A novel technique for markerless, self-sorted 4D-CBCT: feasibility study

A review on 4D cone-beam CT (4D-CBCT) in radiation therapy: Technical advances and clinical applications

Cone-beam CT (CBCT) is the most commonly used onboard imaging technique for target localization in radiation therapy. Conventional 3D CBCT acquires x-ray cone-beam projections at multiple angles around the patient to reconstruct 3D images of the patient in the treatment room. However, despite its wide usage, 3D CBCT is limited in imaging disease sites affected by respiratory motions or other dynamic changes within the body, as it lacks time-resolved information. To overcome this limitation, 4D-CBCT was developed to incorporate a time dimension in the imaging to account for the patient's motion during the acquisitions. For example, respiration-correlated 4D-CBCT divides the breathing cycles into different phase bins and reconstructs 3D images for each phase bin, ultimately generating a complete set of 4D images. 4D-CBCT is valuable for localizing tumors in the thoracic and abdominal regions where the localization accuracy is affected by respiratory motions. This is especially important for hypofractionated stereotactic body radiation therapy (SBRT), which delivers much higher fractional doses in fewer fractions than conventional fractionated treatments. Nonetheless, 4D-CBCT does face certain limitations, including long scanning times, high imaging doses, and compromised image quality due to the necessity of acquiring sufficient x-ray projections for each respiratory phase. In order to address these challenges, numerous methods have been developed to achieve fast, low-dose, and high-quality 4D-CBCT. This paper aims to review the technical developments surrounding 4D-CBCT comprehensively. It will explore conventional algorithms and recent deep learning-based approaches, delving into their capabilities and limitations. Additionally, the paper will discuss the potential clinical applications of 4D-CBCT and outline a future roadmap, highlighting areas for further research and development. Through this exploration, the readers will better understand 4D-CBCT's capabilities and potential to enhance radiation therapy.

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning

A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images. The MSD-GAN is trained using simulated CBCT datasets from patient planning CT images. The enhanced images are further used to estimate the deformable vector field (DVF) among breathing phases using a deformable image registration method. The estimated DVF is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction approach to generate 4D CBCT images. The proposed MSD-GAN is compared with U-Net on the performance of image enhancement. Results show that the proposed method significantly outperforms the total variation regularization-based iterative reconstruction approach and the method using only MSD-GAN to enhance original phase-sorted images in simulation and patient studies on 4D reconstruction quality. The MSD-GAN also shows higher accuracy than the U-Net. The proposed method enables a practical way for 4D-CBCT imaging from a single routine scan in upper abdominal cancer treatment including liver and pancreatic tumors.

View-sharing for 4D magnetic resonance imaging with randomized projection-encoding enables improvements of respiratory motion imaging for treatment planning in abdominothoracic radiotherapy

Background and Purpose

The accuracy and precision of radiation therapy are dependent on the characterization of organ-at-risk and target motion. This work aims to demonstrate a 4D magnetic resonance imaging (MRI) method for improving spatial and temporal resolution in respiratory motion imaging for treatment planning in abdominothoracic radiotherapy.

Materials and Methods

The spatial and temporal resolution of phase-resolved respiratory imaging is improved by considering a novel sampling function based on quasi-random projection-encoding and peripheral k-space view-sharing. The respiratory signal is determined directly from k-space, obviating the need for an external surrogate marker. The average breathing curve is used to optimize spatial resolution and temporal blurring by limiting the extent of data sharing in the Fourier domain. Improvements in image quality are characterized by evaluating changes in signal-to-noise ratio (SNR), resolution, target detection, and level of artifact. The method is validated in simulations, in a dynamic phantom, and in-vivo imaging.

Results

Sharing of high-frequency k-space data, driven by the average breathing curve, improves spatial resolution and reduces artifacts. Although equal sharing of k-space data improves resolution and SNR in stationary features, phases with large temporal changes accumulate significant artifacts due to averaging of high frequency features. In the absence of view-sharing, no averaging and detection artifacts are observed while spatial resolution is degraded.

Conclusions

The use of a quasi-random sampling function, with view-sharing driven by the average breathing curve, provides a feasible method for self-navigated 4D-MRI at improved spatial resolution.

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.

Real-Time Respiratory Tumor Motion Prediction Based on a Temporal Convolutional Neural Network: Prediction Model Development Study

Background

The dynamic tracking of tumors with radiation beams in radiation therapy requires the prediction of real-time target locations prior to beam delivery, as treatment involving radiation beams and gating tracking results in time latency.

Objective

In this study, a deep learning model that was based on a temporal convolutional neural network was developed to predict internal target locations by using multiple external markers.

Methods

Respiratory signals from 69 treatment fractions of 21 patients with cancer who were treated with the CyberKnife Synchrony device (Accuray Incorporated) were used to train and test the model. The reported model’s performance was evaluated by comparing the model to a long short-term memory model in terms of the root mean square errors (RMSEs) of real and predicted respiratory signals. The effect of the number of external markers was also investigated.

Results

The average RMSEs of predicted (ahead time=400 ms) respiratory motion in the superior-inferior, anterior-posterior, and left-right directions and in 3D space were 0.49 mm, 0.28 mm, 0.25 mm, and 0.67 mm, respectively.

Conclusions

The experiment results demonstrated that the temporal convolutional neural network–based respiratory prediction model could predict respiratory signals with submillimeter accuracy.

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

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

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network

PURPOSE

To design a convolutional recurrent neural network (CRNN) that calculates three-dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruction of 4D-CBCT images.

METHOD

Under an IRB-approved clinical lung protocol, kilovoltage (kV) projections of the setup CBCT were collected in free-breathing. Concurrently, an electromagnetic signal-guided system recorded motion traces of three transponders implanted in or near the tumor. Convolutional recurrent neural network was designed to utilize a convolutional neural network (CNN) for extracting relevant features of the kV projections around the tumor, followed by a recurrent neural network for analyzing the temporal patterns of the moving features. Convolutional recurrent neural network was trained on the simultaneously collected kV projections and motion traces, subsequently utilized to calculate motion traces solely based on the continuous feed of kV projections. To enhance performance, CRNN was also facilitated by frequent calibrations (e.g., at 10° gantry rotation intervals) derived from cross-correlation-based registrations between kV projections and templates created from the planning 4DCT. Convolutional recurrent neural network was validated on a leave-one-out strategy using data from 11 lung patients, including 5500 kV images. The root-mean-square error between the CRNN and motion traces was calculated to evaluate the localization accuracy.

RESULT

Three-dimensional displacement around the simulation position shown in the Calypso traces was 3.4 ± 1.7 mm. Using motion traces as ground truth, the 3D localization error of CRNN with calibrations was 1.3 ± 1.4 mm. CRNN had a success rate of 86 ± 8% in determining whether the motion was within a 3D displacement window of 2 mm. The latency was 20 ms when CRNN ran on a high-performance computer cluster.

