Respiratory Phase Research Articles

A Pilot Study of Synchronization of Respiration-Induced Motions in the Duodenum and Stomach for the Primary Tumor in Radiation Therapy for Pancreatic Cancer Using 4-Dimensional Computed Tomography

PurposeWe investigated the synchronization of respiration-induced motions at the primary tumor and organs at risk at radiation planning for pancreatic cancer. Methods and MaterialsFour-dimensional computed tomography images were acquired under the condition of shallow free breathing in patients with pancreatic cancer. The gross tumor volume (GTV), duodenum (DU), and stomach (ST) were contoured. The center of mass was computed for each 4-dimensional volume of interest. The respiration dependence of coordinates for the center of each volume of interest was computed relative to its location at the 50% (maximum exhalation) phase. Based on the shift of the GTV, we investigated the synchronization of respiration-induced motions between each contouring target. We examined the differences in the volume averaged dose to the ST and DU in each respiratory phase. ResultsNine patients with pancreatic cancer were analyzed in this study. The mean maximum 3-dimensional excursions at the GTV, DU, and ST were 9.6, 9.8, and 11.4 mm, respectively. At phase 0% and 90% (inhale phases), mean distance changes in the positional relationship with the GTV were 0.3 and 0.7 mm respectively for the DU and −2.5 and −2.4 mm respectively for the ST. There was no significant respiration associated change (RAC) between each respiratory phase in the DU (P = .568), and there was a significant RAC in the ST (P < .001). There was a significant RAC of the volume averaged dose to the ST (P = .023). ConclusionsOur results indicate that the DU but not the ST might move synchronously with GTV due to respiration.

Phase2Phase: Respiratory Motion-Resolved Reconstruction of Free-Breathing Magnetic Resonance Imaging Using Deep Learning Without a Ground Truth for Improved Liver Imaging.

Respiratory binning of free-breathing magnetic resonance imaging data reduces motion blurring; however, it exacerbates noise and introduces severe artifacts due to undersampling. Deep neural networks can remove artifacts and noise but usually require high-quality ground truth images for training. This study aimed to develop a network that can be trained without this requirement. This retrospective study was conducted on 33 participants enrolled between November 2016 and June 2019. Free-breathing magnetic resonance imaging was performed using a radial acquisition. Self-navigation was used to bin the k-space data into 10 respiratory phases. To simulate short acquisitions, subsets of radial spokes were used in reconstructing images with multicoil nonuniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and 2 deep learning methods: UNet3DPhase and Phase2Phase (P2P). UNet3DPhase was trained using a high-quality ground truth, whereas P2P was trained using noisy images with streaking artifacts. Two radiologists blinded to the reconstruction methods independently reviewed the sharpness, contrast, and artifact-freeness of the end-expiration images reconstructed from data collected at 16% of the Nyquist sampling rate. The generalized estimating equation method was used for statistical comparison. Motion vector fields were derived to examine the respiratory motion range of 4-dimensional images reconstructed using different methods. A total of 15 healthy participants and 18 patients with hepatic malignancy (50 ± 15 years, 6 women) were enrolled. Both reviewers found that the UNet3DPhase and P2P images had higher contrast (P < 0.01) and fewer artifacts (P < 0.01) than the CS images. The UNet3DPhase and P2P images were reported to be sharper than the CS images by 1 reviewer (P < 0.01) but not by the other reviewer (P = 0.22, P = 0.18). UNet3DPhase and P2P were similar in sharpness and contrast, whereas UNet3DPhase had fewer artifacts (P < 0.01). The motion vector lengths for the MCNUFFT800 and P2P800 images were comparable (10.5 ± 4.2 mm and 9.9 ± 4.0 mm, respectively), whereas both were significantly larger than CS2000 (7.0 ± 3.9 mm; P < 0.0001) and UNnet3DPhase800 (6.9 ± 3.2; P < 0.0001) images. Without a ground truth, P2P can reconstruct sharp, artifact-free, and high-contrast respiratory motion-resolved images from highly undersampled data. Unlike the CS and UNet3DPhase methods, P2P did not artificially reduce the respiratory motion range.

The acoustical and perceptual features of snore-related sounds inpatients with obstructive sleep apnea sleeping with the dynamic mandibular advancement system MATRx plus®.

