Dataset Information

Predictive value of pediatric respiratory-induced diaphragm motion quantified using pre-treatment 4DCT and CBCTs.

ABSTRACT:

Background

In adults, a single pre-treatment four-dimensional CT (4D-CT) acquisition is often used to account for respiratory-induced target motion during radiotherapy. However, studies have indicated that a 4D-CT is not always representative for respiratory motion. Our aim was to investigate whether respiratory-induced diaphragm motion in children on a single pre-treatment 4DCT can accurately predict respiratory-induced diaphragm motion as observed on cone beam CTs (CBCTs).

Methods

Twelve patients (mean age 14.5 yrs.; range 8.6-17.9 yrs) were retrospectively included based on visibility of the diaphragm on abdominal or thoracic imaging data acquired during free breathing. A 4DCT for planning purposes and daily/weekly CBCTs (total 125; range 4-29 per patient) acquired prior to dose delivery were available. The amplitude, corresponding to the difference in position of the diaphragm in cranial-caudal direction in end-inspiration and end-expiration phases, was extracted from the 4DCT (A_4DCT). The amplitude in CBCTs (A_CBCT) was defined as displacement between averaged in- and expiration diaphragm positions on corresponding projection images, and the distribution of A_CBCT was compared to A_4DCT (one-sample t-test, significance level p?< 0.05).

Results

Over all patients, the mean A_4DCT was 10.4 mm and the mean A_CBCT 11.6 mm. For 9/12 patients, A_4DCT differed significantly (p CBCT. Differences >?3 mm were found in 69/125 CBCTs (55%), with A_4DCT mostly underestimating A_CBCT. For 7/12 patients, diaphragm positions differed significantly from the baseline position.

Conclusion

Respiratory-induced diaphragm motion determined on 4DCT does not accurately predict the daily respiratory-induced diaphragm motion observed on CBCTs, as the amplitude and baseline position differed statistically significantly in the majority of patients. Regular monitoring of respiratory motion during the treatment course using CBCTs could yield a higher accuracy when a daily adaptation to the actual breathing amplitude takes place.

SUBMITTER: Huijskens SC

PROVIDER: S-EPMC6180457 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predictive value of pediatric respiratory-induced diaphragm motion quantified using pre-treatment 4DCT and CBCTs.

Huijskens Sophie C SC van Dijk Irma W E M IWEM Visser Jorrit J Balgobind Brian V BV Rasch Coen R N CRN Alderliesten Tanja T Bel Arjan A

Radiation oncology (London, England) 20181011 1

<h4>Background</h4>In adults, a single pre-treatment four-dimensional CT (4D-CT) acquisition is often used to account for respiratory-induced target motion during radiotherapy. However, studies have indicated that a 4D-CT is not always representative for respiratory motion. Our aim was to investigate whether respiratory-induced diaphragm motion in children on a single pre-treatment 4DCT can accurately predict respiratory-induced diaphragm motion as observed on cone beam CTs (CBCTs).<h4>Methods</ ...[more]

PMID: 30305118

Similar Datasets

Project description:To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients (fv) to account for most of the spectrum energy (Σfv2). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves (R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors, providing an automatic robust tool to evaluate diaphragm motion.

