Four-dimensional volume-of-interest reconstruction for cone-beam computed tomography-guided radiation therapy

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.

Clinical and Dosimetric Impact of 2D kV Motion Monitoring and Intervention in Liver Stereotactic Body Radiation Therapy

Purpose

Positional errors resulting from motion are a principal challenge across all disease sites in radiation therapy. This is particularly pertinent when treating lesions in the liver with stereotactic body radiation therapy (SBRT). To achieve dose escalation and margin reduction for liver SBRT, kV real-time imaging interventions may serve as a potential solution. In this study, we report results of a retrospective cohort of liver patients treated using real-time 2D kV-image guidance SBRT with emphasis on the impact of (1) clinical workflow, (2) treatment accuracy, and (3) tumor dose.

Methods and Materials

Data from 33 patients treated with 41 courses of liver SBRT were analyzed. During treatment, planar kV images orthogonal to the treatment beam were acquired to determine treatment interventions, namely treatment pauses (ie, adequacy of gating thresholds) or treatment shifts. Patients were shifted if internal markers were >3 mm, corresponding to the PTV margin used, from the expected reference condition. The frequency, duration, and nature of treatment interventions (ie, pause vs shift) were recorded, and the dosimetric impact associated with treatment shifts was estimated using a machine learning dosimetric model.

Results

Of all fractions delivered, 39% required intervention, which took on average 1.9 ± 1.6 minutes and occurred more frequently in treatments lasting longer than 7 minutes. The median realignment shift was 5.7 mm in size, and the effect of these shifts on minimum tumor dose in simulated clinical scenarios ranged from 0% to 50% of prescription dose per fraction.

Conclusion

Real-time kV-based imaging interventions for liver SBRT minimally affect clinical workflow and dosimetrically benefit patients. This potential solution for addressing positional errors from motion addresses concerns about target accuracy and may enable safe dose escalation and margin reduction in the context of liver SBRT.

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.

Iterative volume of interest based 4D cone-beam CT

PURPOSE

4D cone-beam CT (CBCT) has potential applications in soft tissue alignment and tumor motion verification at the time of radiation treatment. However, prominent streak artifacts with conventional image reconstructions have limited its clinical use and alternative reconstructions are generally too computationally expensive for the time available. We propose an iterative volume of interest based (I4D VOI) reconstruction technique, where 4D reconstruction is only performed within a VOI, to limit streak artifacts with limited added computation time.

METHODS

The I4D VOI technique is compared to standard cone-beam filtered back projection (FDK), an FDK VOI technique, and unconstrained total variation (TV) minimization by comparing tumor motion quantification errors and image quality. 14 long CBCT scans (6.5 to 12 min) of patients receiving radiation treatment for lung cancer were used for the comparison. Rigid registration between phase images of FDK reconstructions using all projections were used to quantify the gold standard motion. Projections were removed to simulate 2 minute scans and these new projection sets were used for each of the test reconstructions.

RESULTS

Excluding two patients where registration failed, the average root mean square (RMS) error for each method was as follows: 1.5 ± 0.2 mm for FDK, 1.4 ± 0.2 mm for FDK VOI, 1.3 ± 0.2 mm for I4D VOI, 1.7 ± 0.4 mm for low regularization TV minimization, and 1.1 ± 0.2 mm for high regularization TV minimization. No significant difference was observed between RMS error for I4D VOI and the other methods, except for unsmoothed FDK VOI (P = 0.02). An increase in RMS error difference between I4D VOI and smoothed FDK VOI was observed going from 2 min to 1 min scans (0.1 mm to 0.3 mm, P = 0.20 to P = 0.09).

CONCLUSIONS

I4D VOI and FDK VOI reconstruction measured tumor trajectories with equivalent accuracy as TV minimization with improved bony anatomy image quality and computation time (I4D VOI was approximately 15 and 95 times faster than low and high regularization TV minimization, respectively). Within the VOI, streak artifact reduction compared to FDK VOI may be beneficial for tumor visualization and motion measurement, but requires further study.

Evaluation of gantry speed on image quality and imaging dose for 4D cone-beam CT acquisition

Background

This study investigates the effect of gantry speed on 4DCBCT image quality and dose for the Varian On-Board Imager®.

Methods

A thoracic 4DCBCT protocol was designed using a 125 kVp spectrum. Image quality parameters were evaluated for 4DCBCT acquisition using Catphan® phantom with real-time position management™ system for gantry speeds varying between 1.0 to 6.0°/s. Superior-inferior motion of the phantom was executed using a sinusoidal waveform with five second period. Scans were retrospectively sorted into 4 phases (CBCT-4 ph) and 10 phases (CBCT-10 ph); average 4DCBCT (CBCT-ave), using all image data from the 4DCBCT acquisitions was also evaluated. The 4DCBCT images were evaluated using the following image quality metrics: spatial resolution, contrast-to-noise ratio (CNR), and uniformity index (UI). Additionally, Hounsfield unit (HU) sensitivity compared to a baseline CBCT and percent differences and RMS errors (RMSE) of excursion were also determined. Imaging dose was evaluated using an IBA CC13 ion chamber placed within CIRS Thorax phantom using the same sinusoidal motion and image acquisition settings as mentioned above.