The effect of snoring on the bed partner can be studied through the evaluation of in situ sound records by the bed partner or unspecialized raters as a proxy of real-life snoring perception. The aim was to characterize perceptual snore events through acoustical features in patients withobstructive sleep apnea (OSA) with an advanced mandibular position. Thirty-minute sound samples of 29 patients withOSA were retrieved from overnight, in-home recordings of a study to validate the MATRx plus® dynamic mandibular advancement system. Three unspecialized raters identified sound events and classified them as noise, snore, or breathing. The raters provided ratings for classification certainty and annoyance. Data were analyzed with respect to respiratory phases, and annoyance. When subdividing perceptual events based on respiratory phase, the logarithm-transformed Mean Power, Spectral Centroid, and Snore Factor differed significantly between event types, although not substantially for the spectral centroid. The variability within event type was high and distributions suggested the presence of subpopulations. The general linear model (GLM) showed a significant patient effect. Inspiration segments occurred in 65% of snore events, expiration segments in 54%. The annoyance correlated with the logarithm of mean power (r = 0.48) and the Snore Factor (0.46). Perceptual sound events identified by non-experts contain a non-negligible mixture of expiration and inspiration phases making the characterization through acoustical features complex. The present study reveals that subpopulations mayexist, and patient-specific features need to be introduced.

Inspiratory and Phase‐Spanning Dorsolateral Pontine Neurons Are Silent During Fentanyl‐Induced Apnea

Overdoses caused by the opioid agonist fentanyl have increased exponentially in recent years. These overdose deaths are the result of inadequate ventilation. While much research has focused on opioid modulation of medullary respiratory nuclei, pontine mediated effects of opioids have received less attention. Given the interconnectivity of the pontine Kölliker-Fuse/Parabrachial complex (KF/PB) with rhythmogenic nuclei in the medulla, their involvement in controlling respiratory rate and pattern, and their sensitivity to opioids, we investigate the role they play in opioid mediated respiratory depression and hypothesize that dorsolateral pontine inspiratory, phase-spanning, and post-inspiratory neurons are silenced by opioids. To address this, we have done single unit recordings in the working heart brainstem in situ rat preparation. Dorsolateral pontine neural units (n=48) were recorded extracellularly and systemically exposed to fentanyl and then the opioid antagonist naloxone. A phase-fraction 2-D histogram (heatmap) was generated to compare all unit's action potential spiking over respiratory phases and during periods following fentanyl administration. Each neural unit was categorized at baseline into 4 main groups of discharge identity: Inspiratory, Expiratory, Phase-spanning, and Tonic. Systemic application of fentanyl (300nM) resulted in a consistent decline in fictive respiratory output, transitioned to fast shallowing breathing, and eventually resulted in cessation of respiratory output (apnea). Out of 48 extracellular single unit recordings in the dorsolateral pons, 25 (52%) were completely silent (firing frequency of 0 Hz) suggesting post-synaptic opioid receptor activation during fentanyl-induced apnea; 17/20 inspiratory, 6/12 phase spanning (4/9 IE & 2/3 EI), 1/13 expiratory (1/5 Post I Dec) and 1/3 tonic neurons. To note, many neurons that were not completely silenced by opioids did fire at a much lower firing frequency, suggesting pre-synaptic opioid receptors also contribute to opioid-induced loss of pontine neural activity. Our hypothesis was partially supported, as the majority of inspiratory neurons (17/20) and a subpopulation of phase-spanning neurons (6/12) were completely silenced by fentanyl. Surprisingly post-inspiratory neurons were mainly insensitive to opioids (4/5). These results suggest that loss of post-inspiratory activity with DAMGO injection into the KF may depend critically on post-synaptic inhibition of inspiratory and a subset of IE phase-spanning neurons, which in turn, could decrease local pre-synaptic excitation of post-inspiratory neurons. The majority of pontine expiratory neurons were not silenced by fentanyl (12/13). Due to the KF/PB role in expiratory duration, sustained activity of pontine expiratory neurons may facilitate sustained apnea during opioid overdose. These results warrant further investigation into the contribution of the dorsolateral pons in respiratory pattern formation and opioid-induced respiratory depression.

Investigation of the effect of acquisition schemes on time-resolved magnetic resonance fingerprinting

