Interfractional Positional Variability of Fiducial Markers and Primary Tumors in Locally Advanced Non-Small-Cell Lung Cancer During Audiovisual Biofeedback Radiotherapy

DiffRecon: Diffusion-based CT reconstruction with cross-modal deformable fusion for DR-guided non-coplanar radiotherapy

In non-coplanar radiotherapy, DR is commonly used for image guiding which needs to fuse intraoperative DR with preoperative CT. But this fusion task performs poorly, suffering from unaligned and dimensional differences between DR and CT. CT reconstruction estimated from DR could facilitate this challenge. Thus, We propose a unified generation and registration framework, named DiffRecon, for intraoperative CT reconstruction based on DR using the diffusion model. Specifically, we use the generation model for synthesizing intraoperative CTs to eliminate dimensional differences and the registration model for aligning synthetic CTs to improve reconstruction. To ensure clinical usability, CT is not only estimated from DR but the preoperative CT is also introduced as prior. We design a dual-encoder to learn prior knowledge and spatial deformation among pre- and intra-operative CT pairs and DR parallelly for 2D/3D feature deformable conversion. To calibrate the cross-modal fusion, we insert cross-attention modules to enhance the 2D/3D feature interaction between dual encoders. DiffRecon has been evaluated by both image quality metrics and dosimetric indicators. The high image synthesis metrics are with RMSE of 0.02±0.01, PSNR of 44.92±3.26, and SSIM of 0.994±0.003. The mean gamma passing rates between rCT and sCT for 1%/1 mm, 2%/2 mm and 3%/3 mm acceptance criteria are 95.2%, 99.4% and 99.9% respectively. The proposed DiffRecon can reconstruct CT accurately from a single DR projection with excellent image generation quality and dosimetric accuracy. These demonstrate that the method can be applied in non-coplanar adaptive radiotherapy workflows.

Intra- and inter-fractional variations of tumors with fiducial markers measured using respiratory-correlated computed tomography images for respiratory gated lung stereotactic body radiation therapy

PURPOSE

This study evaluated the intra- and inter-fractional variation of tumors with fiducial markers (FMs), relative to the tumor-FM distance, to establish how close an FM should be inserted for respiratory-gated stereotactic body radiation therapy (RG-SBRT).

METHODS

Forty-five lung tumors treated with RG-SBRT were enrolled. End-expiratory computed tomography (CT) (CTplan) and four-dimensional-CT (4D-CT) scans were obtained for planning. End-expiratory CT (CTfr) scanning was performed before each fraction. The FMs were divided into two groups based on the median tumor-FM distance in the CTplan (Dp). For the intra-fractional variation, the correlations between the corresponding tumor and FM intra-fractional motions, defined as the centroid coordinates of those in each 0-90% phase, with the 50% phase of 4D-CT as the origin, were calculated in the left-right, anterior-posterior, and superior-inferior directions. Furthermore, the maximum difference in the tumor-FM distance in each phase of 4D-CT scan, based on those in the 50% phase of 4D-CT scan (Dmax), was obtained. Inter-fractional variation was defined as the maximum distance between the tumors in CTplan and CTfr, when the CT scans were fused based on each FM or vertebra.

RESULTS

The median Dp was 26.1 mm. While FM intra-fractional motions were significantly and strongly correlated with the tumor intra-fractional motions in only anterior-posterior and superior-inferior directions for the Dp > 26 mm group, they were significantly and strongly correlated in all directions for the Dp ≤ 26 mm group. In all directions, Dmax values of the Dp ≤ 26 mm group were lower than those of the Dp > 26 mm group. The inter-fractional variations based on the Dp ≤ 26 mm were smaller than those on the Dp > 26 mm and on the vertebra in all directions.

CONCLUSIONS

Regarding intra- and inter-fractional variation, FMs for Dp ≤ 26 mm can increase the accuracy for RG-SBRT.

Real-time estimation of lung deformation from body surface using a general CoordConv CNN

BACKGROUND AND OBJECTIVE

Estimating the three-dimensional (3D) deformation of the lung is important for accurate dose delivery in radiotherapy and precise surgical guidance in lung surgery navigation. Additional 4D-CT information is often required to eliminate the effect of individual variations and obtain a more accurate estimation of lung deformation. However, this results in increased radiation dose. Therefore, we propose a novel method that estimates lung tissue deformation from depth maps and two CT phases per patient.

METHODS

The method models the 3D motion of each voxel as a linear displacement along a direction vector, with a variable amplitude and phase that depend on the voxel location. The direction vector and amplitude are derived from the registration of the CT images at the end-of-exhale (EOE) and the end-of-inhale (EOI) phases. The voxel phase is estimated by a neural network. Coordinate convolution (CoordConv) is used to fuse multimodal data and embed absolute position information. The network takes the front and side views as well as the previous phase views as inputs to enhance accuracy.

RESULTS

We evaluate the proposed method on two datasets: DIR-Lab and 4D-Lung, and obtain average errors of 2.11 mm and 1.36 mm, respectively. The method achieves real-time performance of less than 7 ms per frame on a NVIDIA GeForce 2080Ti GPU.

CONCLUSION

Compared with previous methods, our method achieves comparable or even better accuracy with less CT phases.

Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization

Objective.We demonstrate a novel focus stacking technique to improve spatial resolution of single-event particle radiography (pRad), and exploit its potential for 3D feature detection.Approach.Focus stacking, used typically in optical photography and microscopy, is a technique to combine multiple images with different focal depths into a single super-resolution image. Each pixel in the final image is chosen from the image with the largest gradient at that pixel's position. pRad data can be reconstructed at different depths in the patient based on an estimate of each particle's trajectory (called distance-driven binning; DDB). For a given feature, there is a depth of reconstruction for which the spatial resolution of DDB is maximal. Focus stacking can hence be applied to a series of DDB images reconstructed from a single pRad acquisition for different depths, yielding both a high-resolution projection and information on the features' radiological depth at the same time. We demonstrate this technique with Geant4 simulated pRads of a water phantom (20 cm thick) with five bone cube inserts at different depths (1 × 1 × 1 cm3) and a lung cancer patient.Main results.For proton radiography of the cube phantom, focus stacking achieved a median resolution improvement of 136% compared to a state-of-the-art maximum likelihood pRad reconstruction algorithm and a median of 28% compared to DDB where the reconstruction depth was the center of each cube. For the lung patient, resolution was visually improved, without loss in accuracy. The focus stacking method also enabled to estimate the depth of the cubes within few millimeters accuracy, except for one shallow cube, where the depth was underestimated by 2.5 cm.Significance.Focus stacking utilizes the inherent 3D information encoded in pRad by the particle's scattering, overcoming current spatial resolution limits. It further opens possibilities for 3D feature localization. Therefore, focus stacking holds great potential for future pRad applications.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Real-time liver tumor localization via combined surface imaging and a single x-ray projection