Results

Spatial resolution decreased linearly from 5.93 to 3.82 lp/cm as gantry speed increased from 1.0 to 6.0°/s. CNR decreased linearly from 4.80 to 1.82 with gantry speed increasing from 1.0 to 6.0°/s, respectively. No noteworthy variations in UI, HU sensitivity, or excursion metrics were observed with changes in gantry speed. Ion chamber dose rates measured ranged from 2.30 (lung) to 5.18 (bone) E-3 cGy/mAs.

Conclusions

A quantitative analysis of the Varian OBI’s 4DCBCT capabilities was explored. Changing gantry speed changes the number of projections used for reconstruction, affecting both image quality and imaging dose if x-ray tube current is held constant. From the results of this study, a gantry speed between 2 and 3°/s was optimal when considering image quality, dose, and reconstruction time. The future of 4DCBCT clinical utility relies on further investigation of image acquisition and reconstruction optimization.

[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.

A method for volumetric imaging in radiotherapy using single x-ray projection

PURPOSE

It is an intriguing problem to generate an instantaneous volumetric image based on the corresponding x-ray projection. The purpose of this study is to develop a new method to achieve this goal via a sparse learning approach.

METHODS

To extract motion information hidden in projection images, the authors partitioned a projection image into small rectangular patches. The authors utilized a sparse learning method to automatically select patches that have a high correlation with principal component analysis (PCA) coefficients of a lung motion model. A model that maps the patch intensity to the PCA coefficients was built along with the patch selection process. Based on this model, a measured projection can be used to predict the PCA coefficients, which are then further used to generate a motion vector field and hence a volumetric image. The authors have also proposed an intensity baseline correction method based on the partitioned projection, in which the first and the second moments of pixel intensities at a patch in a simulated projection image are matched with those in a measured one via a linear transformation. The proposed method has been validated in both simulated data and real phantom data.

RESULTS

The algorithm is able to identify patches that contain relevant motion information such as the diaphragm region. It is found that an intensity baseline correction step is important to remove the systematic error in the motion prediction. For the simulation case, the sparse learning model reduced the prediction error for the first PCA coefficient to 5%, compared to the 10% error when sparse learning was not used, and the 95th percentile error for the predicted motion vector was reduced from 2.40 to 0.92 mm. In the phantom case with a regular tumor motion, the predicted tumor trajectory was successfully reconstructed with a 0.82 mm error for tumor center localization compared to a 1.66 mm error without using the sparse learning method. When the tumor motion was driven by a real patient breathing signal with irregular periods and amplitudes, the average tumor center error was 0.6 mm. The algorithm robustness with respect to sparsity level, patch size, and presence or absence of diaphragm, as well as computation time, has also been studied.

CONCLUSIONS

The authors have developed a new method that automatically identifies motion information from an x-ray projection, based on which a volumetric image is generated.

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.

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

PURPOSE

4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol.

METHODS

The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency.

RESULTS

The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3-0.5 mm for patients 1-3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1-1.5 min per phase.

CONCLUSIONS

High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.

Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT

PURPOSE

In image-guided radiation therapy (IGRT) valuable information for patient positioning, dose verification, and adaptive treatment planning is provided by an additional kV imaging unit. However, due to the limited gantry rotation speed during treatment the typical acquisition time is quite long. Tomographic images of the thorax suffer from motion blurring or, if a gated 4D reconstruction is performed, from significant streak artifacts. Our purpose is to provide a method that reliably estimates respiratory motion in presence of severe artifacts. The estimated motion vector fields are then used for motion-compensated image reconstruction to provide high quality respiratory-correlated 4D volumes for on-board cone-beam CT (CBCT) scans.

METHODS

The proposed motion estimation method consists of a model that explicitly addresses image artifacts because in presence of severe artifacts state-of-the-art registration methods tend to register artifacts rather than anatomy. Our artifact model, e.g., generates streak artifacts very similar to those included in the gated 4D CBCT images, but it does not include respiratory motion. In combination with a registration strategy, the model gives an error estimate that is used to compensate the corresponding errors of the motion vector fields that are estimated from the gated 4D CBCT images. The algorithm is tested in combination with a cyclic registration approach using temporal constraints and with a standard 3D-3D registration approach. A qualitative and quantitative evaluation of the motion-compensated results was performed using simulated rawdata created on basis of clinical CT data. Further evaluation includes patient data which were scanned with an on-board CBCT system.