Purpose. This study aims to investigate the feasibility of different acquisition methods for time-resolved magnetic resonance fingerprinting (TR-MRF) in computer simulation. Methods. An extended cardiac-torso (XCAT) phantom is used to generate abdominal T1, T2, and proton density maps for MRF simulation. The simulated MRF technique consists of an IR-FISP MRF sequence with spiral trajectory acquisition. MRF maps were simulated with different numbers of repetitions from 1 to 15. Three different methods were used to generate TR-MRF maps: (1) continuous acquisition without delay between MRF repetitions; (2) continuous acquisition with 5 s delay between MRF repetitions; (3) triggered acquisition with variable delay between MRF repetitions to allow the next acquisition to start at different respiration phase. After the generation of TR-MRF maps, the image quality indexes including the absolute T1 and T2 values, signal-to-noise-ratio (SNR), tumor-to-liver contrast-to-noise ratio, error in the amplitude of diaphragm motion and tumor volume error were used to evaluate the reconstructed parameter maps. Three volunteers were recruited to test the feasibility of the selected acquisition method. Results. Dynamic MR parametric maps using three different acquisition methods were estimated. The overall and liver T1 value error, liver SNR in T1 and T2 maps, and tumor SNR from T1 maps from triggered method is statistically significantly better than the other two methods (p-value < 0.05). The other image quality indexes have no significant difference between the triggered method and the other two continuous acquisition methods. All image quality indexes exhibit no significant difference between the acquisition methods with 0 s and 5 s delay. The triggered method was successfully performed in three healthy volunteers. Conclusion. TR-MRF technique was investigated using three different acquisition methods in computer simulation where the triggered method showed better performance than the other two methods. The triggered method has been tested successfully in healthy volunteers.

OFx: A method of 4D image construction from free-breathing non-gated MRI slice acquisitions of the thorax via optical flux.

Since real-time 4D dynamic magnetic resonance imaging (dMRI) methods with adequate spatial and temporal resolution for imaging the pediatric thorax are currently not available, free-breathing slice acquisitions followed by appropriate 4D construction methods are currently employed. Self-gating methods, which extract breathing signals only from image information without any external gating technology, have much potential for this purpose, such as for use in studying pediatric thoracic insufficiency syndrome (TIS). Patients with TIS frequently suffer from extreme malformations of the chest wall, diaphragm, and spine, leading to breathing that is very complex, including deep or shallow respiratory cycles. Existing 4D construction methods cannot perform satisfactorily in this scenario, and most are not fully automatic, requiring manual interactive operations. In this paper, we propose a novel fully automatic 4D image construction method based on an image-derived concept called flux to address these challenges. We utilized 25 dMRI data sets from 25 pediatric subjects with no known thoracic anomalies and 58 dMRI data sets from 29 patients with TIS where each patient had a dMRI scan before and after surgery. A time sequence of 80 slices are acquired at each sagittal location continuously at a rate of ~480ms per slice under free-breathing conditions, with 30-40 sagittal locations across the chest for each subject depending on the thoracic size. In our approach, we first extract the breathing signal for each sagittal location based on the flux of the optical flow vector field of the body region from the image time series. Here, for each time point of respiratory phase, the net flux of the body region can be regarded as the flux going into or out of the body region, which we term Optical Flux (OFx). OFx provides a very robust representation of the real breathing motion of the thorax. OFx allows us to perform a full analysis of all respiratory cycles, extract only normal cycles in a robust manner, and map all extracted normal cycles on to one cosine respiration model for each sagittal location. Subsequently, we re-sample one normal cycle from the respiration model for each location independently. The normal cycle models associated with the different sagittal locations are finally composited to form the final constructed 4D image. We employ several metrics to evaluate the quality of the 4D construction results: Eie - error in locating time instants corresponding to end inspiration and end expiration; Eto - deviation from correct temporal order in each detected normal cycle; Ess - deviation in spatial smoothness; and Esc - deviation from spatial continuity as scored by a reader. The means and standard deviations of these metrics for normal subjects and TIS patients are found to be, respectively: Eie: 0.25±0.05 and 0.38±0.16 in units of time instance (ideal value=0); Eto: 2.7% ± 2.3% and 1.8%±2% (ideal value=0%); Ess: 0.5±0.17 and 0.54±0.25 in pixel units (ideal value=0); Esc: 4.6±0.48 and 4.56±0.98 (score range: best=5, worst=1). The results show that the OFx method achieves excellent spatial and temporal continuity and its yield was 100% meaning that it successfully performed 4D construction on every data set tested. Compared to a recently published method, OFx is fully automatic requiring about 5min of computational time per study starting from acquired dMRI scans. The method achieves high temporal and spatial continuity even on complex TIS data sets that include many abnormal respiratory cycles. A new 4D dMRI construction method based on the concept of optical flux is presented which is fully automatic and very robust in deriving respiratory signals purely from dynamic image sequences even when presented with complex breathing patterns due to severe disease conditions like TIS. Evaluations show that its accuracy is comparable to the variations found in manual annotations. An important characteristic of the method is that it is independent of the number of sagittal locations used in the construction process, which suggests that it is applicable to imaging techniques where data are acquired at only a few sagittal locations instead of the full width of the thorax. The method is not tied to any specific imaging modality, as demonstrated in this paper on not just dMRI but dynamic computed tomography (CT) as well.

