Phase and amplitude binning for 4D-CT imaging

Abdelnour, A. Farras; Nehmeh, Sadek; Pan, Tinsu; Humm, John L.; Vernon, Phil; Schöder, Heiko; Rosenzweig, Kenneth E.; Mageras, G.; Yorke, Ellen; Larson, Steven M.; Erdi, Yusuf E.

doi:10.1088/0031-9155/52/12/012

Cited by 124 publications

(119 citation statements)

References 24 publications

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“…Abdelnour et al 49 shows that amplitude binning can be more accurate than phase binning when using 4DCT imaging. Moreover, for irregular target trajectories, phase binning can lead to large reconstruction errors that need to be addressed using complex iterative techniques.…”

Section: Discussionmentioning

confidence: 99%

An optimization algorithm for 3D real-time lung tumor tracking during arc therapy using kV projection images

et al. 2013

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Purpose: To develop a real-time markerless 3D tumor tracking using kilovoltage (kV) cone-beam CT (CBCT) projection images during volumetric modulated arc therapy (VMAT) treatment of lung tumors. Methods:The authors have developed a method to identify the position of lung tumors during VMAT treatment, where the current mean 3D position is detected and subsequently the real time 3D position is obtained. The mean position is evaluated by iteratively minimizing an observation error function between the tumor coordinate detected in the imaging plane and the coordinate of the corresponding projection of the estimated mean position. The 3D trajectory is reconstructed using the same optimization formalism, where an observation error function is minimized for tumor positions confined within a predefined amplitude bin as determined from the superiorinferior tumor motion. Dynamic phantom experiments were performed and image data acquired during patient treatment were analyzed to characterize the reconstruction ability of the proposed method. Results:The proposed algorithm needs to acquire kV projection data until a certain gantry angle is passed through, termed the black-out angle, before accurate estimation mean 3D tumor position is possible. The black-out angle for the mean position method is approximately 20• , while for the 3D trajectory reconstruction an additional ∼15• is required. The mean 3D position and 3D trajectory reconstruction are accurate within ±0.5 mm. Conclusions:The authors present a real-time tracking framework to locate lung tumors during VMAT treatment using an optimization algorithm applied to CBCT kV projection images taken concomitantly with the treatment delivery. The authors' technique does not introduce significant additional dose and can be used for real-time treatment monitoring. © 2013 Author(s)

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Section: Discussionmentioning

confidence: 99%

An optimization algorithm for 3D real-time lung tumor tracking during arc therapy using kV projection images

et al. 2013

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“…These mismatches become visible as artifacts in reconstructed 4D CT images. [1][2][3][4][5][6][7][8] These artifacts appear as undefined or irregular boundaries in the 4D CT images and cause systematic errors in patient contouring and dose calculations. Various studies have shown that the magnitude of artifacts resulting from retrospective displacement sorting is less severe than those resulting from retrospective phase sorting.…”

Section: Introductionmentioning

confidence: 99%

“…Various studies have shown that the magnitude of artifacts resulting from retrospective displacement sorting is less severe than those resulting from retrospective phase sorting. 1,3,5,7 Recently it was found that at least one artifact appeared in retrospectively phase sorted 4D CT images for 45 out of 50 patients. 9 The problem with retrospective displacement sorting is, on the other hand, that data may be insufficient at some couch positions because of variations in the respiratory motion.…”

Section: Introductionmentioning

confidence: 99%

“…This is also evident from the inability of retrospective phase sorting methods to produce artifact-free 4D CT images. 1,3,5,7 However, it is assumed here that if the displacement and velocity of the respiratory trace match for two different cycles, it can also be assumed that the acceleration, and therefore the instantaneous force on the tumor, is equal or nearly equal, resulting in the same deformation and trajectory. It is therefore hypothesized that this method can produce accurate reconstructed 4D CT images in five dimensional parameter space, by sorting 2D CT images not only by using a temporal parameter, but also an "anatomical state" parameter.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy in the localization of thoracic and abdominal tumors using respiratory displacement, velocity, and phase