Objective. Real-time imaging, a building block of real-time adaptive radiotherapy, provides instantaneous knowledge of anatomical motion to drive delivery adaptation to improve patient safety and treatment efficacy. The temporal constraint of real-time imaging (<500 milliseconds) significantly limits the imaging signals that can be acquired, rendering volumetric imaging and 3D tumor localization extremely challenging. Real-time liver imaging is particularly difficult, compounded by the low soft tissue contrast within the liver. We proposed a deep learning (DL)-based framework (Surf-X-Bio), to track 3D liver tumor motion in real-time from combined optical surface image and a single on-board x-ray projection.Approach. Surf-X-Bio performs mesh-based deformable registration to track/localize liver tumors volumetrically via three steps. First, a DL model was built to estimate liver boundary motion from an optical surface image, using learnt motion correlations between the respiratory-induced external body surface and liver boundary. Second, the residual liver boundary motion estimation error was further corrected by a graph neural network-based DL model, using information extracted from a single x-ray projection. Finally, a biomechanical modeling-driven DL model was applied to solve the intra-liver motion for tumor localization, using the liver boundary motion derived via prior steps.Main results. Surf-X-Bio demonstrated higher accuracy and better robustness in tumor localization, as compared to surface-image-only and x-ray-only models. By Surf-X-Bio, the mean (±s.d.) 95-percentile Hausdorff distance of the liver boundary from the 'ground-truth' decreased from 9.8 (±4.5) (before motion estimation) to 2.4 (±1.6) mm. The mean (±s.d.) center-of-mass localization error of the liver tumors decreased from 8.3 (±4.8) to 1.9 (±1.6) mm.Significance. Surf-X-Bio can accurately track liver tumors from combined surface imaging and x-ray imaging. The fast computational speed (<250 milliseconds per inference) allows it to be applied clinically for real-time motion management and adaptive radiotherapy.

Thoracic motion-compensated cone-beam computed tomography in under 20 seconds on a fast-rotating linac: A simulation study

BACKGROUND

Rapid kV cone-beam computed tomography (CBCT) scans are achievable in under 20 s on select linear accelerator systems to generate volumetric images in three dimensions (3D). Daily pre-treatment four-dimensional CBCT (4DCBCT) is recommended in image-guided lung radiotherapy to mitigate the detrimental effects of respiratory motion on treatment quality.

PURPOSE

To demonstrate the potential for thoracic 4DCBCT reconstruction using projection data that was simulated using a clinical rapid 3DCBCT acquisition protocol.

METHODS

We simulated conventional (1320 projections over 4 min) and rapid (491 projections over 16.6 s) CBCT acquisitions using 4D computed tomography (CT) volumes of 14 lung cancer patients. Conventional acquisition data were reconstructed using the 4D Feldkamp-Davis-Kress (FDK) algorithm. Rapid acquisition data were reconstructed using 3DFDK, 4DFDK, and Motion-Compensated FDK (MCFDK). Image quality was evaluated using Contrast-to-Noise Ratio (CNR), Tissue Interface Width (TIW), Root-Mean-Square Error (RMSE), and Structural SIMilarity (SSIM).

RESULTS

The conventional acquisition 4DFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.96, 8.02 mm, 83.5, and 0.54, respectively. The rapid acquisition 3DFDK reconstructions had median CNR, TIW, RMSE, and SSIM of 2.99, 13.6 mm, 112, and 0.44 respectively. The rapid acquisition MCFDK reconstructions had median phase averaged CNR, TIW, RMSE, and SSIM of 2.98, 10.2 mm, 103, and 0.46, respectively. Rapid acquisition 4DFDK reconstruction quality was insufficient for any practical use due to sparse angular projection sampling.

CONCLUSIONS

Results suggest that 4D motion-compensated reconstruction of rapid acquisition thoracic CBCT data are feasible with image quality approaching conventional acquisition CBCT data reconstructed using standard 4DFDK.

2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking

Two-dimensional (2D)/three-dimensional (3D) registration is critical in clinical applications. However, existing methods suffer from long alignment times and high doses. In this paper, a non-rigid 2D/3D registration method based on deep learning with orthogonal angle projections is proposed. The application can quickly achieve alignment using only two orthogonal angle projections. We tested the method with lungs (with and without tumors) and phantom data. The results show that the Dice and normalized cross-correlations are greater than 0.97 and 0.92, respectively, and the registration time is less than 1.2 seconds. In addition, the proposed model showed the ability to track lung tumors, highlighting the clinical potential of the proposed method.

NuTracker: a coordinate-based neural network representation of lung motion for intrafraction tumor tracking with various surrogates in radiotherapy

Objective. Tracking tumors and surrounding tissues in real-time is critical for reducing errors and uncertainties during radiotherapy. Existing methods are either limited by the linear representation or scale poorly with the volume resolution. To address both issues, we propose a novel coordinate-based neural network representation of lung motion to predict the instantaneous 3D volume at arbitrary spatial resolution from various surrogates: patient surface, fiducial marker, and single kV projection.Approach. The proposed model, namely NuTracker, decomposes the 4DCT into a template volume and dense displacement fields (DDFs), and uses two coordinate neural networks to predict them from spatial coordinates and surrogate states. The predicted template is spatially warped with the predicted DDF to produce the deformed volume for a given surrogate state. The nonlinear coordinate networks enable representing complex motion at infinite resolution. The decomposition allows imposing different regularizations on the spatial and temporal domains. The meta-learning and multi-task learning are used to train NuTracker across patients and tasks, so that commonalities and differences can be exploited. NuTracker was evaluated on seven patients implanted with markers using a leave-one-phase-out procedure.Main results. The 3D marker localization error is 0.66 mm on average and <1 mm at 95th-percentile, which is about 26% and 32% improvement over the predominant linear methods. The tumor coverage and image quality are improved by 5.7% and 11% in terms of dice and PSNR. The difference in the localization error for different surrogates is small and is not statistically significant. Cross-population learning and multi-task learning contribute to performance. The model tolerates surrogate drift to a certain extent.Significance. NuTracker can provide accurate estimation for entire tumor volume based on various surrogates at infinite resolution. It is of great potential to apply the coordinate network to other imaging modalities, e.g. 4DCBCT and other tasks, e.g. 4D dose calculation.

Dlung: Unsupervised Few-Shot Diffeomorphic Respiratory Motion Modeling

Lung image registration plays an important role in lung analysis applications, such as respiratory motion modeling. Unsupervised learning-based image registration methods that can compute the deformation without the requirement of supervision attract much attention. However, it is noteworthy that they have two drawbacks: they do not handle the problem of limited data and do not guarantee diffeomorphic (topology-preserving) properties, especially when large deformation exists in lung scans. In this paper, we present an unsupervised few-shot learning-based diffeomorphic lung image registration, namely Dlung. We employ fine-tuning techniques to solve the problem of limited data and apply the scaling and squaring method to accomplish the diffeomorphic registration. Furthermore, atlas-based registration on spatio-temporal (4D) images is performed and thoroughly compared with baseline methods. Dlung achieves the highest accuracy with diffeomorphic properties. It constructs accurate and fast respiratory motion models with limited data. This research extends our knowledge of respiratory motion modeling.