Reduction of margin to compensate the respiratory tumor motion by the analysis of dosimetric internal target volume in lung SBRT with nonuniform volume prescription method.

To develop a dosimetric internal target volume (ITV) margin (DIM) for respiratory motion in lung stereotactic body radiotherapy (SBRT) and to evaluate DIM with a nonuniform volume prescription (NVP) and the point prescription (PP). Volumetric modulated arc therapy (VMAT) treatment plans with PP and NVP were created on a heterogeneous programmable respiratory motion phantom, with a tumor (30-mm diameter) inside a cylindrical lung insert. The tumor was defined as the gross tumor volume (GTV), equal to the clinical target volume (CTV). Five-millimeter and 0-mm margins were used for the ITV and setup margins, respectively. The phantom was moved in cranio-caudal direction with a biquadratic sinusoidal waveform with a 4-s cycle and an amplitude of ±5-10mm. The interplay effect was evaluated by measuring the dose profile with a film in the sagittal plane for different respiratory periods and different initial respiratory phases. DIM was based on the respiratory motion amplitude that satisfied 100% and 95% coverage of the prescribed dose by the minimum dose of the CTV. Moreover, the absolute dose was measured with and without respiratory motion for NVP by a pinpoint chamber. The dose difference in the tumor region due to the interplay effect was within 1.0%. The gamma passing rate was over 95.1% for different respiratory periods and 98.6% for different initial respiratory phases. DIM with PP was almost equivalent to the margin of the respiratory motion. However, DIM with NVP was 2.0 and 1.8 times larger than the margin of the respiratory motion for the 100% and 95% coverage of the prescribed doses, respectively. The interplay effects experienced between the MLC sequence and tumor motion were negligible for NVP. The DIM analysis revealed that the margin to compensate the respiratory tumor motion could be reduced by more than 44-50% for NVP in SBRT.

Using the Diaphragm as a Tracking Surrogate in CyberKnife Synchrony Treatment.

BackgroundIn this study, we assessed the usefulness of diaphragm surrogate tracking in the design of a respiratory model for CyberKnife Synchrony treatment of lung tumors.Material/MethodsTwenty-four patients with lung cancer who underwent stereotactic body radiotherapy with CyberKnife between April and November 2019 were enrolled. Simulation plans for each patient were designed using Xsight lung tracking (XLT) and diaphragm tracking (DT) methods, and tumor visualization tests were performed. The offset consistency at each respiratory phase was analyzed. The relative distance along the alignment center of the superior-inferior (SI) axis in the 2 projections (dxAB), uncertainty (%), and average standard error (AvgStdErr)/maximum standard error (MAXStdErr) were also analyzed.ResultsBland-Altman analyses revealed that the average differences±standard deviation (SD) between XLT and DT tracking methods were 0.4±2.9 mm, 0.3±4.35 mm, and -1.8±6.8 mm for the SI, left-right (LR), and anterior-posterior (AP) directions, respectively. These results indicated high consistency in the SI and LR directions and poor consistency in the AP direction. Uncertainty differed significantly between XLT and DT (22.813±5.721% vs 9.384±3.799%; t=−5.236; P=0.0008), but we found no significant differences in dxAB, AvgStdErr, or MAXStdErr.ConclusionsIn the majority of cases, motion tracking by XLT and DT was consistent and synchronized in the SI directions, but not in the LR and AP directions. With a boundary margin of 0.3±4.35 mm and 1.8±6.8 mm for the LR and AP directions, DT may contribute to better implementation of CyberKnife Synchrony treatment in patients with lung tumors near the diaphragm that cannot be seen in tumor visualization tests.

A 2D/3D Non-rigid Registration Method for Lung Images Based on a Non-linear Correlation Between Displacement Vectors and Similarity Measures

