A novel internal target volume definition based on velocity and time of respiratory target motion for external beam radiotherapy

This study aimed to develop a novel internal target volume (ITV) definition for respiratory motion targets, considering target motion velocity and time. The proposed ITV was evaluated in respiratory-gated radiotherapy. An ITV modified with target motion velocity and time (ITVvt) was defined as an ITV that includes a target motion based on target motion velocity and time. The target motion velocity was calculated using four-dimensional computed tomography (4DCT) images. The ITVvts were created from phantom and clinical 4DCT images. The phantom 4DCT images were acquired using a solid phantom that moved with a sinusoidal waveform (peak-to-peak amplitudes of 10 and 20 mm and cycles of 2-6 s). The clinical 4DCT images were obtained from eight lung cancer cases. In respiratory-gated radiotherapy, the ITVvt was compared with conventional ITVs for beam times of 0.5-2 s within the gating window. The conventional ITV was created by adding a uniform margin as the maximum motion within the gating window. In the phantom images, the maximum volume difference between the ITVvt and conventional ITV was -81.9%. In the clinical images, the maximum volume difference was -53.6%. Shorter respiratory cycles and longer BTs resulted in smaller ITVvt compared with the conventional ITV. Therefore, the proposed ITVvt plan could be used to reduce treatment volumes and doses to normal tissues.

Reconstruction of multi-phase parametric maps in 4D-magnetic resonance fingerprinting (4D-MRF) by optimization of local T1 and T2 sensitivities

BACKGROUND

Time-resolved magnetic resonance fingerprinting (MRF), or 4D-MRF, has been demonstrated its feasibility in motion management in radiotherapy (RT). However, the prohibitive long acquisition time is one of challenges of the clinical implementation of 4D-MRF. The shortening of acquisition time causes data insufficiency in each respiratory phase, leading to poor accuracies and consistencies of the predicted tissues' properties of each phase.

PURPOSE

To develop a technique for the reconstruction of multi-phase parametric maps in four-dimensional magnetic resonance fingerprinting (4D-MRF) through the optimization of local T1 and T2 sensitivities.

METHODS

The proposed technique employed an iterative optimization to tailor the data arrangement of each phase by manipulation of inter-phase frames, such that the T1 and T2 sensitivities, which were quantified by the modified Minkowski distance, of the truncated signal evolution curve was maximized. The multi-phase signal evolution curves were modified by sliding window reconstruction and inter-phase frame sharing (SWIFS). Motion correction (MC) and dot product matching were sequentially performed on the modified signal evolution and dictionary to reconstruct the multi-parametric maps. The proposed technique was evaluated by numerical simulations using the extended cardiac-torso (XCAT) phantom with regular and irregular breathing patterns, and by in vivo MRF data of three health volunteers and six liver cancer patients acquired at a 3.0 T scanner.

RESULTS

In simulation study, the proposed SWIFS approach achieved the overall mean absolute percentage error (MAPE) of 8.62% ± 1.59% and 16.2% ± 3.88% for the eight-phases T1 and T2 maps, respectively, in the sagittal view with irregular breathing patterns. In contrast, the overall MAPE of T1 and T2 maps generated by the conventional approach with multiple MRF repetitions were 22.1% ± 11.0% and 30.8% ± 14.9%, respectively. For in-vivo study, the predicted mean T1 and T2 of liver by the proposed SWIFS approach were 795 ms ± 38.9 ms and 58.3 ms ± 11.7 ms, respectively.

CONCLUSIONS

Both simulation and in vivo results showed that the approach empowered by T1 and T2 sensitivities optimization and sliding window under the shortened acquisition of MRF had superior performance in the estimation of multi-phase T1 and T2 maps as compared to the conventional approach with oversampling of MRF data.

Deformable lung 4DCT image registration via landmark-driven cycle network

BACKGROUND

An automated, accurate, and efficient lung four-dimensional computed tomography (4DCT) image registration method is clinically important to quantify respiratory motion for optimal motion management.

PURPOSE

The purpose of this work is to develop a weakly supervised deep learning method for 4DCT lung deformable image registration (DIR).

METHODS

The landmark-driven cycle network is proposed as a deep learning platform that performs DIR of individual phase datasets in a simulation 4DCT. This proposed network comprises a generator and a discriminator. The generator accepts moving and target CTs as input and outputs the deformation vector fields (DVFs) to match the two CTs. It is optimized during both forward and backward paths to enhance the bi-directionality of DVF generation. Further, the landmarks are used to weakly supervise the generator network. Landmark-driven loss is used to guide the generator's training. The discriminator then judges the realism of the deformed CT to provide extra DVF regularization.

RESULTS

We performed four-fold cross-validation on 10 4DCT datasets from the public DIR-Lab dataset and a hold-out test on our clinic dataset, which included 50 4DCT datasets. The DIR-Lab dataset was used to evaluate the performance of the proposed method against other methods in the literature by calculating the DIR-Lab Target Registration Error (TRE). The proposed method outperformed other deep learning-based methods on the DIR-Lab datasets in terms of TRE. Bi-directional and landmark-driven loss were shown to be effective for obtaining high registration accuracy. The mean and standard deviation of TRE for the DIR-Lab datasets was 1.20 ± 0.72 mm and the mean absolute error (MAE) and structural similarity index (SSIM) for our datasets were 32.1 ± 11.6 HU and 0.979 ± 0.011, respectively.

CONCLUSION

The landmark-driven cycle network has been validated and tested for automatic deformable image registration of patients' lung 4DCTs with results comparable to or better than competing methods.

Feasibility Study of Convolutional Long ShortTerm Memory Network for Pulmonary Movement Prediction in CT Images

Background

During X-ray imaging, pulmonary movements can cause many image artifacts. To tackle this issue, several studies, including mathematical algorithms and 2D-3D image registration methods, have been presented. Recently, the application of deep artificial neural networks has been considered for image generation and prediction.

Objective

In this study, a convolutional long short-term memory (ConvLSTM) neural network is used to predict spatiotemporal 4DCT images.

Material and Methods

In this analytical analysis study, two ConvLSTM structures, consisting of stacked ConvLSTM models along with the hyperparameter optimizer algorithm and a new design of the ConvLSTM model are proposed. The hyperparameter optimizer algorithm in the conventional ConvLSTM includes the number of layers, number of filters, kernel size, epoch number, optimizer, and learning rate. The two ConvLSTM structures were also evaluated through six experiments based on Root Mean Square Error (RMSE) and structural similarity index (SSIM).

Results

Comparing the two networks demonstrates that the new design of the ConvLSTM network is faster, more accurate, and more reliable in comparison to the tuned-stacked ConvLSTM model. For all patients, the estimated RMSE and SSIM were 3.17 and 0.988, respectively, and a significant improvement can be observed in comparison to the previous studies.

Conclusion

Overall, the results of the new design of the ConvLSTM network show excellent performances in terms of RMSE and SSIM. Also, the generated CT images with the new design of the ConvLSTM model show a good consistency with the corresponding references regarding registration accuracy and robustness.

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-733 V. 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 ± 1.02 mm versus Anzai with 1.75 ± 1.54 and 5.85 ± 3.61 mm 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 2.3 mm 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.

A comprehensive quality assurance program for four-dimensional computed tomography in radiotherapy

This study aimed to develop and validate a comprehensive, reproducible and automatic 4DCT Quality Assurance (QA) workflow (QAMotion) that evaluates image accuracy across various regular and irregular breathing patterns. Volume and amplitude deviations, CT number accuracy, and spatial integrity were used as evaluation metrics. For repeatability tests, tolerances were respected with a mean CT number deviation < 10 HU, volume deviation < 2% and diameter and amplitude deviation < 2 mm except for irregular amplitude curves for which an amplitude deviation up to 6 mm was measured. QAMotion was able to flag image artefacts for our clinical 4DCT system.

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.

Effect of subject motion and gantry rotation speed on image quality and dose delivery in CT-guided radiotherapy

PURPOSE

To investigate the effects of subject motion and gantry rotation speed on computed tomography (CT) image quality over a range of image acquisition speeds for fan-beam (FB) and cone-beam (CB) CT scanners, and quantify the geometric and dosimetric errors introduced by FB and CB sampling in the context of adaptive radiotherapy.

METHODS

Images of motion phantoms were acquired using four CT scanners with gantry rotation speeds of 0.5 s/rotation (denoted FB-0.5), 1.9 s/rotation (FB-1.9), 16.6 s/rotation (CB-16.6), and 60.0 s/rotation (CB-60.0). A phantom presenting various tissue densities undergoing motion with 4-s period and ranging in amplitude from ±0.5 to ±10.0 mm was used to characterize motion artifacts (streaks), motion blur (edge-spread function, ESF), and geometric inaccuracy (excursion of insert centroids and distortion of known shape). An anthropomorphic abdomen phantom undergoing ±2.5-mm motion with 4-s period was used to simulate an adaptive radiotherapy workflow, and relative geometric and dosimetric errors were compared between scanners.

RESULTS

At ±2.5-mm motion, phantom measurements demonstrated mean ± SD ESF widths of 0.6 ± 0.0, 1.3 ± 0.4, 2.0 ± 1.1, and 2.9 ± 2.0 mm and geometric inaccuracy (excursion) of 2.7 ± 0.4, 4.1 ± 1.2, 2.6 ± 0.7, and 2.0 ± 0.5 mm for the FB-0.5, FB-1.9, CB-16.6, and CB-60.0 scanners, respectively. The results demonstrated nonmonotonic trends with scanner speed for FB and CB geometries. Geometric and dosimetric errors in adaptive radiotherapy plans were largest for the slowest (CB-60.0) scanner and similar for the three faster systems (CB-16.6, FB-1.9, and FB-0.5).

