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Respiratory triggered 4D cone-beam computed tomography: a novel method to reduce imaging dose.

ABSTRACT: A novel method called respiratory triggered 4D cone-beam computed tomography (RT 4D CBCT) is described whereby imaging dose can be reduced without degrading image quality. RT 4D CBCT utilizes a respiratory signal to trigger projections such that only a single projection is assigned to a given respiratory bin for each breathing cycle. In contrast, commercial 4D CBCT does not actively use the respiratory signal to minimize image dose.To compare RT 4D CBCT with conventional 4D CBCT, 3600 CBCT projections of a thorax phantom were gathered and reconstructed to generate a ground truth CBCT dataset. Simulation pairs of conventional 4D CBCT acquisitions and RT 4D CBCT acquisitions were developed assuming a sinusoidal respiratory signal which governs the selection of projections from the pool of 3600 original projections. The RT 4D CBCT acquisition triggers a single projection when the respiratory signal enters a desired acquisition bin; the conventional acquisition does not use a respiratory trigger and projections are acquired at a constant frequency. Acquisition parameters studied were breathing period, acquisition time, and imager frequency. The performance of RT 4D CBCT using phase based and displacement based sorting was also studied. Image quality was quantified by calculating difference images of the test dataset from the ground truth dataset. Imaging dose was calculated by counting projections.Using phase based sorting RT 4D CBCT results in 47% less imaging dose on average compared to conventional 4D CBCT. Image quality differences were less than 4% at worst. Using displacement based sorting RT 4D CBCT results in 57% less imaging dose on average, than conventional 4D CBCT methods; however, image quality was 26% worse with RT 4D CBCT.Simulation studies have shown that RT 4D CBCT reduces imaging dose while maintaining comparable image quality for phase based 4D CBCT; image quality is degraded for displacement based RT 4D CBCT in its current implementation.

SUBMITTER: Cooper BJ

PROVIDER: S-EPMC3612115 | biostudies-other | 2013 Apr

REPOSITORIES: biostudies-other

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Project description:BackgroundMotion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling.PurposeThis study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images.MethodsA 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC).ResultsThe trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average (± standard deviation) RMSE in mm-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040 ± 0.0009, 0.0029 ± 0.0002, 0.0024 ± 0.0003 and 0.0021 ± 0.0003. Corresponding NCC were 0.84 ± 0.05, 0.91 ± 0.05, 0.91 ± 0.05 and 0.93 ± 0.04.ConclusionsCNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.

Project description:The variety of cone-beam computed tomography (CBCT) machines and their applications has rapidly increased in recent years, making the dose evaluation of individual devices an important issue. Patient doses from CBCT were assessed with two different methods: optically stimulated luminescence dosimeter (OSLD) measurements and Monte Carlo (MC) simulations, in four different examination modes. Based on an analysis of the measurement process and the obtained values, a recommendation is made regarding which method is more practical and efficient for acquiring the effective dose of CBCT. Twenty-two OSLDs were calibrated and equipped in human phantoms of head and neck organs. They were exposed to radiation from two CBCT units-CS9300 (Carestream Dental LLC, Atlanta, Georgia) and RAYSCAN α+ (Ray Co. Ltd, Hwaseong-si, Korea)-using two different examination modes. The dose recorded using the OSLDs was used to calculate the organ dose and the effective dose for each unit in each examination mode. These values were also calculated using MC software, PCXMC (STUK, Helsinki, Finland). The organ doses and effective doses obtained using both methods were compared for each examination mode of the individual units. The OSLD-measured effective dose value was higher than that obtained using the MC method for each examination mode, except the dual jaw mode of CS9300. The percent difference of the effective dose between the two methods ranged from 4.0% to 14.3%. The dose difference between the methods decreased as the field of view became smaller. The organ dose values varied according to the method, although the overall trend was similar for both methods. The organs showing high doses were mostly consistent for both methods. In this study, the effective dose obtained by OSLD measurements and MC simulations were compared, and both methods were described in detail. As a relatively efficient and easy-to-perform method, we cautiously suggest using MC simulations for dose evaluations in the future.

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Respiratory triggered 4D cone-beam computed tomography: a novel method to reduce imaging dose.

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