Tracking moving objects with megavoltage portal imaging: A feasibility studya)

Dense feature-based motion estimation in MV fluoroscopy during dynamic tumor tracking treatment: preliminary study on reduced aperture and partial occlusion handling

Quality assurance solutions to complement available motion compensation technologies are central for their safe routine implementation and success of treatment. This work presents a dense feature-based method for soft-tissue tumor motion estimation in megavoltage (MV) beam's-eye-view (BEV) projections for potential intra-treatment monitoring during dynamic tumor tracking (DTT). Dense sampling and matching principles were employed to track a gridded set of features landmarks (FLs) in MV-BEV projections and estimate tumor motion, capable to overcome reduced field aperture and partial occlusion challenges. The algorithm's performance was evaluated by retrospectively applying it to fluoroscopic sequences acquired at ∼2 frames s^-1 (fps) for a dynamic phantom and two lung stereotactic body radiation therapy (SBRT) patients treated with DTT on the Vero SBRT system. First, a field-specific train image is initialized by sampling the tumor region at, S, pixel intervals on a grid using a representative frame from a stream of query frames. Sampled FLs are locally characterized in the form of descriptor vectors and geometric attributes representing the target. For motion tracking, subsequent query frames are likewise sampled, corresponding feature descriptors determined, and then patch-wise matched to the training set based on their descriptors and geometric relationships. FLs with high correspondence are pruned and used to estimate tumor displacement. In scenarios of partial occlusions, position is estimated from the set of correctly (visible) FLs on past observations. Reconstructed trajectories were benchmarked against ground-truth manual tracking using the root-mean-square (RMS) as a metric of positional accuracy. A total of 19 fluoroscopy sequences were analyzed. This included scenarios of field aperture obstruction during three-dimensional conformal, as well as step-and-shoot intensity modulated radiotherapy (IMRT) delivery assisted with DTT. The algorithm resolved target motion satisfactorily. The RMS was <1.2 mm and <1.8 mm for the phantom and the clinical dataset, respectively. Dense tracking showed promising results to overcome localization challenges at the field penumbra and partial obstruction by multi-leaf collimator (MLC). Motion retrieval was possible in ∼66% of the control points studied. In addition to MLC obstruction, changes in the external/internal breathing dynamics and baseline drifts were a major source of estimation bias. Dense feature-based tracking is a viable alternative. The algorithm is rotation-/scale-invariant and robust to photometric changes. Tracking multiple features may help overcome partial occlusion challenges by the MLC. This in turn opens up new possibilities for motion detection and intra-treatment monitoring during IMRT and potentially VMAT.

Evaluating a potential technique with local optical flow vectors for automatic organ-at-risk (OAR) intrusion detection and avoidance during radiotherapy

Various techniques of deep inspiration breath hold (DIBH) have been used to mitigate the likelihood and risk of exposing the heart, an organ-at-risk (OAR) for unintended radiation during left breast radiotherapy. However, issues of reproducibility of these techniques warrant further investigation into the feasibility of detecting the intrusion of an OAR into the treatment field during intra-fractional treatment delivery. The increase of high-dose, low-fraction radiotherapy treatments makes it important to immediately adapt treatment once an OAR is detected in the treatment field. This proof-of-concept implementation includes an algorithm that detects and tracks the motion at the edges of a treatment field and a control algorithm that adapts the treatment aperture according to the motion detected. In accordance to the AAPM Task-Group (TG-132) report, image registration techniques should be verified with virtual and physical phantoms prior to clinical application. Since most OARs move as a result of respiration-induced motion, we have used a lung phantom to generate images of a generic OAR intruding into a treatment field with known velocity. The phantom was programmed to move with sinusoidal and lung patient tumor motion patterns and the accuracy of intrusion tracking and MLC adaptation were benchmarked with the ground truth-programmed motion of the OAR. The motions were recorded with an electronic portal imaging device (EPID). An optimal cluster size of 9  ×  9 motion vectors was found to provide the smallest average absolute position error of 0.3 mm. A strong linear correlation between the adapted MLC leaves and the actual OAR position was observed. The algorithm had a mean position tracking error of  -0.4  ±  0.3 mm and a precision of 1.1 mm. It is possible to adapt MLC leaves based on the motion detected at the edges of the irradiated field, and it would be feasible to shield an unplanned intrusion of an OAR into the treatment field.

