OBJECTIVE: To investigate the variability and diagnostic efficacy of respiratory-gated (RG) PET/CT based radiomics features compared to ungated (UG) PET/CT in the differentiation of non-small cell lung cancer (NSCLC) and benign lesions.
METHODS: 117 patients with suspected lung lesions from March 2020 to May 2021 and consent to undergo UG PET/CT and chest RG PET/CT (including phase-based quiescent period gating, pQPG and phase-matched 4D PET/CT, 4DRG) were prospectively included. 377 radiomics features were extracted from PET images of each scan. Paired t test was used to compare UG and RG features for inter-scan variability analysis. We developed three radiomics models with UG and RG features (i.e. UGModel, pQPGModel and 4DRGModel). ROC curves were used to compare diagnostic efficiencies, and the model-level comparison of diagnostic value was performed by five-fold cross-validation. A P value < 0.05 was considered as statistically significant.
RESULTS: A total of 111 patients (average age ± standard deviation was 59.1 ± 11.6 y, range, 29 - 88 y, and 63 were males) with 209 lung lesions were analyzed for features variability and the subgroup of 126 non-metastasis lesions in 91 patients without treatment before PET/CT were included for diagnosis analysis. 101/377 (26.8 %) 4DRG features and 82/377 (21.8 %) pQPG features showed significant difference compared to UG features (both P<0.05). 61/377 (16.2 %) and 59/377 (15.6 %) of them showed significantly better discriminant ability (ΔAUC% (i.e. (AUCRG - AUCUG) / AUCUG×100 %) > 0 and P<0.05) in malignant recognition, respectively. For the model-level comparison, 4DRGModel achieved the highest diagnostic efficacy (sen 73.2 %, spe 87.3 %) compared with UGModel (sen 57.7 %, spe 76.4 %) and pQPGModel (sen 63.4 %, spe 81.8 %).
CONCLUSIONS: RG PET/CT performs better in the quantitative assessment of metabolic heterogeneity for lung lesions and the subsequent diagnosis in patients with NSCLC compared with UG PET/CT.

OBJECTIVE: Respiratory movement has an important impact on the radiotherapy for lung tumor. Respiratory gating technology is helpful to improve the accuracy of target delineation. This study investigated the value of prospective and retrospective respiratory gating simulations in target delineation and radiotherapy plan design for solitary pulmonary tumors (SPTs) in radiotherapy.
METHODS: The enrolled patients underwent CT simulation with three-dimensional (3D) CT non gating, prospective respiratory gating, and retrospective respiratory gating simulation. The target volumes were delineated on three sets of CT images, and radiotherapy plans were prepared accordingly. Tumor displacements and movement information obtained using the two respiratory gating approaches, as well as the target volumes and dosimetry parameters in the radiotherapy plan were compared.
RESULTS: No significant difference was observed in tumor displacement measured using the two gating methods (p > 0.05). However, the internal gross tumor volumes (IGTVs), internal target volumes (ITVs), and planning target volumes (PTVs) based on the retrospective respiratory gating simulation were larger than those obtained using prospective gating (group A: pIGTV = 0.041, pITV = 0.003, pPTV = 0.008; group B: pIGTV = 0.025, pITV = 0.039, pPTV = 0.004). The two-gating PTVs were both smaller than those delineated on 3D non gating images (p < 0.001). V5Gy, V10Gy, V20Gy, V30Gy, and mean lung dose in the two gated radiotherapy plans were lower than those in the 3D non gating plan (p < 0.001); however, no significant difference was observed between the two gating plans (p > 0.05).
CONCLUSIONS: The application of respiratory gating could reduce the target volume and the radiation dose that the normal lung tissue received. Compared to prospective respiratory gating, the retrospective gating provides more information about tumor movement in PTV.