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

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

A deep unsupervised learning framework for the 4D CBCT artifact correction

Objective. Four-dimensional cone-beam computed tomography (4D CBCT) has unique advantages in moving target localization, tracking and therapeutic dose accumulation in adaptive radiotherapy. However, the severe fringe artifacts and noise degradation caused by 4D CBCT reconstruction restrict its clinical application. We propose a novel deep unsupervised learning model to generate the high-quality 4D CBCT from the poor-quality 4D CBCT. Approach. The proposed model uses a contrastive loss function to preserve the anatomical structure in the corrected image. To preserve the relationship between the input and output image, we use a multilayer, patch-based method rather than operate on entire images. Furthermore, we draw negatives from within the input 4D CBCT rather than from the rest of the dataset. Main results. The results showed that the streak and motion artifacts were significantly suppressed. The spatial resolution of the pulmonary vessels and microstructure were also improved. To demonstrate the results in the different directions, we make the animation to show the different views of the predicted correction image in the supplementary animation. Significance. The proposed method can be integrated into any 4D CBCT reconstruction method and maybe a practical way to enhance the image quality of the 4D CBCT.

Lung-CRNet: A convolutional recurrent neural network for lung 4DCT image registration

PURPOSE

Deformable image registration (DIR) of lung four-dimensional computed tomography (4DCT) plays a vital role in a wide range of clinical applications. Most of the existing deep learning-based lung 4DCT DIR methods focus on pairwise registration which aims to register two images with large deformation. However, the temporal continuities of deformation fields between phases are ignored. This paper proposes a fast and accurate deep learning-based lung 4DCT DIR approach that leverages the temporal component of 4DCT images.

METHODS

We present Lung-CRNet, an end-to-end convolutional recurrent registration neural network for lung 4DCT images and reformulate 4DCT DIR as a spatiotemporal sequence predicting problem in which the input is a sequence of three-dimensional computed tomography images from the inspiratory phase to the expiratory phase in a respiratory cycle. The first phase in the sequence is selected as the only reference image and the rest as moving images. Multiple convolutional gated recurrent units (ConvGRUs) are stacked to capture the temporal clues between images. The proposed network is trained in an unsupervised way using a spatial transformer layer. During inference, Lung-CRNet is able to yield the respective displacement field for each reference-moving image pair in the input sequence.

RESULTS

We have trained the proposed network using a publicly available lung 4DCT dataset and evaluated performance on the widely used the DIR-Lab dataset. The mean and standard deviation of target registration error are 1.56 ± 1.05 mm on the DIR-Lab dataset. The computation time for each forward prediction is less than 1 s on average.

CONCLUSIONS

The proposed Lung-CRNet is comparable to the existing state-of-the-art deep learning-based 4DCT DIR methods in both accuracy and speed. Additionally, the architecture of Lung-CRNet can be generalized to suit other groupwise registration tasks which align multiple images simultaneously.

Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy

During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of respiratory motion and the latency of linear accelerator (LINAC) systems. In the proposed study, we first use the Lucas-Kanade pyramidal optical flow algorithm to perform deformable image registration (DIR) of chest computed tomography (CT) scan images of four patients with lung cancer. We then track three internal points close to the lung tumor based on the previously computed deformation field and predict their position with a recurrent neural network (RNN) trained using real-time recurrent learning (RTRL) and gradient clipping. The breathing data is quite regular, sampled at approximately 2.5 Hz, and includes artificially added drift in the spine direction. The amplitude of the motion of the tracked points ranged from 12.0 mm to 22.7 mm. Finally, we propose a simple method for recovering and predicting three-dimensional (3D) tumor images from the tracked points and the initial tumor image, based on a linear correspondence model and the Nadaraya-Watson non-linear regression. The root-mean-square (RMS) error, maximum error and jitter corresponding to the RNN prediction on the test set were smaller than the same performance measures obtained with linear prediction and least mean squares (LMS). In particular, the maximum prediction error associated with the RNN, equal to 1.51 mm, is respectively 16.1% and 5.0% lower than the error given by a linear predictor and LMS. The average prediction time per time step with RTRL is equal to 119 ms, which is less than the 400 ms marker position sampling time. The tumor position in the predicted images appears visually correct, which is confirmed by the high mean cross-correlation between the original and predicted images, equal to 0.955. The standard deviation of the Gaussian kernel and the number of layers in the optical flow algorithm were the parameters having the most significant impact on registration performance. Their optimization led respectively to a 31.3% and 36.2% decrease in the registration error. Using only a single layer proved to be detrimental to the registration quality because tissue motion in the lower part of the lung has a high amplitude relative to the resolution of the CT scan images. The random initialization of the hidden units and the number of these hidden units were found to be the most important factors affecting the performance of the RNN. Increasing the number of hidden units from 15 to 250 led to a 56.3% decrease in the prediction error on the cross-validation data. Similarly, optimizing the standard deviation of the initial Gaussian distribution of the synaptic weights σ_init^RNN led to a 28.4% decrease in the prediction error on the cross-validation data, with the error minimized for σ_init^RNN=0.02 with the four patients.

Investigation of inter-fraction target motion variations in the context of pencil beam scanned proton therapy in non-small cell lung cancer patients

Purpose

For locally advanced‐stage non‐small cell lung cancer (NSCLC), inter‐fraction target motion variations during the whole time span of a fractionated treatment course are assessed in a large and representative patient cohort. The primary objective is to develop a suitable motion monitoring strategy for pencil beam scanning proton therapy (PBS‐PT) treatments of NSCLC patients during free breathing.

Methods

Weekly 4D computed tomography (4DCT; 41 patients) and daily 4D cone beam computed tomography (4DCBCT; 10 of 41 patients) scans were analyzed for a fully fractionated treatment course. Gross tumor volumes (GTVs) were contoured and the 3D displacement vectors of the centroid positions were compared for all scans. Furthermore, motion amplitude variations in different lung segments were statistically analyzed. The dosimetric impact of target motion variations and target motion assessment was investigated in exemplary patient cases.

Results

The median observed centroid motion was 3.4 mm (range: 0.2–12.4 mm) with an average variation of 2.2 mm (range: 0.1–8.8 mm). Ten of 32 patients (31.3%) with an initial motion <5 mm increased beyond a 5‐mm motion amplitude during the treatment course. Motion observed in the 4DCBCT scans deviated on average 1.5 mm (range: 0.0–6.0 mm) from the motion observed in the 4DCTs. Larger motion variations for one example patient compromised treatment plan robustness while no dosimetric influence was seen due to motion assessment biases in another example case.

Conclusions

Target motion variations were investigated during the course of radiotherapy for NSCLC patients. Patients with initial GTV motion amplitudes of < 2 mm can be assumed to be stable in motion during the treatment course. For treatments of NSCLC patients who exhibit motion amplitudes of > 2 mm, 4DCBCT should be considered for motion monitoring due to substantial motion variations observed.

