Developing and optimizing an x-ray scatter control and reduction technique is one of the major challenges for cone beam computed tomography (CBCT) because CBCT will be much less immune to scatter than fan-beam CT. X-ray scatter reduces image contrast, increases image noise and introduces reconstruction error into CBCT. To reduce scatter interference, a practical algorithm that is based upon the beam stop array technique and image sequence processing has been developed on a flat panel detector-based CBCT prototype scanner. This paper presents a beam stop array-based scatter correction algorithm and the evaluation results through phantom studies. The results indicate that the beam stop array-based scatter correction algorithm is practical and effective to reduce and correct x-ray scatter for a CBCT imaging task.

Purpose The incorporation of cone‐beam computed tomography (CBCT) has allowed for enhanced image‐guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning‐based method for generating high quality corrected CBCT (CCBCT) images is proposed. Methods The proposed method integrates a residual block concept into a cycle‐consistent adversarial network (cycle‐GAN) framework, called res‐cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle‐GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end‐to‐end CBCT‐to‐CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), normalized cross‐correlation (NCC) indices, and spatial non‐uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning‐based CBCT correction method. Results Overall, the MAE, PSNR, NCC, and SNU were 13.0 HU, 37.5 dB, 0.99, and 0.05 in the brain, 16.1 HU, 30.7 dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning‐based method. Conclusions The authors have developed a novel deep learning‐based method to generate high‐quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.

PurposeCurrent clinical application of cone‐beam CT (CBCT) is limited to patient setup. Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT‐based adaptive planning presently impractical. In this study, we developed a deep‐learning‐based approach to improve CBCT image quality and HU accuracy for potential extended clinical use in CBCT‐guided pancreatic adaptive radiotherapy.MethodsThirty patients previously treated with pancreas SBRT were included. The CBCT acquired prior to the first fraction of treatment was registered to the planning CT for training and generation of synthetic CT (sCT). A self‐attention cycle generative adversarial network (cycleGAN) was used to generate CBCT‐based sCT. For the cohort of 30 patients, the CT‐based contours and treatment plans were transferred to the first fraction CBCTs and sCTs for dosimetric comparison.ResultsAt the site of abdomen, mean absolute error (MAE) between CT and sCT was 56.89 ± 13.84 HU, comparing to 81.06 ± 15.86 HU between CT and the raw CBCT. No significant differences (P > 0.05) were observed in the PTV and OAR dose‐volume‐histogram (DVH) metrics between the CT‐ and sCT‐based plans, while significant differences (P < 0.05) were found between the CT‐ and the CBCT‐based plans.ConclusionsThe image similarity and dosimetric agreement between the CT and sCT‐based plans validated the dose calculation accuracy carried by sCT. The CBCT‐based sCT approach can potentially increase treatment precision and thus minimize gastrointestinal toxicity.

Coronary artery imaging with x-ray computed tomography (CT) is one of the most recent advancements in CT clinical applications. Although existing "state-of-the-art" clinical protocols today utilize helical data acquisition, it suffers from the lack of ability to handle irregular heart rate and relatively high x-ray dose to patients. In this paper, we propose a step-and-shoot data acquisition protocol that significantly overcomes these shortcomings. The key to the proposed protocol is the large volume coverage (40 mm) enabled by the cone beam CT scanner, which allows the coverage of the entire heart in 3 to 4 steps. In addition, we propose a gated complementary reconstruction algorithm that overcomes the longitudinal truncation problem resulting from the cone beam geometry. Computer simulations, phantom experiments, and clinical studies were conducted to validate our approach.