A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging

A novel method was developed to track lung tumor motion in real time during radiation therapy with the purpose to allow target radiation dose escalation while simultaneously reducing the dose to sensitive structures, thereby increasing local control without increasing toxicity. This method analyzes beam’s eye view radiation therapy treatment megavoltage (MV) images with simulated digitally reconstructed radiographs (DRRs) as references. Instead of comparing global DRRs with projection images, this method incorporates a technique that divides the global composite DRR and the corresponding MV projection into sub-images called tiles. Registration is performed independently on tile pairs in order to reduce the effects of global discrepancies due to scattering or imaging modality differences. This algorithm was evaluated by phantom studies while simulated tumors were controlled to move with various patterns in a complex humanoid torso. Approximately 15,000 phantom MV images were acquired at nine gantry angles, with different tumors moving within ranges between 10 and 20 mm. Tumors were successfully identified on every projection with a total maximum/average error of 1.84/0.98 mm. This algorithm was also applied to over 5,000 frames of MV projections acquired during radiation therapy of five lung cancer patients. This tumor-tracking methodology is capable of accurately locating lung tumors during treatment without implanting any internal fiducial markers nor delivering extra imaging radiation doses.

Enhancement of megavoltage electronic portal images for markerless tumor tracking

Purpose

The poor quality of megavoltage (MV) images from electronic portal imaging device (EPID) hinders visual verification of tumor targeting accuracy particularly during markerless tumor tracking. The aim of this study was to investigate the effect of a few representative image processing treatments on visual verification and detection capability of tumors under auto tracking.

Methods

Images of QC‐3 quality phantom, a single patient's setup image, and cine images of two‐lung cancer patients were acquired. Three image processing methods were individually employed to the same original images. For each deblurring, contrast enhancement, and denoising, a total variation deconvolution, contrast‐limited adaptive histogram equalization (CLAHE), and median filter were adopted, respectively. To study the effect of image enhancement on tumor auto‐detection, a tumor tracking algorithm was adopted in which the tumor position was determined as the minimum point of the mean of the sum of squared pixel differences (MSSD) between two images. The detectability and accuracy were compared.

Results

Deblurring of a quality phantom image yielded sharper edges, while the contrast‐enhanced image was more readable with improved structural differentiation. Meanwhile, the denoising operation resulted in noise reduction, however, at the cost of sharpness. Based on comparison of pixel value profiles, contrast enhancement outperformed others in image perception. During the tracking experiment, only contrast enhancement resulted in tumor detection in all images using our tracking algorithm. Deblurring failed to determine the target position in two frames out of a total of 75 images. For original and denoised set, target location was not determined for the same five images. Meanwhile, deblurred image showed increased detection accuracy compared with the original set. The denoised image resulted in decreased accuracy. In the case of contrast‐improved set, the tracking accuracy was nearly maintained as that of the original image.

Conclusions

Considering the effect of each processing on tumor tracking and the visual perception in a limited time, contrast enhancement would be the first consideration to visually verify the tracking accuracy of tumors on MV EPID without sacrificing tumor detectability and detection accuracy.

Tracking tumor boundary in MV-EPID images without implanted markers: A feasibility study

PURPOSE

To develop a markerless tracking algorithm to track the tumor boundary in megavoltage (MV)-electronic portal imaging device (EPID) images for image-guided radiation therapy.

METHODS

A level set method (LSM)-based algorithm is developed to track tumor boundary in EPID image sequences. Given an EPID image sequence, an initial curve is manually specified in the first frame. Driven by a region-scalable energy fitting function, the initial curve automatically evolves toward the tumor boundary and stops on the desired boundary while the energy function reaches its minimum. For the subsequent frames, the tracking algorithm updates the initial curve by using the tracking result in the previous frame and reuses the LSM to detect the tumor boundary in the subsequent frame so that the tracking processing can be continued without user intervention. The tracking algorithm is tested on three image datasets, including a 4-D phantom EPID image sequence, four digitally deformable phantom image sequences with different noise levels, and four clinical EPID image sequences acquired in lung cancer treatment. The tracking accuracy is evaluated based on two metrics: centroid localization error (CLE) and volume overlap index (VOI) between the tracking result and the ground truth.