CONCLUSIONS

CRNN is able to provide accurate localization of lung tumors with aid from frequent recalibrations using the conventional cross-correlation-based registration approach, and has the potential to remove reliance on the implanted fiducials.

Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning

4-Dimensional cone-beam computed tomography (4D-CBCT) offers several key advantages over conventional 3D-CBCT in moving target localization/delineation, structure de-blurring, target motion tracking, treatment dose accumulation and adaptive radiation therapy. However, the use of the 4D-CBCT in current radiation therapy practices has been limited, mostly due to its sub-optimal image quality from limited angular sampling of cone-beam projections. In this study, we summarized the recent developments of 4D-CBCT reconstruction techniques for image quality improvement, and introduced our developments of a new 4D-CBCT reconstruction technique which features simultaneous motion estimation and image reconstruction (SMEIR). Based on the original SMEIR scheme, biomechanical modeling-guided SMEIR (SMEIR-Bio) was introduced to further improve the reconstruction accuracy of fine details in lung 4D-CBCTs. To improve the efficiency of reconstruction, we recently developed a U-net-based deformation-vector-field (DVF) optimization technique to leverage a population-based deep learning scheme to improve the accuracy of intra-lung DVFs (SMEIR-Unet), without explicit biomechanical modeling. Details of each of the SMEIR, SMEIR-Bio and SMEIR-Unet techniques were included in this study, along with the corresponding results comparing the reconstruction accuracy in terms of CBCT images and the DVFs. We also discussed the application prospects of the SMEIR-type techniques in image-guided radiation therapy and adaptive radiation therapy, and presented potential schemes on future developments to achieve faster and more accurate 4D-CBCT imaging.

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.

Dosimetric evaluation of 4D-CBCT reconstructed by Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for carbon ion therapy of lung cancer

PURPOSE

Motion management is critical for the efficacy of carbon ion therapy for moving targets such as lung tumors. We evaluated the feasibility of using four-dimensional cone beam computed tomography (4D-CBCT) reconstructed by Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for dose calculation and accumulation in carbon ion treatment of lung cancer.

METHODS

Motion-compensated 4D-CBCT images were reconstructed with the SMEIR algorithm to capture the most updated anatomy and motion with an updated interphase motion model on the treatment day. Projections of all CBCT phases were simulated from the planning 4D-CT by the ray tracing technique. Treatment planning and dose calculation were performed with a GPU-based Monte Carlo dose calculation software for carbon ion therapy. The treatment plan was optimized on the average computed tomography (CT) to obtain optimal intensity of the carbon ions. From the optimized plan, dose distributions on individual phases of 4D-CT and 4D-CBCT were calculated by the Monte Carlo-based dose engine. Dose accumulation was performed on 4D-CBCT images using deformable vector fields (DVF) generated by SMEIR. The accumulated planning target volume (PTV) dose based on 4D-CBCT was then compared to the accumulated dose calculated on 4D-CT, where the DVFs between different phases were obtained by the demons deformable registration algorithm.

RESULTS

Dose value histograms (DVH) as well as absolute deviations of the maximum dose ( Δ D max ), mean dose ( Δ D mean ), and dose coverage metrics ( Δ V 95 % and Δ V 100 % ) for PTV were quantitatively evaluated for the two sets of plans. Good agreement was found between the 4D-CT and 4D-CBCT-based PTV-DVH curves. The average values of Δ D max , Δ D mean , Δ V 95 % , and Δ V 100 % calculated between the 4D-CT and SMEIR-4D-CBCT-based plans were 1.91 % , 3.55 % , 2.12%, and 1.15 % , respectively, for the PTVs of ten patient case studies.

CONCLUSIONS

Based on these results, SMEIR-reconstructed 4D-CBCTs can potentially be used for motion estimation, dose evaluation, and adaptive treatment planning in lung cancer carbon ion therapy.

Design and validation of a MV/kV imaging-based markerless tracking system for assessing real-time lung tumor motion

PURPOSE

Localizing lung tumors during treatment delivery is critical for managing respiratory motion, ensuring tumor coverage, and reducing toxicities. The purpose of this project is to develop a real-time system that performs markerless tracking of lung tumors using simultaneously acquired MV and kV images during radiotherapy of lung cancer with volumetric modulated arc therapy.

METHOD

Continuous MV/kV images were simultaneously acquired during dose delivery. In the subsequent analysis, a gantry angle-specific region of interest was defined according to the treatment aperture. After removing imaging artifacts, processed MV/kV images were directly registered to the corresponding daily setup cone-beam CT (CBCT) projections that served as reference images. The registration objective function consisted of a sum of normalized cross-correlation, weighted by the contrast-to-noise ratio of each MV and kV image. The calculated 3D shifts of the tumor were corrected by the displacements between the CBCT projections and the planning respiratory correlated CT (RCCT) to generate motion traces referred to a specific respiratory phase. The accuracy of the algorithm was evaluated on both anthropomorphic phantom and patient studies. The phantom consisted of localizing a 3D printed tumor, embedded in a thorax phantom, in an arc delivery. In an IRB-approved study, data were obtained from VMAT treatments of two lung cancer patients with three electromagnetic (Calypso) beacon transponders implanted in airways near the lung tumor.

RESULT

In the phantom study, the root mean square error (RMSE) between the registered and actual (programmed couch movement) target position was 1.2 mm measured by the MV/kV imaging system, which was smaller compared to the MV or kV alone, of 4.1 and 1.3 mm, respectively. In the patient study, the mean and standard deviation discrepancy between electromagnetic-based tumor position and the MV/KV-markerless approach was -0.2 ± 0.6 mm, 0.2 ± 1.0 mm, and -1.2 ± 1.5 mm along the superior-inferior, anterior-posterior, and left-right directions, respectively; resulting in a 3D displacement discrepancy of 2.0 ± 1.1 mm. Poor contrast around the tumor was the main contribution to registration uncertainties.

CONCLUSION

The combined MV/kV imaging system can provide real-time 3D localization of lung tumor, with comparable accuracy to the electromagnetic-based system when features of tumors are detectable. Careful design of a registration algorithm and a VMAT plan that maximizes the tumor visibility are key elements for a successful MV/KV localization strategy.

Two methods for reducing moving metal artifacts in cone-beam CT

PURPOSE

In image-guided radiation therapy, fiducial markers or clips are often used to determine the position of the tumor. These markers lead to streak artifacts in cone-beam CT (CBCT) scans. Standard inpainting-based metal artifact reduction (MAR) methods fail to remove these artifacts in cases of large motion. We propose two methods to effectively reduce artifacts caused by moving metal inserts.