Project description:PurposePredicting complex patterns of respiration can benefit the management of the respiratory motion for radiation therapy of lung cancer. The purpose of the present work was to develop a patient-specific, physiologically relevant respiratory motion model which is capable of predicting lung tumor motion over a complete normal breathing cycle.MethodsCurrently employed techniques for generating the lung geometry from four-dimensional computed tomography data tend to lose details of mesh topology due to excessive surface smoothening. Some of the existing models apply displacement boundary conditions instead of the intrapleural pressure as the actual motive force for respiration, while others ignore the nonlinearity of lung tissues or the mechanics of pleural sliding. An intermediate nonuniform rational basis spline surface representation is used to avoid multiple geometric smoothing procedures used in the computational mesh preparation. Measured chest pressure-volume relationships are used to simulate pressure loading on the surface of the model for a given lung volume, as in actual breathing. A hyperelastic model, developed from experimental observations, has been used to model the lung tissue material. Pleural sliding on the inside of the ribcage has also been considered.ResultsThe finite-element model has been validated using landmarks from four patient CT data sets over 34 breathing phases. The average differences of end-inspiration in position between the landmarks and those predicted by the model are observed to be 0.450 +/- 0.330 cm for Patient P1, 0.387 +/- 0.169 cm for Patient P2, 0.319 +/- 0.186 cm for Patient P3, and 0.204 +/- 0.102 cm for Patient P4 in the magnitude of error vector, respectively. The average errors of prediction at landmarks over multiple breathing phases in superior-inferior direction are less than 3 mm.ConclusionsThe prediction capability of pressure-volume curve driven nonlinear finite-element model is consistent over the entire breathing cycle. The biomechanical parameters in the model are physiologically measurable, so that the results can be extended to other patients and additional neighboring organs affected by respiratory motion.

Project description:PurposeThis study aims to investigate the agreement of three software packages in measuring baseline ischemic core volume (ICV) and penumbra volume (PV), and determine their predictive values for unfavorable clinical outcome in patients with endovascular thrombectomy (EVT).MethodsPatients with acute ischemic stroke who underwent computed tomographic perfusion (CTP) were recruited. Baseline CTP measurements including ICV and PV were calculated by three software packages of IntelliSpace Portal (ISP), Rapid Processing of Perfusion and Diffusion (RAPID), and fast-processing of ischemic stroke (F-STROKE). All patients received EVT, and the modified Rankin scale (mRS) at 90 days after EVT was assessed to determine the clinical outcomes (favorable: mRS = 0-2; unfavorable: mRS = 3-6). The agreement of CTP measurements among three software packages was determined using intraclass correlation coefficient (ICC). The associations between CTP measurements and unfavorable clinical outcome were analyzed using logistic regression. Receiver operating characteristic curves were conducted to calculate the area under the curve (AUC) of CTP measurements in predicting unfavorable clinical outcome.ResultsOf 223 recruited patients (68.2 ± 11.3 years old; 145 males), 17.0% had unfavorable clinical outcome after EVT. Excellent agreement between F-STROKE and RAPID was found in measuring ICV (ICC 0.965; 95% CI 0.956-0.973) and PV (ICC 0.966; 95% CI 0.956-0.973). ICVs measured by three software packages were significantly associated with unfavorable clinical outcome before (odds ratios 1.012-1.018, all P < 0.01) and after (odds ratios 1.003-1.014, all P < 0.05) adjusted for confounding factors (age, gender, TOAST classification, and NIHSS on admission). In predicting unfavorable clinical outcome, ICV measured by F-STROKE showed similar performance to that measured by RAPID (AUC 0.701 vs. 0.717) but higher performance than that measured by ISP (AUC 0.629).ConclusionsThe software of F-STROKE has excellent agreement with the widely used analysis tool of RAPID in measuring ICV and PV. The ischemic core volume measured by both F-STROKE and RAPID is a stronger predictor for unfavorable clinical outcome after EVT compared to ISP.