This paper presents a novel 2D/3D non-rigid registration method for lung lesions tracking in image-guided diagnoses and treatments. Preoperative 3D lung CT volumes were obtained at a series of respiratory phases and important anatomical points were extracted. A CT volume was selected as reference volume and others were considered floating volumes. Displacement vectors of anatomical points were calculated using coherent point drift (CPD) and diffeomorphic-demon methods. For each CT volume, 2D digitally reconstructed radiographs (DRR) were generated by ray projection to simulate intraoperative 2D X-ray, and grayscale-based similarity measures of DRR between reference and floating volumes were calculated. A pulmonary respiration model was constructed using cubic polynomial, which represents the non-linear correlation between displacement vectors and similarity measures. During operation, the pulmonary respiration model used X-ray to obtain displacement vectors of important anatomical points; deformation fields were calculated from the displacement vectors through B-spline transformations. Finally, simulated lung CT volumes corresponding to intraoperative X-ray were generated by applying deformation fields to reference volume. The proposed method was compared to a state-of-the-art 2D/3D non-rigid registration method named respiratory-phase matching. Experimental results on two datasets respectively from a lung cancer patient and an ex vivo pig lung, showed mean registration accuracy of the proposed method was 1.57 ± 0.92 mm and 2.65 ± 1.35 mm; an improvement of 23% and 9% over the matching method. Moreover, mean lung structure overlaps were 0.91 and 0.88, comparable to matching method. The proposed method has potential in aiding lung intervention diagnoses and real-time lesion tracking.

DEEP GENERATIVE STORM MODEL FOR DYNAMIC IMAGING.

We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The non-linear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well as the latent vectors are jointly estimated from the undersampled measurements using stochastic gradient descent. We penalize the norm of the gradient of the generator to encourage the learning of a smooth surface/manifold, while temporal gradients of the latent vectors are penalized to encourage the time series to be smooth. The main benefits of the proposed scheme are (a) the quite significant reduction in memory demand compared to the analysis based SToRM model, and (b) the spatial regularization brought in by the CNN model. We also introduce efficient progressive approaches to minimize the computational complexity of the algorithm.

An Automated Jet Nebulizer with Dynamic Flow Regulation.

PurposeThe present study is focused on designing an automated jet nebulizer that possesses the capability of dynamic flow regulation. In the case of existing equipment, 50% of the aerosol is lost to the atmosphere through the vent, during the exhalation phase of respiration. Desired effects of nebulization may not be achieved by neglecting this poor administration technique. There may be adverse effects like bronchospasm and exposure to high drug concentrations. MethodsThe proposed nebulizer is composed of two modes as “Compressed Air” mode and the “Oxygen Therapy” mode. The automated triggering from one mode to another will be dependent upon the percentage of oxygen saturation of the patient, monitored from the SpO2 sensor. The compressed airflow will be delivered to the patient according to the minute ventilation, derived with the aid of a temperature sensor–based algorithm. The compressor controller circuitry ensures that the patient receives optimum level of compressed air as per the flow rate. At the end of the drug delivery, if the liquid level sensor detects the absence of medication within the nebulizer chamber, the nebulization process will be terminated. The dynamic regulation of the motor speed with respect to the minute ventilation was accomplished.ResultsA laminar flow was obtained from the outlet of the compressor towards the nebulizer tubing, and a turbulent flow was obtained within the chamber. No excessive turbulent flows or rotational flow patterns were detected.ConclusionDetecting the drug levels in the nebulizing chamber will prevent continuous workup and useful in situations where back-to-back nebulization is required. Oxygen therapy mode identifies the patient’s desaturation and important where the patient can be already hypoxic or have a ventilation-perfusion mismatch, but may be disadvantageous in severe COPD patients. The aforesaid results could certainly lead to the improvements of the existing nebulizers.

Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula

Background and Purpose4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times. MethodsTwo 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study. ResultsFor 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm3) were each reconstructed from gridded images in only 28 seconds per subject. Excellent agreement was found between deep-learning-based and joint MoCo-HDTV-reconstructed MRI (average SSIM ≥ 0.96, NIQE scores 7.94 and 5.66). Deep-learning-based 4D-MRI were clinically acceptable for target and organ-at-risk delineation. Tumour positions agreed within 0.7 mm on midposition images. ConclusionOur results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.

An MRI framework for respiratory motion modelling validation.

IntroductionRespiratory motion models establish a correspondence between respiratory‐correlated (RC) 4‐dimensional (4D) imaging and respiratory surrogates, to estimate time‐resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine‐MRI and (ii) additional 4DMRI data for testing intra‐/inter‐fraction breathing motion variability.MethodsRepeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter‐fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine‐MRI and 4DMRI respiratory phases, respectively.ResultsThe median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm3), with higher values in the presence of large inter‐fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks’ position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra‐ and inter‐fraction motion, respectively. The range of motion described in the cine‐MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter‐fraction baseline variations occurred.ConclusionsResults suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI‐guided radiotherapy.

