Cone-beam computed tomography (CBCT) is one of the most frequently used tools to diagnose hard tissues such as teeth and bones. However, the CBCT scanners generally suffer from a long image acquisition time, which can result in motion artifacts in the resultant volume images. The motion artifacts mainly come from the mechanical vibration of system components and the unintended movement of the patients during data acquisition. In this study, we propose a method to reduce motion artifacts in CBCT images. The relative misalignments between the flat-panel detector and the object can be calculated by aligning two limited-angle tomography images reconstructed at the opposite sides of the circular trajectory. Finally the motion artifacts in the resultant CT images can be suppressed without pre-calibration procedures by compensating found misalignments for CT reconstruction. The proposed algorithm will be helpful for the development of real-time motion artifact reduction in CT images.
There is an increasing call for radiation dose tracking from medical examinations and patient-specific dose management has become a great concern. Especially, since computed tomography (CT) can lead to a significant amount of patient dose, fast and accurate CT dose estimation has become an important issue. For real-time scan protocol optimization and patient-specific dose management in cone-beam CT (CBCT), we introduce a deep-learning approach that estimates the absorbed dose distributions from CT scan data. The deep convolutional neural network model based on U-Net architecture is trained to predict the absorbed dose distribution from CT images. The model is trained in 3 different strategies that utilize datasets in 2D, 2.5D (slice-based), and 3D (image-based) forms. The validation of the proposed method is performed by comparative analysis with the Monte Carlo (MC) simulations for typical dentoalveolar CBCT protocols which consider the anthropomorphic head phantoms as a patient. The proposed approach shows good agreement with the MC method while consuming a significantly lower computational cost. This study will be useful for the development of dental CBCT imaging techniques in terms of patient-specific dose management.
An analytical algorithm for the estimation of the patient-specific dose distributions in cone-beam computed tomography is introduced. The developed dose estimation method requires the reconstructed voxel data in values of linear attenuation coefficients and the scanning protocol. The algorithm first calculates the dose distribution due to the primary beam attenuation along the beam path between the source and each reconstructed voxel in conjunction with the solid angle subtended by given voxel. Then, this primary dose voxel map becomes the source for the dose distribution due to the scattered photons. For the pre-calculated primary dose value in a given voxel, the scatter dose values to all the other voxels are similarly calculated as the primary. The developed algorithm shows a good agreement with the Monte Carlo (MC) simulation for an anthropomorphic head-and-neck phantom. The accuracy of the analytical method is investigated by comparing estimates with the MC estimates and the strategy for computational acceleration is discussed in terms of the number of projections used for reconstruction, the number of spectral bins of the incident x-ray spectrum, the number of voxels, and the extent of scattering ranges.
Dental computed tomography (CT) typically uses a cone-beam geometry with a flat-panel detector. Although the flat-panel detector normally covers the maxillary and mandibular jaws, the cone-beam scan can deliver the dose to organs that are located out of the direct beam path but sensitive to radiation damage, such as eye lens. For typical dentoalveolar cone-beam CT (CBCT) scans, this study investigates the absorbed dose distributions in the head and neck using the Monte Carlo technique, and quantifies specific organ doses. Then, we design an intensity-modulated CBCT scan protocol that can provide a higher tomographic image quality at a lesser patient dose. The beam-intensity modulation includes the changes of tube current (mA) and/or voltage (kVp) during circular scanning, and the modulation scenarios are designed considering the cervical spine through which x-ray beam attenuates largely. We assess the noise-to-dose performances for various intensity-modulation scenarios, and compare the results with that obtained for the conventional scan.
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