CONCLUSIONS

Clinically standard CB-60.0 demonstrates strong image quality degradation in the presence of subject motion, which is mitigated through faster CBCT or FBCT. Although motion blur is minimized for FB-0.5 and FB-1.9, such systems suffer from increased geometric distortion compared to CB-16.6. Each system reflects tradeoffs in image artifacts and geometric inaccuracies that affect treatment delivery/dosimetric error and should be considered in the design of next-generation CT-guided radiotherapy systems.

A new method for assessing lung tumor motion in radiotherapy using dynamic chest radiography

Dynamic chest radiography (DCR) is a recent advanced modality to acquire dynamic and functional images. We developed a new method using DCR and the free analysis software, Kinovea, to assess lung tumor motion. This study aimed to demonstrate the usefulness of our method. Phantom and clinical studies were performed. In the phantom study, dynamic images of a moving lead sphere were acquired using DCR, and the motion of the phantom was tracked using Kinovea in a DCR video. The amplitude of phantom motion was measured and compared with a predetermined baseline amplitude. In a clinical study, DCR and respiratory‐gated four‐dimensional computed tomography (4D‐CT) were performed on 15 patients who underwent stereotactic body radiation therapy for lung tumors. The amplitudes of tumor motion in DCR and 4D‐CT were measured in the superior‐inferior (SI), left‐right (LR), and anterior‐posterior (AP) directions, and the square root of the sum of squares (SRSS) of the amplitude was calculated in all directions. Spearman's rank correlation and the Wilcoxon signed‐rank test were performed to determine the correlations of the amplitudes of tumor motion obtained using DCR and 4D‐CT. In the phantom study, the absolute mean error between the measured and predetermined amplitudes was 0.60 mm (range: 0.061.53 mm). In the clinical study, the amplitudes of tumor motion obtained using DCR correlated significantly with those of 4D‐CT in the SI and LR directions, as did the SRSS values. The median amplitudes for DCR were significantly higher than those for 4D‐CT in all (SI, LR, and AP) directions, as were the SRSS values. Our proposed method based on DCR and Kinovea is useful for assessing lung tumor motion, visually and quantitatively. Therefore, DCR has potential as a new modality for evaluating lung tumor motion in radiotherapy.

Quality assurance of a breathing controlled four-dimensional computed tomography algorithm

•

Initial quality assurance of a novel breathing-controlled four-dimensional computed tomography algorithm.

•

Assessment of geometry, motion representation and image quality for regular and irregular breathing.

•

No clinically relevant differences in results for regular and irregular breathing.

•

Only minor differences in tumor geometry representation and image quality compared to static three-dimensional computed tomography.

•

Table flexion has no clinically relevant impact on geometry representation.

Background & purpose

Material & methods

Results

Conclusions

Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy

Purpose

The purpose of this study was to evaluate the accuracy of a lung stereotactic body radiotherapy (SBRT) treatment plan with the target of a newly predicted internal target volume (ITV_predict) and the feasibility of its clinical application. ITV_predict was automatically generated by our in-house deep learning model according to the cone-beam CT (CBCT) image database.

Method

A retrospective study of 45 patients who underwent SBRT was involved, and Mask R-CNN based algorithm model helped to predict the internal target volume (ITV) using the CBCT image database. The geometric accuracy of ITV_predict was verified by the Dice Similarity Coefficient (DSC), 3D Motion Range (R_3D), Relative Volume Index (RVI), and Hausdorff Distance (HD). The PTV_predict was generated by ITV_predict, which was registered and then projected on free-breath CT (FBCT) images. The PTV_FBCT was margined from the GTV on FBCT images gross tumor volume on free-breath CT (GTV_FBCT). Treatment plans with the target of Predict planning target volume on CBCT images (PTV_predict) and planning target volume on free-breath CT (PTV_FBCT) were respectively re-established, and the dosimetric parameters included the ratio of the volume of patients receiving at least the prescribed dose to the volume of PTV (R_100%), the ratio of the volume of patients receiving at least 50% of the prescribed dose to the volume of PTV in the Radiation Therapy Oncology Group (RTOG) 0813 Trial (R_50%), Gradient Index (GI), and the maximum dose 2 cm from the PTV (D_2cm), which were evaluated via Plan_4DCT, plan which based on PTV_predict (Plan_predict), and plan which based on PTV_FBCT (Plan_FBCT).

Result

The geometric results showed that there existed a good correlation between ITV_predict and ITV on the 4-dimensional CT [ITV_4DCT; DSC= 0.83 ±0.18]. However, the average volume of ITV_predict was 10% less than that of ITV_4DCT (p = 0.333). No significant difference in dose coverage was found in V_100% for the ITV with 99.98 ± 0.04% in the ITV_4DCT vs. 97.56 ± 4.71% in the ITV_predict (p = 0.162). Dosimetry parameters of PTV, including R_100%, R_50%, GI and D_2cm showed no statistically significant difference between each plan (p > 0.05).

Conclusion

Dosimetric parameters of Plan_predict are clinically comparable to those of the original Plan_4DCT. This study confirmed that the treatment plan based on ITV_predict produced by our model could automatically meet clinical requirements. Thus, for patients undergoing lung SBRT, the model has great potential for using CBCT images for ITV contouring which can be used in treatment planning.

First clinical evaluation of breathing controlled four-dimensional computed tomography imaging

Background and Purpose

Four-dimensional computed tomography (4DCT) has become an essential part of radiotherapy planning but is often affected by artifacts. A new breathing controlled 4DCT (i4DCT) algorithm has been introduced. This study aims to present the first clinical data and to evaluate the achieved image quality, projection data coverage and beam-on time.

Material & Methods

The analysis included i4DCT data for 129 scans of patients with thoracic tumors. Projection data coverage and beam-on time were evaluated. Additionally, image quality was exemplarily discussed and rated by ten clinical experts with a 5-score-scale for 30 patients with large variations in their breathing pattern (‘challenging subgroup’). Rated images were reconstructed amplitude- and phase-based.

Results

Expert scoring revealed that 78% (amplitude-based) and 63% (phase-based) of the challenging subgroup were artifact-free (rating ≥4). For the entire cohort, average beam-on time per couch position was 4.9 ± 1.6 s. For the challenging subgroup, time increased slightly but not significantly compared to the remaining patients (5.1 s vs. 4.9 s; p = 0.64). Median projection data coverage was 93% and 94% for inhalation and exhalation, respectively, for the entire cohort. The comparison for the subgroup and the remaining patients revealed a small but significant decrease of the median coverage values for the challenging cases (inhalation: 90% vs. 94%, p = 0.02; exhalation: 93% vs. 94%, p = 0.02).

Conclusions

This first clinical evaluation of i4DCT shows very promising results in terms of image quality and projection data coverage. The results agree with and support the results of previous i4DCT phantom studies.

Ventilation measurements using fast-helical free-breathing computed tomography

PURPOSE

To examine the use of multiple fast-helical free breathing computed tomography (FHFBCT) scans for ventilation measurement.

METHODS

Ten patients were scanned 25 times in alternating directions using a FHFBCT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. Regions-of-interest (ROIs) were selected from the upper right lungs of each patient for analysis. The ROIs were first registered using a published registration technique (pTV). A subsequent follow-up registration employed an objective function with two terms, a ventilation-adjusted Hounsfield Unit difference and a conservation-of-mass term labeled ΔΓ that denoted the difference between the deformation Jacobian and the tissue density ratio. The ventilations were calculated voxel-by-voxel as the slope of a first-order fit of the Jacobian as a function of the breathing amplitude.

RESULTS

The ventilations of the 10 patients showed different patterns and magnitudes. The average ventilation calculated from the deformation vector fields (DVFs) of the pTV and secondary registration was nearly identical, but the standard deviation of the voxel-to-voxel differences was approximately 0.1. The mean of the 90^th percentile values of ΔΓ was reduced from 0.153 to 0.079 between the pTV and secondary registration, implying first that the secondary registration improved the conservation-of-mass criterion by almost 50% and that on average the correspondence between the Jacobian and density ratios as demonstrated by ΔΓ was less than 0.1. This improvement occurred in spite of the average of the 90th percentile changes in the DVF magnitudes being only 0.58 mm.

CONCLUSIONS

This work introduces the use of multiple free-breathing CT scans for free-breathing ventilation measurements. The approach has some benefits over the traditional use of 4-dimensional CT (4DCT) or breath-hold scans. The benefit over 4DCT is that FHFBCT does not have sorting artifacts. The benefits over breath-hold scans include the relatively small motion induced by quiet respiration versus deep-inspiration breath hold and the potential for characterizing dynamic breathing processes that disappear during breath hold.

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

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

Non-rigid image registration of 4D-MRI data for improved delineation of moving tumors

BACKGROUND

To increase the image quality of end-expiratory and end-inspiratory phases of retrospective respiratory self-gated 4D MRI data sets using non-rigid image registration for improved target delineation of moving tumors.

METHODS

End-expiratory and end-inspiratory phases of volunteer and patient 4D MRI data sets are used as targets for non-rigid image registration of all other phases using two different registration schemes: In the first, all phases are registered directly (dir-Reg) while next neighbors are successively registered until the target is reached in the second (nn-Reg). Resulting data sets are quantitatively compared using diaphragm and tumor sharpness and the coefficient of variation of regions of interest in the lung, liver, and heart. Qualitative assessment of the patient data regarding noise level, tumor delineation, and overall image quality was performed by blinded reading based on a 4 point Likert scale.