Reducing 4DCBCT scan time and dose through motion compensated acquisition and reconstruction

Conventional 4DCBCT captures 1320 projections across 4 min. Adaptive 4DCBCT has been developed to reduce imaging dose and scan time. This study investigated reconstruction algorithms that best complement adaptive 4DCBCT acquisition for reducing imaging dose and scan time whilst maintaining or improving image quality compared to conventional 4DCBCT acquisition using real patient data from the first 10 adaptive 4DCBCT patients. Adaptive 4DCBCT was implemented in the ADaptive CT Acquisition for Personalized Thoracic imaging clinical trial. Adaptive 4DCBCT modulates gantry rotation speed and kV acquisition rate in response to the patient’s real-time respiratory signal, ensuring even angular spacing between projections at each respiratory phase. We examined the first 10 lung cancer radiotherapy patients that received adaptive 4DCBCT. Fast, 200-projection scans over 60–80 s, and slower, 600-projection scans over ∼240 s, were obtained after routine patient treatment and compared against conventional 4DCBCT acquisition. Adaptive 4DCBCT acquisitions were reconstructed using Feldkamp−Davis−Kress (FDK), McKinnon–Bates (MKB), Motion Compensated FDK (MCFDK) and Motion Compensated MKB (MCMKB) algorithms. Reconstructions were assessed via, Structural SIMilarity (SSIM), Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR), Tissue Interface Sharpness of Diaphragm (TIS-D) and Tumor (TIS-T). The 200- and 600-projection adaptive 4DCBCT acquisition corresponded to 85% and 55% reduction in imaging dose, shorter and similar scan times of approximately 90 s and 236 s respectively, compared to conventional 4DCBCT acquisition. 200- and 600-projection adaptive 4DCBCT reconstructions achieved more than 0.900 SSIM relative to conventional 4DCBCT acquisition. Compared to conventional 4DCBCT acquisition, 200-projection adaptive 4DCBCT reconstructions achieved higher SNR, CNR, TIS-T, TIS-D with motion compensated algorithms, MCFDK (208%, 159%, 174%, 247%) and MCMKB (214%, 173%, 266%, 245%) respectively. The 200-projection adaptive 4DCBCT MCFDK- and MCMKB-reconstruction results show image quality improvements are possible even with 85% fewer projections acquired. We established acquisition-reconstruction protocols that provide substantial reductions in imaging time and dose whilst improving image quality.

The relationship between the continuous movement of the dancer and the respiratory phase: focusing on the differences due to the experience of the female college student dancer

The relationship between the continuous movement of the dancer and the respiratory phase: focusing on the differences due to the experience of the female college student dancer

P20.06 Dosimetric Comparison of the Real and Virtual Plans in Non-Small Cell Lung Cancer Patients Treated With Respiratory Motion Management

We conducted a prospective study to increase tumor control, reducing side effects and dosimetric comparison of the real and virtual plans by using motion management in 40 patients with locally advanced non-small cell lung cancer. The study was approved by the ethics (2018-7/7) and scientific committee (OUAP (T) 2019/2) as scientific research project in our university. Simulation images were obtained using free breathing in the treatment position using axial cine mode 16-slice 4D-CT scanner and RPM in 10 respiratory phases. In the MIP phase, PTV with a 5 mm border in each direction was created for the ITV including the tumor and involved nodes. A volumetric arc treatment plan was established by taking the 98% of prescription dose to 95% of PTV and also taking into account the risky organ doses. Patients were treated with CBCT imaging on LINAC device every other day. For virtual plan, GTV for tumor and involved nodes and CTV according to ICRU83 were created in the free respiratory phase. PTV was created with a 5 mm margin to CTV and virtual treatment plan was created with the same prescribed dose. 10 patients were treated till December 2019. The median age was 59 years (49-72) and the M/F ratio was 8/2. Histopathological diagnosis were SCC (4), adenocarcinoma (5), LCNEC (1). AJCC18 staging were listed as IA2 (1), IIIA (2), IIIB (4), IIIC (2) and IVB (1). The median RT dose was 6300 cGy (5940-6400) with 32 fractions (30-35). In the third month, 2 complete, 3 partial responses and 1 locoregonal progression were detected by PET/CT. ITVtm was 46.98 and GTVtm was 31.27 cc (p = 0.66), while PTV volumes were 173.53 and 494.50 cc (p = 0.008) in treatment and virtual plans, respectively. The tumor volume was higher but PTV volume was lower in the treatment plans. D95 for ITVtm and GTVtm were found 6311 and 6326 cGy (p = 0.72) whereas D95 for PTV 6297 and 6051 cGy (p = 0.13), respectively. The mean lung dose, V5, V20 and V30 were 15.14 vs 16.07 Gy (p = 0.077), 55.95% vs 63.8% (p = 0.087), 26.44% vs 31.55% (p = 0.23) and 17.44% vs 20.64 (p = 0.085), median heart dose was 6.03 and 10.36 Gy (p = 0.043), heart V50 was 8.2% vs 33.9% (p = 0.007), median esophageal dose was 18.94 vs 21.43 Gy (p = 0.35) and esophagus V50 11.39% vs 18% (p = 0.69), the median and maximum spinal cord dose was 8.11 vs 8.22 Gy (p = 0.61) and 33.15 vs 37.39 Gy, respectively (p = 0.22), in treatment and virtual plans. It was observed that all risky organ doses were less in treatment plans than virtual plans with statistical significance in heart doses. In this study, it was shown that the target volume did not increase, the target was covered with 95% accuracy and the risky organ doses were lower in 10 patients who were treated with 4D-CT planning and respiratory motion management.