SPARE: Sparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan

PURPOSE

Currently, four-dimensional (4D) cone-beam computed tomography (CBCT) requires a 3-4 min full-fan scan to ensure usable image quality. Recent advancements in sparse-view 4D-CBCT reconstruction have opened the possibility to reduce scan time and dose. The aim of this study is to provide a common framework for systematically evaluating algorithms for 4D-CBCT reconstruction from a 1-min scan. Using this framework, the AAPM-sponsored SPARE Challenge was conducted in 2018 to identify and compare state-of-the-art algorithms.

METHODS

A clinically realistic CBCT dataset was simulated using patient CT volumes from the 4D-Lung database. The selected patients had multiple 4D-CT sessions, where the first 4D-CT was used as the prior CT, and the rest were used as the ground truth volumes for simulating CBCT projections. A GPU-based Monte Carlo tool was used to simulate the primary, scatter, and quantum noise signals. A total of 32 CBCT scans of nine patients were generated. Additional qualitative analysis was performed on a clinical Varian and clinical Elekta dataset to validate the simulation study. Participants were blinded from the ground truth, and were given 3 months to apply their reconstruction algorithms to the projection data. The submitted reconstructions were analyzed in terms of root-mean-squared-error (RMSE) and structural similarity index (SSIM) with the ground truth within four different region-of-interests (ROI) - patient body, lungs, planning target volume (PTV), and bony anatomy. Geometric accuracy was quantified as the alignment error of the PTV.

RESULTS

Twenty teams participated in the challenge, with five teams completing the challenge. Techniques involved in the five methods included iterative optimization, motion-compensation, and deformation of the prior 4D-CT. All five methods rendered significant reduction in noise and streaking artifacts when compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm. The RMS of the three-dimensional (3D) target registration error of the five methods ranged from 1.79 to 3.00 mm. Qualitative observations from the Varian and Elekta datasets mostly concur with those from the simulation dataset. Each of the methods was found to have its own strengths and weaknesses. Overall, the MA-ROOSTER method, which utilizes a 4D-CT motion model for temporal regularization, had the best and most consistent image quality and accuracy.

CONCLUSION

The SPARE Challenge represents the first framework for systematically evaluating state-of-the-art algorithms for 4D-CBCT reconstruction from a 1-min scan. Results suggest the potential for reducing scan time and dose for 4D-CBCT. The challenge dataset and analysis framework are publicly available for benchmarking future reconstruction algorithms.

Anatomical Adaptation-Early Clinical Evidence of Benefit and Future Needs in Lung Cancer

Definitive treatment of locally advanced non-small-cell lung cancer with radiation is challenging. During the course of treatment, anatomical changes such as tumor regression, tumor displacement/deformation, pleural effusion, and/or atelectasis can result in a deviation of the administered radiation dose from the intended prescribed treatment and thereby worsen local control and toxicity. Adaptive radiotherapy can help correct for these changes and can be generally categorized into 3 philosophical paradigms: (1) maintenance of prescribed dose to the initially defined target volume; (2) dose reduction to healthy organs while maintaining initial prescribed dose to a regressing tumor volume; or (3) dose escalation to a regressing tumor volume with isotoxicity to healthy organs. Numerous single institution studies have investigated these methods, and results from large prospective clinical trials will hopefully provide consensus on the method, utility, and efficacy of implementing adaptive radiation therapy (ART) in a clinical setting. Additional development into standardization and automation of the ART workflow, specifically in identifying when ART is warranted and in reducing the manual clinical effort needed to produce an adaptive plan, will be paramount to making ART feasible for the broader radiation therapy community.

A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images

PURPOSE

Real-time image-guided adaptive radiation therapy (IGART) requires accurate marker segmentation to resolve three-dimensional (3D) motion based on two-dimensional (2D) fluoroscopic images. Most common marker segmentation methods require prior knowledge of marker properties to construct a template. If marker properties are not known, an additional learning period is required to build the template which exposes the patient to an additional imaging dose. This work investigates a deep learning-based fiducial marker classifier for use in real-time IGART that requires no prior patient-specific data or additional learning periods. The proposed tracking system uses convolutional neural network (CNN) models to segment cylindrical and arbitrarily shaped fiducial markers.

METHODS

The tracking system uses a tracking window approach to perform sliding window classification of each implanted marker. Three cylindrical marker training datasets were generated from phantom kilovoltage (kV) and patient intrafraction images with increasing levels of megavoltage (MV) scatter. The cylindrical shaped marker CNNs were validated on unseen kV fluoroscopic images from 12 fractions of 10 prostate cancer patients with implanted gold fiducials. For the training and validation of the arbitrarily shaped marker CNNs, cone beam computed tomography (CBCT) projection images from ten fractions of seven lung cancer patients with implanted coiled markers were used. The arbitrarily shaped marker CNNs were trained using three patients and the other four unseen patients were used for validation. The effects of full training using a compact CNN (four layers with learnable weights) and transfer learning using a pretrained CNN (AlexNet, eight layers with learnable weights) were analyzed. Each CNN was evaluated using a Precision-Recall curve (PRC), the area under the PRC plot (AUC), and by the calculation of sensitivity and specificity. The tracking system was assessed using the validation data and the accuracy was quantified by calculating the mean error, root-mean-square error (RMSE) and the 1st and 99th percentiles of the error.

RESULTS

The fully trained CNN on the dataset with moderate noise levels had a sensitivity of 99.00% and specificity of 98.92%. Transfer learning of AlexNet resulted in a sensitivity and specificity of 99.42% and 98.13%, respectively, for the same datasets. For the arbitrarily shaped marker CNNs, the sensitivity was 98.58% and specificity was 98.97% for the fully trained CNN. The transfer learning CNN had a sensitivity and specificity of 98.49% and 99.56%, respectively. The CNNs were successfully incorporated into a multiple object tracking system for both cylindrical and arbitrarily shaped markers. The cylindrical shaped marker tracking had a mean RMSE of 1.6 ± 0.2 pixels and 1.3 ± 0.4 pixels in the x- and y-directions, respectively. The arbitrarily shaped marker tracking had a mean RMSE of 3.0 ± 0.5 pixels and 2.2 ± 0.4 pixels in the x- and y-directions, respectively.

CONCLUSION

With deep learning CNNs, high classification performances on unseen patient images were achieved for both cylindrical and arbitrarily shaped markers. Furthermore, the application of CNN models to intrafraction monitoring was demonstrated using a simple tracking system. The results demonstrate that CNN models can be used to track markers without prior knowledge of the marker properties or an additional learning period.

Data-driven respiratory motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) using groupwise deformable registration

PURPOSE

To demonstrate the feasibility of using a purely data-driven, a posteriori respiratory motion modeling and reconstruction compensation method to improve 4D-CBCT image quality under clinically relevant image acquisition conditions.

