4DCT Reconstruction Research Articles

Pt4: Phantom-4D, an open-source software tool for creating time-evolving 3D phantoms

pt4, an open-source software tool to describe time-evolving phantoms is presented. pt4 allows users to create detailed time-evolving phantoms for testing novel 4D-CT reconstruction algorithms. Ground-truth volumes and simulated X-ray projections can be produced at arbitrary time-points during time-evolution and with customisable pixel dimensions, noise models, and X-ray source trajectories. Phantoms are built up from 3D primitives whose parameters and attenuation can be made arbitrary functions of time. This feature permits both complex continuous and discontinuous time-evolution necessary for thorough testing of 4D-CT reconstruction algorithms. Various phantoms built using pt4 are also presented to demonstrate the versatility of pt4 phantom description. pt4 is written in C++ and is highly parallelised leading to a performant implementation which is feasible to use for up to thousand-of-voxel volume pixel dimensions.

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

Background and ObjectiveIn radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts and erroneously characterizes the averaged breathing motion. Furthermore, conventional 4D-CBCT can only be generated post-hoc using the full sequence of kV projections after the treatment is complete, limiting its utility. Hence, our purpose is to develop a deep-learning motion model for estimating 3D+t CT images from treatment kV projection series. MethodsWe propose an end-to-end learning-based 3D motion modelling and 4DCT reconstruction model named 4D-Precise, abbreviated from Probabilistic reconstruction of image sequences from CBCT kV projections. The model estimates voxel-wise motion fields and simultaneously reconstructs a 3DCT volume at any arbitrary time point of the input projections by transforming a reference CT volume. Developing a Torch-DRR module, it enables end-to-end training by computing Digitally Reconstructed Radiographs (DRRs) in PyTorch. During training, DRRs with matching projection angles to the input kVs are automatically extracted from reconstructed volumes and their structural dissimilarity to inputs is penalised. We introduced a novel loss function to regulate spatio-temporal motion field variations across the CT scan, leveraging planning 4DCT for prior motion distribution estimation. ResultsThe model is trained patient-specifically using three kV scan series, each including over 1200 angular/temporal projections, and tested on three other scan series. Imaging data from five patients are analysed here. Also, the model is validated on a simulated paired 4DCT-DRR dataset created using the Surrogate Parametrised Respiratory Motion Modelling (SuPReMo). The results demonstrate that the reconstructed volumes by 4D-Precise closely resemble the ground-truth volumes in terms of Dice, volume similarity, mean contour distance, and Hausdorff distance, whereas 4D-Precise achieves smoother deformations and fewer negative Jacobian determinants compared to SuPReMo. ConclusionsUnlike conventional 4DCT reconstruction techniques that ignore breath inter-cycle motion variations, the proposed model computes both intra-cycle and inter-cycle motions. It represents motion over an extended timeframe, covering several minutes of kV scan series.

Motion-compensated 4DCT reconstruction from single-beat cardiac CT scans using convolutional networks.

We proposed a deep learning-based method for single-heartbeat 4D cardiac CT reconstruction, where a single cardiac cycle was split into multiple phases for reconstruction. First, we pre-reconstruct each phase using the projection data from itself and the neighboring phases. The pre-reconstructions are fed into a supervised registration network to generate the deformation fields between different phases. The deformation fields are trained so that it can match the ground truth images from the corresponding phases. The deformation fields are then used in the FBP-and-wrap method for motion-compensated reconstruction, where a subsequent network is used to remove residual artifacts. The proposed method was validated with simulation data from 40 4D cardiac CT scans and demonstrated improved RMSE and SSIM and less blurring compared to FBP and PICCS.

Dose reduction in sequence scanning 4D CT imaging through respiratory signal-guided tube current modulation: A feasibility study.