Randomized Controlled Trial of Ultrasound-guided Fluid Resuscitation of Sepsis-Induced Hypoperfusion and Septic Shock.

IntroductionThe ultrasound measurement of inferior vena cava (IVC) diameter change during respiratory phase to guide fluid resuscitation in shock patients is widely performed, but the benefit on reducing the mortality of sepsis patients is questionable. The study objective was to evaluate the 30-day mortality rate of patients with sepsis-induced tissue hypoperfusion (SITH) and septic shock (SS) treated with ultrasound-guided fluid management (UGFM) using ultrasonographic change of the IVC diameter during respiration compared with those treated with the usual-care strategy.MethodsThis was a randomized controlled trial conducted in an urban, university-affiliated tertiary-care hospital. Adult patients with SITH/SS were randomized to receive treatment with UGFM using respiratory change of the IVC (UGFM strategy) or with the usual-care strategy during the first six hours after emergency department (ED) arrival. We compared the 30-day mortality rate and other clinical outcomes between the two groups.ResultsA total of 202 patients were enrolled, 101 in each group (UGFM vs usual-care strategy) for intention-to-treat analysis. There was no significant difference in 30-day overall mortality between the two groups (18.8% and 19.8% in the usual-care and UGFM strategy, respectively; p > 0.05 by log rank test). Neither was there a difference in six-hour lactate clearance, a change in the sequential organ failure assessment score, or length of hospital stay. However, the cumulative fluid amount given in 24 hours was significantly lower in the UGFM arm.ConclusionIn our ED setting, the use of respiratory change of IVC diameter determined by point-of-care ultrasound to guide initial fluid resuscitation in SITH/SS ED patients did not improve the 30-day survival probability or other clinical parameters compared to the usual-care strategy. However, the IVC ultrasound-guided resuscitation was associated with less amount of fluid used.

Influences of gas explosion on acute blast lung injury and time phase changes of pulmonary function in rats under real roadway environment

Objective: The aims of this study were to investigate the effect of gas explosion on rats and to explore the pulmonary function alterations associated with gas explosion-induced acute blast lung injury (ABLI) in real roadway environment. Methods: In April 2018, the large coal mine gas explosion test roadway and explosion test system were used to simulate the real gas explosion roadway environment, fixed the cage and set the explosion parameters. 72 SD rats, male, SPF grade, were randomly divided into nine groups by completely random grouping method according to their body weight: control group, close range group (160 m) , and long range group (240 m) . In each group, there were wound groups (24 h group and 48h group, 8/group, total 48 in six groups) and no wound groups (8/group, total 24 in three groups) . Except for the control group, the other groups were placed in cages at different distances under anesthesia, the experiment of gas explosion was carried out by placing the rats in a position that could force the lungs. The changes of respiratory function of the rats in the non-invasive group were monitored with pulmonary function instrument at 2 h, 24 h, 48 h, 72 h and 168h after the explosion, and were killed under anesthesia 7 days later; the rats in invasive groups were anesthetized and killed at 24 h, 48 h and 168 h, respectively. Gross observation, lung wet-dry ratio and lung histopathology were performed. Results: Compared with the control group, f (respiratory frequency, f) , MV (minute ventilation, MV) , PEF (peak expiratory flow rate, PEF) , PIF (peak inspiratory flow rate, PIF) and EF50 (1/2 tidal volume expiratory flow, EF50) of rats in the close and long range groups decreased significantly after gas explosion 2 h. PAU (respiration pause, PAU) , Te (expiratory time, Te) , Ti (inspiratory time, Ti) and Tr (relaxation time, Tr) were significantly increased (P<0.05) . After 48 h, TV (tidal volume, TV) , Penh (enhanced respiration pause, Penh) , PAU, and PIF of rats in the long range group were significantly increased (P<0.05) . After 72 h, MV in the long range group was significantly decreased (P<0.05) . Compared with the control group, Penh, PAU, Ti and Te were significantly decreased after 168 h in the close and long range groups, with statistical significance (P<0.05) . At the same time, the body weight of rats in different range groups was significantly decreased (P<0.05) . In addition, both HE staining and routine observation of lung tissues of rats in different range groups showed that gas explosion caused pulmonary edema, obviously congested pulmonary capillaries, a large number of inflammatory cells and infiltrated red blood cells. Conclusion: Gas explosion in real roadway environment can cause the change of respiratory function phase and lung tissue damage in rats, suggesting that the model of gas explosion-induced ABLI has been initially established successfully, which would provide a basis for further study on the pathogenesis of ABLI.