METHODS

The first method (MMAR) utilizes a coarse metal segmentation in the image domain and a refined segmentation in the rawdata domain. After an initial reconstruction, metal is segmented and forward projected giving a coarse metal mask in the rawdata domain. Inside the coarse mask, metal is segmented by utilizing a 2D Sobel filter. Metal is removed by linear interpolation in the refined metal mask. The second method (MoCoMAR) utilizes a motion compensation (MoCo) algorithm [Med Phys. 2013;40:101913] that provides us with a motion-free volume (3D) or with a time series of motion-free volumes (4D). We then apply the normalized metal artifact reduction (NMAR) [Med Phys. 2010;37:5482-5493] to these MoCo volumes. Both methods were applied to three CBCT data sets of patients with metal inserts in the thorax or abdomen region and a 4D thorax simulation. The results were compared to volumes corrected by a standard MAR1 [Radiology. 1987;164:576-577].

RESULTS

MMAR and MoCoMAR were able to remove all artifacts caused by moving metal inserts for the patients and the simulation. Both new methods outperformed the standard MAR1, which was only able to remove artifacts caused by metal inserts with little or no motion.

CONCLUSIONS

In this work, two new methods to remove artifacts caused by moving metal inserts are introduced. Both methods showed good results for a simulation and three patients. While the first method (MMAR) works without any prior knowledge, the second method (MoCoMAR) requires a respiratory signal for the MoCo step and is computationally more demanding and gives no benefit over MMAR, unless MoCo images are desired.

Fast 4D cone-beam CT from 60 s acquisitions

Background & purpose

Four dimensional Cone beam CT (CBCT) has many potential benefits for radiotherapy but suffers from poor image quality, long acquisition times, and/or long reconstruction times. In this work we present a fast iterative reconstruction algorithm for 4D reconstruction of fast acquisition cone beam CT, as well as a new method for temporal regularization and compare to state of the art methods for 4D CBCT.

Materials & methods

Regularization parameters for the iterative algorithms were found automatically via computer optimization on 60 s acquisitions using the XCAT phantom. Nineteen lung cancer patients were scanned with 60 s arcs using the onboard image on a Varian trilogy linear accelerator. Images were reconstructed using an accelerated ordered subset algorithm. A frequency based temporal regularization algorithm was developed and compared to the McKinnon-Bates algorithm, 4D total variation and prior images compressed sensing (PICCS).

Results

All reconstructions were completed in 60 s or less. The proposed method provided a structural similarity of 0.915, compared with 0.786 for the classic McKinnon-bates method. For the patient study, it provided fewer image artefacts than PICCS, and better spatial resolution than 4D TV.

Conclusion

Four dimensional iterative CBCT reconstruction was done in less than 60 s, demonstrating the clinical feasibility. The frequency based method outperformed 4D total variation and PICCS on the simulated data, and for patients allowed for tumor location based on 60 s acquisitions, even for slowly breathing patients. It should thus be suitable for routine clinical use.

Application of the 4-D XCAT Phantoms in Biomedical Imaging and Beyond

The four-dimensional (4-D) eXtended CArdiac-Torso (XCAT) series of phantoms was developed to provide accurate computerized models of the human anatomy and physiology. The XCAT series encompasses a vast population of phantoms of varying ages from newborn to adult, each including parameterized models for the cardiac and respiratory motions. With great flexibility in the XCAT's design, any number of body sizes, different anatomies, cardiac or respiratory motions or patterns, patient positions and orientations, and spatial resolutions can be simulated. As such, the XCAT phantoms are gaining a wide use in biomedical imaging research. There they can provide a virtual patient base from which to quantitatively evaluate and improve imaging instrumentation, data acquisition, techniques, and image reconstruction and processing methods which can lead to improved image quality and more accurate clinical diagnoses. The phantoms have also found great use in radiation dosimetry, radiation therapy, medical device design, and even the security and defense industry. This review paper highlights some specific areas in which the XCAT phantoms have found use within biomedical imaging and other fields. From these examples, we illustrate the increasingly important role that computerized phantoms and computer simulation are playing in the research community.

Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network

To improve the prediction accuracy of respiratory signals using adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for gated treatment of moving target in radiation therapy. The respiratory signals acquired using a real-time position management (RPM) device from 138 previous 4DCT scans were retrospectively used in this study. The ADMLP-NN was composed of several artificial neural networks (ANNs) which were used as weaker predictors to compose a stronger predictor. The respiratory signal was initially smoothed using a Savitzky-Golay finite impulse response smoothing filter (S-G filter). Then, several similar multi-layer perceptron neural networks (MLP-NNs) were configured to estimate future respiratory signal position from its previous positions. Finally, an adaptive boosting (Adaboost) decision algorithm was used to set weights for each MLP-NN based on the sample prediction error of each MLP-NN. Two prediction methods, MLP-NN and ADMLP-NN (MLP-NN plus adaptive boosting), were evaluated by calculating correlation coefficient and root-mean-square-error between true and predicted signals. For predicting 500 ms ahead of prediction, average correlation coefficients were improved from 0.83 (MLP-NN method) to 0.89 (ADMLP-NN method). The average of root-mean-square-error (relative unit) for 500 ms ahead of prediction using ADMLP-NN were reduced by 27.9%, compared to those using MLP-NN. The preliminary results demonstrate that the ADMLP-NN respiratory prediction method is more accurate than the MLP-NN method and can improve the respiration prediction accuracy.

A Novel Method of Cone Beam CT Projection Binning Based on Image Registration

Accurate sorting of beam projections is important in 4D cone beam computed tomography (4D CBCT) to improve the quality of the reconstructed 4D CBCT image by removing motion-induced artifacts. We propose image registration-based projection binning (IRPB), a novel marker-less binning method for 4D CBCT projections, which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing tissue feature point trajectories. We conducted experiments with two phantom and six patient datasets, including both regular and irregular respirations. In experiments, we compared the performance of the proposed IRPB, Amsterdam Shroud method (AS), Fourier transform-based method (FT), and local intensity feature tracking method (LIFT). The results showed that the average absolute phase shift of IRPB was 3.74 projections and 0.48 projections less than that of FT and LIFT, respectively. AS lost the most breathing cycles in the respiration extraction for the five patient datasets, so we could not compare the average absolute phase shift between IRPB and AS. Based on the peak signal-to-noise ratio (PSNR) of the reconstructed 4D CBCT images, IRPB had 5.08, 1.05, and 2.90 dB larger PSNR than AS, FT, and LIFT, respectively. The average Structure SIMilarity Index (SSIM) of the 4D CBCT image reconstructed by IRPB, AS, and LIFT were 0.87, 0.74, 0.84, and 0.70, respectively. These results demonstrated that IRPB has superior performance to the other standard methods.