Respiratory signal-guided 4D CT sequence scanning such as the recently introduced Intelligent 4D CT (i4DCT) approach reduces image artifacts compared to conventional 4D CT, especially for irregular breathing. i4DCT selects beam-on periods during scanning such that data sufficiency conditions are fulfilled for each couch position. However, covering entire breathing cycles during beam-on periods leads to redundant projection data and unnecessary dose to the patient during long exhalation phases. We propose and evaluate the feasibility of respiratory signal-guided dose modulation (i.e., temporary reduction of the CT tube current) to reduce the i4DCT imaging dose while maintaining high projection data coverage for image reconstruction. The study is designed as an in-silico feasibility study. Dose down- and up-regulation criteria were defined based on the patients' breathing signals and their representative breathing cycle learned before and during scanning. The evaluation (including an analysis of the impact of the dose modulation criteria parameters) was based on 510 clinical 4D CT breathing curves. Dose reduction was determined as the fraction of the downregulated dose delivery time to the overall beam-on time. Furthermore, under the assumption of a 10-phase 4D CT and amplitude-based reconstruction, beam-on periods were considered negatively affected by dose modulation if the downregulation period covered an entire phase-specific amplitude range for a specific breathing phase (i.e., no appropriate reconstruction of the phase image possible for this specific beam-on period). Corresponding phase-specific amplitude bins are subsequently denoted as compromised bins. Dose modulation resulted in a median dose reduction of 10.4% (lower quartile: 7.4%, upper quartile: 13.8%, maximum: 28.6%; all values corresponding to a default parameterization of the dose modulation criteria). Compromised bins were observed in 1.0% of the beam-on periods (72 / 7370 periods) and affected 10.6% of the curves (54/510 curves). The extent of possible dose modulation depends strongly on the individual breathing patterns and is weakly correlated with the median breathing cycle length (Spearman correlation coefficient 0.22, p<0.001). Moreover, the fraction of beam-on periods with compromised bins is weakly anti-correlated with the patient's median breathing cycle length (Spearman correlation coefficient -0.24; p<0.001). Among the curves with the 17% longest average breathing cycles, no negatively affected beam-on periods were observed. Respiratory signal-guided dose modulation for i4DCT imaging is feasible and promises to significantly reduce the imaging dose with little impact on projection data coverage. However, the impact on image quality remains to be investigated in a follow-up study.

Comparative evaluation of a surface-based respiratory monitoring system against a pressure sensor for 4DCT image reconstruction in phantoms.

Four-dimensional computed tomography (4DCT), which relies on breathing-induced motion, requires realistic surrogate information of breathing variations to reconstruct the tumor trajectory and motion variability of normal tissues accurately. Therefore, the SimRT surface-guided respiratory monitoring system has been installed on a Siemens CT scanner. This work evaluated the temporal and spatial accuracy of SimRT versus our commonly used pressure sensor, AZ-733V. A dynamic thorax phantom was used to reproduce regular and irregular breathing patterns acquired by SimRT and Anzai. Various parameters of the recorded breathing patterns, including mean absolute deviations (MAD), Pearson correlations (PC), and tagging precision, were investigated and compared to ground-truth. Furthermore, 4DCT reconstructions were analyzed to assess the volume discrepancy, shape deformation and tumor trajectory. Compared to the ground-truth, SimRT more precisely reproduced the breathing patterns with a MAD range of 0.37±0.27 and 0.92±1.02mm versus Anzai with 1.75±1.54 and 5.85±3.61mm for regular and irregular breathing patterns, respectively. Additionally, SimRT provided a more robust PC of 0.994±0.009 and 0.936±0.062 for all investigated breathing patterns. Further, the peak and valley recognition were found to be more accurate and stable using SimRT. The comparison of tumor trajectories revealed discrepancies up to 7.2 and 2.3mm for Anzai and SimRT, respectively. Moreover, volume discrepancies up to 1.71±1.62% and 1.24±2.02% were found for both Anzai and SimRT, respectively. SimRT was validated across various breathing patterns and showed a more precise and stable breathing tracking, (i) independent of the amplitude and period, (ii) and without placing any physical devices on the patient's body. These findings resulted in a more accurate temporal and spatial accuracy, thus leading to a more realistic 4DCT reconstruction and breathing-adapted treatment planning.

4DCT is long overdue for improvement.

4DCT is long overdue for improvement.

Individualized breathing trace quality assurance for lung radiotherapy patients undergoing 4DCT simulation.