OBJECTIVE: To propose a straightforward and time-efficient quality assurance (QA) approach of beam time delay for respiratory-gated radiotherapy and validate the proposed method on typical respiratory gating systems, Catalyst™ and AlignRT™.
METHODS: The QA apparatus was composed of a motion platform and a Winston-Lutz cube phantom (WL3) embedded with metal balls. The apparatus was first scanned in CT-Sim and two types of QA plans specific for beam on and beam off time delay, respectively, were designed. Static reference images and motion testing images of the WL3 cube were acquired with EPID. By comparing the position differences of the embedded metal balls in the motion and reference images, beam time delays were determined. The proposed approach was validated on three linacs with either Catalyst™ or AlignRT™ respiratory gating systems. To investigate the impact of energy and dose rate on beam time delay, a range of QA plans with Eclipse (V15.7) were devised with varying energy and dose rates.
RESULTS: For all energies, the beam on time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 92.13 ± $ \\pm $ 5.79 ms, 123.11 ± $ \\pm $ 6.44 ms, and 303.44 ± $ \\pm $ 4.28 ms, respectively. The beam off time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 121.87 ± $ \\pm $ 1.34 ms, 119.33 ± $ \\pm $ 0.75 ms, and 97.69 ± $ \\pm $ 2.02 ms, respectively. Furthermore, the beam on delays decreased slightly as dose rates increased for all gating systems, whereas the beam off delays remained unaffected.
CONCLUSIONS: The validation results demonstrate the proposed QA approach of beam time delay for respiratory-gated radiotherapy was both reproducible and time-efficient to practice for institutions to customize accordingly.

Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data. It enables the reconstruction of long-term dynamic patterns using only a limited number of time snapshots. The quasi-periodic motion of a deformable lung tumor caused by respiratory motion makes it suitable for treatment using DMD. The direct application of the DMD method to analyze the respiratory motion of the tumor is impractical because the tumor is three-dimensional and spans multiple CT slices. To predict the respiratory movement of lung tumors, a method called uniform angular interval (UAI) sampling was developed to generate snapshot vectors of equal length, which are suitable for DMD analysis. The effectiveness of this approach was confirmed by applying the UAI-DMD method to the 4D-CT data of ten patients with lung cancer. The results indicate that the UAI-DMD method effectively approximates the lung tumor\'s deformable boundary surface and nonlinear motion trajectories. The estimated tumor centroid is within 2 mm of the manually delineated centroid, a smaller margin of error compared to the traditional BSpline interpolation method, which has a margin of 3 mm. This methodology has the potential to be extended to reconstruct the 20-phase respiratory movement of a lung tumor based on dynamic features from 10-phase 4D-CT data, thereby enabling more accurate estimation of the planned target volume (PTV).

OBJECTIVE: Beam delivery latency in respiratory-gated particle therapy systems is a crucial issue to dose delivery accuracy. The aim of this study is to develop a multi-channel signal acquisition platform for investigating gating latencies occurring within RPM respiratory gating system (Varian, USA) and ProBeam proton treatment system (Varian, USA) individually.
METHODS: The multi-channel signal acquisition platform consisted of several electronic components, including a string position sensor for target motion detection, a photodiode for proton beam sensing, an interfacing board for accessing the trigger signal between the respiratory gating system and the proton treatment system, a signal acquisition device for sampling and synchronizing signals from the aforementioned components, and a laptop for controlling the signal acquisition device and data storage. RPM system latencies were determined by comparing the expected gating phases extracted from the motion signal with the trigger signal\'s state turning points. ProBeam system latencies were assessed by comparing the state turning points of the trigger signal with the beam signal. The total beam delivery latencies were calculated as the sum of delays in the respiratory gating system and the cyclotron proton treatment system. During latency measurements, simulated sinusoidal motion were applied at different amplitudes and periods for complete beam delivery latency evaluation under different breathing patterns. Each breathing pattern was repeated 30 times for statistical analysis.
RESULTS: The measured gating ON/OFF latencies in the RPM system were found to be 104.20 ± 13.64 ms and 113.60 ± 14.98 ms, respectively. The measured gating ON/OFF delays in the ProBeam system were 108.29 ± 0.85 ms and 1.20 ± 0.04 ms, respectively. The total beam ON/OFF latencies were determined to be 212.50 ± 13.64 ms and 114.80 ± 14.98 ms.
CONCLUSIONS: With the developed multi-channel signal acquisition platform, it was able to investigate the gating lags happened in both the respiratory gating system and the proton treatment system. The resolution of the platform is enough to distinguish the delays at the millisecond time level. Both the respiratory gating system and the proton treatment system made contributions to gating latency. Both systems contributed nearly equally to the total beam ON latency, with approximately 100 ms. In contrast, the respiratory gating system was the dominant contributor to the total beam OFF latency.