Langner

Keall

2009

Medical Physics

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Current four dimensional ͑4D͒ computed tomography ͑CT͒ reconstruction techniques are retrospectively created based on either the phase or displacement of the respiratory signal. Both techniques have known limitations which cause clinically significant motion artifacts in 4D CT images. These artifacts, which appear as undefined or irregular boundaries in the 4D CT images, cause systematic errors in patient contouring and dose calculations. The aim of this work was to evaluate the reproducibility of tumor position as a function of displacement, phase, and velocity of the respiratory signal, respectively, in order to determine the optimum parameter or combination of parameters to use in order to minimize artifacts in 4D CT images or to accurately deliver radiation to relevant structures during treatment. Method and materials: Estimated tumor centroid position and respiratory signal data were acquired with the Cyberknife Synchrony system for 26 thoracic radiotherapy patients ͑52 fractions͒. A reference respiratory cycle was calculated for each patient. Displacement, phase, and velocity of ten data points were calculated from this reference respiratory cycle, where each point represents an image bin. The corresponding tumor position was then sorted into these image bins if the phase, displacement, simultaneous displacement and phase, or simultaneous displacement and velocity of the respiratory signal were within tolerances of 0.5 mm for displacement and 0.5 mm/ s for velocity, respectively, from the corresponding data of the reference cycle for each image bin. Results: The mean of the standard deviations of tumor positions over all bins and all fractions for the superior-inferior direction were 2.13Ϯ 1.01 mm for phase sorting, 1.20Ϯ 0.76 mm for displacement sorting, 1.20Ϯ 0.71 mm for simultaneous displacement and phase sorting, and 1.10Ϯ 0.71 mm for simultaneous displacement and velocity sorting, with maximum deviations of 43.0, 16.1, 15.5, and 14.1 mm for each scenario, respectively. The same trend was observed for the anterior-posterior and left-right directions. A linear dependence was observed between the mean of the standard deviations of tumor positions over all fractions as a function of the velocity of the respiratory signal at each bin for all the sorting scenarios. A substantially larger gradient for the phase sorting scenario, compared to the other scenarios, suggests that tumor localization will become increasingly less accurate as the velocity of the tumor increases during a breathing cycle, e.g., if the amplitude of motion increases while the period of the respiratory cycle stays constant or during mid inhale or exhale phases of the respiratory cycle. Conclusion: This study illustrates that position of a tumor can be determined more accurately if displacement and velocity are used simultaneously as sorting parameters for 4D CT images or during treatment. A real-time displacement and velocity based 4D CT image sorting method may therefore produce fewer and smaller artifacts in 4D CT images than current r...

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“…Within the context of 4D CT imaging (3-dimensional CT volumes plus time), the robustness of this phase model was unreliable in the case of irregular breathing (3). Alternative approaches accounting for irregular breathing cycles have been proposed, such as adaptive phase binning accounting for the length of individual cycles or, alternatively, the use of respiration amplitude binning (4).…”

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confidence: 99%

Generation of 4-Dimensional CT Images Based on 4-Dimensional PET–Derived Motion Fields

et al. 2013

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Respiratory motion can potentially reduce accuracy in anatomic and functional image fusion from multimodality systems. It can blur the uptake of small lesions and lead to significant activity underestimation. Solutions presented to date include respiration-synchronized anatomic and functional acquisitions. To increase the signal-to-noise ratio of the synchronized PET images, methods using nonrigid transformations during the reconstruction process have been proposed. In most of these methods, 4-dimensional (4D) CT images were used to derive the required deformation matrices. However, variations between acquired 4D PET and corresponding CT image series due to differences in respiratory conditions during PET and CT acquisitions have been reported. In addition, the radiation dose burden resulting from a 4D CT acquisition may not be justifiable for every patient. Methods: In this paper, we present a method for the generation of dynamic CT images from the combination of one reference CT image and deformation matrices obtained from the elastic registration of 4D PET images not corrected for attenuation. On the one hand, our approach eliminates the need for the acquisition of dynamic CT. On the other hand, it also ensures a good match between CT and PET images, allowing accurate attenuation correction to be performed for respirationsynchronized PET acquisitions. Results: The proposed method was first validated on Monte Carlo-simulated datasets, and then on patient datasets (n 5 4) by comparing generated 4D CT images with the corresponding acquired original CT images. Different levels of PET image statistical quality were considered in order to investigate the impact of image noise in the derivation of the 4D CT series. Conclusion: Our results suggest that clinically relevant PET acquisition times can be used for the implementation of such an approach, making this an even more attractive solution considering the absence of the extra dose given by a standard 4D CT acquisition. Finally, this approach may be applicable to other multimodality devices such as PET/MR.

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Phase and amplitude binning for 4D-CT imaging

Cited by 124 publications

References 24 publications

An optimization algorithm for 3D real-time lung tumor tracking during arc therapy using kV projection images

An optimization algorithm for 3D real-time lung tumor tracking during arc therapy using kV projection images

Accuracy in the localization of thoracic and abdominal tumors using respiratory displacement, velocity, and phase

Generation of 4-Dimensional CT Images Based on 4-Dimensional PET–Derived Motion Fields

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