Project description:IntroductionThere is a variety of tools being used in clinical practice for the prediction of weaning success from mechanical ventilation. However, their diagnostic performances are less than satisfactory. The purpose of this study is to investigate the value of serial changes in diaphragm function measured by ultrasound during the spontaneous breathing trial (SBT) as a weaning predictor.Methods and analysisThis is a prospective observational study conducted in a 10-bed medical emergency intensive care unit (EICU) in a university-affiliated hospital. The study will be performed from November 2016 to December 2017. All patients in the EICU who are expected to have mechanical ventilation for more than 48 hours through endotracheal tube are potentially eligible for this study. Patients will be included if they fulfil the criteria for SBT. All enrolled patients will be ventilated with an Evita-4 by using volume assist control mode prior to SBT. Positive end-expiratory pressure (PEEP) will be set to 5 cmH2O and fractional inspired oxygen (FiO2) will be set to a value below 0.5 that guarantees oxygen saturation by pulse oximetry (SpO2) greater than 90%. Enrolled patients will undergo SBT for 2 hours in semirecumbent position. During the SBT, the patients will breathe through the ventilator circuit by using flow triggering (2 L/min) with automatic tube compensation of 100% and 5 cmH2O PEEP. The FiO2 will be set to the same value as used before SBT. If the patients fail to tolerate the SBT, the trial will be discontinued immediately and the ventilation mode will be switched to that used before the trial. Patients who pass the 2-hour SBT will be extubated. Right diaphragm excursion and bilateral diaphragm thickening fraction will be measured by ultrasonography during spontaneous breathing. Images will be obtained immediately prior to the SBT, and at 5, 30, 60, 90 and 120 min after the initiation of SBT. Rapid shallow breathing index will be simultaneously calculated at the bedside by a respiratory nurse.Ethics and disseminationThe study protocol is approved by the ethics committee of Sir Run Run Shaw Hospital, an affiliate of Zhejiang University, Medical College. The results will be published in a peer-reviewed journal and shared with the worldwide medical community.Trial registration numberISRCTN42917473; Pre-results.

Project description:In lung radiotherapy, variations in cycle-to-cycle breathing results in four-dimensional computed tomography imaging artifacts, leading to inaccurate beam coverage and tumor targeting. In previous studies, the effect of audiovisual (AV) biofeedback on the external respiratory signal reproducibility has been investigated but the internal anatomy motion has not been fully studied. The aim of this study is to test the hypothesis that AV biofeedback improves diaphragm motion reproducibility of internal anatomy using magnetic resonance imaging (MRI).To test the hypothesis 15 healthy human subjects were enrolled in an ethics-approved AV biofeedback study consisting of two imaging sessions spaced ∼1 week apart. Within each session MR images were acquired under free breathing and AV biofeedback conditions. The respiratory signal to the AV biofeedback system utilized optical monitoring of an external marker placed on the abdomen. Synchronously, serial thoracic 2D MR images were obtained to measure the diaphragm motion using a fast gradient-recalled-echo MR pulse sequence in both coronal and sagittal planes. The improvement in the diaphragm motion reproducibility using the AV biofeedback system was quantified by comparing cycle-to-cycle variability in displacement, respiratory period, and baseline drift. Additionally, the variation in improvement between the two sessions was also quantified.The average root mean square error (RMSE) of diaphragm cycle-to-cycle displacement was reduced from 2.6 mm with free breathing to 1.6 mm (38% reduction) with the implementation of AV biofeedback (p-value < 0.0001). The average RMSE of the respiratory period was reduced from 1.7 s with free breathing to 0.3 s (82% reduction) with AV biofeedback (p-value < 0.0001). Additionally, the average baseline drift obtained using a linear fit was reduced from 1.6 mm∕min with free breathing to 0.9 mm∕min (44% reduction) with AV biofeedback (p-value = 0.012). The diaphragm motion reproducibility improvements with AV biofeedback were consistent with the abdominal motion reproducibility that was observed from the external marker motion variation.This study was the first to investigate the potential of AV biofeedback to improve the motion reproducibility of internal anatomy using MRI. The study demonstrated the significant improvement in diaphragm motion reproducibility using AV biofeedback combined with MRI. This system can potentially provide clinically beneficial motion management of internal anatomy in MRI and radiotherapy.

Dataset Information

Predictive value of pediatric respiratory-induced diaphragm motion quantified using pre-treatment 4DCT and CBCTs.

Background

Methods

Results

Conclusion

Publications

Predictive value of pediatric respiratory-induced diaphragm motion quantified using pre-treatment 4DCT and CBCTs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets