Extracting respiratory signals from thoracic cone beam CT projections

Yan, Hao; Wang, Xiaoyu; Yin, Wotao; Pan, Tinsu; Ahmad, Moiz; Mou, Xuanqin; Cerviño, Laura; Jia, Xun; Jiang, Steve B

doi:10.1088/0031-9155/58/5/1447

Cited by 48 publications

(69 citation statements)

References 54 publications

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“…In conventional 4DCBCT and 4DCT applications the phase signal is computed retrospectively from the respiratory signal extracted from an external sensor, such as the RPM system from Varian Medical Systems, or from the images themselves (Zijp et al 2004) and (Yan et al 2013). In order to acquire projections in phase bins, RMG-4DCBCT needs to know the phase of the breathing signal in real-time as new respiratory data arrives.…”

Section: Real-time Phase Calculationmentioning

confidence: 99%

Reducing 4DCBCT imaging time and dose: the first implementation of variable gantry speed 4DCBCT on a linear accelerator

O’Brien¹,

Stankovic²,

Sonke³

et al. 2017

Phys. Med. Biol.

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Abstract. Four dimensional cone beam computed tomography (4DCBCT) uses a constant gantry speed and imaging frequency that are independent of the patient's breathing rate. Using a technique called Respiratory Motion Guided 4DCBCT (RMG-4DCBCT), we have previously demonstrated that by varying the gantry speed and imaging frequency, in response to changes in the patient's real-time respiratory signal, the imaging dose can be reduced by 50-70%. RMG-4DCBCT optimally computes a patient specific gantry trajectory to eliminate streaking artefacts and projection clustering that is inherent in 4DCBCT imaging. The gantry trajectory is continuously updated as projection data is acquired and the patient's breathing changes. The aim of this study was to realise RMG-4DCBCT for the first time on a linear accelerator.To change the gantry speed in real-time a potentiometer under microcontroller control was used to adjust the current supplied to an Elekta Synergy's gantry motor. A real-time feedback loop was developed on the microcontroller to modulate the gantry speed and projection acquisition in response to the real-time respiratory signal so that either 40, RMG-4DCBCT 40 , or 60, RMG-4DCBCT 60 , uniformly spaced projections were acquired in 10 phase bins. Images of the CIRS dynamic Thorax phantom were acquired with sinusoidal breathing periods ranging from 2s to 8s together with two breathing traces from lung cancer patients. Image quality was assessed using the contrast to noise ratio (CNR) and edge response width (ERW).For the average patient, with a 3.8s breathing period, the imaging time and image dose were reduced by 37% and 70% respectively. Across all respiratory rates, RMG-4DCBCT 40 had a CNR in the range of 6.5 to 7.5, and RMG-4DCBCT 60 had a CNR between 8.7 and 9.7, indicating that RMG-4DCBCT allows consistent and controllable CNR. In comparison, the CNR for conventional 4DCBCT drops from 20.4 to 6.2 as the breathing rate increases from 2s to 8s. With RMG-4DCBCT, the ERW in the direction of motion of the imaging insert decreases from 2.1mm to 1.1mm as the breathing rate increases from 2s to 8s while for conventional 4DCBCT the ERW increases from 1.9mm to 2.5mm.Image quality can be controlled during 4DCBCT acquisition by varying the gantry speed and the projection acquisition in response to the patient's real-time respiratory signal. However, although the image sharpness, i.e., ERW, is improved with RMG-4DCBCT, the ERW depends on the patient's breathing rate and breathing regularity.

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Section: Real-time Phase Calculationmentioning

confidence: 99%

Reducing 4DCBCT imaging time and dose: the first implementation of variable gantry speed 4DCBCT on a linear accelerator

O’Brien¹,

Stankovic²,

Sonke³

et al. 2017

Phys. Med. Biol.

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“…50 In principle, 4D-CT image at an arbitrary phase can serve as the p-CT f p . However, in order to minimize the motion blur in the p-CT, 0% (maximum inhale) or 50% (maximum exhale) phase of the 4D-CT is recommended.…”

Section: A3 Implementationmentioning

confidence: 99%

“…In each case, 4D-CBCT projection images are acquired. The respiratory signal is obtained by analyzing those projections 50 Projections are then distributed to each breathing phase according to the obtained signal.…”

Section: B1 Experimental Datamentioning

confidence: 99%

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

et al. 2014

Self Cite

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Purpose: 4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. Methods: The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. Results: The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3-0.5 mm for patients 1-3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1-1.5 min per phase. Conclusions: High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm.

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“…One widely-applied methodology is the PCA to encode the respiratory motion, where the space spanned by the leading eigenvectors is employed to represent the original data. In respiratory motion studies, PCA has been utilized to simplify the problem set in various approaches (Yan et al 2013, Zhang et al 2013, Mishra et al 2014, Wilms et al 2014, Dhou et al 2015). Another methodology is manifold learning: projecting a manifold in higher dimensional space to a lower dimensional space while preserving the local neighborhood (Wachinger et al 2012, Usman et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning

Wei

Huang

et al. 2015

Biomed. Phys. Eng. Express

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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.

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Extracting respiratory signals from thoracic cone beam CT projections

Cited by 48 publications

References 54 publications

Reducing 4DCBCT imaging time and dose: the first implementation of variable gantry speed 4DCBCT on a linear accelerator

Reducing 4DCBCT imaging time and dose: the first implementation of variable gantry speed 4DCBCT on a linear accelerator

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning

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