Clinical Study of Orthogonal-View Phase-Matched Digital Tomosynthesis for Lung Tumor Localization

Background and Purpose:

Compared to cone-beam computed tomography, digital tomosynthesis imaging has the benefits of shorter scanning time, less imaging dose, and better mechanical clearance for tumor localization in radiation therapy. However, for lung tumors, the localization accuracy of the conventional digital tomosynthesis technique is affected by the lack of depth information and the existence of lung tumor motion. This study investigates the clinical feasibility of using an orthogonal-view phase-matched digital tomosynthesis technique to improve the accuracy of lung tumor localization.

Materials and Methods:

The proposed orthogonal-view phase-matched digital tomosynthesis technique benefits from 2 major features: (1) it acquires orthogonal-view projections to improve the depth information in reconstructed digital tomosynthesis images and (2) it applies respiratory phase-matching to incorporate patient motion information into the synthesized reference digital tomosynthesis sets, which helps to improve the localization accuracy of moving lung tumors. A retrospective study enrolling 14 patients was performed to evaluate the accuracy of the orthogonal-view phase-matched digital tomosynthesis technique. Phantom studies were also performed using an anthropomorphic phantom to investigate the feasibility of using intratreatment aggregated kV and beams’ eye view cine MV projections for orthogonal-view phase-matched digital tomosynthesis imaging. The localization accuracy of the orthogonal-view phase-matched digital tomosynthesis technique was compared to that of the single-view digital tomosynthesis techniques and the digital tomosynthesis techniques without phase-matching.

Results:

The orthogonal-view phase-matched digital tomosynthesis technique outperforms the other digital tomosynthesis techniques in tumor localization accuracy for both the patient study and the phantom study. For the patient study, the orthogonal-view phase-matched digital tomosynthesis technique localizes the tumor to an average (± standard deviation) error of 1.8 (0.7) mm for a 30° total scan angle. For the phantom study using aggregated kV–MV projections, the orthogonal-view phase-matched digital tomosynthesis localizes the tumor to an average error within 1 mm for varying magnitudes of scan angles.

Conclusion:

The pilot clinical study shows that the orthogonal-view phase-matched digital tomosynthesis technique enables fast and accurate localization of moving lung tumors.

Estimating 4D-CBCT from prior information and extremely limited angle projections using structural PCA and weighted free-form deformation for lung radiotherapy

PURPOSE

To investigate the feasibility of using structural-based principal component analysis (PCA) motion-modeling and weighted free-form deformation to estimate on-board 4D-CBCT using prior information and extremely limited angle projections for potential 4D target verification of lung radiotherapy.

METHODS

A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In the previous method, each phase of the 4D-CBCT was generated by deforming a prior CT volume. The DFM was solved by a motion model extracted by a global PCA and free-form deformation (GMM-FD) technique, using a data fidelity constraint and deformation energy minimization. In this study, a new structural PCA method was developed to build a structural motion model (SMM) by accounting for potential relative motion pattern changes between different anatomical structures from simulation to treatment. The motion model extracted from planning 4DCT was divided into two structures: tumor and body excluding tumor, and the parameters of both structures were optimized together. Weighted free-form deformation (WFD) was employed afterwards to introduce flexibility in adjusting the weightings of different structures in the data fidelity constraint based on clinical interests. XCAT (computerized patient model) simulation with a 30 mm diameter lesion was simulated with various anatomical and respiratory changes from planning 4D-CT to on-board volume to evaluate the method. The estimation accuracy was evaluated by the volume percent difference (VPD)/center-of-mass-shift (COMS) between lesions in the estimated and "ground-truth" on-board 4D-CBCT. Different on-board projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy. The method was also evaluated against three lung patients.

RESULTS

The SMM-WFD method achieved substantially better accuracy than the GMM-FD method for CBCT estimation using extremely small scan angles or projections. Using orthogonal 15° scanning angles, the VPD/COMS were 3.47 ± 2.94% and 0.23 ± 0.22 mm for SMM-WFD and 25.23 ± 19.01% and 2.58 ± 2.54 mm for GMM-FD among all eight XCAT scenarios. Compared to GMM-FD, SMM-WFD was more robust against reduction of the scanning angles down to orthogonal 10° with VPD/COMS of 6.21 ± 5.61% and 0.39 ± 0.49 mm, and more robust against reduction of projection numbers down to only 8 projections in total for both orthogonal-view 30° and orthogonal-view 15° scan angles. SMM-WFD method was also more robust than the GMM-FD method against increasing levels of noise in the projection images. Additionally, the SMM-WFD technique provided better tumor estimation for all three lung patients compared to the GMM-FD technique.

CONCLUSION

Compared to the GMM-FD technique, the SMM-WFD technique can substantially improve the 4D-CBCT estimation accuracy using extremely small scan angles and low number of projections to provide fast low dose 4D target verification.

Simultaneous 4D-CBCT reconstruction with sliding motion constraint

PURPOSE

Current approaches using deformable vector field (DVF) for motion-compensated 4D-cone beam CT (CBCT) reconstruction typically utilize an isotropically smoothed DVF between different respiration phases. Such isotropically smoothed DVF does not work well if sliding motion exists between neighboring organs. This study investigated an anisotropic motion modeling scheme by extracting organ boundary local motions (e.g., sliding) and incorporated them into 4D-CBCT reconstruction to optimize the motion modeling and reconstruction methods.

METHODS

Initially, a modified simultaneous algebraic reconstruction technique (mSART) was applied to reconstruct high quality reference phase CBCT using all phase projections. The initial DVFs were precalculated and subsequently updated to achieve the optimized solution. During the DVF update, sliding motion estimation was performed by matching the measured projections to the forward projection of the deformed reference phase CBCT. In this process, each moving organ boundary was first segmented. The normal vectors of the boundary DVF were then extracted and incorporated for further DVF optimization. The regularization term in the objective function adaptively regularizes the DVF by (1) isotopically smoothing the DVF within each organ; (2) smoothing the DVF at boundary along the normal direction; and (3) leaving the tangent direction of boundary DVF unsmoothed (i.e., allowing for sliding motion). A nonlinear conjugate gradient optimizer was used. The algorithm was validated on a digital cubic tube phantom with sliding motion, nonuniform rotational B-spline based cardiac-torso (NCAT) phantom, and two anonymized patient data. The relative reconstruction error (RE), the motion trajectory's root mean square error (RMSE) together with its maximum error (MaxE), and the Dice coefficient of the lung boundary were calculated to evaluate the algorithm performance.