RESULTS

The median coefficient of variation was lower for both registration schemes compared to the target. Median dir-Reg coefficient of variation of all ROIs was 5.6% lower for expiration and 7.0% lower for inspiration compared with nn-Reg. Statistical significant differences between the two schemes were found in all comparisons. Median sharpness in inspiration is lower compared to expiration sharpness in all cases. Registered data sets were rated better compared to the targets in all categories. Over all categories, mean expiration scores were 2.92 ± 0.18 for the target, 3.19 ± 0.22 for nn-Reg and 3.56 ± 0.14 for dir-Reg and mean inspiration scores 2.25 ± 0.12 for the target, 2.72 ± 215 0.04 for nn-Reg and 3.78 ± 0.04 for dir-Reg.

CONCLUSIONS

In this work, end-expiratory and inspiratory phases of a 4D MRI data sets are used as targets for non-rigid image registration of all other phases. It is qualitatively and quantitatively shown that image quality of the targets can be significantly enhanced leading to improved target delineation of moving tumors.

Use of 4D-CT for radiotherapy planning and reality in France: Data from a national survey

PURPOSE

Lung and some digestive tumours move during a respiratory cycle. Four-dimensional scanography (4D-CT) is commonly used in treatment planning to account for respiratory motion. Although many French radiotherapy centres are now equipped, there are no guidelines on this subject to date. We wanted to draw up a description of the use of the 4D-CT for the treatment planning in France.

METHODS AND MATERIAL

We conducted a survey in all French radiotherapy centres between March and April 2017.

RESULTS

One hundred and seventy-two were contacted. The participation rate was 88.37%. The use of the 4D-CT seems to be common and concerned planning for 15.28% of kidney and adrenal cancers, 19.72% of pancreatic cancers, 27.78% of oesophageal cancers and 73.24% of lung cancers in case of normofractionated treatments. The use of the 4D-CT was also widespread in the case of stereotactic body radiation therapy: with 61.11% in the case of pulmonary irradiation and 34.72% in the case of hepatic irradiation. Many centres declared they carried out several 4D-CT for treatment planning (29, 55% in case of stereotactic body radiation therapy for lung tumours and 20% for liver tumours). Private centres tend to repeat 4D-CT more.

CONCLUSION

Although the use of the 4D-CT appears to be developing, it remains very heterogeneous. To date, the repetition of the 4D-CT has been very poorly studied and could be the subject of clinical studies, allowing to define in which indications and for which populations there is a real benefit.

Enhancement of Long-Term External-Internal Correlation by Phase-Shift Detection and Correction Based on Concurrent External Bellows and Internal Navigator Signals

Purpose

The purpose of this study was to enhance the correlation between external and internal respiratory motions by dynamically determining and correcting the patient-specific phase shift between external and internal respiratory waveforms acquired concurrently during respiratory-correlated 4-dimensional magnetic resonance imaging scans.

Methods and Materials

Internal-navigator and external-bellows waveforms were acquired simultaneously during 6- to 15-minute respiratory-correlated 4-dimensional magnetic resonance imaging scans in 10 healthy participants under an institutional review board–approved protocol. The navigator was placed at the right lung–diaphragm interface, and the bellows were placed ∼5 cm inferior to the sternum. Three segments of each respiratory waveform, at the beginning, middle, and end of a scan, were analyzed. Three phase-domain methods were employed to estimate the phase shift, including analytical signal analysis, phase-space oval fitting, and principal component analysis. A robust strategy for estimating the phase shift was realized by combining these methods in a weighted average and by eliminating outliers (>2 σ) caused by breathing irregularities. Whether phase-shift correction affects the external-internal correlation was evaluated. The cross-correlation between the 2 waveforms in the time domain provided an independent check of the correlation enhancement.

Results

Phase-shift correction significantly enhanced the external-internal correlation in all participants across the entire 6- to 15-minute scans. On average, the correlation increased from 0.45 ± 0.28 to 0.85 ± 0.15 for the combined method. The combined method exhibited a 99.5% success rate and revealed that the phase of the external waveform leads that of the internal waveform in all 10 participants by 57 ^o ± 17^o (1.6 ± 0.5 bins) on average. Seven participants exhibited highly reproducible phase shifts over time, evidenced by standard deviations (σ) < 4^o, whereas 8^o < σ < 12^o in the remaining 3 participants. Regardless, phase-shift correction significantly improved the correlation in all participants.

Conclusions

Correcting the phase shift estimated by the phase-domain methods provides a new approach for enhancing the correlation between external and internal respiratory motions. This strategy holds promise for improving the accuracy of respiratory-gated radiation therapy.

A post-processing method based on interphase motion correction and averaging to improve image quality of 4D magnetic resonance imaging: a clinical feasibility study

METHODS:

Nine patients (seven pancreas, one liver, and one lung) were recruited. 4D-MRI was performed using two prototype k-space sorted techniques, stack-of-stars (SOS) and koosh-ball (KB) acquisitions. Post-processing using MoCoAve was implemented for both methods. Image quality score, apparent SNR (aSNR), sharpness, motion trajectory and standard deviation (σ_GTV) of the gross tumor volumes were compared between original and MoCoAve image sets.

RESULTS:

All subjects successfully underwent 4D-MRI scans and MoCoAve was performed on all data sets. Significantly higher image quality scores (2.64 ± 0.39 vs 1.18 ± 0.34, p = 0.001) and aSNR (37.6 ± 15.3 vs 18.1 ± 5.7, p = 0.001) was observed in the MoCoAve images when compared to the original images. High correlation in tumor motion trajectories in the superoinferior direction (SI: 0.91 ± 0.08) and weaker in the anteroposterior (AP: 0.51 ± 0.44) and mediolateral (ML: 0.37 ± 0.23) directions, similar image sharpness (0.367 ± 0.068 vs 0.369 ± 0.072, p = 0.805), and minimal average absolute difference (0.47 ± 0.34  mm) of the motion trajectory profiles was found between the two image sets. The σ_GTV in pancreas patients was significantly (p = 0.039) lower in MoCoAve images (1.48 ± 1.35  cm3) than in the original images (2.17 ± 1.31  cm3).

CONCLUSION:

MoCoAve using interphase motion correction and averaging has shown promise as a post-processing method for improving k-space sorted (SOS and KB) 4D-MRI image quality in thoracic and abdominal cancer patients.

ADVANCES IN KNOWLEDGE:

The proposed method is an image based post-processing method that could be applied to many k-space sorted 4D-MRI methods for improved image quality and signal-to-noise ratio while preserving image sharpness and respiratory motion fidelity. It is a useful technique for the radiotherapy planning community who are interested in using 4D-MRI but aren't satisfied with their current MR image quality.

Investigation of thoracic four-dimensional CT-based dimension reduction technique for extracting the robust radiomic features

Robust feature selection in radiomic analysis is often implemented using the RIDER test-retest datasets. However, the CT Protocol between the facility and test-retest datasets are different. Therefore, we investigated possibility to select robust features using thoracic four-dimensional CT (4D-CT) scans that are available from patients receiving radiation therapy. In 4D-CT datasets of 14 lung cancer patients who underwent stereotactic body radiotherapy (SBRT) and 14 test-retest datasets of non-small cell lung cancer (NSCLC), 1170 radiomic features (shape: n = 16, statistics: n = 32, texture: n = 1122) were extracted. A concordance correlation coefficient (CCC) > 0.85 was used to select robust features. We compared the robust features in various 4D-CT group with those in test-retest. The total number of robust features was a range between 846/1170 (72%) and 970/1170 (83%) in all 4D-CT groups with three breathing phases (40%-60%); however, that was a range between 44/1170 (4%) and 476/1170 (41%) in all 4D-CT groups with 10 breathing phases. In test-retest, the total number of robust features was 967/1170 (83%); thus, the number of robust features in 4D-CT was almost equal to that in test-retest by using 40-60% breathing phases. In 4D-CT, respiratory motion is a factor that greatly affects the robustness of features, thus by using only 40-60% breathing phases, excessive dimension reduction will be able to be prevented in any 4D-CT datasets, and select robust features suitable for CT protocol of your own facility.

Safety-oriented design of in-house software for new techniques: A case study using a model-based 4DCT protocol

PURPOSE

In-house software is commonly employed to implement new imaging and therapy techniques before commercial solutions are available. Risk analysis methods, as detailed in the TG-100 report of the American Association of Physicists in Medicine, provide a framework for quality management of processes but offer little guidance on software design. In this work, we examine a novel model-based four-dimensional computed tomography (4DCT) protocol using the TG-100 approach and describe two additional methods for promoting safety of the associated in-house software.

METHODS

To implement a previously published model-based 4DCT protocol, in-house software was necessary for tasks such as synchronizing a respiratory signal to computed tomography images, deformable image registration (DIR), model parameter fitting, and interfacing with a treatment planning system. A process map was generated detailing the workflow. Failure modes and effects analysis (FMEA) was performed to identify critical steps and guide quality interventions. Software system safety was addressed through writing "use cases," narratives that characterize the behavior of the software, for all major operations to elicit safety requirements. Safety requirements were codified using the easy approach to requirements syntax (EARS) to ensure testability and eliminate ambiguity.

RESULTS

Sixty-one failure modes were identified and assigned risk priority numbers using FMEA. Resultant quality management interventions include integration of a comprehensive reporting and logging system into the software, mandating daily and monthly equipment quality assurance procedures, and a checklist to be completed at image acquisition. Use cases and resulting safety requirements informed the design of needed in-house software as well as a suite of tests performed during the image generation process.

