Evaluation of the combined use of two different respiratory monitoring systems for 4D CT simulation and gated treatment

Comparison of two different respiratory monitoring systems with 4D-CT images for target volume definition in patients undergoing para-aortic nodal irradiation

OBJECTIVE

Today, respiratory movement can be monitored and recorded with different methods during a simulation on a four-dimensional (4D) computed tomography (CT) device to be used in radiotherapy planning. A synchronized respiratory monitoring system (RPM) with an externally equipped device is one of these methods. Another method is to create 4D images of the patient's breathing phases without the need for extra equipment, with an anatomy-based software program integrated into the CT device. Our aim is to compare the RPM system and the software system (Deviceless) which are two different respiratory monitoring methods used in tracking moving targets during 4D-CT imaging and to assess their clinical usability.

METHODS

Ten patients who underwent paraaortic nodal irradiation were enrolled. The simulation was performed using intravenous contrast material on a 4D-CT device with both respiratory monitoring methods. The right/left kidneys and renal arteries were chosen as references to evaluate abdominal organ movement. It was then manually contoured one by one on both sets of images. The images were compared volumetrically and geometrically after rigid reconstruction. The similarity between the contours was determined by the Dice index. Wilcoxon test was used for statistical comparisons.

RESULTS

The motion of the kidneys in all three directions was found to be 0.0 cm in both methods. The shifts in the right/left renal arteries were submillimetric. The Dice index showed a high similarity in both kidney and renal artery contours.

CONCLUSION

In our study, no difference was found between RPM and Deviceless systems used for tracking and detection of moving targets during simulation in 4D-CT. Both methods can be used safely for radiotherapy planning according to the available possibilities in the clinic.

CT respiratory motion synthesis using joint supervised and adversarial learning

Objective.Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery.Approach.In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude.Main results.Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim).Significance.This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found athttps://github.com/cyiheng/Dynagan.

Motion-correction strategies for enhancing whole-body PET imaging

Positron Emission Tomography (PET) is a powerful medical imaging technique widely used for detection and monitoring of disease. However, PET imaging can be adversely affected by patient motion, leading to degraded image quality and diagnostic capability. Hence, motion gating schemes have been developed to monitor various motion sources including head motion, respiratory motion, and cardiac motion. The approaches for these techniques have commonly come in the form of hardware-driven gating and data-driven gating, where the distinguishing aspect is the use of external hardware to make motion measurements vs. deriving these measures from the data itself. The implementation of these techniques helps correct for motion artifacts and improves tracer uptake measurements. With the great impact that these methods have on the diagnostic and quantitative quality of PET images, much research has been performed in this area, and this paper outlines the various approaches that have been developed as applied to whole-body PET imaging.

Simulation of a new respiratory phase sorting method for 4D-imaging using optical surface information towards precision radiotherapy

BACKGROUND

Respiratory signal detection is critical for 4-dimensional (4D) imaging. This study proposes and evaluates a novel phase sorting method using optical surface imaging (OSI), aiming to improve the precision of radiotherapy.

METHOD

Based on 4D Extended Cardiac-Torso (XCAT) digital phantom, OSI in point cloud format was generated from the body segmentation, and image projections were simulated using the geometries of Varian 4D kV cone-beam-CT (CBCT). Respiratory signals were extracted respectively from the segmented diaphragm image (reference method) and OSI respectively, where Gaussian Mixture Model and Principal Component Analysis (PCA) were used for image registration and dimension reduction respectively. Breathing frequencies were compared using Fast-Fourier-Transform. Consistency of 4DCBCT images reconstructed using Maximum Likelihood Expectation Maximization algorithm was also evaluated quantitatively, where high consistency can be suggested by lower Root-Mean-Square-Error (RMSE), Structural-Similarity-Index (SSIM) value closer to 1, and larger Peak-Signal-To-Noise-Ratio (PSNR) respectively.

RESULTS

High consistency of breathing frequencies was observed between the diaphragm-based (0.232 Hz) and OSI-based (0.251 Hz) signals, with a slight discrepancy of 0.019Hz. Using end of expiration (EOE) and end of inspiration (EOI) phases as examples, the mean±1SD values of the 80 transverse, 100 coronal and 120 sagittal planes were 0.967, 0,972, 0.974 (SSIM); 1.657 ± 0.368, 1.464 ± 0.104, 1.479 ± 0.297 (RMSE); and 40.501 ± 1.737, 41.532 ± 1.464, 41.553 ± 1.910 (PSNR) for the EOE; and 0.969, 0.973, 0.973 (SSIM); 1.686 ± 0.278, 1.422 ± 0.089, 1.489 ± 0.238 (RMSE); and 40.535 ± 1.539, 41.605 ± 0.534, 41.401 ± 1.496 (PSNR) for EOI respectively.