RESULTS

For the cubic tube and NCAT phantom tests, the REs are 10.2% and 7.4% with sliding motion compensation, compared to 13.4% and 8.9% without sliding modeling. The motion trajectory's RMSE and MaxE for NCAT phantom tests are 0.5 and 0.8 mm with sliding motion constraint compared to 3.5 and 7.3 mm without sliding motion modeling. The Dice coefficients for both NCAT phantom and the patients show a consistent trend that sliding motion constraint achieves better similarity for segmented lung boundary compared with the ground truth or patient reference.

CONCLUSIONS

A sliding motion-compensated 4D-CBCT reconstruction and the motion modeling scheme was developed. Both phantom and patient study demonstrated the improved accuracy and motion modeling accuracy in reconstructed 4D-CBCT.

Markerless Four-Dimensional-Cone Beam Computed Tomography Projection-Phase Sorting Using Prior Knowledge and Patient Motion Modeling: A Feasibility Study

AIM

During cancer radiotherapy treatment, on-board four-dimensional-cone beam computed tomography (4D-CBCT) provides important patient 4D volumetric information for tumor target verification. Reconstruction of 4D-CBCT images requires sorting of acquired projections into different respiratory phases. Traditional phase sorting methods are either based on external surrogates, which might miscorrelate with internal structures; or on 2D internal structures, which require specific organ presence or slow gantry rotations. The aim of this study is to investigate the feasibility of a 3D motion modeling-based method for markerless 4D-CBCT projection-phase sorting.

METHODS

Patient 4D-CT images acquired during simulation are used as prior images. Principal component analysis (PCA) is used to extract three major respiratory deformation patterns. On-board patient image volume is considered as a deformation of the prior CT at the end-expiration phase. Coefficients of the principal deformation patterns are solved for each on-board projection by matching it with the digitally reconstructed radiograph (DRR) of the deformed prior CT. The primary PCA coefficients are used for the projection-phase sorting.

RESULTS

PCA coefficients solved in nine digital phantoms (XCATs) showed the same pattern as the breathing motions in both the anteroposterior and superoinferior directions. The mean phase sorting differences were below 2% and percentages of phase difference < 10% were 100% for all the nine XCAT phantoms. Five lung cancer patient results showed mean phase difference ranging from 1.62% to 2.23%. The percentage of projections within 10% phase difference ranged from 98.4% to 100% and those within 5% phase difference ranged from 88.9% to 99.8%.

CONCLUSION

The study demonstrated the feasibility of using PCA coefficients for 4D-CBCT projection-phase sorting. High sorting accuracy in both digital phantoms and patient cases was achieved. This method provides an accurate and robust tool for automatic 4D-CBCT projection sorting using 3D motion modeling without the need of external surrogate or internal markers.

Extraction of Respiratory Signal Based on Image Clustering and Intensity Parameters at Radiotherapy with External Beam: A Comparative Study

BACKGROUND

Since tumors located in thorax region of body mainly move due to respiration, in the modern radiotherapy, there have been many attempts such as; external markers, strain gage and spirometer represent for monitoring patients' breathing signal. With the advent of fluoroscopy technique, indirect methods were proposed as an alternative approach to extract patients' breathing signals.

MATERIALS AND METHODS

The purpose of this study is to extract respiratory signals using two available methods based on clustering and intensity strategies on medical image dataset of XCAT phantom.

RESULTS

For testing and evaluation methods, correlation coefficient, standard division, amplitude ratio and different phases are utilized. Phantom study showed excellent match between correlation coefficient, standard division, amplitude ratio and different phase. Both techniques segmenting medical images are robust due to their inherent mathematical properties. Using clustering strategy, lung region borders are remarkably extracted regarding intensity-based method. This may also affect the amount of amplitude signal.

CONCLUSION

To evaluate the performance of these methods, results are compared with slice body volume (SBV) method. Moreover, all methods have shown the same correlation coefficient of 99%, but at different amplitude ratio and different phase. In SBV method, standard division and different phase are better than clustering and intensity methods with SDR=4.71 mm, and SDL=4.12 mm and average different phase 1.47 %, but amplitude ration of clustering method is significantly more remarkable than SBV and intensity methods.

Lung diaphragm tracking in CBCT images using spatio-temporal MRF

In EBRT in order to monitor the intra fraction motion of thoracic and abdominal tumors, one of the standard approaches is to use the lung diaphragm apex as an internal marker. However, tracking the position of the apex from image based observations is a challenging problem, as it undergoes both position and shape variation. The purpose of this paper is to propose an alternative method for tracking the ipsi-lateral hemidiaphragm apex (IHDA) position on Cone Beam Computed Tomography (CBCT) projection images. A hierarchical method is proposed to track the IHDA position across the frames. The diaphragm state is modeled as a spatio-temporal Markov Random Field (MRF). The likelihood function is derived from the votes based on 4D-Hough space. The optimal state of the diaphragm is obtained by solving the associated energy minimization problem using graph-cuts. A heterogeneous GPU implementation is provided for the method using CUDA framework and the performance is compared with that of CPU implementation. The method was tested using 15 clinical CBCT images. The results demonstrate that the MRF formulation outperforms the full search method in terms of accuracy. The GPU based heterogeneous implementation of the proposed algorithm takes about 25s, which is 16% improvement over the existing benchmark. The proposed MRF formulation considers all the possible combinations from the 4D-Hough space and therefore results in better tracking accuracy. The GPU based implementation exploits the inherent parallelism in our algorithm to accelerate the performance thereby increasing the viability of the approach for clinical use.

[Impact of the Infrared Monitor Signal Pattern on Accuracy of Target Imaging in 4-dimensional Cone-beam Computed Tomography]

To realize the high precision radiotherapy, localized radiation field of the moving target is very important, and visualization of a temporal location of the target can help to improve the accuracy of the target localization. However, conditions of the breathing and the patient's own motion differ from the situation of the treatment planning. Therefore, positions of the tumor are affected by these changes. In this study, we implemented a method to reconstruct target motions obtained with the 4D CBCT using the sorted projection data according to the phase and displacement of the extracorporeal infrared monitor signal, and evaluated the proposed method with a moving phantom. In this method, motion cycles and positions of the marker were sorted to reconstruct the image, and evaluated the image quality affected by changes in the cycle, phase, and positions of the marker. As a result, we realized the visualization of the moving target using the sorted projection data according to the infrared monitor signal. This method was based on the projection binning, in which the signal of the infrared monitor was surrogate of the tumor motion. Thus, further major efforts are needed to ensure the accuracy of the infrared monitor signal.

Robust breathing signal extraction from cone beam CT projections based on adaptive and global optimization techniques

We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.

Internal respiratory surrogate in multislice 4D CT using a combination of Fourier transform and anatomical features

PURPOSE

The purpose of this study was to develop a novel algorithm to create a robust internal respiratory signal (IRS) for retrospective sorting of four-dimensional (4D) computed tomography (CT) images.