CONCLUSIONS

TG-100 methods were used to construct a process-level quality management program for a 4DCT imaging protocol. Two supplemental tools from the field of requirements engineering facilitated elicitation and codification of safety requirements that informed the design and testing of in-house software necessary to implement the protocol. These general tools can be applied to promote safety when in-house software is needed to bring new techniques to the clinic.

Simulation of dosimetry impact of 4DCT uncertainty in 4D dose calculation for lung SBRT

Background

Due to the heterogeneity of patient’s individual respiratory motion pattern in lung stereotactic body radiotherapy (SBRT), treatment planning dose assessment using a traditional four-dimensional computed tomography (4DCT_traditional) images based on a uniform breathing curve may not represent the true treatment dose delivered to the patient. The purpose of this study was to evaluate the accumulated dose discrepancy between based on the 4DCT_traditional and true 4DCT (4DCT_true) that incorporated with the patient’s real entire breathing motion. The study also explored a novel 4D robust planning strategy to compensate for such heterogeneity respiratory motion uncertainties.

Methods

Simulated and measured patient specific breathing curves were used to generate 4D targets motion CT images. Volumetric-modulated arc therapy (VMAT) was planned using two arcs. Accumulated dose was obtained by recalculating the plan dose on each individual phase image and then deformed the dose from each phase image to the reference image. The “4 D dose” (D^4D) and “true dose” (D^true) were the accumulated dose based on the 4DCT_traditional and 4DCT_true respectively. The average worse case dose discrepancy (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta D} $$\end{document}ΔD¯) between D^4D and D^true in all treatment fraction was calculated to evaluate dosimetric /planning parameters and correlate them with the heterogeneity of respiratory-induced motion patterns. A novel 4D robust optimization strategy for VMAT (4D Ro-VMAT) based on the probability density function(pdf) of breathing curve was proposed to improve the target coverage in the presence of heterogeneity respiratory motion. The data were assessed with a paired t-tests.

Results

With increasing breathing amplitude from 5 to 20 mm, target \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta {D}_{99}} $$\end{document}ΔD99¯, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta {D}_{95}} $$\end{document}ΔD95¯ increased from 1.59,1.39 to 10.15%,8.66% respectively. When the standard deviation of breathing amplitude increased from 15 to 35% of the mean amplitude, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta {D}_{99}} $$\end{document}ΔD99¯, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \overline{\Delta {D}_{95}} $$\end{document}ΔD95¯ increased from 4.06,3.48 to 10.25%,6.63% respectively. The 4D Ro-VMAT plan significantly improve the target dose compared to VMAT plan.

Conclusion

When the breathing curve amplitude is more than 10 mm and standard deviation of amplitude is higher than 25% of mean amplitude, special care is needed to choose an appropriated dose accumulation approach to evaluate lung SBRT plan target coverage robustness. The proposed 4D Ro_VMAT strategy based on the pdf of patient specific breathing curve could effectively compensate such uncertainties.

The effectiveness of 4DCT in children and adults: A pooled analysis

BACKGROUND

While four-dimensional computed tomography (4DCT) is extensively used in adults, reluctance remains to use 4DCT in children. Day-to-day (interfractional) variability and irregular respiration (intrafractional variability) have shown to be limiting factors of 4DCT effectiveness in adults. In order to evaluate 4DCT applicability in children, the purpose of this study is to quantify inter- and intrafractional variability of respiratory motion in children and adults. The pooled analysis enables a solid comparison to reveal if 4DCT application for planning purposes in children could be valid.

METHODS/MATERIALS

We retrospectively included 90 patients (45 children and 45 adults), for whom the diaphragm was visible on abdominal/thoracic free-breathing cone beam CTs (480 pediatric, 524 adult CBCTs). For each CBCT, the cranial-caudal position of end-exhale and end-inhale positions of the right diaphragm dome were manually selected in the projection images. The difference in position between both phases defines the amplitude. Cycle time equaled inspiratory plus expiratory time. We analyzed the variability of the inter- and intrafractional respiratory-induced diaphragm motion.

RESULTS

Ranges of respiratory motion characteristics were large in both children and adults (amplitude: 4-17 vs 5-24 mm, cycle time 2.1-3.9 vs 2.7-6.5 s). The mean amplitude was slightly smaller in children than in adults (10.7 vs 12.3 mm; P = 0.06). Interfractional amplitude variability was statistically significantly smaller in children than in adults (1.4 vs 2.2 mm; P = 0.00). Mean cycle time was statistically significantly shorter in children (2.9 vs 3.6 s; P = 0.00). Additionally, intrafractional cycle time variability was statistically significantly smaller in children (0.5 vs 0.7 s; P = 0.00).

CONCLUSIONS

Overall variability is smaller in children than in adults, indicating that respiratory motion is more regular in children than in adults. This implies that a single pretreatment 4DCT could be a good representation of daily respiratory motion in children and will be at least equally beneficial for planning purposes as it is in adults.

Quantification accuracy for dynamic contrast enhanced (DCE) CT imaging: phantom and quality assurance framework

PURPOSE

Standardization and protocol optimization is essential for quantification of Dynamic Contrast Enhanced CT as an imaging biomarker. Currently, no commercially available quality assurance (QA) phantoms can provide for testing a complete set of imaging parameters pertaining to routine quality control for contrast-enhanced (CE) CT, as well as spatiotemporal accuracy. The purpose of this work was, therefore: (a) developing a solid calibration phantom for routine CE CT quality assurance; (b) investigating the sensitivity of CECT to organ motion, and (c) characterizing a volumetric CT scanner for CECT.

METHODS

CECT calibration phantom consisting of an acrylic uniform cylinder containing multiple capsules of varying diameters and orientations was designed and built. The capsules contain different solid density materials mimicking iodine contrast enhancement. Sensitivity and accuracy of CECT measurements on all capsules was performed using a 320-slice CT scanner for a range of scan parameters both with and without phantom motion along the transaxial axis of the scanner.

RESULTS

Routine commissioning tests such as uniformity, spatial resolution and image noise were successfully determined using the CECT phantom. Partial volume effect and motion blurring both contribute to a general decrease in contrast enhancement and this was further dependent on capsule orientation (least pronounced for the transaxial orientation). Scanning with a rotation time of less than 0.5 s, the effect of blurring is less than 3% for all orientations and phantom speeds.

CONCLUSION

A new robust contrast calibration phantom was developed and used to evaluate the performance of a 320-slice volumetric CT scanner for DCE-CT.

Quantitative evaluation of the performance of different deformable image registration algorithms in helical, axial, and cone-beam CT images using a mobile phantom

The goal of this project is to investigate quantitatively the performance of different deformable image registration algorithms (DIR) with helical (HCT), axial (ACT), and cone-beam CT (CBCT). The variations in the CT-number values and lengths of well-known targets moving with controlled motion were evaluated. Four DIR algorithms: Demons, Fast-Demons, Horn-Schunck and Lucas-Kanade were used to register intramodality CT images of a mobile phantom scanned with different imaging techniques. The phantom had three water-equivalent targets inserted in a low-density foam with different lengths (10-40 mm) and moved with adjustable motion amplitudes (0-20 mm) and frequencies (0-0.5 Hz). The variations in the CT-number level, volumes and shapes of these targets were measured from the spread-out of the CT-number distributions. In CBCT, most of the DIR algorithms were able to produce the actual lengths of the mobile targets; however, the CT-number values obtained from the DIR algorithms deviated from the actual CT-number of the targets. In HCT, the DIR algorithms were successful in deforming the images of the mobile targets to the images of the stationary targets producing the CT-number values and lengths of the targets for motion amplitudes <20 mm. Similarly in ACT, all DIR algorithms produced the actual CT-number values and lengths of the stationary targets for low-motion amplitudes <15 mm. The optical flow-based DIR algorithms such as the Horn-Schunck and Lucas-Kanade performed better than the Demons and Fast-Demons that are based on attraction forces particularly at large motion amplitudes. In conclusion, most of the DIR algorithms did not reproduce well the CT-number values and lengths of the targets in images that have artifacts induced by large motion amplitudes. The deviations in the CT-number values and variations in the volume of the mobile targets in the deformed CT images produced by the different DIR algorithms need to be considered carefully in the treatment planning for accurate dose calculation dose coverage of the tumor, and sparing of critical structures.

Dependence of subject-specific parameters for a fast helical CT respiratory motion model on breathing rate: an animal study

To determine if the parameters relating lung tissue displacement to a breathing surrogate signal in a previously published respiratory motion model vary with the rate of breathing during image acquisition. An anesthetized pig was imaged using multiple fast helical scans to sample the breathing cycle with simultaneous surrogate monitoring. Three datasets were collected while the animal was mechanically ventilated with different respiratory rates: 12 bpm (breaths per minute), 17 bpm, and 24 bpm. Three sets of motion model parameters describing the correspondences between surrogate signals and tissue displacements were determined. The model error was calculated individually for each dataset, as well asfor pairs of parameters and surrogate signals from different experiments. The values of one model parameter, a vector field denoted [Formula: see text] which related tissue displacement to surrogate amplitude, determined for each experiment were compared. The mean model error of the three datasets was 1.00  ±  0.36 mm with a 95th percentile value of 1.69 mm. The mean error computed from all combinations of parameters and surrogate signals from different datasets was 1.14  ±  0.42 mm with a 95th percentile of 1.95 mm. The mean difference in [Formula: see text] over all pairs of experiments was 4.7%  ±  5.4%, and the 95th percentile was 16.8%. The mean angle between pairs of [Formula: see text] was 5.0  ±  4.0 degrees, with a 95th percentile of 13.2 mm. The motion model parameters were largely unaffected by changes in the breathing rate during image acquisition. The mean error associated with mismatched sets of parameters and surrogate signals was 0.14 mm greater than the error achieved when using parameters and surrogate signals acquired with the same breathing rate, while maximum respiratory motion was 23.23 mm on average.