Free-breathing abdominal chemical exchange saturation transfer (CEST) has great potential for clinical application, but its technical implementation remains challenging. This study aimed to propose and evaluate a free-breathing abdominal CEST sequence. The proposed sequence employed respiratory gating (ResGat) to synchronize the data acquisition with respiratory motion and performed a water presaturation module before the CEST saturation to abolish the influence of respiration-induced repetition time variation. In vivo experiments were performed to compare different respiratory motion-control strategies and B0 offset correction methods, and to evaluate the effectiveness and necessity of the quasi-steady-state (QUASS) approach for correcting the influence of the water presaturation module on CEST signal. ResGat with a target expiratory phase of 0.5 resulted in a higher structural similarity index and a lower coefficient of variation on consecutively acquired CEST S0 images than breath-holding (BH) and respiratory triggering (all p < 0.05). B0 maps derived from the abdominal CEST dataset itself were more stable for B0 correction, compared with the separately acquired B0 maps by a dual-echo time scan and B0 maps derived from the water saturation shift referencing approach. Compared with BH, ResGat yielded more homogeneous magnetization transfer ratio asymmetry maps at 3.5 ppm (standard deviation: 3.96% vs. 3.19%, p = 0.036) and a lower mean squared difference between scan and rescan (27.52‱ vs. 16.82‱, p = 0.004). The QUASS approach could correct the water presaturation-induced CEST signal change, but its necessity for in vivo scanning needs further verification. The proposed free-breathing abdominal CEST sequence using ResGat had an acquisition efficiency of approximately four times that using BH. In conclusion, the proposed free-breathing abdominal CEST sequence using ResGat and water presaturation has a higher acquisition efficiency and image quality than abdominal CEST using BH.

OBJECTIVE: To improve the scan efficiency of thoracic aorta vessel wall imaging using a self-gating (SG)-based motion correction scheme.
METHODS: A slab-selective variable-flip-angle 3D turbo spin-echo (SPACE) sequence was modified to acquire SG signals and imaging data. Cartesian sampling with a tiny golden-step spiral profile ordering was used to obtain the imaging data during the systolic period, and then the image data were subsequently corrected based on the SG signals and binned to different respiratory cycles. Finally, respiratory artifacts were estimated from image-based registration of 3D undersampled respiratory bins that were reconstructed with L1 iterative self-consistent parallel imaging reconstruction (SPIRiT). This method was evaluated in 11 healthy volunteers and compared against conventional diaphragmatic navigator-gated acquisition to assess the feasibility of the proposed framework.
RESULTS: Results showed that the proposed method achieved image quality comparable to that of conventional diaphragmatic navigator-gated acquisition with an average scan time of 4 min. The sharpness of the vessel wall and the definition of the liver boundary were in good agreement with the navigator-gated acquisition, which took approximately above 8.5 min depend on the respiratory rate. Further valuation of this technique in patients will be conducted to determine its clinical use.

OBJECTIVE: This study aimed to investigate the performance of respiratory-gating imaging with reduced acquisition time using the total-body positron emission tomography/computed tomography (PET/CT) scanner.
METHODS: Imaging data of 71 patients with suspect malignancies who underwent total-body 2-[18F]-fluoro-2-deoxy-D-glucose PET/CT for 15 min with respiration recorded were analyzed. For each examination, four reconstructions were performed: Ungated-15, using all coincidences; Ungated-5, using data of the first 5 min; Gated-15 using all coincidences but with respiratory gating; and Gated-6 using data of the first 6 min with respiratory gating. Lesions were quantified and image quality was evaluated; both were compared between the four image sets.
RESULTS: A total of 390 lesions were found in the thorax and upper abdomen. Lesion detectability was significantly higher in gated-15 (97.2%) than in ungated-15 (93.6%, p = 0.001) and ungated-5 (92.3%, p = 0.001), but comparable to Gated-6 (95.9%, p = 0.993). A total of 131 lesions were selected for quantitative analyses. Lesions in Gated-15 presented significantly larger standardized uptake values, tumor-to-liver ratio, and tumor-to-blood ratio, but smaller metabolic tumor volume, compared to those in Ungated-15 and Ungated-5 (all p < 0.001). These differences were more obvious in small lesions and in lesions from sites other than mediastinum/retroperitoneum. However, these indices were not significantly different between Gated-15 and Gated-6. Higher, but acceptable, image noise was identified in gated images than in ungated images.
CONCLUSIONS: Respiratory-gating imaging with reduced scanning time using the total-body PET/CT scanner is superior to ungated imaging and can be used in the clinic.
CONCLUSIONS: • In PET imaging, respiratory gating can improve lesion presentation and detectability but requires longer imaging time. • This single-center study showed that the total-body PET scanner allows respiratory-gated imaging with reduced and clinically acceptable scanning time.