RESULTS

For the 4-D phantom image sequence, the CLE is 0.23 ± 0.20 mm, and VOI is 95.6% ± 0.2%. For the digital phantom image sequences, the total CLE and VOI are 0.11 ± 0.08 mm and 96.7% ± 0.7%, respectively. In addition, for the clinical EPID image sequences, the proposed algorithm achieves 0.32 ± 0.77 mm in the CLE and 72.1% ± 5.5% in the VOI. These results demonstrate the effectiveness of the authors' proposed method both in tumor localization and boundary tracking in EPID images. In addition, compared with two existing tracking algorithms, the proposed method achieves a higher accuracy in tumor localization.

CONCLUSIONS

In this paper, the authors presented a feasibility study of tracking tumor boundary in EPID images by using a LSM-based algorithm. Experimental results conducted on phantom and clinical EPID images demonstrated the effectiveness of the tracking algorithm for visible tumor target. Compared with previous tracking methods, the authors' algorithm has the potential to improve the tracking accuracy in radiation therapy. In addition, real-time tumor boundary information within the irradiation field will be potentially useful for further applications, such as adaptive beam delivery, dose evaluation.

Megavoltage conebeam CT cine as final verification of treatment plan in lung stereotactic body radiotherapy

INTRODUCTION

To analyse the clinical impact of megavoltage conebeam computed tomography (MV-CBCT) cine on internal target volume (ITV) coverage in lung stereotactic body radiotherapy (SBRT).

METHODS

One hundred and six patients received lung SBRT. All underwent 4D computed tomography simulation followed by treatment via image guided 3D conformal or intensity modulated radiation. Prior to SBRT, all patients underwent MV-CBCT cine, in which raw projections are displayed as beam's-eye-view fluoroscopic series with the planning target volume (PTV) projected onto each image, enabling verification of tumour motion relative to the PTV and assessment of adequacy of treatment margin.

RESULTS

Megavoltage conebeam computed tomography cine was completed 1-2 days prior to SBRT. Four patients (3.8%) had insufficient ITV coverage inferiorly at cine review. All four plans were changed by adding 5 mm on the PTV margin inferiorly. The mean change in PTV volumes was 3.9 cubic centimetres (cc) (range 1.85-6.32 cc). Repeat cine was performed after plan modification to ensure adequate PTV coverage in the modified plans.

CONCLUSIONS

PTV margin was adequate in the majority of patients with this technique. MV-CBCT cine did show insufficient coverage in a small subset of patients. Insufficient PTV margins may be a function of 4D CT simulation inadequacies or deficiencies in visualizing the ITV inferior border in the full-inhale phase. MV-CBCT cine is a valuable tool for final verification of PTV margins.

Automated marker tracking using noisy X-ray images degraded by the treatment beam

This study demonstrates the feasibility of automated marker tracking for the real-time detection of intrafractional target motion using noisy kilovoltage (kV) X-ray images degraded by the megavoltage (MV) treatment beam. The authors previously introduced the in-line imaging geometry, in which the flat-panel detector (FPD) is mounted directly underneath the treatment head of the linear accelerator. They found that the 121 kVp image quality was severely compromised by the 6 MV beam passing through the FPD at the same time. Specific MV-induced artefacts present a considerable challenge for automated marker detection algorithms. For this study, the authors developed a new imaging geometry by re-positioning the FPD and the X-ray tube. This improved the contrast-to-noise-ratio between 40% and 72% at the 1.2 mAs/image exposure setting. The increase in image quality clearly facilitates the quick and stable detection of motion with the aid of a template matching algorithm. The setup was tested with an anthropomorphic lung phantom (including an artificial lung tumour). In the tumour one or three Calypso beacons were embedded to achieve better contrast during MV radiation. For a single beacon, image acquisition and automated marker detection typically took around 76 ± 6 ms. The success rate was found to be highly dependent on imaging dose and gantry angle. To eliminate possible false detections, the authors implemented a training phase prior to treatment beam irradiation and also introduced speed limits for motion between subsequent images.