RESULTS

The model-based motion estimation method is nearly insensitive to image artifacts of gated 4D reconstructions as they are caused by angular undersampling. The motion is accurately estimated and our motion-compensated image reconstruction algorithm can correct for it. Motion artifacts of 3D standard reconstruction are significantly reduced, while almost no new artifacts are introduced.

CONCLUSIONS

Using the artifact model allows to accurately estimate and compensate for patient motion, even if the initial reconstructions are of very low image quality. Using our approach together with a cyclic registration algorithm yields a combination which shows almost no sensitivity to sparse-view artifacts and thus ensures both high spatial and high temporal resolution.

Dosimetric effect of respiratory motion on volumetric-modulated arc therapy-based lung SBRT treatment delivered by TrueBeam machine with flattening filter-free beam

The purpose of this study is to investigate the interplay effect between dynamic MLC movement and tumor respiratory motion in volumetric‐modulated arc therapy (VMAT)‐based lung SBRT treatment delivered by the flattening filter‐free (FFF) beam of a Varian TrueBeam machine. Six lung cancer patients with tumor motions ranging between 0.5–1.6 cm were recruited in this study. All patients underwent 4D‐CT scan with audiocoaching. A two‐arc VMAT plan was retrospectively generated using Varian's Eclipse planning system for each patient. To explicitly describe the interplay effect, the contributions of each control point in the original static VMAT plans to each respiratory phase were calculated, and then ten new VMAT plans corresponding to different respiratory phases were generated and imported back into Eclipse planning system to calculate the radiation dose based on the CT images of related respiratory phase. An in‐house 4D dose calculation program with deformable registration capacity was used to calculate the accumulative 4D dose distribution of the targets. For all patients, the PTV coverage dropped significantly with increased respiratory motion amplitude. However, V100 and D90 of the GTV and GTV+5mm, which mimic the target with setup error of less than 5 mm, were either unchanged or slightly increased up to 1.2%, and the variations of their minimum doses were less than 3.2%. Our results indicated that for VMAT‐based lung SBRT treatment delivered by FFF beam of TrueBeam machine, the impact of interplay effects on target coverage is insignificant, as long as a sufficient margin was given.

PACS number: 87.53.Ly

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.

Four-dimensional cone beam CT reconstruction and enhancement using a temporal nonlocal means method

PURPOSE

Four-dimensional cone beam computed tomography (4D-CBCT) has been developed to provide respiratory phase-resolved volumetric imaging in image guided radiation therapy. Conventionally, it is reconstructed by first sorting the x-ray projections into multiple respiratory phase bins according to a breathing signal extracted either from the projection images or some external surrogates, and then reconstructing a 3D CBCT image in each phase bin independently using FDK algorithm. This method requires adequate number of projections for each phase, which can be achieved using a low gantry rotation or multiple gantry rotations. Inadequate number of projections in each phase bin results in low quality 4D-CBCT images with obvious streaking artifacts. 4D-CBCT images at different breathing phases share a lot of redundant information, because they represent the same anatomy captured at slightly different temporal points. Taking this redundancy along the temporal dimension into account can in principle facilitate the reconstruction in the situation of inadequate number of projection images. In this work, the authors propose two novel 4D-CBCT algorithms: an iterative reconstruction algorithm and an enhancement algorithm, utilizing a temporal nonlocal means (TNLM) method.

METHODS

The authors define a TNLM energy term for a given set of 4D-CBCT images. Minimization of this term favors those 4D-CBCT images such that any anatomical features at one spatial point at one phase can be found in a nearby spatial point at neighboring phases. 4D-CBCT reconstruction is achieved by minimizing a total energy containing a data fidelity term and the TNLM energy term. As for the image enhancement, 4D-CBCT images generated by the FDK algorithm are enhanced by minimizing the TNLM function while keeping the enhanced images close to the FDK results. A forward-backward splitting algorithm and a Gauss-Jacobi iteration method are employed to solve the problems. The algorithms implementation on GPU is designed to avoid redundant and uncoalesced memory access, in order to ensure a high computational efficiency. Our algorithms have been tested on a digital NURBS-based cardiac-torso phantom and a clinical patient case.

RESULTS

The reconstruction algorithm and the enhancement algorithm generate visually similar 4D-CBCT images, both better than the FDK results. Quantitative evaluations indicate that, compared with the FDK results, our reconstruction method improves contrast-to-noise-ratio (CNR) by a factor of 2.56-3.13 and our enhancement method increases the CNR by 2.75-3.33 times. The enhancement method also removes over 80% of the streak artifacts from the FDK results. The total computation time is 509-683 s for the reconstruction algorithm and 524-540 s for the enhancement algorithm on an NVIDIA Tesla C1060 GPU card.

CONCLUSIONS

By innovatively taking the temporal redundancy among 4D-CBCT images into consideration, the proposed algorithms can produce high quality 4D-CBCT images with much less streak artifacts than the FDK results, in the situation of inadequate number of projections.