The recently available gated T1-weighted imaging with the Dixon technique enables the synchronized gating signal for both MR acquisition and PET reconstruction. Herein, we evaluated the clinical value of this MR-gated PET reconstruction in the thoracic-abdominal PET/MRI compared with non-MR-gated method.
Twenty patients (28 hypermetabolic target lesions) underwent PET/MRI. Four types of PET images were reconstructed: non-MR-gating + gated attenuation correction (AC) (group A), MR-gating + gated AC (group B), non-MR-gating + breath-hold (BH) AC (group C), and MR-gating + BH AC (group D). A 4-point objective scale (from well match to obvious mismatch was scored from 3 to 0) was proposed to evaluate the mismatch. The detection rate and quantitative metrics were also evaluated.
In the patient-based analysis, for groups A through D, the detection rates were 90%, 100%, 85%, and 90% as well as 95%, 100%, 85%, and 85%, assessed by readers 1 and 2, respectively, and significant difference of mismatch score was observed with the highest proportion of 3 points in group B (85%, 90%, 35%, and 40%, and 80%, 90%, 35%, and 20%, assessed by readers 1 and 2, respectively). The lesion-based analysis demonstrated significant differences in quantitative metrics for groups A through D (all P\'s < 0.05), with the highest quantitative metrics in group B (SUVmax: 7.49 ± 3.37, 8.45 ± 3.82, 6.90 ± 3.24, and 7.69 ± 3.50; SUVmean: 3.90 ± 1.60, 4.34 ± 1.84, 3.67 ± 1.61, and 4.03 ± 1.81; SUVpeak: 5.60 ± 2.50, 6.10 ± 2.80, 5.22 ± 2.40, and 5.65 ± 2.68; signal-to-noise ratio: 136.06 ± 90.58, 136.24 ± 81.63, 99.52 ± 53.16, and 107.57 ± 69.05).
The MR-gated reconstruction using gated AC reduced the mismatch between MR and PET images and improved the thoracic-abdominal PET image quality in simultaneous PET/MRI systems.

Motion compensation can eliminate inconsistencies of respiratory movement during image acquisitions for precise vascular reconstruction in the clinical diagnosis of vascular disease from x-ray angiographic image sequences. In x-ray-based vascular interventional therapy, motion modeling can simulate the process of organ deformation driven by motion signals to display a dynamic organ on angiograms without contrast agent injection. Automatic respiratory signal estimation from x-ray angiographic image sequences is essential for motion compensation and modeling. The effects of respiratory motion, cardiac impulses, and tremors on structures in the chest and abdomen bring difficulty in extracting accurate respiratory signals individually. In this study, an end-to-end deep learning framework based on a motion-flow-guided recurrent network is proposed to address the aforementioned problem. The proposed method utilizes a convolutional neural network to learn the spatial features of every single frame, and a recurrent neural network to learn the temporal features of the entire sequence. The combination of the two networks can effectively analyze the image sequence to realize respiratory signal estimation. In addition, the motion-flow between consecutive frames is introduced to provide a dynamic constraint of spatial features, which enables the recurrent network to learn better temporal features from dynamic spatial features than from static spatial features. We demonstrate the advantages of our approach on designed datasets which contain coronary and hepatic angiographic sequences with diaphragm structures, and coronary angiographic sequences without diaphragm structures. Our method improves over state-of-the-art manifold-learning-based methods by 85.7%, 81.5% and 75.3% in respiratory signal accuracy metric on these datasets. The results demonstrate that the proposed method can effectively estimate respiratory signals from multiple motion patterns.