Comparison of lung tumor motion measured using a model-based 4DCT technique and a commercial protocol

PURPOSE

To compare lung tumor motion measured with a model-based technique to commercial 4-dimensional computed tomography (4DCT) scans and describe a workflow for using model-based 4DCT as a clinical simulation protocol.

METHODS AND MATERIALS

Twenty patients were imaged using a model-based technique and commercial 4DCT. Tumor motion was measured on each commercial 4DCT dataset and was calculated on model-based datasets for 3 breathing amplitude percentile intervals: 5th to 85th, 5th to 95th, and 0th to 100th. Internal target volumes (ITVs) were defined on the 4DCT and 5th to 85th interval datasets and compared using Dice similarity. Images were evaluated for noise and rated by 2 radiation oncologists for artifacts.

RESULTS

Mean differences in tumor motion magnitude between commercial and model-based images were 0.47 ± 3.0, 1.63 ± 3.17, and 5.16 ± 4.90 mm for the 5th to 85th, 5th to 95th, and 0th to 100th amplitude intervals, respectively. Dice coefficients between ITVs defined on commercial and 5th to 85th model-based images had a mean value of 0.77 ± 0.09. Single standard deviation image noise was 11.6 ± 9.6 HU in the liver and 6.8 ± 4.7 HU in the aorta for the model-based images compared with 57.7 ± 30 and 33.7 ± 15.4 for commercial 4DCT. Mean model error within the ITV regions was 1.71 ± 0.81 mm. Model-based images exhibited reduced presence of artifacts at the tumor compared with commercial images.

CONCLUSION

Tumor motion measured with the model-based technique using the 5th to 85th percentile breathing amplitude interval corresponded more closely to commercial 4DCT than the 5th to 95th or 0th to 100th intervals, which showed greater motion on average. The model-based technique tended to display increased tumor motion when breathing amplitude intervals wider than 5th to 85th were used because of the influence of unusually deep inhalations. These results suggest that care must be taken in selecting the appropriate interval during image generation when using model-based 4DCT methods.

Improved vessel-tissue contrast and image quality in 3D radial sampling-based 4D-MRI

Purpose

In radiation treatment planning for thoracic and abdominal tumors, 4D‐MRI has shown promise in respiratory motion characterization with improved soft‐tissue contrast compared to clinical standard, 4D computed tomography (4D‐CT). This study aimed to further improve vessel–tissue contrast and overall image quality in 3D radial sampling‐based 4D‐MRI using a slab‐selective (SS) excitation approach.

Methods

The technique was implemented in a 3D radial sampling with self‐gating‐based k‐space sorting sequence. The SS excitation approach was compared to a non‐selective (NS) approach in six cancer patients and two healthy volunteers at 3T. Improvements in vessel–tissue contrast ratio (CR) and vessel signal‐to‐noise ratio (SNR) were analyzed in five of the eight subjects. Image quality was visually assessed in all subjects on a 4‐point scale (0: poor; 3: excellent). Tumor (patients) and pancreas (healthy) motion trajectories were compared between the two imaging approaches.

Results

Compared with NS‐4D‐MRI, SS‐4D‐MRI significantly improved the overall vessel–tissue CR (2.60 ± 3.97 vs. 1.03 ± 1.44, P < 0.05), SNR (63.33 ± 38.45 vs. 35.74 ± 28.59, P < 0.05), and image quality score (2.6 ± 0.5 vs. 1.4 ± 0.5, P = 0.02). Motion trajectories from the two approaches exhibited strong correlation in the superior–inferior (0.96 ± 0.06), but weaker in the anterior–posterior (0.78 ± 0.24) and medial–lateral directions (0.46 ± 0.44).

Conclusions

The proposed 4D‐MRI with slab‐selectively excited 3D radial sampling allows for improved blood SNR, vessel–tissue CR, and image quality.

ITV, mid-ventilation, gating or couch tracking - A comparison of respiratory motion-management techniques based on 4D dose calculations

PURPOSE

Respiratory motion-management techniques (MMT) aim to ensure tumor dose coverage while sparing lung tissue. Dynamic treatment-couch tracking of the moving tumor is a promising new MMT and was compared to the internal-target-volume (ITV) concept, the mid-ventilation (MidV) principle and the gating approach in a planning study based on 4D dose calculations.

METHODS

For twenty patients with lung lesions, planning target volumes (PTV) were adapted to the MMT and stereotactic body radiotherapy treatments were prepared with the 65%-isodose enclosing the PTV. For tracking, three concepts for target volume definition were considered: Including the gross tumor volume of one phase (single-phase tracking), including deformations between phases (multi-phase tracking) and additionally including tracking latencies of a couch tracking system (reliable couch tracking). The accumulated tumor and lung doses were estimated with 4D dose calculations based on 4D-CT datasets and deformable image registration.

RESULTS

Single-phase tracking showed the lowest ipsilateral lung Dmean (median: 3.3Gy), followed by multi-phase tracking, gating, reliable couch tracking, MidV and ITV concepts (3.6, 3.8, 4.1, 4.3 and 4.8Gy). The 4D dose calculations showed the MidV and single-phase tracking overestimated the target mean dose (-2.3% and -1.3%), while it was slightly underestimated by the other MMT (<+1%).

CONCLUSION

The ITV concept ensures tumor coverage, but exposes the lung tissue to a higher dose. The MidV, gating and tracking concepts were shown to reduce the lung dose. Neglecting non-translational changes of the tumor in the target volume definition for tracking results in a slightly reduced target coverage. The slightly inferior dose coverage for MidV should be considered when applying this technique clinically.

Automated identification and reduction of artifacts in cine four-dimensional computed tomography (4DCT) images using respiratory motion model

PURPOSE

Four-dimensional computed tomography (4DCT) images are often marred by artifacts that substantially degrade image quality and confound image interpretation. Human observation remains the standard method of 4DCT artifact evaluation, which is time-consuming and subjective. We develop a method to automatically identify and reduce artifacts in cine 4DCT images.

METHODS

We proposed an algorithm that consisted of two main stages: deformable image registration and respiratory motion simulation. Specifically, each 4DCT phase image was registered to the breath-holding CT image using the block-matching method, with erroneous spatial matches removed by the least median of squares filter and the full displacement vector field generated by the moving least squares interpolation. The lung's respiratory motion trajectory was then recovered from the displacement vector field using the parameterized polynomial function, with fitting parameters estimated by combinatorial optimization. In this way, artifacts were located according to deviations between image points and their motion trajectories and further corrected based on position prediction.

RESULTS

The mean spatial error (standard deviation) was 1.00 (0.85) mm after registration as opposed to 6.96 (4.61) mm before registration. In addition, we took human observation conducted by medical experts as the gold standard. The average sensitivity, specificity, and accuracy of the proposed method in artifact identification were 0.97, 0.84, and 0.89, respectively.

CONCLUSIONS

The proposed method identified and reduced artifacts accurately and automatically, providing an alternative way to analyze 4DCT image quality and to correct problematic images for radiation therapy.

A method for improved 4D-computed tomography data acquisition

In four-dimensional time-dependent computed tomography (4D-CT) of the lungs, irregularities in breathing movements can cause errors in data acquisition, or even data loss. We present a method based on sending a synthetic, regular breathing signal to the CT instead of the real signal, which ensures 4D-CT data sets without data loss. Subsequent correction of the signal based on the real breathing curve enables an accurate reconstruction of the size and movement of the target volume. This makes it possible to plan radiation treatment based on the obtained data. The method was tested with dynamic thorax phantom measurements using synthetic and real breathing patterns.

Objective function to obtain multiple representative waveforms for a novel helical CT scan protocol

PURPOSE

To develop objective functions for selecting multiple representative respiratory waveforms. A specific application considered is to reduce the number of swiping scans in a novel helical CT scan protocol to harvest efficiency and dose reduction benefit.

METHODS

The authors consider a general class of potential objective functions consisting of weighted norms on pointwise profile differentials. The authors utilize the Lagrangian approach and derive proper conditions on the formulation based on first and second order optimality conditions. The derived objective functions are applied to clinically acquired respiratory trajectories for swipe subset selection to verify the validity and generality of the proposed rationale. An end-to-end 4DCT reconstruction comparison is performed using a swipe subset of data corresponding to 3 out of the full 25 waveforms to assess the consequence in image quality and dose.

RESULTS

Their results show that maximizing the proposed objective function with the suggested parameters yields maximal spread of trajectories among the selected subset. 4DCT Reconstruction using the chosen subset of data indicates the potential for further dose reduction by about 5 to 10 folds without significant sacrifice in image quality. Experimental results also support further generalization to include slice prioritization.

CONCLUSIONS

The authors have derived a formulation that is both simple and general as a metric to quantify the spread of a set of respiratory trajectories, which can be used for subset selection with potential computation and dose reduction benefit when applied to a newly developed helical 4DCT scan protocol.

Four-Dimensional Magnetic Resonance Imaging With 3-Dimensional Radial Sampling and Self-Gating-Based K-Space Sorting: Early Clinical Experience on Pancreatic Cancer Patients

PURPOSE

To apply a novel self-gating k-space sorted 4-dimensional MRI (SG-KS-4D-MRI) method to overcome limitations due to anisotropic resolution and rebinning artifacts and to monitor pancreatic tumor motion.