METHODS

The proposed algorithm combines information from the Fourier transform of the CT images and from internal anatomical features to form the IRS. The algorithm first extracts potential respiratory signals from low-frequency components in the Fourier space and selected anatomical features in the image space. A clustering algorithm then constructs groups of potential respiratory signals with similar temporal oscillation patterns. The clustered group with the largest number of similar signals is chosen to form the final IRS. To evaluate the performance of the proposed algorithm, the IRS was computed and compared with the external respiratory signal from the real-time position management (RPM) system on 80 patients.

RESULTS

In 72 (90%) of the 4D CT data sets tested, the IRS computed by the authors' proposed algorithm matched with the RPM signal based on their normalized cross correlation. For these data sets with matching respiratory signals, the average difference between the end inspiration times (Δtins) in the IRS and RPM signal was 0.11 s, and only 2.1% of Δtins were more than 0.5 s apart. In the eight (10%) 4D CT data sets in which the IRS and the RPM signal did not match, the average Δtins was 0.73 s in the nonmatching couch positions, and 35.4% of them had a Δtins greater than 0.5 s. At couch positions in which IRS did not match the RPM signal, a correlation-based metric indicated poorer matching of neighboring couch positions in the RPM-sorted images. This implied that, when IRS did not match the RPM signal, the images sorted using the IRS showed fewer artifacts than the clinical images sorted using the RPM signal.

CONCLUSIONS

The authors' proposed algorithm can generate robust IRSs that can be used for retrospective sorting of 4D CT data. The algorithm is completely automatic and requires very little processing time. The algorithm is cost efficient and can be easily adopted for everyday clinical use.

Preliminary clinical evaluation of a 4D-CBCT estimation technique using prior information and limited-angle projections

BACKGROUND AND PURPOSE

A technique has been previously reported to estimate high-quality 4D-CBCT using prior information and limited-angle projections. This study is to investigate its clinical feasibility through both phantom and patient studies.

MATERIALS AND METHODS

The new technique used to estimate 4D-CBCT is called MMFD-NCC. It is based on the previously reported motion modeling and free-form deformation (MMFD) method, with the introduction of normalized-cross-correlation (NCC) as a new similarity metric. The clinical feasibility of this technique was evaluated by assessing the accuracy of estimated anatomical structures in comparison to those in the 'ground-truth' reference 4D-CBCTs, using data obtained from a physical phantom and three lung cancer patients. Both volume percentage error (VPE) and center-of-mass error (COME) of the estimated tumor volume were used as the evaluation metrics.

RESULTS

The average VPE/COME of the tumor in the prior image was 257.1%/10.1 mm for the phantom study and 55.6%/3.8 mm for the patient study. Using only orthogonal-view 30° projections, the MMFD-NCC has reduced the corresponding values to 7.7%/1.2 mm and 9.6%/1.1 mm, respectively.

CONCLUSION

The MMFD-NCC technique is able to estimate 4D-CBCT images with geometrical accuracy of the tumor within 10% VPE and 2 mm COME, which can be used to improve the localization accuracy of radiotherapy.

Evaluation of intrinsic respiratory signal determination methods for 4D CBCT adapted for mice

PURPOSE

4D CT imaging in mice is important in a variety of areas including studies of lung function and tumor motion. A necessary step in 4D imaging is obtaining a respiratory signal, which can be done through an external system or intrinsically through the projection images. A number of methods have been developed that can successfully determine the respiratory signal from cone-beam projection images of humans, however only a few have been utilized in a preclinical setting and most of these rely on step-and-shoot style imaging. The purpose of this work is to assess and make adaptions of several successful methods developed for humans for an image-guided preclinical radiation therapy system.

METHODS

Respiratory signals were determined from the projection images of free-breathing mice scanned on the X-RAD system using four methods: the so-called Amsterdam shroud method, a method based on the phase of the Fourier transform, a pixel intensity method, and a center of mass method. The Amsterdam shroud method was modified so the sharp inspiration peaks associated with anesthetized mouse breathing could be detected. Respiratory signals were used to sort projections into phase bins and 4D images were reconstructed. Error and standard deviation in the assignment of phase bins for the four methods compared to a manual method considered to be ground truth were calculated for a range of region of interest (ROI) sizes. Qualitative comparisons were additionally made between the 4D images obtained using each of the methods and the manual method.

RESULTS

4D images were successfully created for all mice with each of the respiratory signal extraction methods. Only minimal qualitative differences were noted between each of the methods and the manual method. The average error (and standard deviation) in phase bin assignment was 0.24 ± 0.08 (0.49 ± 0.11) phase bins for the Fourier transform method, 0.09 ± 0.03 (0.31 ± 0.08) phase bins for the modified Amsterdam shroud method, 0.09 ± 0.02 (0.33 ± 0.07) phase bins for the intensity method, and 0.37 ± 0.10 (0.57 ± 0.08) phase bins for the center of mass method. Little dependence on ROI size was noted for the modified Amsterdam shroud and intensity methods while the Fourier transform and center of mass methods showed a noticeable dependence on the ROI size.

CONCLUSIONS

The modified Amsterdam shroud, Fourier transform, and intensity respiratory signal methods are sufficiently accurate to be used for 4D imaging on the X-RAD system and show improvement over the existing center of mass method. The intensity and modified Amsterdam shroud methods are recommended due to their high accuracy and low dependence on ROI size.

Image quality in thoracic 4D cone-beam CT: a sensitivity analysis of respiratory signal, binning method, reconstruction algorithm, and projection angular spacing

PURPOSE

Respiratory signal, binning method, and reconstruction algorithm are three major controllable factors affecting image quality in thoracic 4D cone-beam CT (4D-CBCT), which is widely used in image guided radiotherapy (IGRT). Previous studies have investigated each of these factors individually, but no integrated sensitivity analysis has been performed. In addition, projection angular spacing is also a key factor in reconstruction, but how it affects image quality is not obvious. An investigation of the impacts of these four factors on image quality can help determine the most effective strategy in improving 4D-CBCT for IGRT.

METHODS

Fourteen 4D-CBCT patient projection datasets with various respiratory motion features were reconstructed with the following controllable factors: (i) respiratory signal (real-time position management, projection image intensity analysis, or fiducial marker tracking), (ii) binning method (phase, displacement, or equal-projection-density displacement binning), and (iii) reconstruction algorithm [Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), or adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS)]. The image quality was quantified using signal-to-noise ratio (SNR), contrast-to-noise ratio, and edge-response width in order to assess noise/streaking and blur. The SNR values were also analyzed with respect to the maximum, mean, and root-mean-squared-error (RMSE) projection angular spacing to investigate how projection angular spacing affects image quality.