4DCT simulation is a popular solution for radiotherapy simulation of lung cancer patients as it allows the clinician to gain an appreciation for target motion during the patient breathing cycle. Resultant binning of images and production of the 4DCT dataset relies heavily on the recorded breathing trace; but quality assurance is not routinely performed on these and there lacks any substantial recommendations thereof. An application was created for Windows in C# that was able to analyze the VXP breathing trace files from Varian RPM/RGSC and quantify various metrics associated with the patient breathing cycle. This data was then used to consider errors in voluming of targets for several example cases in order to justify recommendations on quality assurance. For 281 real patient breathing traces from 4DCT simulation of lung targets, notable differences were found between RGSC and application calculations of phase data. For any new patient without individualized QA, the average marked phase calculation (which is used for 4DCT reconstruction) is only accurate to within 19% of the actual phases. The error in BPM within the scan due to breathing rate variation is 37%. The uncertainty in amplitude due to breathing variation is 34% in the mean. Phase uncertainty leads to misbinning which we have shown can lead to missing 66% of the target for gated treatment. Variation in inhalation/exhalation level leads to voluming errors which, without individualized QA, can be assumed to be 11% (PTV is smaller than actual). Without individualized quality assurance of patient breathing traces, large uncertainties have to be assumed for metrics of both phase and amplitude, leading to clinically significant uncertainties in treatment. It is recommended to perform individualized quality assurance as this provides the clinician with an accurate quantification of uncertainty for their patient.

Effects of reconstruction methods on dose distribution for lung stereotactic body radiotherapy treatment plans.

The aim of the present study was to investigate the effect of tumour motion on various imaging strategies as well as on treatment plan accuracy for lung stereotactic body radiotherapy treatment (SBRT) cases. The ExacTrac gating phantom and paraffin were used to investigate respiratory motion and represent a lung tumour, respectively. Four-dimensional computed tomography (4DCT) imaging was performed, while the phantom was moving sinusoidally with 4s cycling time with three different amplitudes of 8, 16, and 24mm. Reconstructions were done with maximum (MIP) and average intensity projection (AIP) methods. Comparisons of target density and volume were performed using two reconstruction techniques and references values. Volumetric modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT) were planned based on reconstructed computed tomography (CT) sets, and it was examined how density variations affect the dose-volume histogram (DVH) parameters. 4D cone beam computed tomography (CBCT) was performed with the Elekta Versa HD linac imaging system before irradiation and compared with 3D CBCT. Thus, various combinations of 4DCT reconstruction methods and treatment alignment methods have been investigated. Point measurements as well as 2 and 3D dose measurements were done by optically stimulated luminescence (OSL), gafchromic films, and electronic portal imaging devices (EPIDs), respectively. The mean volume reduction was 7.8% for the AIP and 2.6% for the MIP method. The obtained Hounsfield Unit (HU) values were lower for AIP and higher for MIP when compared with the reference volume density. In DVH analysis, there were no statistical differences for D95%, D98%, and Dmean (p > 0.05). However, D2% was significantly affected by HU changes (p < 0.01). A positional variation was obtained up to 2mm in moving direction when 4D CBCT was applied after 3D CBCT. Dosimetric measurements showed that the main part of the observed dose deviation was due to movement. In lung SBRT treatment plans, D2% doses differ significantly according to the reconstruction method. Additionally, it has been observed that setups based on 3D imaging can cause a positional error of up to 2mm compared to setups based on 4D imaging. It is concluded that MIP has advantages over AIP in defining internal target volume (ITV) in lung SBRT applications. In addition, 4D CBCT and 3D EPID dosimetry are recommended for lung SBRT treatments.

Deep learning improves image quality and radiomics reproducibility for high-speed four-dimensional computed tomography reconstruction

