatial-temporal information.

3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models

3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we developed and performed initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and used these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparison to ground truth digital and physical phantom images. The performance of 4DCBCT-based and 4DCT-based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery.

REAL-TIME LUNG TUMOUR MOTION MODELING FOR ADAPTIVE RADIATION THERAPY USING LEGO MINDSTORMS

An anthropomorphic phantom was built using LEGO Mindstorms® and programmed in LabVIEW® for Adaptive Radiation Therapy (ART) purpose, to simulate the processes of breathing in the lung district during treatments. A thoracic cavity is prototyped by means of an 8 ribs apparatus and 2 artificial tumor masses, driven by intelligent brick LINUX® OS CPU. An optical surface tracking system (VisionRT®) and a QUASAR™ phantom allow correlation between physiological, robotic motion and surrogated signal. Patient's breathing phases are acquired instantaneously by InfraRed/UltraSound sensors. Through 4DCT images, tumor center of mass are individuated and tracked during respiration, to link internal–external organs motion. To quantify the degree of divergences due to dynamics organs deformation, a 4D function was obtained and simulated by our phantom. Sinusoidal signals (6, 10, 12, 15 and 17 Breaths per Minute-BPM) were used for evaluating and commissioning, thereby obtained a correlation coefficient (0.90–0.94) between QUASAR and LEGO. Validated on ideal conditions, phantom was tested in clinical practice. Breaths and CT study of 12 patients were analyzed. Fitting of real breath sinograms returned a mean R value of 0.94 (0.83–0.98) with best model performance achieved in signals with respiratory frequency less than 20 BPM. By using LEGO it is possible to reproduce real patients conditions and simulate normal and even abnormal behavior during the course of therapy, allowing spatial motion estimation.

An image-based method to synchronize cone-beam CT and optical surface tracking

The integration of in-room X-ray imaging and optical surface tracking has gained increasing importance in the field of image guided radiotherapy (IGRT). An essential step for this integration consists of temporally synchronizing the acquisition of X-ray projections and surface data. We present an image-based method for the synchronization of cone-beam computed tomography (CBCT) and optical surface systems, which does not require the use of additional hardware. The method is based on optically tracking the motion of a component of the CBCT/gantry unit, which rotates during the acquisition of the CBCT scan. A calibration procedure was implemented to relate the position of the rotating component identified by the optical system with the time elapsed since the beginning of the CBCT scan, thus obtaining the temporal correspondence between the acquisition of X-ray projections and surface data. The accuracy of the proposed synchronization method was evaluated on a motorized moving phantom, performing eight simultaneous acquisitions with an Elekta Synergy CBCT machine and the AlignRT optical device. The median time difference between the sinusoidal peaks of phantom motion signals extracted from the synchronized CBCT and AlignRT systems ranged between -3.1 and 12.9 msec, with a maximum interquartile range of 14.4 msec. The method was also applied to clinical data acquired from seven lung cancer patients, demonstrating the potential of the proposed approach in estimating the individual and daily variations in respiratory parameters and motion correlation of internal and external structures. The presented synchronization method can be particularly useful for tumor tracking applications in extracranial radiation treatments, especially in the field of patient-specific breathing models, based on the correlation between internal tumor motion and external surface surrogates.

Quantification of an external motion surrogate for quality assurance in lung cancer radiation therapy

The purpose of this work was to validate the stability of the end exhale position in deep expiration breath hold (DEBH) technique for quality assurance in stereotactic lung tumor radiation therapy. Furthermore, a motion analysis was performed for 20 patients to evaluate breathing periods and baseline drifts based on an external surrogate. This trajectory was detected using stereo infrared (IR) cameras and reflective body markers. The respiratory waveform showed large interpatient differences in the end exhale position during irradiation up to 18.8 mm compared to the global minimum. This position depends significantly on the tumor volume. Also the baseline drifts, which occur mostly in posterior direction, are affected by the tumor size. Breathing periods, which depend mostly on the patient age, were in a range between 2.4 s and 7.0 s. Fifteen out of 20 patients, who showed a reproducible end exhale position with a deviation of less than 5 mm, might benefit from DEBH due to smaller planning target volumes (PTV) compared to free breathing irradiation and hence sparing of healthy tissue. Patients with larger uncertainties should be treated with more complex motion compensation techniques.