Suitability of markerless EPID tracking for tumor position verification in gated radiotherapy

PURPOSE

To maximize the benefits of respiratory gated radiotherapy (RGRT) of lung tumors real-time verification of the tumor position is required. This work investigates the feasibility of markerless tracking of lung tumors during beam-on time in electronic portal imaging device (EPID) images of the MV therapeutic beam.

METHODS

EPID movies were acquired at ∼2 fps for seven lung cancer patients with tumor peak-to-peak motion ranges between 7.8 and 17.9 mm (mean: 13.7 mm) undergoing stereotactic body radiotherapy. The external breathing motion of the abdomen was synchronously measured. Both datasets were retrospectively analyzed in PortalTrack, an in-house developed tracking software. The authors define a three-step procedure to run the simulations: (1) gating window definition, (2) gated-beam delivery simulation, and (3) tumor tracking. First, an amplitude threshold level was set on the external signal, defining the onset of beam-on/-off signals. This information was then mapped onto a sequence of EPID images to generate stamps of beam-on/-hold periods throughout the EPID movies in PortalTrack, by obscuring the frames corresponding to beam-off times. Last, tumor motion in the superior-inferior direction was determined on portal images by the tracking algorithm during beam-on time. The residual motion inside the gating window as well as target coverage (TC) and the marginal target displacement (MTD) were used as measures to quantify tumor position variability.

RESULTS

Tumor position monitoring and estimation from beam's-eye-view images during RGRT was possible in 67% of the analyzed beams. For a reference gating window of 5 mm, deviations ranging from 2% to 86% (35% on average) were recorded between the reference and measured residual motion. TC (range: 62%-93%; mean: 77%) losses were correlated with false positives incidence rates resulting mostly from intra-/inter-beam baseline drifts, as well as sudden cycle-to-cycle fluctuations in exhale positions. Both phenomena can lead to considerable deviations (with MTD values up to a maximum of 7.8 mm) from the intended tumor position, and in turn may result in a marginal miss. The difference between tumor traces determined within the gating window against ground truth trajectory maps was 1.1 ± 0.7 mm on average (range: 0.4-2.3 mm).

CONCLUSIONS

In this retrospective analysis of motion data, it is demonstrated that the system is capable of determining tumor positions in the plane perpendicular to the beam direction without the aid of fiducial markers, and may hence be suitable as an online verification tool in RGRT. It may be possible to use the tracking information to enable on-the-fly corrections to intra-/inter-beam variations by adapting the gating window by means of a robotic couch.

Markerless Lung Tumor Motion Tracking by Dynamic Decomposition of X-Ray Image Intensity

We propose a new markerless tracking technique of lung tumor motion by using an X-ray fluoroscopic image sequence for real-time image-guided radiation therapy (IGRT). A core innovation of the new technique is to extract a moving tumor intensity component from the fluoroscopic image intensity. The fluoroscopic intensity is the superimposition of intensity components of all the structures passed through by the X-ray. The tumor can then be extracted by decomposing the fluoroscopic intensity into the tumor intensity component and the others. The decomposition problem for more than two structures is ill posed, but it can be transformed into a well-posed one by temporally accumulating constraints that must be satisfied by the decomposed moving tumor component and the rest of the intensity components. The extracted tumor image can then be used to achieve accurate tumor motion tracking without implanted markers that are widely used in the current tracking techniques. The performance evaluation showed that the extraction error was sufficiently small and the extracted tumor tracking achieved a high and sufficient accuracy less than 1 mm for clinical datasets. These results clearly demonstrate the usefulness of the proposed method for markerless tumor motion tracking.