Background and purposeHybrid iterative reconstruction (HIR) is the most commonly used algorithm for four-dimensional computed tomography (4DCT) reconstruction due to its high speed. However, the image quality is worse than that of model-based iterative reconstruction (MIR). Different reconstruction methods affect the stability of radiomics features. Herein, we developed a deep learning method to improve the quality and radiomics reproducibility of the high-speed reconstruction. Materials and methodsThe 4DCT images of 70 patients were reconstructed using both the HIR and MIR algorithms. A cycle-consistent adversarial network was adopted to learn the mapping from HIR to MIR, and then generate synthetic MIR (sMIR) images from HIR. The performance was evaluated using the testing set (10 patients). ResultsThe total reconstruction times for the HIR, MIR, and proposed sMIR images were approximately 2.5, 15, and 3.1 mins, respectively. The quality of sMIR images was close to that of MIR and was superior to that of HIR images, with noise reduced by 45–77% and contrast-to-noise ratio improved by 91–296%. The concordance correlation coefficients (CCC) of radiomic features improved from 0.89 ± 0.15 for HIR to 0.97 ± 0.07 for the proposed sMIR. The percentage of reproducible features (CCC ≥ 0.85) increased from 76.08% for HIR to 95.86% for sMIR, with an improvement of 19.78%. ConclusionCompared to existing HIR algorithm, the proposed method improves the image quality and radiomics reproducibility of 4DCT images under high-speed reconstruction. It is computationally efficient and has potential to be integrated into any CT system.

4D CT reconstruction method for periodically moving objects in a high-speed camera CT system

Purpose of this study is to enable 4D CT reconstruction of objects in periodic motion by using a high-speed CT system. 4D CT reconstruction is a promising technique for dynamic internal inspection of products. In general, 4D CT reconstruction means that a volume is generated at each time frame from projection images scanned while an object is in motion. Proposed method in this study is phase sorting reconstruction using motion period of object. Proposed method was validated using simulated and real data. In addition, this study explored and validated several methods for sorting projections by motion phase.

Adjoint image warping using multivariate splines with application to four-dimensional computed tomography.

PurposeAdjoint image warping is an important tool to solve image reconstruction problems that warp the unknown image in the forward model. This includes four‐dimensional computed tomography (4D‐CT) models in which images are compared against recorded projection images of various time frames using image warping as a model of the motion. The inversion of these models requires the adjoint of image warping, which up to now has been substituted by approximations. We introduce an efficient implementation of the exact adjoints of multivariate spline based image warping, and compare it against previously used alternatives.MethodsUsing symbolic computer algebra, we computed a list of 64 polynomials that allow us to compute a matrix representation of trivariate cubic image warping. By combining an on‐the‐fly computation of this matrix with a parallelized implementation of columnwise matrix multiplication, we obtained an efficient, low memory implementation of the adjoint action of 3D cubic image warping. We used this operator in the solution of a previously proposed 4D‐CT reconstruction model in which the image of a single subscan was compared against projection data of multiple subscans by warping and then projecting the image. We compared the properties of our exact adjoint with those of approximate adjoints by warping along inverted motion.ResultsOur method requires halve the memory to store motion between subscans, compared to methods that need to compute and store an approximate inverse of the motion. It also avoids the computation time to invert the motion and the tunable parameter of the number of iterations used to perform this inversion. Yet, a similar and often better reconstruction quality was obtained in comparison with these more expensive methods, especially when the motion is large. When compared against a simpler method that is similar to ours in computational demands, our method achieves a higher reconstruction quality in general.ConclusionsOur implementation of the exact adjoint of cubic image warping improves efficiency and provides accurate reconstructions.