METHODS

Evaluated workflows that utilized a combination of groupwise deformable image registration and motion-compensated image reconstruction algorithms. Groupwise registration is an approach that simultaneously registers all temporal frames of a 4D image to a common reference instead of one at a time so as to minimize the influence of any individual time point on the global smoothness or accuracy of the resulting deformation model. Four-dimensional cone-beam CT (4D-CBCT) Feldkamp-Davis-Kress (FDK) reconstructions were registered to either iteratively computed mean respiratory phase (mean-frame) or preselected respiratory phase (fixed-frame) reference images to model respiratory motion. The resulting 4D transformations were used to deform projection data during the FDK backprojection operation to create motion-compensated reconstructions. Tissue interface sharpness (TIS) was defined as the slope of a sigmoid curve fit to a mobile tissue boundary and was used to evaluate image quality in regions susceptible to motion artifacts. Image quality improvement was assessed for 19 clinical cases by evaluating mitigation of view aliasing artifacts, TIS, image noise reduction, and contrast for implanted fiducial markers.

RESULTS

Average (standard deviation) diaphragm TIS recovery relative to initial 4D-CBCT reconstructions was observed to be 87% (46%) using fixed-frame registration alone; 87% (47%) using fixed frame with motion-compensated reconstruction; 101% (68%) using mean-frame registration alone; and 99% (65%) using mean frame with motion-compensated reconstruction. Noise was reduced in sampled soft tissue ROIs by 58% for both fixed-frame registration and registration with motion compensation and by 57% and 58% on average for the corresponding mean-frame methods, respectively. Average improvement in local CNR was observed to be respectively 93% and 98% for fixed-frame registration and registration with motion compensation methods and 116% and 111% for the corresponding mean-frame methods.

CONCLUSION

Data-driven groupwise registration and motion-compensated reconstruction offer a feasible means of improving the quality of 4D-CBCT images acquired under clinical conditions. The addition of motion compensation reconstruction after groupwise registration visibly reduced the impact of view aliasing artifacts for the clinical image datasets studied.

Effectiveness of different rescanning techniques for scanned proton radiotherapy in lung cancer patients

Non-small cell lung cancer (NSCLC) is a tumour type thought to be well-suited for proton radiotherapy. However, the lung region poses many problems related to organ motion and can for actively scanned beams induce severe interplay effects. In this study we investigate four mitigating rescanning techniques: (1) volumetric rescanning, (2) layered rescanning, (3) breath-sampled (BS) layered rescanning, and (4) continuous breath-sampled (CBS) layered rescanning. The breath-sampled methods will spread the layer rescans over a full breathing cycle, resulting in an improved averaging effect at the expense of longer treatment times. In CBS, we aim at further improving the averaging by delivering as many rescans as possible within one breathing cycle. The interplay effect was evaluated for 4D robustly optimized treatment plans (with and without rescanning) for seven NSCLC patients in the treatment planning system RayStation. The optimization and final dose calculation used a Monte Carlo dose engine to account for the density heterogeneities in the lung region. A realistic treatment delivery time structure given from the IBA ScanAlgo simulation tool served as basis for the interplay evaluation. Both slow (2.0 s) and fast (0.1 s) energy switching times were simulated. For all seven studied patients, rescanning improves the dose conformity to the target. The general trend is that the breath-sampled techniques are superior to layered and volumetric rescanning with respect to both target coverage and variability in dose to OARs. The spacing between rescans in our breath-sampled techniques is set at planning, based on the average breathing cycle length obtained in conjunction with CT acquisition. For moderately varied breathing cycle lengths between planning and delivery (up to 15%), the breath-sampled techniques still mitigate the interplay effect well. This shows the potential for smooth implementation at the clinic without additional motion monitoring equipment.

Motion monitoring during a course of lung radiotherapy with anchored electromagnetic transponders : Quantification of inter- and intrafraction motion and variability of relative transponder positions

Purpose

Anchored electromagnetic transponders for tumor motion monitoring during lung radiotherapy were clinically evaluated. First, intrafractional motion patterns were analyzed as well as their interfractional variations. Second, intra- and interfractional changes of the geometric transponder positions were investigated.

Materials and methods

Intrafractional motion data from 7 patients with an upper or middle lobe tumor and three implanted transponders each was used to calculate breathing amplitudes, overall motion amount and motion midlines in three mutual perpendicular directions and three-dimensionally (3D) for 162 fractions. For 6 patients intra- and interfractional variations in transponder distances and in the size of the triangle defined by the transponder locations over the treatment course were determined.

Results

Mean 3D values of all fractions were up to 4.0, 4.6 and 3.4 mm per patient for amplitude, overall motion amount and midline deviation, respectively. Intrafractional transponder distances varied with standard deviations up to 3.2 mm, while a maximal triangle shrinkage of 36.5% over 39 days was observed.

Conclusions

Electromagnetic real-time motion monitoring was feasible for all patients. Detected respiratory motion was on average modest in this small cohort without lower lobe tumors, but changes in motion midline were of the same size as the amplitudes and greater midline motion can be observed in some fractions. Intra- and interfractional variations of the geometric transponder positions can be large, so for reliable motion management correlation between transponder and tumor motion needs to be evaluated per patient.

Electronic supplementary material

The online version of this article (doi: 10.1007/s00066-017-1183-0) contains supplementary material, which is available to authorized users.

A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy

The ability to monitor tumor motion without implanted markers can potentially enable broad access to more accurate and precise lung radiotherapy. A major challenge is that kilovoltage (kV) imaging based methods are rarely able to continuously track the tumor due to the inferior tumor visibility on 2D kV images. Another challenge is the estimation of 3D tumor position based on only 2D imaging information. The aim of this work is to address both challenges by proposing a Bayesian approach for markerless tumor tracking for the first time. The proposed approach adopts the framework of the extended Kalman filter, which combines a prediction and measurement steps to make the optimal tumor position update. For each imaging frame, the tumor position is first predicted by a respiratory-correlated model. The 2D tumor position on the kV image is then measured by template matching. Finally, the prediction and 2D measurement are combined based on the 3D distribution of tumor positions in the past 10 s and the estimated uncertainty of template matching. To investigate the clinical feasibility of the proposed method, a total of 13 lung cancer patient datasets were used for retrospective validation, including 11 cone-beam CT scan pairs and two stereotactic ablative body radiotherapy cases. The ground truths for tumor motion were generated from the the 3D trajectories of implanted markers or beacons. The mean, standard deviation, and 95th percentile of the 3D tracking error were found to range from 1.6-2.9 mm, 0.6-1.5 mm, and 2.6-5.8 mm, respectively. Markerless tumor tracking always resulted in smaller errors compared to the standard of care. The improvement was the most pronounced in the superior-inferior (SI) direction, with up to 9.5 mm reduction in the 95th-percentile SI error for patients with  >10 mm 5th-to-95th percentile SI tumor motion. The percentage of errors with 3D magnitude  <5 mm was 96.5% for markerless tumor tracking and 84.1% for the standard of care. The feasibility of 3D markerless tumor tracking has been demonstrated on realistic clinical scenarios for the first time. The clinical implementation of the proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow. Future work is focused on the clinical and real-time implementation of this method.