CONCLUSIONS

This work proposed and evaluated a novel respiratory phase sorting approach for 4D imaging using optical surface signals, which can potentially be applied to precision radiotherapy. Its potential advantages were non-ionizing, non-invasive, non-contact, and more compatible with various anatomic regions and treatment/imaging systems.

Evidence-based region of interest (ROI) definition for surface-guided radiotherapy (SGRT) of abdominal cancers using deep-inspiration breath-hold (DIBH)

To define and evaluate the appropriate abdominal region of interest (ROI) as a surrogate of diaphragm positioning in deep-inspiration breath-hold (DIBH) for surface-guided radiotherapy (SGRT) of abdominal cancers using 3D optical surface imaging (OSI). Six potential abdominal ROIs were evaluated to calculate their correlations with the diaphragm position using 4DCT images of 20 abdominal patients. Twelve points of interest (POIs) were defined (six on the central soft tissue and six on the bilateral ribs) at three superior-inferior levels, and different sub-groups represented different ROIs. ROI-1 was the largest, containing all 12 POIs from the xiphoid to the umbilicus and between the lateral body midlines while ROI-2 had only eight inferior POIs, ROI-3 had six lateral POIs, and ROI-4 had four superior-lateral POIs over the ribs, ROI-5 contained six central and two most inferior-lateral POIs and ROI-6 contained six central and four inferior-lateral POIs. Internally, the right diaphragm dome was used to represent its positions in 4DCT (0% and 50% within the cycle). The Pearson correlation coefficients were calculated between the diaphragm dome and all 12 external POIs individually or grouped as six ROIs. The quality of the abdominal ROIs was evaluated as potential internal surrogates and, therefore, potential ROIs for SGRT DIBH setup. The four most inferior POIs show the highest mean correlation (r = 0.75) with diaphragmatic motion, and the correlation decreases as POIs move superiorly. The mean correlations are the highest for ROIs with little or no rib support: r = 0.67 for ROI-2, r = 0.64 for ROI-5, and r = 0.63 for ROI-6, while lower for ROIs with rib support: ROI-1 has r = 0.60, ROI-3 has r = 0.50, and ROI-4 has only r = 0.28. This study demonstrates that the rectangular/triangular soft-tissue ROI (with little rib support) is an optimal surrogate for body positioning and diaphragmatic motion, even when treating tumors under the rib cage. This evidence-based ROI definition should be utilized when treating abdominal cancers with free-breathing (FB) and/or DIBH setup.

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: the respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: patient-based datasets with regular and irregular breathing patterns; and pseudopatient-based datasets with regular and irregular breathing patterns. In this study, 'pseudopatients' refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²) of 1.2-1.6%, 0.65-0.8%, and 0.97-0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement.

Stability and Reliability of Enhanced External-Internal Motion Correlation via Dynamic Phase-Shift Corrections Over 30-min Timeframe for Respiratory-Gated Radiotherapy

To assess the stability of patient-specific phase shifts between external- and internal-respiratory motion waveforms, the reliability of enhanced external–internal correlation with phase-shift correction, and the feasibility of guiding respiratory-gated radiotherapy (RGRT) over 30 min. In this clinical feasibility investigation, external bellows and internal-navigator waveforms were simultaneously and prospectively acquired along with two four-dimensional magnetic resonance imaging (4DMRI) scans (6–15 m each) with 15–20 m intervals in 10 volunteers. A bellows was placed 5 cm inferior to the xiphoid to monitor abdominal motion, and an MR navigator was used to track the diaphragmatic motion. The mean phase-domain (MPD) method was applied, which combines three individual phase-calculating methods: phase-space oval fitting, principal component analysis, and analytic signal analysis, weighted by the reciprocal of their residual errors (RE) excluding outliers (RE >2σ). The time-domain cross-correlation (TCC) analysis was applied for comparison. Dynamic phase-shift correction was performed based on the phase shift detected on the fly within two 10 s moving datasets. Simulating bellows-triggered gating, the median and 95% confidence interval for the navigator's position at beam-on/beam-off and %harm (percentage of beam-on time outside the safety margin) were calculated. Averaged across all subjects, the mean phase shifts are found indistinguishable (p > .05) between scan 1 (55˚ ± 9˚) and scan 2 (59˚ ± 11˚). Using the MPD method the averaged correlation increases from 0.56 ± 0.22 to 0.85 ± 0.11 for scan 1 and from 0.47 ± 0.30 to 0.84 ± 0.08 for scan 2. The TCC correction results in similar results. After phase-shift correction, the number of cases that were suitable for amplitude gating (with <10%harm) increased from 2 to 17 out of 20 cases. A patient-specific, stable phase-shift between the external and internal motions was observed and corrected using the MPD and TCC methods, producing long-lasting enhanced motion correlation over 30m. Phase-shift correction offers a feasible strategy for improving the accuracy of tumor-motion prediction during RGRT.