4DCT Reconstruction With a Low-Density Target

4DCT technology is used in radiation oncology to estimate the internal motion of organs due to breathing. While used to determine the internal target volume (ITV), there is a lack of publications that show the precision and accuracy of 4DCT technology with low density targets. In this study, we investigate the accuracy of the Minimum Intensity Projection (mIP) when the target is air and the surrounding medium is acrylic. A Quasar phantom with a 4D PET-CT insert (P/N: 500-3318) filled with air was utilized for the study. The scanning unit was a GE Optima Scanner utilizing Real-Time Positioning ManagementTM (RPM) System. The breathing period is a sinusoidal breathing pattern set to 20 cycles per minute with 4 cm amplitude. A set of two different studies was performed: static, where the phantom was scanned without motion, and dynamic-tracking, where the moving phantom was tracked using RPM. For the dynamic-tracking study, 3D images were created for each of the 10 breathing phases utilizing phase sorting. From these, a 3D mIP image and an average 3D image (AVG) were created. The air cavity was contoured in both studies using Lung window level preset and the diameter, height and volume were determined. The true ITV was calculated using vendor insert specifications. The amplitude of motion was obtained by subtracting the static height from the mIP and the AVG and compared with the amplitude set on the phantom. Next, a CBCT was performed on the phantom using a linear accelerator. The volume of air was contoured on this scan using the same preset as before, and this volume was compared to the volume on the AVG reconstructed 3D image. The mIP had a measured amplitude of 3.97 cm compared with the true set amplitude of 4cm and a target volume of 41.2 cm3 compared with the true ITV volume of 42.74 cm3 resulting in a difference from the true values of -0.8% and -3.6%, respectively. The AVG measured amplitude was 3.90 cm and target volume were 37.2 cm3, resulting in a difference of 2.5% and -13.0%, respectively. The CBCT had a measured amplitude of motion of 3.88 cm, which resulted in a difference from the true amplitude of -3.0%, but when compared against the AVG only resulted in a difference of -0.5%, with the AVG being slightly larger. In addition, the target volume of the CBCT was 36.4 cm3, which was slightly smaller than the AVG target volume of 37.2 cm3, with a difference of -2.2%. This study shows that the motion of a low-density target surrounded by high density material can be accurately estimated using 4DCT technology. The mIP, which is used to determine the ITV margins for treatment for low density targets, accurately depicted the motion of the target to within a millimeter. In addition, the AVG, which is the scan used for treatment planning, accurately matched the CBCT observed target. Further study is required on non-sinusoidal motion to determine 4DCT accuracy.

Evaluating reconstruction algorithms for respiratory motion guided acquisition

Conventional thoracic 4DCBCT scans take 1320 projections over 4 min. This paper investigates which reconstruction algorithms best leverage Respiratory-Motion-Guided (RMG) acquisition in order to reduce scan time and dose while maintaining image quality.We investigated a 200 projection, on average 1 min RMG acquisition. RMG acquisition ensures even angular separation between projections at each respiratory phase by adjusting the imaging gantry rotation to the patient respiratory signal in real time.Conventional 1320 projection data and RMG 200 projection data were simulated from 4DCT volumes of 14 patients. Each patient had an initial 4DCT reconstruction, treated as a planning 4DCT, and a 4DCT reconstruction acquired later, used for 4DCBCT data simulation and evaluation.Reconstructions were computed using the Feldkamp-David-Kress (FDK), McKinnon-Bates (MKB), RecOnstructiOn using Spatial and TEmporal Regularization (ROOSTER), and Motion Compensated FDK (MCFDK) algorithms. We also introduced and evaluated a novel MCMKB algorithm.Image quality was evaluated with Root-Mean-Square Error (RMSE), Structural SIMilarity index (SSIM) and Tissue Interface Sharpness (TIS). Rigid registration of the tumor volume regions between the reconstruction and the ground truth was used to evaluate geometric accuracy.Relative to conventional 4DCBCT acquisition, the RMG acquisition delivered 80% less dose and was on average 70% faster. The conventional-acquisition 4DFDK-reconstruction volumes had mean RMSE, SSIM, TIS and geometric error of 94, 0.9987, 2.69 and 1.19 mm respectively. The RMG-acquisition MCFDK-reconstruction volumes had mean RMSE, SSIM, TIS and geometric error of 113, 0.9986, 1.76 and 1.77 mm respectively with minimal increase in computational cost. These results suggest scan time and dose can be significantly reduced with minimal impact on reconstruction quality by implementing RMG acquisition and motion compensated reconstruction.

Commissioning a four-dimensional Computed Tomography Simulator for minimum target size due to motion in the Anterior-Posterior direction: a procedure and treatment planning recommendations.