Improvement of image quality using amplitude-based respiratory gating in PET-computed tomography scanning.

This study sought to provide data supporting the expanded clinical use of respiratory gating by assessing the diagnostic accuracy of breathing motion correction using amplitude-based respiratory gating. A respiratory movement tracking device was attached to a PET-computed tomography scanner, and images were obtained in respiratory gating mode using a motion phantom that was capable of sensing vertical motion. Specifically, after setting amplitude changes and intervals according to the movement cycle using a total of nine combinations of three waveforms and three amplitude ranges, respiratory motion-corrected images were reconstructed using the filtered back projection method. After defining areas of interest in the acquired images in the same image planes, statistical analyses were performed to compare differences in standardized uptake value (SUV), lesion volume, full width at half maximum (FWHM), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). SUVmax increased by 89.9%, and lesion volume decreased by 27.9%. Full width at half maximum decreased by 53.9%, signal-to-noise ratio increased by 11% and contrast-to-noise ratio increased by 16.3%. Optimal results were obtained when using a rest waveform and 35% duty cycle, in which the change in amplitude in the respiratory phase signal was low, and a constant level of long breaths was maintained. These results demonstrate that respiratory-gated PET-CT imaging can be used to accurately correct for SUV changes and image distortion caused by respiratory motion, thereby providing excellent imaging information and quality.

4D radiomics: impact of 4D-CBCT image quality on radiomic analysis

Purpose. To investigate the impact of 4D-CBCT image quality on radiomic analysis and the efficacy of using deep learning based image enhancement to improve the accuracy of radiomic features of 4D-CBCT. Material and Methods. In this study, 4D-CT data from 16 lung cancer patients were obtained. Digitally reconstructed radiographs (DRRs) were simulated from the 4D-CT, and then used to reconstruct 4D CBCT using the conventional FDK (Feldkamp et al J. Opt. Soc. Am. A 1 612–9) algorithm. Different projection numbers (i.e. 72, 120, 144, 180) and projection angle distributions (i.e. evenly distributed and unevenly distributed using angles from real 4D-CBCT scans) were simulated to generate the corresponding 4D-CBCT. A deep learning model (TecoGAN) was trained on 10 patients and validated on 3 patients to enhance the 4D-CBCT image quality to match with the corresponding ground-truth 4D-CT. The remaining 3 patients with different tumor sizes were used for testing. The radiomic features in 6 different categories, including histogram, GLCM, GLRLM, GLSZM, NGTDM, and wavelet, were extracted from the gross tumor volumes of each phase of original 4D-CBCT, enhanced 4D-CBCT, and 4D-CT. The radiomic features in 4D-CT were used as the ground-truth to evaluate the errors of the radiomic features in the original 4D-CBCT and enhanced 4D-CBCT. Errors in the original 4D-CBCT demonstrated the impact of image quality on radiomic features. Comparison between errors in the original 4D-CBCT and enhanced 4D-CBCT demonstrated the efficacy of using deep learning to improve the radiomic feature accuracy. Results. 4D-CBCT image quality can substantially affect the accuracy of the radiomic features, and the degree of impact is feature-dependent. The deep learning model was able to enhance the anatomical details and edge information in the 4D-CBCT as well as removing other image artifacts. This enhancement of image quality resulted in reduced errors for most radiomic features. The average reduction of radiomics errors for 3 patients are 20.0%, 31.4%, 36.7%, 50.0%, 33.6% and 11.3% for histogram, GLCM, GLRLM, GLSZM, NGTDM and Wavelet features. And the error reduction was more significant for patients with larger tumors. The findings were consistent across different respiratory phases, projection numbers, and angle distributions. Conclusions. The study demonstrated that 4D-CBCT image quality has a significant impact on the radiomic analysis. The deep learning-based augmentation technique proved to be an effective approach to enhance 4D-CBCT image quality to improve the accuracy of radiomic analysis.