Purpose Flat‐panel detector (FPD) based dual‐energy cone‐beam computed tomography (DE‐CBCT) is a promising imaging technique for dedicated clinical applications. In this paper, we proposed a fully analytical method for fast and effective single‐scan DE‐CBCT image reconstruction and decomposition. Methods A rotatable Mo filter was inserted between an x‐ray source and imaged object to alternately produce low and high‐energy x‐ray spectra. First, filtered‐backprojection (FBP) method was applied on down‐sampled projections to reconstruct low and high‐energy images. Then, the two images were converted into a vectorized form represented with an amplitude and an argument image. Using amplitude image as a guide, a joint bilateral filter was applied to denoise the argument image. Then, high‐quality dual‐energy images were recovered from the amplitude image and the denoised argument image. Finally, the recovered dual‐energy images were further used for low‐noise material decomposition and electron density synthesis. Imaging was conducted on a Catphan®600 phantom and an anthropomorphic head phantom. The proposed method was evaluated via comparison with the traditional two‐scan method and a commonly used filtering method (HYPR‐LR). Results On the Catphan®600 phantom, the proposed method successfully reduced streaking artifacts and preserved spatial resolution and noise‐power‐spectrum (NPS) pattern. In the electron density image, the proposed method increased contrast‐to‐noise ratio (CNR) by more than 2.5 times and achieved <1.2% error for electron density values. On the anthropomorphic head phantom, the proposed method greatly improved the soft‐tissue contrast and the fine detail differentiation ability. In the selected ROIs on different human tissues, the differences between the CT number obtained by the proposed method and that by the two‐scan method were less than 4 HU. In the material images, the proposed method suppressed noise by over 75.5% compared with two‐scan results, and by over 40.4% compared with HYPR‐LR results. Implementation of the whole algorithm took 44.5 s for volumetric imaging, including projection preprocessing, FBP reconstruction, joint bilateral filtering, and material decomposition. Conclusions Using down‐sampled projections in single‐scan DE‐CBCT, the proposed method could effectively and efficiently produce high‐quality DE‐CBCT images and low‐noise material decomposition images. This method demonstrated superior performance on spatial resolution enhancement, NPS preservation, noise reduction, and electron density accuracy, indicating better prospect in material differentiation and dose calculation.
Purpose Cone‐beam CT (CBCT) has been widely utilized in image‐guided radiotherapy. Planning CT (pCT)‐aided CBCT scatter correction could further enhance image quality and extend CBCT application to dose calculation and adaptive planning. Nevertheless, existing pCT‐based approaches demand accurate registration between pCT and CBCT, leading to limited imaging performance and increased computational cost when large anatomical discrepancies exist. In this work, we proposed a robust and fast CBCT scatter correction method using local filtration technique and rigid registration between pCT and CBCT (LF‐RR). Methods First of all, the pCT was rigidly registered with CBCT, then forward projection was performed on registered pCT to create scatter‐free projections. The raw scatter signals were obtained via subtracting the scatter‐free projections from the measured CBCT projections. Based on frequency and intensity threshold criteria, reliable scatter signals were selected from the raw scatter signals, and further filtered for global scatter estimation via local filtration technique. Finally, corrected CBCT was reconstructed with the projections generated by subtracting the scatter estimation from the raw CBCT projections using FDK algorithm. The LF‐RR method was evaluated via comparison with another pCT‐based scatter correction method based on Median and Gaussian filters (MG method). Results Proposed method was first validated on an anthropomorphic pelvis phantom, and showed satisfied performance on scatter removal even when anatomical mismatches were intentionally created on pCT. The quantitative analysis was further performed on four clinical CBCT images. Compared with the uncorrected CBCT, CBCT corrected by MG with rigid registration (MG‐RR), MG with deformable registration (MG‐DR), and LF‐RR reduced the CT number error from 79±35 to 25±18,17±13 and 7±3 HU for adipose and from 115±61 to 36±22,30±24, 7±3 HU for muscle, respectively. After correction, the spatial non‐uniformity (SNU) of CBCT corrected with MG‐RR, MG‐DR and LF‐RR was 51±13,60±21, and 21±9 HU for adipose, and 50±22,57±41, and 25±6 HU for muscle, respectively. Meanwhile, the contrast‐to‐noise ratio (CNR) between muscle and adipose was increased by a factor of 2.70, 2.89 and 2.56, respectively. Using the LF‐RR method, the scatter correction of 656 projections can be finished within 10 s and the corrected volumetric images (200 slices) can be obtained within 2 min. Conclusion We developed a fast and robust pCT‐based CBCT scatter correction method which exploits the local‐filtration technique to promote the accuracy of scatter estimation and is resistant to pCT‐to‐CBCT registration uncertainties. Both phantom and patient studies showed the superiority of the proposed correction in imaging accuracy and computational efficiency, indicating promisingfuture clinical application.
Objective. In the traditional beam-blocker based cone beam CT (CBCT) scatter correction, the scatter measured in the region shaded by lead strips was multiplied by a correction factor to directly represent the scatter in the unblocked region. The correction factor optimization is a tedious process and lacks objective stop criterion. To skip the optimization process, an indirect scatter estimation method was developed and validated in phantom imaging. Approach. A beam-blocker made of lead strips was mounted between the X-ray source and object for scatter estimation. The primary signal between lead strips in the blocked region was first calculated by subtracting the measured scatter, and then used to calculate the scatter signal in the unblocked region corresponding to the same attenuation path. The calculated scatter signal was smoothed via local filtration and used to correct the measured projection in the unblocked region. Finally, the CBCT was reconstructed via Feldkamp-Davis-Kress (FDK) algorithm. A Catphan and a head phantom were used to verify the performance of the proposed method in both full- and half-blocker scenarios, and with and without a bow-tie filter. Main Results. For scans without the bow-tie filter, the CT number error was reduced to 3.97±2.27 and 5.51±3.90 HU in the full- and half-blocker scenarios, respectively, for the Catphan, and to 4.01±2.18 and 7.97±4.05 HU for the head phantom. When the bow-tie filter was applied, the CT number error was reduced to 2.29±1.42 and 6.72±0.77 HU in the full- and half-blocker scenarios, respectively, for the Catphan, and 2.35±1.25 and 4.96±1.89 HU for the head phantom. Significance: The proposed method effectively avoids the influence of the inserted beam blocker itself on the scatter intensity estimation, and proves a more practical and robust way for the beam-blocker based scatter correction in CBCT scanning.
Deep learning has achieved great success in many medical imaging tasks without explicit solutions. In this work, learning method was applied to dual-energy cone-beam CT imaging. We proposed a Residual W-shape Network (ResWnet). ResWnet consists of three modules: scatter correction module 𝒮, material decomposition module ℳ, decomposition denoising module 𝒟. Both 𝒮 and 𝒟 use ResUnet architecture, and this lightweight model fuses multi-level features, achieving satisfied performance with a small number of parameters. 𝒮 acts on dual-energy attenuation projections to reduce the scatter contaminations, and 𝒟 acts on material composition projections to suppress the noise. ℳ links the modules 𝒮 and 𝒟, and is used for domain transform from attenuation projections to material projections. This process could be approximated by polynomials with pre-calibrated parameters, that is, ℳ is a known operator in proposed network with no trainable parameters. This helps to reduce model parameters and improve the performance with small training dataset. Using public head CT dataset, we simulated dual-energy cone-beam CT projections and material projections. Proposed ResWnet was trained, validated and tested on this simulated dataset, verifying its effectiveness in projection-domain scatter correction and low-noise decomposition.
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