The purpose of this work is to develop a procedure for commissioning four‐dimensional computed tomography (4DCT) algorithms for minimum target reconstruction size, to quantify the effect of anterior–posterior (AP) motion artifacts on known object reconstruction for periodic and irregular breathing patterns, and to provide treatment planning recommendations for target sizes below a minimum threshold. A mechanical platform enabled AP motion of a rod and lung phantom during 4DCT acquisition. Static, artifact‐free scans of the phantoms were first acquired. AP sinusoidal and patient breathing motion was applied to obtain 4DCT images. 4DCT reconstruction artifacts were assessed by measuring the apparent width and angle of the rod. Comparison of known tumor diameters and volumes between the static image parameters with the 4DCT image sets was used to quantify the extent of AP reconstruction artifact and contour deformation. Examination of the rod width, under sinusoidal motion, found it was best represented during the inhale and exhale phases for all periods and ranges of motion. From the gradient phases, the apparent width of the rod decreased with increasing amplitude and decreasing period. The rod angle appeared larger on the reconstructed images due to the presence of motion artifact. The apparent diameters of the spherical tumors on the gradient phases were larger/equivalent than the true values in the AP/LR direction, respectively, while the exhale phase consistently displayed the spheres at the approximately correct diameter. The Eclipse calculated diameter matched closely with the true diameter on the exhale phase and was found to be larger on the inhale, MIP, and Avg scans. The procedure detailed here may be used during the acceptance and commissioning period of a computed tomography simulator or retroactively when implementing a SBRT program to determine the minimum target size that can be reliably reconstructed.

65. Study of respiratory signals of patients undergoing 4DCT for VMAT lung cancer treatments

Purpose To account for respiratory motion in radiotherapy treatment of lung lesions, 4DCT is often employed. For a correct reconstruction of respiratory phases, periodic and regular breathing is the underlying hypothesis when phase-binning algorithms are used. Aim of this work was to study the respiratory signals acquired during 4DCT and to evaluate whether a correlation exists between deviations from regularity of breathing signal and quality of reconstructed images. Methods and materials 4DCT of 40 patients acquired with a Philips Brilliance Big Bore CT were analyzed in this study. Respiratory signals were exported and an in-house written Matlab routine was used for extracting and processing the waveforms. The duration of each respiratory cycle (CD) was calculated as the distance between two consecutive maxima, while the fraction of inspiration (ID) was calculated as the ratio between the distance minimum–maximum and the duration of the whole cycle. Mean value (M), standard deviation ( σ ) and relative percentage error Er = σ M × 100 were extracted for each patient, the latter being a measurement of breathing regularity. Thereafter, 4DCT image quality was blindly scored from 1 to 4 (1: presence of visible artifacts in the reconstructed phases, 4: absence of visible artifacts). Patients were classified according to the score and mean relative percentage errors and standard deviations were calculated within each scoring class. Results In Fig. 1 mean relative percentage errors versus assigned score are reported for both CD and ID. A correlation between Er and image quality is clearly visible. Threshold values for Er of about 18% for CD and about 15% for ID can be identified as limits between good and bad quality reconstructed images. Conclusion Variations in respiratory cycle and inspiration phase duration clearly correlate with the quality of 4DCT reconstruction and can be used as predictors of image quality. Download : Download high-res image (153KB) Download : Download full-size image

Predicting Increased Inter-Observer Variation in Pre-Treatment Image Guidance for Lung SBRT Patients

To use CT simulation data to identify lung SBRT patients that may be more difficult to register during pre-treatment image guidance. Retrospective image registration was performed for the first and last treatment fraction for 10 lung SBRT patients by 16 individual observers. In the parameter descriptions below, GTV0 is defined as the GTV as contoured using the 0% inhalation phase of a 4DCT reconstruction while surrounding normal lung is defined as the lung volume within the PTV less GTV0. Four metrics were evaluated as possible predictors of higher inter-observer variability, and were named as follows: (1) “target excursion” defined as ITV volume-GTV0 volume, (2) “local target contrast” defined as the area of overlap of the normalized HU distributions for target and surrounding normal lung, (3) “mean HU difference’ defined as local lung mean HU – GTV0 mean HU, and (4) “target density variability” defined as GTV HU standard deviation. Regression analysis was performed to assess significance of the correlation. Three of the 4 quantities evaluated as potential predictive metrics showed statistical correlation with increased inter-observer variation. For “target excursion”, a correlation between ITV-GTV0 volume and mean inter-observer 3D vector difference was observed for both first fraction (r2 = 0.51) and last fraction (r2 = 0.39). For “local target contrast”, one significant outlier was identified having mean local lung HU beyond two standard deviations from the mean of all patients. Upon removal of this outlier, “mean HU difference” (r2 = 0.56 – first fraction, and r2 = 0.56 – last fraction) and “local target contrast” (r2 = 0.48 – first fraction, and r2 = 0.40 – last fraction) were correlated with mean 3D vector difference. Three of 10 patients had inter-observer variability that was considered clinically significant, defined here as having a 95% confidence width greater than 5 mm. All of these patients had higher “target excursion” and/or “local target contrast” scores compared to the remaining patients. Three metrics were shown here to potentially identify patients for which large inter-observer variations would be anticipated and may be used in the future to develop thresholds for additional interventions to mitigate these registration variations.