RESULTS

The choice of respiratory signals was found to have no significant impact on image quality. Displacement-based binning was found to be less prone to motion artifacts compared to phase binning in more than half of the cases, but was shown to suffer from large interbin image quality variation and large projection angular gaps. Both MKB and ASD-POCS resulted in noticeably improved image quality almost 100% of the time relative to FDK. In addition, SNR values were found to increase with decreasing RMSE values of projection angular gaps with strong correlations (r ≈ -0.7) regardless of the reconstruction algorithm used.

CONCLUSIONS

Based on the authors' results, displacement-based binning methods, better reconstruction algorithms, and the acquisition of even projection angular views are the most important factors to consider for improving thoracic 4D-CBCT image quality. In view of the practical issues with displacement-based binning and the fact that projection angular spacing is not currently directly controllable, development of better reconstruction algorithms represents the most effective strategy for improving image quality in thoracic 4D-CBCT for IGRT applications at the current stage.

Differences in respiratory-induced pancreatic tumor motion between 4D treatment planning CT and daily cone beam CT, measured using intratumoral fiducials

BACKGROUND

In radiotherapy, the magnitude of respiratory-induced tumor motion is often measured using a single four-dimensional computed tomography (4DCT). This magnitude is required to determine the internal target volume. The aim of this study was to compare the magnitude of respiratory-induced motion of pancreatic tumors on a single 4DCT with the motion on daily cone beam CT (CBCT) scans during a 3-5-week fractionated radiotherapy scheme. In addition, we investigated changes in the respiratory motion during the treatment course.

MATERIAL AND METHODS

The mean peak-to-peak motion (i.e. magnitude of motion) of pancreatic tumors was measured for 18 patients using intratumoral gold fiducials visible on CBCT scans made prior to each treatment fraction (10-27 CBCTs per patient; 401 CBCTs in total). For each patient, these magnitudes were compared to the magnitude measured on 4DCT. Possible time trends were investigated by applying linear fits to the tumor motion determined from daily CBCTs as a function of treatment day.

RESULTS

We found a significant (p ≤ 0.01) difference between motion magnitude on 4DCT and on CBCT in superior-inferior, anterior-posterior and left-right direction, in 13, 9 and 12 of 18 patients, respectively. In the anterior- posterior and left-right direction no fractions had a difference ≥ 5 mm. In the superior-inferior direction the difference was ≥ 5 mm for 17% of the 401 fractions. In this direction, a significant (p ≤ 0.05) time trend in tumor motion was observed in 4 of 18 patients, but all trends were small (- 0.17-0.10 mm/day) and did not explain the large differences in motion magnitude between 4DCT and CBCT.

CONCLUSION

A single measurement of the respiratory-induced motion magnitude of pancreatic tumors using 4DCT is often not representative for the magnitude during daily treatment over a 3-5-week radiotherapy scheme. For this patient group it may be beneficial to introduce breath-hold to eliminate respiratory-induced tumor motion.

A technique for estimating 4D-CBCT using prior knowledge and limited-angle projections

PURPOSE

To develop a technique to estimate onboard 4D-CBCT using prior information and limited-angle projections for potential 4D target verification of lung radiotherapy.

METHODS

Each phase of onboard 4D-CBCT is considered as a deformation from one selected phase (prior volume) of the planning 4D-CT. The deformation field maps (DFMs) are solved using a motion modeling and free-form deformation (MM-FD) technique. In the MM-FD technique, the DFMs are estimated using a motion model which is extracted from planning 4D-CT based on principal component analysis (PCA). The motion model parameters are optimized by matching the digitally reconstructed radiographs of the deformed volumes to the limited-angle onboard projections (data fidelity constraint). Afterward, the estimated DFMs are fine-tuned using a FD model based on data fidelity constraint and deformation energy minimization. The 4D digital extended-cardiac-torso phantom was used to evaluate the MM-FD technique. A lung patient with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D-CT to onboard volume, including changes of respiration amplitude, lesion size and lesion average-position, and phase shift between lesion and body respiratory cycle. The lesions were contoured in both the estimated and "ground-truth" onboard 4D-CBCT for comparison. 3D volume percentage-difference (VPD) and center-of-mass shift (COMS) were calculated to evaluate the estimation accuracy of three techniques: MM-FD, MM-only, and FD-only. Different onboard projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy.

RESULTS

For all simulated patient and projection acquisition scenarios, the mean VPD (±S.D.)∕COMS (±S.D.) between lesions in prior images and "ground-truth" onboard images were 136.11% (±42.76%)∕15.5 mm (±3.9 mm). Using orthogonal-view 15°-each scan angle, the mean VPD∕COMS between the lesion in estimated and "ground-truth" onboard images for MM-only, FD-only, and MM-FD techniques were 60.10% (±27.17%)∕4.9 mm (±3.0 mm), 96.07% (±31.48%)∕12.1 mm (±3.9 mm) and 11.45% (±9.37%)∕1.3 mm (±1.3 mm), respectively. For orthogonal-view 30°-each scan angle, the corresponding results were 59.16% (±26.66%)∕4.9 mm (±3.0 mm), 75.98% (±27.21%)∕9.9 mm (±4.0 mm), and 5.22% (±2.12%)∕0.5 mm (±0.4 mm). For single-view scan angles of 3°, 30°, and 60°, the results for MM-FD technique were 32.77% (±17.87%)∕3.2 mm (±2.2 mm), 24.57% (±18.18%)∕2.9 mm (±2.0 mm), and 10.48% (±9.50%)∕1.1 mm (±1.3 mm), respectively. For projection angular-sampling-intervals of 0.6°, 1.2°, and 2.5° with the orthogonal-view 30°-each scan angle, the MM-FD technique generated similar VPD (maximum deviation 2.91%) and COMS (maximum deviation 0.6 mm), while sparser sampling yielded larger VPD∕COMS. With equal number of projections, the estimation results using scattered 360° scan angle were slightly better than those using orthogonal-view 30°-each scan angle. The estimation accuracy of MM-FD technique declined as noise level increased.

CONCLUSIONS

The MM-FD technique substantially improves the estimation accuracy for onboard 4D-CBCT using prior planning 4D-CT and limited-angle projections, compared to the MM-only and FD-only techniques. It can potentially be used for the inter/intrafractional 4D-localization verification.

Respiration-phase-matched digital tomosynthesis imaging for moving target verification: a feasibility study

PURPOSE

To develop a respiration-phase-matched digital tomosynthesis (DTS) technique to monitor moving targets, and to evaluate its accuracy for various imaging parameters and anatomical characteristics.