METHODS AND MATERIALS

Ten patients were imaged using 4D-CT, cine 2-dimensional MRI (2D-MRI), and the SG-KS-4D-MRI, which is a spoiled gradient recalled echo sequence with 3-dimensional radial-sampling k-space projections and 1-dimensional projection-based self-gating. Tumor volumes were defined on all phases in both 4D-MRI and 4D-CT and then compared.

RESULTS

An isotropic resolution of 1.56 mm was achieved in the SG-KS-4D-MRI images, which showed superior soft-tissue contrast to 4D-CT and appeared to be free of stitching artifacts. The tumor motion trajectory cross-correlations (mean ± SD) between SG-KS-4D-MRI and cine 2D-MRI in superior-inferior, anterior-posterior, and medial-lateral directions were 0.93 ± 0.03, 0.83 ± 0.10, and 0.74 ± 0.18, respectively. The tumor motion trajectories cross-correlations between SG-KS-4D-MRI and 4D-CT in superior-inferior, anterior-posterior, and medial-lateral directions were 0.91 ± 0.06, 0.72 ± 0.16, and 0.44 ± 0.24, respectively. The average standard deviation of gross tumor volume calculated from the 10 breathing phases was 0.81 cm(3) and 1.02 cm(3) for SG-KS-4D-MRI and 4D-CT, respectively (P=.012).

CONCLUSIONS

A novel SG-KS-4D-MRI acquisition method capable of reconstructing rebinning artifact-free, high-resolution 4D-MRI images was used to quantify pancreas tumor motion. The resultant pancreatic tumor motion trajectories agreed well with 2D-cine-MRI and 4D-CT. The pancreatic tumor volumes shown in the different phases for the SG-KS-4D-MRI were statistically significantly more consistent than those in the 4D-CT.

Rapid estimation of 4DCT motion-artifact severity based on 1D breathing-surrogate periodicity

PURPOSE

Motion artifacts are common in patient four-dimensional computed tomography (4DCT) images, leading to an ill-defined tumor volume with large variations for radiotherapy treatment and a poor foundation with low imaging fidelity for studying respiratory motion. The authors developed a method to estimate 4DCT image quality by establishing a correlation between the severity of motion artifacts in 4DCT images and the periodicity of the corresponding 1D respiratory waveform (1DRW) used for phase binning in 4DCT reconstruction.

METHODS

Discrete Fourier transformation (DFT) was applied to analyze 1DRW periodicity. The breathing periodicity index (BPI) was defined as the sum of the largest five Fourier coefficients, ranging from 0 to 1. Distortional motion artifacts (excluding blurring) of cine-scan 4DCT at the junctions of adjacent couch positions around the diaphragm were classified in three categories: incomplete, overlapping, and duplicate anatomies. To quantify these artifacts, discontinuity of the diaphragm at the junctions was measured in distance and averaged along six directions in three orthogonal views. Artifacts per junction (APJ) across the entire diaphragm were calculated in each breathing phase and phase-averaged APJ¯, defined as motion-artifact severity (MAS), was obtained for each patient. To make MAS independent of patient-specific motion amplitude, two new MAS quantities were defined: MAS(D) is normalized to the maximum diaphragmatic displacement and MAS(V) is normalized to the mean diaphragmatic velocity (the breathing period was obtained from DFT analysis of 1DRW). Twenty-six patients' free-breathing 4DCT images and corresponding 1DRW data were studied.

RESULTS

Higher APJ values were found around midventilation and full inhalation while the lowest APJ values were around full exhalation. The distribution of MAS is close to Poisson distribution with a mean of 2.2 mm. The BPI among the 26 patients was calculated with a value ranging from 0.25 to 0.93. The DFT calculation was within 3 s per 1DRW. Correlations were found between 1DRW periodicity and 4DCT artifact severity: -0.71 for MAS(D) and -0.73 for MAS(V). A BPI greater than 0.85 in a 1DRW suggests minimal motion artifacts in the corresponding 4DCT images.

CONCLUSIONS

The breathing periodicity index and motion-artifact severity index are introduced to assess the relationship between 1DRW and 4DCT. A correlation between 1DRW periodicity and 4DCT artifact severity has been established. The 1DRW periodicity provides a rapid means to estimate 4DCT image quality. The rapid 1DRW analysis and the correlative relationship can be applied prospectively to identify irregular breathers as candidates for breath coaching prior to 4DCT scan and retrospectively to select high-quality 4DCT images for clinical motion-management research.

Assessing and accounting for the impact of respiratory motion on FDG uptake and viable volume for liver lesions in free-breathing PET using respiration-suspended PET images as reference

PURPOSE

To assess and account for the impact of respiratory motion on the variability of activity and volume determination of liver tumor in positron emission tomography (PET) through a comparison between free-breathing (FB) and respiration-suspended (RS) PET images.

METHODS

As part of a PET/computed tomography (CT) guided percutaneous liver ablation procedure performed on a PET/CT scanner, a patient's breathing is suspended on a ventilator, allowing the acquisition of a near-motionless PET and CT reference images of the liver. In this study, baseline RS and FB PET/CT images of 20 patients undergoing thermal ablation were acquired. The RS PET provides near-motionless reference in a human study, and thereby allows a quantitative evaluation of the effect of respiratory motion on PET images obtained under FB conditions. Two methods were applied to calculate tumor activity and volume: (1) threshold-based segmentation (TBS), estimating the total lesion glycolysis (TLG) and the segmented volume and (2) histogram-based estimation (HBE), yielding the background-subtracted lesion (BSL) activity and associated volume. The TBS method employs 50% of the maximum standardized uptake value (SUVmax) as the threshold for tumors with SUVmax≥2× SUVliver-bkg, and tumor activity above this threshold yields TLG50%. The HBE method determines local PET background based on a Gaussian fit of the low SUV peak in a SUV-volume histogram, which is generated within a user-defined and optimized volume of interest containing both local background and lesion uptakes. Voxels with PET intensity above the fitted background were considered to have originated from the tumor and used to calculate the BSL activity and its associated lesion volume.

RESULTS

Respiratory motion caused SUVmax to decrease from RS to FB by -15%±11% (p=0.01). Using TBS method, there was also a decrease in SUVmean (-18%±9%, p=0.01), but an increase in TLG50% (18%±36%) and in the segmented volume (47%±52%, p=0.01) from RS to FB PET images. The background uptake in normal liver was stable, 1%±9%. In contrast, using the HBE method, the differences in both BSL activity and BSL volume from RS to FB were -8%±10% (p=0.005) and 0%±16% (p=0.94), respectively.

CONCLUSIONS

This is the first time that almost motion-free PET images of the human liver were acquired and compared to free-breathing PET. The BSL method's results are more consistent, for the calculation of both tumor activity and volume in RS and FB PET images, than those using conventional TBS. This suggests that the BSL method might be less sensitive to motion blurring and provides an improved estimation of tumor activity and volume in the presence of respiratory motion.

Four-dimensional MRI using three-dimensional radial sampling with respiratory self-gating to characterize temporal phase-resolved respiratory motion in the abdomen

PURPOSE

To develop a four-dimensional MRI (4D-MRI) technique to characterize the average respiratory tumor motion for abdominal radiotherapy planning.

METHODS

A continuous spoiled gradient echo sequence was implemented with 3D radial trajectory and 1D self-gating for respiratory motion detection. Data were retrospectively sorted into different respiratory phases based on their temporal locations within a respiratory cycle, and each phase was reconstructed by means of a self-calibrating CG-SENSE program. Motion phantom, healthy volunteer and patient studies were performed to validate the respiratory motion detected by the proposed method against that from a 2D real-time protocol.

RESULTS

The proposed method successfully visualized the respiratory motion in phantom and human subjects. The 4D-MRI and real-time 2D-MRI yielded comparable superior-inferior (SI) motion amplitudes (intraclass correlation = 0.935) with up-to one pixel mean absolute differences in SI displacements over 10 phases and high cross-correlation between phase-resolved displacements (phantom: 0.985; human: 0.937-0.985). Comparable anterior-posterior and left-right displacements of the tumor or gold fiducial between 4D and real-time 2D-MRI were also observed in the two patients, and the hysteresis effect was shown in their 3D trajectories.

CONCLUSION

We demonstrated the feasibility of the proposed 4D-MRI technique to characterize abdominal respiratory motion, which may provide valuable information for radiotherapy planning.

Evaluation of 4D CT acquisition methods designed to reduce artifacts

Four-dimensional computed tomography (4D CT) is used to account for respiratory motion in radiation treatment planning, but artifacts resulting from the acquisition and postprocessing limit its accuracy. We investigated the efficacy of three experimental 4D CT acquisition methods to reduce artifacts in a prospective institutional review board approved study. Eighteen thoracic patients scheduled to undergo radiation therapy received standard clinical 4D CT scans followed by each of the alternative 4D CT acquisitions: 1) data oversampling, 2) beam gating with breathing irregularities, and 3) rescanning the clinical acquisition acquired during irregular breathing. Relative values of a validated correlation-based artifact metric (CM) determined the best acquisition method per patient. Each 4D CT was processed by an extended phase sorting approach that optimizes the quantitative artifact metric (CM sorting). The clinical acquisitions were also postprocessed by phase sorting for artifact comparison of our current clinical implementation with the experimental methods. The oversampling acquisition achieved the lowest artifact presence among all acquisitions, achieving a 27% reduction from the current clinical 4D CT implementation (95% confidence interval = 34-20). The rescan method presented a significantly higher artifact presence from the clinical acquisition (37%; p < 0.002), the gating acquisition (26%; p < 0.005), and the oversampling acquisition (31%; p < 0.001), while the data lacked evidence of a significant difference between the clinical, gating, and oversampling methods. The oversampling acquisition reduced artifact presence from the current clinical 4D CT implementation to the largest degree and provided the simplest and most reproducible implementation. The rescan acquisition increased artifact presence significantly, compared to all acquisitions, and suffered from combination of data from independent scans over which large internal anatomic shifts occurred.