A fiducial-less tracking method for radiation therapy of liver tumors by diaphragm disparity analysis part 2: validation study by using clinical data

Motion management must be considered in treating liver tumors. One effective approach is real-time tumor tracking, which can be performed by the CyberKnife® Robotic Radiosurgery System through the Synchrony® Respiratory Tracking System. It uses a combination of kV images, LED markers, an infrared camera, and surgically implanted fiducial markers to track tumors under the influence of respiration. However, the use of fiducial markers through an invasive procedure can lead to complications. In our previous simulation study, we were able to demonstrate the feasibility of our proposed fiducial-less tracking technique using a digital phantom under regular respiratory motion. The aim of this study is to further validate this innovative method by using a digital phantom data under the irregular respiratory cycles as well as clinical data from patients under the Cyberknife environment. As performed in our previous simulation study, abdominal 4DCT datasets of one breathing cycle, from the digital phantom and from four patients, were previously generated or acquired. Associated with the breathing cycles in the 4DCT datasets, one set of DRR images (+ 45° or − 45°) was produced for each breathing phase. On each DRR, an outline of the lung-diaphragm border was detected using an edge detection algorithm. The tracked target volume’s gravity center was identified for each phase of the breathing cycle by a MATLAB program, serving as the ground truth for the validation. Using artificial neural networks (ANN), four models for the phantom and six models for the patient data, correlating the diaphragm’s location with the corresponding 3D location of the tracked target volume, were compared. Assessment was performed by using the root-mean-squared error (RMSE) values through the leave-one-out (LOO) validation criterion. The averaged RMSE for the phantom data was 1.05 ± 1.14 mm. When using the patient data from the + 45° projection, the averaged RMSE was 2.13 ± 1.79 mm, while from the − 45° projection, the averaged RMSE was 2.26 ± 2.40 mm. Using the proposed method in both phantom validation and patient data validation, the RMSE is closely related to the 4DCT reconstruction error and to the distance from the lung-diaphragm border to the tracked tumor. We proposed and investigated the fiducial-less tracking method to follow tumor motion in the real-time under the influence of respiration. The study shows the feasibility of accurately predicting the tumor’s position with the use of lung-diaphragm border’s information through available kV images without gold fiducial markers. This developed diaphragm disparity-analysis-based approach, verified with clinically accepted errors, has the potential to replace fiducial markers in clinical applications.

Performance evaluation and first clinical experience with the Varian RGSC module for breath detection of 15 lung cancer patients

The University Hospital of Düsseldorf, Germany (UKD) recently installed the Respiratory Gating for Scanners module (RGSC) (Varian Medical Systems, Palo Alto, USA). The aim of this article is to report on the commissioning and clinical implementation of the RGSC system. The steps encompassed the validation of the manufacturer's specifications including functionality tests using a commercial and in-house developed breathing phantom, to establish calibration procedures, and clinical workflow analysis involving breath acquisition and patient data evaluation. In this context also the RGSC signal without motion was performed to assess the calibration procedure. Reproducibility test were conducted as well with breathing phantoms. Fifteen clinical breath curves were examined in order to assess the impact of treatment related uncertainties such as noises of the CT, patient positioning, movement of the CT table, unintended patient motion. Finally, different binning approaches were assessed and the effect on the CT reconstructions and methodic advantages were investigated. All technical specifications of the manufacturer were confirmed. A baseline drift of 1.83mm of the measured breath curve occurred during longitudinal movement of the CT table. This drift is smaller if the direction of table motion coincides precisely with the level of calibration. If the calibration is carried out on extensions for patient positioning we measured a baseline drift up to 6mm. It was found that especially for a combination of a ceiling mounted IR-camera and amplitude based 4D-CT reconstructions, precise calibration is prerequisite. The evaluations of patient breath curves and corresponding CT reconstructions revealed patient specific aspects and variations, respectively. Consequently patient selection criteria need to be established in parallel with the technical implementation and validation phase of respiratory gating.