Feasibility of using a novel automatic cardiac segmentation algorithm in the clinical routine of lung cancer patients

Incidental radiation exposure to the heart during lung cancer radiotherapy is associated with radiation-induced heart disease and increased rates of mortality. By considering the respiratory-induced motion of the heart it is possible to create a radiotherapy plan that results in a lower overall cardiac dose. This approach is challenging using current clinical practices: manual contouring of the heart is time consuming, and subject to inter- and intra-observer variability. In this work, we investigate the feasibility of our previously developed, atlas-based, automatic heart segmentation tool to delineate the heart in four-dimensional x-ray computed tomography (4D-CT) images. We used a dataset comprising 19 patients receiving radiotherapy for lung cancer, with 4D-CT imaging acquired at 10 respiratory phases and with a maximum intensity projection image generated from these. For each patient, one of four experienced radiation oncologists contoured the heart on each respiratory phase image and the maximum intensity image. Automatic segmentation of the heart on these same patient image sets was achieved using a leave-one-out approach, where for each patient the remaining 18 were used as an atlas set. The consistency of the automatic segmentation relative to manual contouring was evaluated using the Dice similarity coefficient (DSC) and mean absolute surface-to-surface distance (MASD). The DSC and MASD are comparable to inter-observer variability in clinically acceptable whole heart delineations (average DSC > 0.93 and average MASD < 2.0 mm in all the respiratory phases). The comparison between automatic and manual delineations on the maximum intensity images produced an overall mean DSC of 0.929 and a mean MASD of 2.07 mm. The automatic, atlas-based segmentation tool produces clinically consistent and robust heart delineations and is easy to implement in the routine care of lung cancer patients.

Investigation of Internal Target Volumes Using Device and Deviceless Four-dimensional Respiratory Monitoring Systems for Moving Targets in Four-dimensional Computed Tomography Acquisition

AIMS AND OBJECTIVES

The influence of target motion on the reconstructed internal target volume (ITV) for device-based (DB) external surrogate system and Smart deviceless (DL) 4-dimensional (4D) system were compared in a controlled phantom experiment. The volumetric changes in reconstructed ITVs from the average intensity projection (AveIP) images using DB method (Anzai Respiratory Gating System, ANZAI MEDICAL CO., LTD, Japan) and DL method (Smart deviceless 4D system by GE Medical Systems (Chicago, USA)) with the theoretical true volume (ITV_th) for moving target with the increasing target motion in anterior-posterior (A-P), lateral (left-right [L-R]) and inferior-superior (S-I) directions were assessed.

MATERIALS AND METHODS

4D computed tomography (4DCT) of CIRS dynamic phantom (Computerized Imaging Reference Systems Inc., Norfolk, VA, USA) with 2.5 cm diameter spherical target of volume 8.2 cc programmed to move in a cos⁴(x) motion pattern placed in the lung volume were acquired for various target motion pattern using DB and DL method of gating. AveIP images of 10 phase binned image sets were generated and ITVs were delineated.

RESULTS

The maximum absolute percent differences between ITV_ave and ITV_th for DL and DB methods were 15.91% and 4.94 % respectively for target motion of 5 mm in AP with 15 mm S-I direction. When the S-I motion was decreased to 10 mm, the observed % difference of the ITVs were also decreased to 12.5% and 0.3% for DL and DB method. When the lateral [L-R] motion was varied from 0 mm to 5 mm for S-I motion of 5 mm to 15 mm, the differences in the ITVs were significant (P = 0.004) with the maximum absolute percent difference of 18.61% and 4.94 % for DL and DB gating. With the simultaneous motion of the target in all the 3 directions, the difference in the reconstructed ITVs were statistically significant for DL method (P = 0.0002) and insignificant for DB method (P = 0.06) with an average increase of 10% in ITVDL against 2% in the ITVDB. The difference in ITVDL was significant for the target motion above 3 mm in A-P and L-R directions for S-I movement of above 10 mm (P = 0.0002). However, for low excursions of the target movement, no significant difference in the ITVs were observed (P > 0.06). In general, ITVDBs were closer to the ITVth (within 7.8%) than ITVDL (18.61%).

CONCLUSION

The results showed that the DL method is an effective way of image sorting in 4D acquisition for smaller target excursion. When the target motion exceeds 3 mm in A-P and L-R directions with S-I more than 10 mm, DB method is the choice due to its accuracy in reproducing the absolute target volume.