Verification of respiratory-gated radiotherapy with new real-time tumour-tracking radiotherapy system using cine EPID images and a log file

A combined system comprising the TrueBeam linear accelerator and a new real-time tumour-tracking radiotherapy system, SyncTraX, was installed at our institution. The objectives of this study are to develop a method for the verification of respiratory-gated radiotherapy with SyncTraX using cine electronic portal image device (EPID) images and a log file and to verify this treatment in clinical cases. Respiratory-gated radiotherapy was performed using TrueBeam and the SyncTraX system. Cine EPID images and a log file were acquired for a phantom and three patients during the course of the treatment. Digitally reconstructed radiographs (DRRs) were created for each treatment beam using a planning CT set. The cine EPID images, log file, and DRRs were analysed using a developed software. For the phantom case, the accuracy of the proposed method was evaluated to verify the respiratory-gated radiotherapy. For the clinical cases, the intra- and inter-fractional variations of the fiducial marker used as an internal surrogate were calculated to evaluate the gating accuracy and set-up uncertainty in the superior-inferior (SI), anterior-posterior (AP), and left-right (LR) directions. The proposed method achieved high accuracy for the phantom verification. For the clinical cases, the intra- and inter-fractional variations of the fiducial marker were  ⩽3 mm and  ±3 mm in the SI, AP, and LR directions. We proposed a method for the verification of respiratory-gated radiotherapy with SyncTraX using cine EPID images and a log file and showed that this treatment is performed with high accuracy in clinical cases.

A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer

PURPOSE

To describe in detail a dataset consisting of serial four-dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally advanced, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community.

ACQUISITION AND VALIDATION METHODS

As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.4-70.2 Gy using daily 1.8 or 2 Gy fractions. Audio-visual biofeedback was used to minimize breathing irregularity during all fractions, including acquisition of all 4DCT and 4DCBCT acquisitions in all subjects. Target, organs at risk, and implanted fiducial markers were delineated by a physician in the 4DCT images. Image coordinate system origins between 4DCT and 4DCBCT were manipulated in such a way that the images can be used to simulate initial patient setup in the treatment position. 4DCT images were acquired on a 16-slice helical CT simulator with 10 breathing phases and 3 mm slice thickness during simulation. In 13 of the 20 subjects, 4DCTs were also acquired on the same scanner weekly during therapy. Every day, 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that each CBCT projection was time stamped with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with the half bow-tie filter. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases and reconstructed with an in-house FDK reconstruction algorithm. Errors in respiration sorting, reconstruction, and acquisition were carefully identified and corrected.

DATA FORMAT AND USAGE NOTES

4DCT and 4DCBCT images are available in DICOM format and structures through DICOM-RT RTSTRUCT format. All data are stored in the Cancer Imaging Archive (TCIA, http://www.cancerimagingarchive.net/) as collection 4D-Lung and are publicly available.

DISCUSSION

Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, oversampled 4DCBCT, and fiducial markers, this dataset can support studies in image-guided and image-guided adaptive radiotherapy, assessment of 4D voxel trajectory variability, and development and validation of new tools for image registration and motion management.

Liquid fiducial marker performance during radiotherapy of locally advanced non small cell lung cancer

BACKGROUND AND PURPOSE

We analysed the positional and structural stability of a long-term biodegradable liquid fiducial marker (BioXmark) for radiotherapy in patients with locally advanced lung cancer.

MATERIAL AND METHODS

Markers were injected via endoscopic- or endobronchial ultrasound in lymph nodes and reachable primary tumours. Marker volume and Hounsfield Units (HU) changing rates were estimated using breath-hold CBCT. Inter-fraction variation in marker position relative to gross tumour volume (GTV) position was established, as well as the inter-fraction variation in mediastinal marker registration relative to a carina registration through the treatment.

RESULTS

Fifteen patients were included and 29 markers analysed. All markers that were in situ at planning were visible through the treatment. Mean HU was 902±165HU for lymph node and 991±219HU for tumour markers. Volume degradation rates were -5% in lymph nodes and -23% in primary tumours. Three-dimensional inter-fraction variation for marker position relative to the GTV position was -0.1±0.7mm in lymph nodes and -1.5±2.3mm in primary tumours. Inter-fraction variations in marker registration relative to carina registration were -0.4±1.2mm in left-right, 0.2±2.0mm in anterior-posterior and -0.5±2.0mm in cranio-caudal directions.

CONCLUSIONS

The liquid fiducial markers were visible and stable in size and position throughout the treatment course.

Breathing guidance in radiation oncology and radiology: A systematic review of patient and healthy volunteer studies

PURPOSE

The advent of image-guided radiation therapy has led to dramatic improvements in the accuracy of treatment delivery in radiotherapy. Such advancements have highlighted the deleterious impact tumor motion can have on both image quality and radiation treatment delivery. One approach to reducing tumor motion irregularities is the use of breathing guidance systems during imaging and treatment. These systems aim to facilitate regular respiratory motion which in turn improves image quality and radiation treatment accuracy. A review of such research has yet to be performed; it was therefore their aim to perform a systematic review of breathing guidance interventions within the fields of radiation oncology and radiology.

METHODS

From August 1-14, 2014, the following online databases were searched: Medline, Embase, PubMed, and Web of Science. Results of these searches were filtered in accordance to a set of eligibility criteria. The search, filtration, and analysis of articles were conducted in accordance with preferred reporting items for systematic reviews and meta-analyses. Reference lists of included articles, and repeat authors of included articles, were hand-searched.

RESULTS

The systematic search yielded a total of 480 articles, which were filtered down to 27 relevant articles in accordance to the eligibility criteria. These 27 articles detailed the intervention of breathing guidance strategies in controlled studies assessing its impact on such outcomes as breathing regularity, image quality, target coverage, and treatment margins, recruiting either healthy adult volunteers or patients with thoracic or abdominal lesions. In 21/27 studies, significant (p < 0.05) improvements from the use of breathing guidance were observed.

CONCLUSIONS

There is a trend toward the number of breathing guidance studies increasing with time, indicating a growing clinical interest. The results found here indicate that further clinical studies are warranted that quantify the clinical impact of breathing guidance, along with the health technology assessment to determine the advantages and disadvantages of breathing guidance.

Measuring interfraction and intrafraction lung function changes during radiation therapy using four-dimensional cone beam CT ventilation imaging

PURPOSE

Adaptive ventilation guided radiation therapy could minimize the irradiation of healthy lung based on repeat lung ventilation imaging (VI) during treatment. However the efficacy of adaptive ventilation guidance requires that interfraction (e.g., week-to-week), ventilation changes are not washed out by intrafraction (e.g., pre- and postfraction) changes, for example, due to patient breathing variability. The authors hypothesize that patients undergoing lung cancer radiation therapy exhibit larger interfraction ventilation changes compared to intrafraction function changes. To test this, the authors perform the first comparison of interfraction and intrafraction lung VI pairs using four-dimensional cone beam CT ventilation imaging (4D-CBCT VI), a novel technique for functional lung imaging.