Impact of a novel exponential weighted 4DCT reconstructionalgorithm.

PurposeThis work characterizes a novel exponential 4DCT reconstruction algorithm (EXPO), in phantom and patient, to determine its impact on image quality as compared to the standard cosine‐squared weighted 4DCT reconstruction.MethodsA motion platform translated objects in the superior–inferior (S‐I) direction at varied breathing rates (8–20 bpm) and couch pitches (0.06–0.1) to evaluate interplay between parameters. Ten‐phase 4DCTs were acquired and data were reconstructed with cosine squared and EXPO weighting. To quantify the magnitude of image blur, objects were translated in the anterior–posterior (A‐P) and S‐I directions for full‐width half maximum (FWHM) analysis between both 4DCT algorithms and a static case. 4DCT sinogram data for 10 patients were retrospectively reconstructed using both weighting factors. Image subtractions elucidated intensity and boundary differences. Subjective image quality grading (presence of image artifacts, noise, spatial resolution (i.e., lung/liver boundary sharpness), and overall image quality) was conducted yielding 200 evaluations.ResultsAfter taking static object size into account, the FWHM of EXPO reconstructions in the A‐P direction was 3.3 ± 1.7 mm (range: 0–4.9) as compared to cosine squared 9.8 ± 4.0 mm (range: 2.6–14.4). The FWHM of objects translated in the S‐I direction reconstructed with EXPO agreed better with the static FWHM than the cosine‐squared reconstructions. Slower breathing periods, faster couch pitches, and intermediate 4DCT phases had the largest reductions of blurring with EXPO. 18 of 60 comparisons of artifacts were improved with EXPO reconstruction, whereas no appreciable changes were observed in image quality scores. In 18 of 20 cases, EXPO provided sharper images although the reduced projections also increased baseline noise.ConclusionExponential weighted 4DCT offers potential for reducing image blur (i.e., improving image sharpness) in 4DCT with a tendency to reduce artifacts. Future work will involve evaluating the impact on treatment planning including delineation ability and dose calculation.

Clinical workflow optimization to improve 4DCT reconstruction for Toshiba Aquilion CT scanners

Respiratory motion remains a source of major uncertainties in radiotherapy. Respiratory correlated computed tomography (referred to as 4DCT) serves as one way of reducing breathing artifacts in 3D-CTs and allows the investigation of tumor motion over time. The quality of the 4DCT images depends on the data acquisition scheme, which in turn is dependent on the vendor. Specifically, the only way Toshiba Aquilion LB CT scanners can reconstruct 4DCTs is a cycle-based reconstruction using triggers provided by an external surrogate signal. The accuracy is strongly dependent on the method of trigger generation. Two consecutive triggers are used to define a breathing cycle which is divided into respiratory phases of equal duration. The goal of this study is to identify if there are advantages in the usage of local-amplitude based sorting (LAS) of the respiration motion states, in order to reduce image artifacts and improve 4DCT quality. Furthermore, this study addresses the generation and optimization of a clinical workflow using as surrogate motion monitoring system the Sentinel™ (C-RAD AB, Sweden) optical surface scanner in combination with a Toshiba Aquilion LB CT scanner. For that purpose, a phantom study using 10 different breathing waveforms and a retrospective patient study using the 4DCT reconstructions of 10 different patients has been conducted. The error in tumor volume has been reduced from 2.9±3.7% to 2.7±2.6% using optimal cycle-based triggers (manipulated CBS) and to 2.7±2.2% using LAS in the phantom study. Moreover, it was possible to decrease the tumor volume variability from 5.0±3.6% using the original cycle-based triggers (original CBS) to 3.5±2.5% using the optimal triggers and to 3.7±2.7% using LAS in the patient data analysis. We therefore propose the usage of the manipulated CBS, also with regard to an accurate and safe clinical workflow.