A time-varying seasonal autoregressive model-based prediction of respiratory motion for tumor following radiotherapy

To achieve a better therapeutic effect and suppress side effects for lung cancer treatments, latency involved in current radiotherapy devices is aimed to be compensated for improving accuracy of continuous (not gating) irradiation to a respiratory moving tumor. A novel prediction method of lung tumor motion is developed for compensating the latency. An essential core of the method is to extract information valuable for the prediction, that is, the periodic nature inherent in respiratory motion. A seasonal autoregressive model useful to represent periodic motion has been extended to take into account the fluctuation of periodic nature in respiratory motion. The extended model estimates the fluctuation by using a correlation-based analysis for adaptation. The prediction performance of the proposed method was evaluated by using data sets of actual tumor motion and compared with those of the state-of-the-art methods. The proposed method demonstrated a high performance within submillimeter accuracy. That is, the average error of 1.0 s ahead predictions was 0.931 ± 0.055 mm. The accuracy achieved by the proposed method was the best among those by the others. The results suggest that the method can compensate the latency with sufficient accuracy for clinical use and contribute to improve the irradiation accuracy to the moving tumor.

Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies

In this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.

Effect of breathing motion in radiotherapy of breast cancer: 4D dose calculation and motion tracking via EPID

PURPOSE

To evaluate the influence of breathing motion in postoperative whole-breast radiotherapy.

PATIENTS AND METHODS

For ten patients with left-sided breast cancer, radiotherapy treatment plans were generated based on conventional three-dimensional computed tomography (3D CT) studies: two techniques (segmented and wedge-based tangential fields) were compared. The influence of breathing motion on the dose to the target and organs at risk (OARs) was evaluated with four-dimensional (4D) dose calculation based on respiration-correlated CTs. Reproducibility of breathing motion was evaluated with electronic portal images (EPID) acquired in cine mode during treatment.

RESULTS

Differences in dose distributions were small between segmented and wedge techniques based on 3D studies. Because of small motion amplitude of the chest in the 4D CT studies (1.8 mm +/- 0.9 mm), target coverage was reduced by < 5% due to breathing motion. Differences between 3D and 4D dose calculation were similar for segmented and wedge techniques. Blurring of the dose distribution in 4D dose calculation resulted in lower doses to the OARs. Analysis of EPID movies proved good reproducibility of breathing motion observed in the 4D CT study.

CONCLUSION

Breathing motion was of minor relevance in postoperative radiotherapy treatment of breast cancer for both segmented and wedge tangential field techniques.

Three-dimensional spatial modelling of the correlation between abdominal motion and lung tumour motion with breathing

The aim of this research was to investigate whether a spatial correlation could be found between an external 3-D respiratory signal and the tumour trajectory. The respiratory signal was obtained by tracking the abdominal movement and the tumour trajectory was obtained by automatically determining the tumour position in a series of portal images. Three different models, based on Systems Identification, are presented to model the correlation using a 1-D respiratory signal, a 3-D respiratory signal and a 3-D respiratory signal together with previously determined tumour positions. Adequate correlation was found for all models in the direction of the tumour movement with standard deviations of 0.89 mm, 0.72 mm and 0.75 mm, respectively, and model fit of Rt2 = 0.19, 0.63 and 0.82, respectively. Increasing the frame rate for the acquisition of portal images from 3 to 15 frames per second improved the standard deviation and model fit. In summary, it is possible to spatially correlate a 3-D respiratory signal with the tumour trajectory using this approach. The models presented provide a framework that can be extended to include more information if required. A 3-D respiratory signal is preferable to a 1-D signal in modelling the tumour motion that is not along the main axis of tumour movement.

Influence of increased target dose inhomogeneity on margins for breathing motion compensation in conformal stereotactic body radiotherapy

Background

Breathing motion should be considered for stereotactic body radiotherapy (SBRT) of lung tumors. Four-dimensional computer tomography (4D-CT) offers detailed information of tumor motion. The aim of this work is to evaluate the influence of inhomogeneous dose distributions in the presence of breathing induced target motion and to calculate margins for motion compensation.