METHODS

The authors analyzed a total of 215 4D-CBCT scans acquired for 19 locally advanced non-small cell lung cancer (LA-NSCLC) patients over 4-6 weeks of radiation therapy. This set of 215 scans was sorted into 56 interfraction pairs (including first day scans and each of treatment weeks 2, 4, and 6) and 78 intrafraction pairs (including pre/postfraction scans on the same-day), with some scans appearing in both sets. VIs were obtained from the Jacobian determinant of the transform between the 4D-CBCT end-exhale and end-inhale images after deformable image registration. All VIs were deformably registered to their corresponding planning CT and normalized to account for differences in breathing effort, thus facilitating image comparison in terms of (i) voxelwise Spearman correlations, (ii) mean image differences, and (iii) gamma pass rates for all interfraction and intrafraction VI pairs. For the side of the lung ipsilateral to the tumor, we applied two-sided t-tests to determine whether interfraction VI pairs were more different than intrafraction VI pairs.

RESULTS

The (mean ± standard deviation) Spearman correlation for interfraction VI pairs was r̄(Inter)=0.52±0.25, which was significantly lower than for intrafraction pairs (r̄(Intra)=0.67±0.20, p = 0.0002). Conversely, mean absolute ventilation differences were larger for interfraction pairs than for intrafraction pairs, with |ΔV̄(Inter)|=0.42±0.65 and |ΔV̄(Intra)|=0.32±0.53, respectively (p < 10(-15)). Applying a gamma analysis with ventilation/distance tolerance of 25%/10 mm, we observed mean pass rate of (69% ± 20%) for interfraction VIs, which was significantly lower compared to intrafraction pairs (80% ± 15%, with p ∼ 0.0003). Compared to the first day scans, all patients experienced at least one subsequent change in median ipsilateral ventilation ≥10%. Patients experienced both positive and negative ventilation changes throughout treatment, with the maximum change occurring at different weeks for different patients.

CONCLUSIONS

The authors' data support the hypothesis that interfraction ventilation changes are larger than intrafraction ventilation changes for LA-NSCLC patients over a course of conventional lung cancer radiation therapy. Longitudinal ventilation changes are observed to be highly patient-dependent, supporting a possible role for adaptive ventilation guidance based on repeat 4D-CBCT VIs. We anticipate that future improvement of 4D-CBCT image reconstruction algorithms will improve the capability of 4D-CBCT VI to resolve interfraction ventilation changes.

Differences in displacement of the proximal and distal ends of mid-upper thoracic esophageal squamous cell carcinoma

In the present study, clips were used as markers to evaluate displacement differences between proximal and distal ends of esophageal tumors and to test whether their internal target volume (ITV) margins should be determined separately. A total of 23 patients with mid-upper thoracic esophageal squamous-cell carcinoma, a tumor length of ≤8 cm and an esophageal lumen suitable for endoscopic ultrasonography were recruited for the present study. Clips were implanted endoscopically at the proximal and distal ends of the esophageal tumor (upper and lower clips). In a further exploratory study on 16 of the patients, a third clip was placed at the distal esophagus 2 cm above the gastro-esophageal junction (GEJ) (cardiac clip). The clips were contoured for all 10 phases of the four-dimensional computed tomography and the maximum displacements of the clip centroids among different breathing phases in left-right (LR), superior-inferior (SI) and anterior-posterior (AP) directions were marked as x, y and z, respectively. The ITV margins that covered 95% of the LR, SI and AP motion were 2.89, 5.00 and 2.36 mm, respectively. Axial displacement (y) was greater than radial displacement (x, z; P<0.05). It was also revealed that LR(x), SI(y) and AP(z) displacement of cardiac clips was greater than that of upper or lower clips (P<0.05). Differences in the axial and radial displacement of the upper and lower clips indicated that axial and radial ITV margins should be determined separately. However, further study is required on patients in whom the distal tumor end is located in proximity to the GEJ.

Markerless tumor tracking using short kilovoltage imaging arcs for lung image-guided radiotherapy

The ability to monitor tumor motion without implanted markers is clinically advantageous for lung image-guided radiotherapy (IGRT). Existing markerless tracking methods often suffer from overlapping structures and low visibility of tumors on kV projection images. We introduce the short arc tumor tracking (SATT) method to overcome these issues. The proposed method utilizes multiple kV projection images selected from a nine-degree imaging arc to improve tumor localization, and respiratory-correlated 4D cone-beam CT (CBCT) prior knowledge to minimize the effects of overlapping anatomies. The 3D tumor position is solved as an optimization problem with prior knowledge incorporated via regularization. We retrospectively validated SATT on 11 clinical scans from four patients with central tumors. These patients represent challenging scenarios for markerless tumor tracking due to the inferior adjacent contrast. The 3D trajectories of implanted fiducial markers were used as the ground truth for tracking accuracy evaluation. In all cases, the tumors were successfully tracked at all gantry angles. Compared to standard pre-treatment CBCT guidance alone, trajectory errors were significantly smaller with tracking in all cases, and the improvements were the most prominent in the superior-inferior direction. The mean 3D tracking error ranged from 2.2-9.9 mm, which was 0.4-2.6 mm smaller compared to pre-treatment CBCT. In conclusion, we were able to directly track tumors with inferior visibility on kV projection images using SATT. Tumor localization accuracies are significantly better with tracking compared to the current standard of care of lung IGRT. Future work involves the prospective evaluation and clinical implementation of SATT.

Feasibility of markerless tumor tracking by sequential dual-energy fluoroscopy on a clinical tumor tracking system

A novel approach to dual-energy imaging for markerless tumor tracking was proposed consisting of sequential dual-energy fluoroscopy, omitting the need for fast-switching kV generators. The implementation of this approach on a clinical tumor tracking system and its efficacy is shown feasible through optimization of the imaging parameters.

Six degrees-of-freedom prostate and lung tumor motion measurements using kilovoltage intrafraction monitoring

PURPOSE

Tumor positional uncertainty has been identified as a major issue that deteriorates the efficacy of radiation therapy. Tumor rotational movement, which is not well understood, can result in significant geometric and dosimetric inaccuracies. The objective of this study was to measure 6 degrees-of-freedom (6 DoF) prostate and lung tumor motion, focusing on the more novel rotation, using kilovoltage intrafraction monitoring (KIM).

METHODS AND MATERIALS

Continuous kilovoltage (kV) projections of tumors with gold fiducial markers were acquired during radiation therapy for 267 fractions from 10 prostate cancer patients and immediately before or after radiation therapy for 50 fractions from 3 lung cancer patients. The 6 DoF motion measurements were determined from the individual 3-dimensional (3D) marker positions, after using methods to reject spurious and smooth noisy data, using an iterative closest point algorithm.

RESULTS

There were large variations in the magnitude of the tumor rotation among different fractions and patients. Various rotational patterns were observed. The average prostate rotation angles around the left-right (LR), superior-inferior (SI), and anterior-posterior (AP) axes were 1.0 ± 5.0°, 0.6 ± 3.3°, and 0.3 ± 2.0°, respectively. For 35% of the time, the prostate rotated more than 5° about the LR axis, indicating the need for intrafractional adaptation during radiation delivery. For lung patients, the average LR, SI, and AP rotation angles were 0.8 ± 4.2°, -0.8 ± 4.5°, and 1.7 ± 3.1°, respectively. For about 30% of the time, the lung tumors rotated more than 5° around the SI axis. Respiration-induced rotation was detected in 2 of the 3 lung patients.