Modeling and measurement of the variations of CT number distributions for mobile targets in cone-beam computed tomographic imaging

The purpose of this study was to investigate quantitatively by measurement and modeling the variations in CT number distributions of mobile targets in cone-beam CT (CBCT) imaging. CBCT images were acquired for three targets manufactured from homogenous water-equivalent gel that was inserted into a commercial mobile thorax phantom. The phantom moved with a controlled cyclic motion in one-dimension along the superior-inferior direction to simulate patient respiratory motion. Profiles of the CT number distributions of the static and mobile targets were obtained using CBCT images. A mathematical model was developed that predicted the variations in CT number distributions and their dependence on the motion parameters of targets moving in one-dimension using CBCT imaging. The measured CT number distributions for the mobile targets varied considerably, depending on the motion parameters. The extension of the CT number distribution increased linearly with motion amplitude where maximum target elongation reached twice the motion amplitude. The CT number levels of the mobile targets were smeared over a longer distribution; for example, the CT number level for the 20 mm target dropped by nearly 30% at motion amplitude (A) equal to 20 mm in comparison with the CT number distribution of stationary targets. Frequency of motion played an important role in spatial and level variations of the CT number distributions. For example, the level of the CT number profile for the medium target (20 mm) decreased evenly by nearly 50% at A = 20 mm with high motion frequencies. Motion phase did not affect the CT number distributions for prolonged projection acquisition that included several respiratory cycles. The mathematical model of the CT number distributions of mobile targets in CBCT reproduced well the measured CT number distributions and predicted their dependence on the target size and phantom motion parameters such as speed, amplitude, frequency, and phase. The CT number distributions varied considerably with motion in CBCT. A motion model of CT number distribution for mobile targets has been developed in this work that predicted well the variations in the measured CT number profiles and their dependence on motion parameters. The model corrected the CT number distribution retrospective to CT image reconstruction where it used a first-order linear relationship between the number of projections collected in the imaging window of a mobile voxel to obtain the cumulative CT number. This model provides quantitative characterization of motion artifacts on CT number distributions in CBCT that is useful to determine the validity of CT numbers and the accuracy of localization and volume measurement of tumors in diagnostic imaging and interventional applications, such as radiotherapy.

Optimizing geometric accuracy of four-dimensional CT scans acquired using the wall- and couch-mounted Varian® Real-time Position Management™ camera systems

OBJECTIVE

The aim of this study was to identify sources of anatomical misrepresentation owing to the location of camera mounting, tumour motion velocity and image processing artefacts in order to optimize the four-dimensional CT (4DCT) scan protocol and improve geometrical-temporal accuracy.

METHODS

A phantom with an imaging insert was driven with a sinusoidal superior-inferior motion of varying amplitude and period for 4DCT scanning. The length of a high-density cube within the insert was measured using treatment planning software to determine the accuracy of its spatial representation. Scan parameters were varied, including the tube rotation period and the cine time between reconstructed images. A CT image quality phantom was used to measure various image quality signatures under the scan parameters tested.

RESULTS

No significant difference in spatial accuracy was found for 4DCT scans carried out using the wall- or couch-mounted camera for sinusoidal target motion. Greater spatial accuracy was found for 4DCT scans carried out using a tube rotation speed of 0.5 s rather than 1.0 s. The reduction in image quality when using a faster rotation speed was not enough to require an increase in patient dose.

CONCLUSION

The 4DCT accuracy may be increased by optimizing scan parameters, including choosing faster tube rotation speeds. Peak misidentification in the recorded breathing trace may lead to spatial artefacts, and this risk can be reduced by using a couch-mounted infrared camera.

ADVANCES IN KNOWLEDGE

This study explicitly shows that 4DCT scan accuracy is improved by scanning with a faster CT tube rotation speed.

High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images

PURPOSE

For nonsmall cell lung cancer (NSCLC) patients, quantitative image features extracted from computed tomography (CT) images can be used to improve tumor diagnosis, staging, and response assessment. For these findings to be clinically applied, image features need to have high intra and intermachine reproducibility. The objective of this study is to identify CT image features that are reproducible, nonredundant, and informative across multiple machines.

METHODS

Noncontrast-enhanced, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. Two machines ("M1" and "M2") used cine 4D-CT and one machine ("M3") used breath-hold helical 3D-CT. Gross tumor volumes (GTVs) were semiautonomously segmented then pruned by removing voxels with CT numbers less than a prescribed Hounsfield unit (HU) cutoff. Three hundred and twenty eight quantitative image features were extracted from each pruned GTV based on its geometry, intensity histogram, absolute gradient image, co-occurrence matrix, and run-length matrix. For each machine, features with concordance correlation coefficient values greater than 0.90 were considered reproducible. The Dice similarity coefficient (DSC) and the Jaccard index (JI) were used to quantify reproducible feature set agreement between machines. Multimachine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering based on the average correlation between features across multiple machines.

RESULTS

For all image types, GTV pruning was found to negatively affect reproducibility (reported results use no HU cutoff). The reproducible feature percentage was highest for average images (M1 = 90.5%, M2 = 94.5%, M1∩M2 = 86.3%), intermediate for end-exhale images (M1 = 75.0%, M2 = 71.0%, M1∩M2 = 52.1%), and lowest for breath-hold images (M3 = 61.0%). Between M1 and M2, the reproducible feature sets generated from end-exhale images were relatively machine-sensitive (DSC = 0.71, JI = 0.55), and the reproducible feature sets generated from average images were relatively machine-insensitive (DSC = 0.90, JI = 0.87). Histograms of feature pair correlation distances indicated that feature redundancy was machine-sensitive and image type sensitive. After hierarchical clustering, 38 features, 28 features, and 33 features were found to be reproducible and nonredundant for M1∩M2 (average images), M1∩M2 (end-exhale images), and M3, respectively. When blinded to the presence of test-retest images, hierarchical clustering showed that the selected features were informative by correctly pairing 55 out of 56 test-retest images using only their reproducible, nonredundant feature set values.

CONCLUSIONS

Image feature reproducibility and redundancy depended on both the CT machine and the CT image type. For each image type, the authors found a set of cross-machine reproducible, nonredundant, and informative image features that would be useful for future image-based models. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multimachine reproducibility and are the best candidates for clinical correlation.

Assessment of a quantitative metric for 4D CT artifact evaluation by observer consensus

The benefits of four-dimensional computed tomography (4D CT) are limited by the presence of artifacts that remain difficult to quantify. A correlation-based metric previously proposed for ciné 4D CT artifact identification was further validated as an independent artifact evaluator by using a novel qualitative assessment featuring a group of observers reaching a consensus decision on artifact location and magnitude. The consensus group evaluated ten ciné 4D CT scans for artifacts over each breathing phase of coronal lung views assuming one artifact per couch location. Each artifact was assigned a magnitude score of 1-5, 1 indicating lowest severity and 5 indicating highest severity. Consensus group results served as the ground truth for assessment of the correlation metric. The ten patients were split into two cohorts; cohort 1 generated an artifact identification threshold derived from receiver operating characteristic analysis using the Youden Index, while cohort 2 generated sensitivity and specificity values from application of the artifact threshold. The Pearson correlation coefficient was calculated between the correlation metric values and the consensus group scores for both cohorts. The average sensitivity and specificity values found with application of the artifact threshold were 0.703 and 0.476, respectively. The correlation coefficients of artifact magnitudes for cohort 1 and 2 were 0.80 and 0.61, respectively, (p < 0.001 for both); these correlation coefficients included a few scans with only two of the five possible magnitude scores. Artifact incidence was associated with breathing phase (p < 0.002), with presentation less likely near maximum exhale. Overall, the correlation metric allowed accurate and automated artifact identification. The consensus group evaluation resulted in efficient qualitative scoring, reduced interobserver variation, and provided consistent identification of artifact location and magnitudes.

A novel fast helical 4D-CT acquisition technique to generate low-noise sorting artifact-free images at user-selected breathing phases

PURPOSE

To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts.

METHODS AND MATERIALS

Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling.

RESULTS

Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm.

CONCLUSIONS

The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact-free images at a patient dose similar to or less than current 4D-CT techniques.

The effect of irregular breathing patterns on internal target volumes in four-dimensional CT and cone-beam CT images in the context of stereotactic lung radiotherapy

PURPOSE

Stereotactic lung radiotherapy is complicated by tumor motion from patient respiration. Four-dimensional CT (4DCT) imaging is a motion compensation method used in treatment planning to generate a maximum intensity projection (MIP) internal target volume (ITV). Image guided radiotherapy during treatment may involve acquiring a volumetric cone-beam CT (CBCT) image and visually aligning the tumor to the planning 4DCT MIP ITV contour. Moving targets imaged with CBCT can appear blurred and currently there are no studies reporting on the effect that irregular breathing patterns have on CBCT volumes and their alignment to 4DCT MIP ITV contours. The objective of this work was therefore to image a phantom moving with irregular breathing patterns to determine whether any configurations resulted in errors in volume contouring or alignment.