Methods

Based on 4D-CT examinations, the probability density function of pulmonary tumors was generated for ten patients. The time-accumulated dose to the tumor was calculated using one-dimensional (1D) convolution simulations of a 'static' dose distribution and target probability density function (PDF). In analogy to stereotactic body radiotherapy (SBRT), different degrees of dose inhomogeneity were allowed in the target volume: minimum doses of 100% were prescribed to the edge of the target and maximum doses varied between 102% (P102) and 150% (P150). The dose loss due to breathing motion was quantified and margins were added until this loss was completely compensated.

Results

With the time-weighted mean tumor position as the isocentre, a close correlation with a quadratic relationship between the standard deviation of the PDF and the margin size was observed. Increased dose inhomogeneity in the target volume required smaller margins for motion compensation: margins of 2.5 mm, 2.4 mm and 1.3 mm were sufficient for compensation of 11.5 mm motion range and standard deviation of 3.9 mm in P105, P125 and P150, respectively. This effect of smaller margins for increased dose inhomogeneity was observed for all patients. Optimal sparing of the organ-at-risk surrounding the target was achieved for dose prescriptions P105 to P118. The internal target volume concept over-compensated breathing motion with higher than planned doses to the target and increased doses to the surrounding normal tissue.

Conclusion

Treatment planning with inhomogeneous dose distributions in the target volume required smaller margins for compensation of breathing induced target motion with the consequence of lower doses to the surrounding organs-at-risk.

Potential of image-guidance, gating and real-time tracking to improve accuracy in pulmonary stereotactic body radiotherapy

PURPOSE

To evaluate the potential of image-guidance, gating and real-time tumor tracking to improve accuracy in pulmonary stereotactic body radiotherapy (SBRT).

MATERIALS AND METHODS

Safety margins for compensation of inter- and intra-fractional uncertainties of the target position were calculated based on SBRT treatments of 43 patients with pre- and post-treatment cone-beam CT imaging. Safety margins for compensation of breathing motion were evaluated for 17 pulmonary tumors using respiratory correlated CT, model-based segmentation of 4D-CT images and voxel-based dose accumulation; the target in the mid-ventilation position was the reference.

RESULTS

Because of large inter-fractional base-line shifts of the tumor, stereotactic patient positioning and image-guidance based on the bony anatomy required safety margins of 12 mm and 9 mm, respectively. Four-dimensional image-guidance targeting the tumor itself and intra-fractional tumor tracking reduced margins to <5 mm and <3 mm, respectively. Additional safety margins are required to compensate for breathing motion. A quadratic relationship between tumor motion and margins for motion compensation was observed: safety margins of 2.4mm and 6mm were calculated for compensation of 10 mm and 20 mm motion amplitudes in cranio-caudal direction, respectively.

CONCLUSION

Four-dimensional image-guidance with pre-treatment verification of the target position and online correction of errors reduced safety margins most effectively in pulmonary SBRT.

How does CT image noise affect 3D deformable image registration for image-guided radiotherapy planning?

PURPOSE

To measure the sensitivity of deformable image registration to image noise. Deformable image registration can be used to map organ contours and other treatment planning data from one CT to another. These CT studies can be acquired with either conventional fan-beam CT systems or more novel cone-beam CT techniques. However, cone-beam CT images can have higher noise levels than fan-beam CT, which might reduce registration accuracy. We have investigated the effect of image quality differences on the deformable registration of fan-beam CTs and CTs with simulated cone-beam noise.

METHOD

Our study used three CT studies for each of five prostate patients. Each CT was contoured by three experienced radiation oncologists. For each patient, one CT was designated the source image and the other two were target images. A deformable image registration process was used to register each source CT to each target CT and then transfer the manually drawn treatment planning contours from the source CT to the target CTs. The accuracy of the automatically transferred contours (and thus of the deformable registration process) was assessed by comparing them to the manual contours on the target CTs, with the differences evaluated with respect to interobserver variability in the manual contours. Then each of the target CTs was modified to include increased noise characteristic of cone-beam CT and the tests were repeated. Changes in registration accuracy due to increased noise were detected by monitoring changes in the automatically transferred contours.

RESULTS

We found that the additional noise caused no significant loss of registration accuracy at magnitudes that exceeded what would normally be found in an actual cone-beam CT.