CONCLUSIONS

The prostate and lung tumors were found to undergo rotations of more than 5° for about a third of the time. The lung tumor data represent the first 6 DoF tumor motion measured by kV images. The 6 DoF KIM method can enable rotational and translational adaptive radiation therapy and potentially reduce treatment margins.

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.

Strategies of dose escalation in the treatment of locally advanced non-small cell lung cancer: image guidance and beyond

Radiation dose in the setting of chemo-radiation for locally advanced non-small cell lung cancer (NSCLC) has been historically limited by the risk of normal tissue toxicity and this has been hypothesized to correlate with the poor results in regard to local tumor recurrences. Dose escalation, as a means to improve local control, with concurrent chemotherapy has been shown to be feasible with three-dimensional conformal radiotherapy in early phase studies with good clinical outcome. However, the potential superiority of moderate dose escalation to 74 Gy has not been shown in phase III randomized studies. In this review, the limitations in target volume definition in previous studies; and the factors that may be critical to safe dose escalation in the treatment of locally advanced NSCLC, such as respiratory motion management, image guidance, intensity modulation, FDG-positron emission tomography incorporation in the treatment planning process, and adaptive radiotherapy, are discussed. These factors, along with novel treatment approaches that have emerged in recent years, are proposed to warrant further investigation in future trials in a more comprehensive and integrated fashion.

Prospective evaluation of dual-energy imaging in patients undergoing image guided radiation therapy for lung cancer: initial clinical results

PURPOSE

A prospective feasibility study was conducted to investigate the utility of dual-energy (DE) imaging compared to conventional x-ray imaging for patients undergoing kV-based image guided radiation therapy (IGRT) for lung cancer.

METHODS AND MATERIALS

An institutional review board-approved feasibility study enrolled patients with lung cancer undergoing IGRT and was initiated in September 2011. During daily setup, 2 sequential respiration-gated x-ray images were obtained using an on-board imager. Imaging was composed of 1 standard x-ray image at 120 kVp (1 mAs) and a second image obtained at 60 kVp (4 mAs). Weighted logarithmic subtraction of the 2 images was performed offline to create a soft tissue-selective DE image. Conventional and DE images were evaluated by measuring relative contrast and contrast-to-noise ratios (CNR) and also by comparing spatial localization, using both approaches. Imaging dose was assessed using a calibrated ion chamber.

RESULTS

To date, 10 patients with stage IA to IIIA lung cancer were enrolled and 57 DE images were analyzed. DE subtraction resulted in complete suppression of overlying bone in all 57 DE images, with an average improvement in relative contrast of 4.7 ± 3.3 over that of 120 kVp x-ray images (P<.0002). The improvement in relative contrast with DE imaging was seen for both smaller (gross tumor volume [GTV] ≤5 cc) and larger tumors (GTV >5 cc), with average relative contrast improvement ratios of 3.4 ± 4.1 and 5.4 ± 3.6, respectively. Moreover, the GTV was reliably localized in 95% of the DE images versus 74% of the single energy (SE images, (P=.004). Mean skin dose per DE image set was 0.44 ± 0.03 mGy versus 0.43 ± 0.03 mGy, using conventional kV imaging parameters.

CONCLUSIONS

Initial results of this feasibility study suggest that DE thoracic imaging may enhance tumor localization in lung cancer patients receiving kV-based IGRT without increasing imaging dose.

Interfraction displacement of primary tumor and involved lymph nodes relative to anatomic landmarks in image guided radiation therapy of locally advanced lung cancer

PURPOSE

To analyze primary tumor (PT) and lymph node (LN) position changes relative to each other and relative to anatomic landmarks during conventionally fractionated radiation therapy for patients with locally advanced lung cancer.

METHODS AND MATERIALS

In 12 patients with locally advanced non-small cell lung cancer PT, LN, carina, and 1 thoracic vertebra were manually contoured on weekly 4-dimensional fan-beam CT scans. Systematic and random interfraction displacements of all contoured structures were identified in the 3 cardinal directions, and resulting setup margins were calculated. Time trends and the effect of volume changes on displacements were analyzed.

RESULTS

Three-dimensional displacement vectors and systematic/random interfraction displacements were smaller for carina than for vertebra both for PT and LN. For PT, mean (SD) 3-dimensional displacement vectors with carina-based alignment were 7 (4) mm versus 9 (5) mm with bony anatomy (P<.0001). For LN, smaller displacements were found with carina- (5 [3] mm, P<.0001) and vertebra-based (6 [3] mm, P=.002) alignment compared with using PT for setup (8 [5] mm). Primary tumor and LN displacements relative to bone and carina were independent (P>.05). Displacements between PT and bone (P=.04) and between PT and LN (P=.01) were significantly correlated with PT volume regression. Displacements between LN and carina were correlated with LN volume change (P=.03).

CONCLUSIONS

Carina-based setup results in a more reproducible PT and LN alignment than bony anatomy setup. Considering the independence of PT and LN displacement and the impact of volume regression on displacements over time, repeated CT imaging even with PT-based alignment is recommended in locally advanced disease.

Stability of percutaneously implanted markers for lung stereotactic radiotherapy

The purpose of this study was to evaluate the stability of complex markers implanted into lung tumors throughout a course of stereotactic body radiotherapy (SBRT). Fifteen patients referred for lung SBRT were prospectively included. Radio‐opaque markers were implanted percutaneously, guided by computed tomography (CT). Deep inspiration breath‐hold CT scans (BHCT) were acquired at planning and on three treatment days. The treatment days' BHCTs were registered to the planning BHCT. Intraobserver uncertainty in both tumor and marker registration was determined. Deviations in the difference between tumor and marker‐based image registrations of the BHCT scans during treatment quantified the marker stability. Marker position deviation relative to tumor position of less than 2 mm in all three dimensions was considered acceptable for treatment delivery precision. Intra‐observer uncertainties for image registration in the left‐right (LR), anterior‐posterior (AP), craniocaudal (CC) directions and three‐dimensional vector (3D) were 0.9 mm, 0.9 mm, 1.0 mm, and 1.1 mm (SD) for tumor registration and 0.3 mm, 0. 5 mm, 0.7 mm, and 0.7 mm (SD) for marker registration. Mean 3D differences for tumor registrations on all days were significantly larger than for 3D marker registrations (p = 0.007). Overall median differences between tumor and marker position were 0.0 mm (range ‐2.9 to 2.6 mm) in LR, 0.0 mm (‐1.8 to 1.5 mm) in AP, and ‐0.2 mm (‐2.6 to 2.8 mm) in CC directions. Four patients had deviations exceeding 2 mm in one or more registrations throughout the SBRT course. This is the first study to evaluate stability of complex markers implanted percutaneously into lung tumors for image guidance in SBRT. We conclude that the observed stability of marker position within the tumor indicates that complex markers can be used as surrogates for tumor position during a short course of SBRT as long as the uncertainties related to their position within the tumor are incorporated into the planning target volume.

PACS number: 87.57.nj, 87.55.ne