METHODS

A Perspex thorax phantom was used to simulate a patient. Three wooden "lung" inserts with embedded Perspex "lesions" were moved up to 4 cm with computer-generated motion patterns, and up to 1 cm with patient-specific breathing patterns. The phantom was imaged on 4DCT and CBCT with the same acquisition settings used for stereotactic lung patients in the clinic and the volumes on all phantom images were contoured. This project assessed the volumes for qualitative and quantitative changes including volume, length of the volume, and errors in alignment between CBCT volumes and 4DCT MIP ITV contours.

RESULTS

When motion was introduced 4DCT and CBCT volumes were reduced by up to 20% and 30% and shortened by up to 7 and 11 mm, respectively, indicating that volume was being under-represented at the extremes of motion. Banding artifacts were present in 4DCT MIP images, while CBCT volumes were largely reduced in contrast. When variable amplitudes from patient traces were used and CBCT ITVs were compared to 4DCT MIP ITVs there was a distinct trend in reduced ITV with increasing amplitude that was not seen when compared to true ITVs. Breathing patterns with a rest period following expiration resulted in well-defined superior edges and were better aligned using an edge-to-edge alignment technique. In most cases, sinusoidal motion patterns resulted in the closest agreements to true values and the smallest misalignments.

CONCLUSIONS

Strategies are needed to compensate for volume losses at the extremes of motion for both 4DCT MIP and CBCT images for larger and varied amplitudes, and for patterns with rest periods following expiration. Lesions moving greater than 2 cm would warrant larger treatment margins added to the 4DCT MIP ITV to account for the volume being under-represented at the extremes of motion. Lesions moving with a rest period following expiration would be better aligned using an edge-to-edge alignment technique. Sinusoidal patterns represented the ideal clinical scenario, reinforcing the importance of investigating clinically relevant motions and their effects on 4DCT MIP and CBCT volumes. Since most patients do not breathe sinusoidally this may lead to misinterpretation of previous studies using only sinusoidal motion.

Experimental evaluations of the accuracy of 3D and 4D planning in robotic tracking stereotactic body radiotherapy for lung cancers

PURPOSE

Due to the complexity of 4D target tracking radiotherapy, the accuracy of this treatment strategy should be experimentally validated against established standard 3D technique. This work compared the accuracy of 3D and 4D dose calculations in respiration tracking stereotactic body radiotherapy (SBRT).

METHODS

Using the 4D planning module of the CyberKnife treatment planning system, treatment plans for a moving target and a static off-target cord structure were created on different four-dimensional computed tomography (4D-CT) datasets of a thorax phantom moving in different ranges. The 4D planning system used B-splines deformable image registrations (DIR) to accumulate dose distributions calculated on different breathing geometries, each corresponding to a static 3D-CT image of the 4D-CT dataset, onto a reference image to compose a 4D dose distribution. For each motion, 4D optimization was performed to generate a 4D treatment plan of the moving target. For comparison with standard 3D planning, each 4D plan was copied to the reference end-exhale images and a standard 3D dose calculation was followed. Treatment plans of the off-target structure were first obtained by standard 3D optimization on the end-exhale images. Subsequently, they were applied to recalculate the 4D dose distributions using DIRs. All dose distributions that were initially obtained using the ray-tracing algorithm with equivalent path-length heterogeneity correction (3D EPL and 4D EPL) were recalculated by a Monte Carlo algorithm (3D MC and 4D MC) to further investigate the effects of dose calculation algorithms. The calculated 3D EPL, 3D MC, 4D EPL, and 4D MC dose distributions were compared to measurements by Gafchromic EBT2 films in the axial and coronal planes of the moving target object, and the coronal plane for the static off-target object based on the γ metric at 5%/3mm criteria (γ5%/3mm). Treatment plans were considered acceptable if the percentage of pixels passing γ5%/3mm (Pγ<1) ≥ 90%.

RESULTS

The averaged Pγ<1 values of the 3D EPL, 3D MC, 4D EPL, and 4D MC dose calculation methods for the moving target plans are 95%, 95%, 94%, and 95% for reproducible motion, and 95%, 96%, 94%, and 93% for nonreproducible motion during actual treatment delivery. The overall measured target dose distributions are in better agreement with the 3DMC dose distributions than the 4DMC dose distributions. Conversely, measured dose distributions agree much better with the 4D EPL/MC than the 3D EPL/MC dose distributions in the static off-target structure, resulting in higher Pγ<1 values with 4D EPL/MC (91%) vs 3D EPL (24%) and 3D MC (25%). Systematic changes of target motion reduced the averaged Pγ<1 to 47% and 53% for 4D EPL and 4D MC dose calculations, and 22% for 3D EPL/MC dose calculations in the off-target films.

CONCLUSIONS

In robotic tracking SBRT, 4D treatment planning was found to yield better prediction of the dose distributions in the off-target structure, but not necessarily in the moving target, compared to standard 3D treatment planning, for reproducible and nonreproducible target motion. It is important to ensure on a patient-by-patient basis that the cumulative uncertainty associated with the 4D-CT artifacts, deformable image registration, and motion variability is significantly smaller than the cumulative uncertainty occurred in standard 3D planning in order to make 4D planning a justified option.

Analysis of the advantage of individual PTVs defined on axial 3D CT and 4D CT images for liver cancer

The purpose of this study was to compare positional and volumetric differences of planning target volumes (PTVs) defined on axial three dimensional CT (3D CT) and four dimensional CT (4D CT) for liver cancer. Fourteen patients with liver cancer underwent 3D CT and 4D CT simulation scans during free breathing. The tumor motion was measured by 4D CT. Three internal target volumes (ITVs) were produced based on the clinical target volume from 3DCT (CTV3D): i) A conventional ITV (ITVconv) was produced by adding 10 mm in CC direction and 5 mm in LR and and AP directions to CTV3D; ii) A specific ITV (ITVspec) was created using a specific margin in transaxial direction; iii) ITVvector was produced by adding an isotropic margin derived from the individual tumor motion vector. ITV4D was defined on the fusion of CTVs on all phases of 4D CT. PTVs were generated by adding a 5 mm setup margin to ITVs. The average centroid shifts between PTVs derived from 3DCT and PTV4D in left–right (LR), anterior–posterior (AP), and cranial–caudal (CC) directions were close to zero. Comparing PTV4D to PTVconv, PTVspec, and PTVvector resulted in a decrease in volume size by 33.18% ±12.39%, 24.95% ±13.01%, 48.08% ±15.32%, respectively. The mean degree of inclusions (DI) of PTV4D in PTVconv, and PTV4D in PTVspec, and PTV4D in PTVvector was 0.98, 0.97, and 0.99, which showed no significant correlation to tumor motion vector (r=‐0.470, 0.259, and 0.244; p=0.090, 0.371, and 0.401). The mean DIs of PTVconv in PTV4D, PTVspec in PTV4D, and PTVvector in PTV4D was 0.66, 0.73, and 0.52. The size of individual PTV from 4D CT is significantly less than that of PTVs from 3DCT. The position of targets derived from axial 3DCT images scatters around the center of 4D targets randomly. Compared to conventional PTV, the use of 3D CT‐based PTVs with individual margins cannot significantly reduce normal tissues being unnecessarily irradiated, but may contribute to reducing the risk of missing targets for tumors with large motion.

PACS number: 87

Comparison of two respiration monitoring systems for 4D imaging with a Siemens CT using a new dynamic breathing phantom

Four-dimensional computed tomography (4D-CT) requires breathing information from the patient, and for this, several systems are available. Testing of these systems, under realistic conditions, requires a phantom with a moving target and an expandable outer contour. An anthropomorphic phantom was developed to simulate patient breathing as well as lung tumor motion. Using the phantom, an optical camera system (GateCT) and a pressure sensor (AZ-733V) were simultaneously operated, and 4D-CTs were reconstructed with a Siemens CT using the provided local-amplitude-based sorting algorithm. The comparison of the tumor trajectories of both systems revealed discrepancies up to 9.7 mm. Breathing signal differences, such as baseline drift, temporal resolution and noise level were shown not to be the reason for this. Instead, the variability of the sampling interval and the accuracy of the sampling rate value written on the header of the GateCT-signal file were identified as the cause. Interpolation to regular sampling intervals and correction of the sampling rate to the actual value removed the observed discrepancies. Consistently, the introduction of sampling interval variability and inaccurate sampling rate values into the header of the AZ-733V file distorted the tumor trajectory for this system. These results underline the importance of testing new equipment thoroughly, especially if components of different manufacturers are combined.

A practical method of identifying data loss in 4DCT

BACKGROUND AND PURPOSE

The design, testing and clinical implementation of a simple quality assurance tool which allows quick and accurate identification of regions of data loss and data interpolation in 4DCT data sets is reported.

MATERIALS AND METHODS

A 4DCT model, dependent on gantry rotation time and pitch, was developed to allow an understanding of the data collection and reconstruction processes. To test this model, 4DCT scans of a phantom were acquired using a Siemens SOMATOM Sensation 40 slice CT scanner. A radio-opaque rod mounted under the couch top was present during the phantom scans.

RESULTS

The model predicts that periodic regions of data loss occur when the respiration rate drops below a critical value. These results are verified by experimental data. Regions of data loss result in breaks in the imaged radio-opaque rod.

CONCLUSIONS

Regions of data loss in 4DCT data sets can be difficult to detect. Mounting a radio-opaque rod under the couch top allows regions of data loss and data interpolation to be quickly assessed on a patient by patient basis. This quality assurance tool has been successfully implemented into clinical use. The results of this work have implications for quality assurance programmes for 4DCT scanning.