METHODS

Previously developed 3D-DTS techniques, registering onboard DTS (OB-DTS, reconstructed from onboard projections) to reference DTS (R-DTS, reconstructed from DRRs of 3D reference CT), are inadequate to monitor moving targets. The authors' proposed respiration-phase-matched DTS technique registers OB-DTS to R-DTS reconstructed from DRRs generated by the same phase images of 4D reference CT as the corresponding onboard projections. To evaluate the improved accuracy of the author's technique, the authors performed thoracic phantom studies using (1) simulation with the 4D digital extended-cardiac-torso (XCAT) phantom, and (2) experiments with an anthropomorphic motion phantom. The studies were performed for various: respiratory cycle (RC), scan angle, and fraction of RC contained therein. Also, the authors assessed the accuracy of their technique relative to target size/location, and respiration inconsistencies from the R-DTS to OB-DTS.

RESULTS

In both simulation and experimental studies, the respiration-phase-matched DTS technique is significantly more accurate in determining moving target positions. For 324 different scenarios simulated by XCAT, the respiration-phase-matched DTS technique localizes the 3D target position to errors of 1.07 ± 0.57 mm (mean ± S.D.), as compared to (a) 2.58 ± 1.37 and (b) 7.37 ± 4.18 mm, for 3D-DTS using 3D reference CT of (a) average intensity projection and (b) free-breathing CT. For 60 scenarios evaluated through experimental study, the uncertainties corresponding to those above are 1.24 ± 0.87, 2.42 ± 1.80, and 5.77 ± 6.45 mm, respectively. For a given scan angle, the accuracy of respiration-phase-matched DTS technique is less dependent on RC and the fraction of RC included in the scan. Increasing scan angle improves its accuracy. For different target locations, the targets near the chest wall or in the middle of lung provide higher registration accuracy compared to those near the mediastinum and diaphragm. Larger targets provide higher registration accuracy than small targets. Different respiratory cycle inconsistencies from R-DTS to OB-DTS minimally affect the registration accuracy. Increasing the respiratory amplitude inconsistencies will decrease the accuracy.

CONCLUSIONS

The respiration-phase-matched DTS is more accurate and robust in determining moving target positions than 3D-DTS. It has potential application in pretreatment setup, post-treatment analysis, and intrafractional target verification.

Registration-based reconstruction of four-dimensional cone beam computed tomography

We present a new method for reconstruction of 4-D cone beam computed tomography from an undersampled set of X-ray projections. The novelty of the proposed method lies in utilizing optical flow based registration to facilitate that each temporal phase is reconstructed from the full set of acquired projections. The reconstruction of each phase thus exhibits limited aliasing despite significant intra-phase undersampling. The method is fully self-contained. Initially an approximate 4-D volume is reconstructed and an inter-phase registration based hereon. A subsequent reconstruction pass integrates the optical flow estimation in a cost function formulation in which the X-ray projections from all temporal phases are considered for the reconstruction of each individual phase. Quantitative and qualitative evaluations were performed through reconstruction of both a numerical phantom and a clinical dataset. The obtained reconstructions are compared to the state-of-the-art alternatives of total variation regularization and prior image constrained compressed sensing. Our studies show that the proposed method is the better overall "compromise" in the depiction of both moving and stationary anatomical structures.

Evaluation of the effect of respiratory and anatomical variables on a Fourier technique for markerless, self-sorted 4D-CBCT

A novel technique based on Fourier transform theory has been developed that directly extracts respiratory information from projections without the use of external surrogates. While the feasibility has been demonstrated with three patients, a more extensive validation is necessary. Therefore, the purpose of this work is to investigate the effects of a variety of respiratory and anatomical scenarios on the performance of the technique with the 4D digital extended cardiac torso phantom. FT-phase and FT-magnitude methods were each applied to identify peak-inspiration projections and quantitatively compared to the gold standard of visual identification. Both methods proved to be robust across the studied scenarios with average differences in respiratory phase <10% and percentage of projections assigned within 10% of the gold standard >90%, when incorporating minor modifications to region-of-interest (ROI) selection and/or low-frequency location for select cases of DA and lung percentage in the field of view of the projection. Nevertheless, in the instance where one method initially faltered, the other method prevailed and successfully identified peak-inspiration projections. This is promising because it suggests that the two methods provide complementary information to each other. To ensure appropriate clinical adaptation of markerless, self-sorted four-dimensional cone-beam CT (4D-CBCT), perhaps an optimal integration of the two methods can be developed.

Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: A Simulation and a Phantom Study

Long acquisition times lead to image artifacts in thoracic C-arm CT. Motion blur caused by respiratory motion leads to decreased image quality in many clinical applications. We introduce an image-based method to estimate and compensate respiratory motion in C-arm CT based on diaphragm motion. In order to estimate respiratory motion, we track the contour of the diaphragm in the projection image sequence. Using a motion corrected triangulation approach on the diaphragm vertex, we are able to estimate a motion signal. The estimated motion signal is used to compensate for respiratory motion in the target region, for example, heart or lungs. First, we evaluated our approach in a simulation study using XCAT. As ground truth data was available, a quantitative evaluation was performed. We observed an improvement of about 14% using the structural similarity index. In a real phantom study, using the artiCHEST phantom, we investigated the visibility of bronchial tubes in a porcine lung. Compared to an uncompensated scan, the visibility of bronchial structures is improved drastically. Preliminary results indicate that this kind of motion compensation can deliver a first step in reconstruction image quality improvement. Compared to ground truth data, image quality is still considerably reduced.

Extracting respiratory signals from thoracic cone beam CT projections

The patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such a signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principal component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely the Amsterdam Shroud method, the intensity analysis method and the Fourier-transform-based phase analysis method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting a respiratory signal. We also identified the applicability of each existing method.

Local intensity feature tracking and motion modeling for respiratory signal extraction in cone beam CT projections

Accounting for respiration motion during imaging can help improve targeting precision in radiation therapy. We propose local intensity feature tracking (LIFT), a novel markerless breath phase sorting method in cone beam computed tomography (CBCT) scan images. The contributions of this study are twofold. First, LIFT extracts the respiratory signal from the CBCT projections of the thorax depending only on tissue feature points that exhibit respiration. Second, the extracted respiratory signal is shown to correlate with standard respiration signals. LIFT extracts feature points in the first CBCT projection of a sequence and tracks those points in consecutive projections forming trajectories. Clustering is applied to select trajectories showing an oscillating behavior similar to the breath motion. Those "breathing" trajectories are used in a 3-D reconstruction approach to recover the 3-D motion of the lung which represents the respiratory signal. Experiments were conducted on datasets exhibiting regular and irregular breathing patterns. Results showed that LIFT-based respiratory signal correlates with the diaphragm position-based signal with an average phase shift of 1.68 projections as well as with the internal marker-based signal with an average phase shift of 1.78 projections. LIFT was able to detect the respiratory signal in all projections of all datasets.