SUMMARY

We conclude that noise levels in cone-beam CTs that might reduce manual contouring accuracy do not reduce image registration and automatic contouring accuracy.

Registration of DRRs and portal images for verification of stereotactic body radiotherapy: a feasibility study in lung cancer treatment

Image guidance has become a pre-requisite for hypofractionated radiotherapy where the applied dose per fraction is increased. Particularly in stereotactic body radiotherapy (SBRT) for lung tumours, one has to account for set-up errors and intrafraction tumour motion. In our feasibility study, we compared digitally reconstructed radiographs (DRRs) of lung lesions with MV portal images (PIs) to obtain the displacement of the tumour before irradiation. The verification of the tumour position was performed by rigid intensity based registration and three different merit functions such as the sum of squared pixel intensity differences, normalized cross correlation and normalized mutual information. The registration process then provided a translation vector that defines the displacement of the target in order to align the tumour with the isocentre. To evaluate the registration algorithms, 163 test images were created and subsequently, a lung phantom containing an 8 cm(3) tumour was built. In a further step, the registration process was applied on patient data, containing 38 tumours in 113 fractions. To potentially improve registration outcome, two filter types (histogram equalization and display equalization) were applied and their impact on the registration process was evaluated. Generated test images showed an increase in successful registrations when applying a histogram equalization filter whereas the lung phantom study proved the accuracy of the selected algorithms, i.e. deviations of the calculated translation vector for all test algorithms were below 1 mm. For clinical patient data, successful registrations occurred in about 59% of anterior-posterior (AP) and 46% of lateral projections, respectively. When patients with a clinical target volume smaller than 10 cm(3) were excluded, successful registrations go up to 90% in AP and 50% in lateral projection. In addition, a reliable identification of the tumour position was found to be difficult for clinical target volumes at the periphery of the lung, close to backbone or diaphragm. Moreover, tumour movement during shallow breathing strongly influences image acquisition for patient positioning. Recapitulating, 2D/3D image registration for lung tumours is an attractive alternative compared to conventional CT verification of the tumour position. Nevertheless, size and location of the tumour are limiting parameters for an accurate registration process.

Real-time respiration monitoring using the radiotherapy treatment beam and four-dimensional computed tomography (4DCT)—a conceptual study

Real-time knowledge of intra-fraction motion, such as respiration, is essential for four-dimensional (4D) radiotherapy. Surrogate-based and internal-fiducial-based methods may suffer from one or many drawbacks such as false correlation, being invasive, delivering extra patient radiation, and requiring complicated hardware and software development and implementation. In this paper we develop a simple non-surrogate, non-invasive method to monitor respiratory motion during radiotherapy treatments in real time. This method directly utilizes the treatment beam and thus imposes no additional radiation to the patient. The method requires a pre-treatment 4DCT and a real-time detector system. The method combines off-line processes with on-line processes. The off-line processes include 4DCT imaging and pre-calculating detector signals at each phase of the 4DCT based on the planned fluence map and the detector response function. The on-line processes include measuring detector signal from the treatment beam, and correlating the measured detector signal with the pre-calculated signals. The respiration phase is determined as the position of peak correlation. We tested our method with extensive simulations based on a TomoTherapy machine and a 4DCT of a lung cancer patient. Three types of simulations were implemented to mimic the clinical situations. Each type of simulation used three different TomoTherapy delivery sinograms, each with 800 to 1000 projections, as input fluences. Three arbitrary breathing patterns were simulated and two dose levels, 2 Gy/fraction and 2 cGy/fraction, were used for simulations to study the robustness of this method against detector quantum noise. The algorithm was used to determine the breathing phases and this result was compared with the simulated breathing patterns. For the 2 Gy/fraction simulations, the respiration phases were accurately determined within one phase error in real time for most projections of the treatment, except for a few projections at the start and end of the treatment in which beam intensities were extremely low. At 2 cGy/fraction dose level, the method can still determine the respiration phase very well with less than 10% of projections having more than two phases (approximately 1 s) error. This technique can also be applied in other delivery systems such as orthogonal x-ray systems, although in those cases it would entail the delivery of additional non-treatment radiation.