Tremendous efforts have been devoted to develop efficient deep blue organic light-emitting diodes (OLEDs) materials with CIE y < 0.10 (Commission International de L’Eclairage (CIE)) and match the National Television System Committee (NTSC) standard blue CIE (x, y) coordinates of (0.14, 0.08) for display applications. However, deep blue fluorescent materials with an external quantum efficiency (EQE) over 5% are still rare. Herein, we report a phenanthroimidazole–sulfone hybrid donor–acceptor (D–A) molecule with efficient deep blue emission. D–A structure molecular design has been proven to be an effective strategy to obtain high electroluminescence (EL) efficiency. In general, charge transfer (CT) exciton formed between donor and acceptor is a weak coulomb bonded hole–electron pair and is favorable for the spin flip that can turn triplet excitons into singlet ones. However, the photoluminescence quantum yield (PLQY) of CT exciton is usually very low. On the other hand, a locally excited (LE) state normally possesses high PLQY owing to the almost overlapped orbital distributions. Hence, a highly mixed or hybrid local and charge transfer (HLCT) excited state would be ideal to simultaneously achieve both a large fraction of singlet formation and a high PLQY and eventually achieve high EL efficiency. On the basis of such concept, phenanthroimidazole is chosen as a weak donor and sulfone as a moderate acceptor to construct a D–A type molecule named as PMSO. The PMSO exhibits HLCT excited state properties. The doped device shows deep blue electroluminescence with an emission peak of 445 nm and CIE (0.152, 0.077). The maximum external quantum efficiency (EQE) is 6.8% with small efficiency roll-off. The device performance is among the best results of deep blue OLEDs reported so far.

Organic light‐emitting diodes (OLEDs) can promise flexible, light weight, energy conservation, and many other advantages for next‐generation display and lighting applications. However, achieving efficient blue electroluminescence still remains a challenge. Though both phosphorescent and thermally activated delayed fluorescence materials can realize high‐efficiency via effective triplet utilization, they need to be doped into appropriate host materials and often suffer from certain degree of efficiency roll‐off. Therefore, developing efficient blue‐emitting materials suitable for nondoped device with little efficiency roll‐off is of great significance in terms of practical applications. Herein, a phenanthroimidazole−anthracene blue‐emitting material is reported that can attain high efficiency at high luminescence in nondoped OLEDs. The maximum external quantum efficiency (EQE) of nondoped device is 9.44% which is acquired at the luminescence of 1000 cd m−2. The EQE is still as high as 8.09% even the luminescence reaches 10 000 cd m−2. The maximum luminescence is ≈57 000 cd m−2. The electroluminescence (EL) spectrum shows an emission peak of 470 nm and the Commission International de L'Eclairage (CIE) coordinates is (0.14, 0.19) at the voltage of 7 V. To the best of the knowledge, this is among the best results of nondoped blue EL devices.

Based on the structure of the original helical FDK algorithm, a three-dimensional (3D)-weighted cone beam filtered backprojection (CB-FBP) algorithm is proposed for image reconstruction in volumetric CT under helical source trajectory. In addition to its dependence on view and fan angles, the 3D weighting utilizes the cone angle dependency of a ray to improve reconstruction accuracy. The 3D weighting is ray-dependent and the underlying mechanism is to give a favourable weight to the ray with the smaller cone angle out of a pair of conjugate rays but an unfavourable weight to the ray with the larger cone angle out of the conjugate ray pair. The proposed 3D-weighted helical CB-FBP reconstruction algorithm is implemented in the cone-parallel geometry that can improve noise uniformity and image generation speed significantly. Under the cone-parallel geometry, the filtering is naturally carried out along the tangential direction of the helical source trajectory. By exploring the 3D weighting's dependence on cone angle, the proposed helical 3D-weighted CB-FBP reconstruction algorithm can provide significantly improved reconstruction accuracy at moderate cone angle and high helical pitches. The 3D-weighted CB-FBP algorithm is experimentally evaluated by computer-simulated phantoms and phantoms scanned by a diagnostic volumetric CT system with a detector dimension of 64 x 0.625 mm over various helical pitches. The computer simulation study shows that the 3D weighting enables the proposed algorithm to reach reconstruction accuracy comparable to that of exact CB reconstruction algorithms, such as the Katsevich algorithm, under a moderate cone angle (4 degrees) and various helical pitches. Meanwhile, the experimental evaluation using the phantoms scanned by a volumetric CT system shows that the spatial resolution along the z-direction and noise characteristics of the proposed 3D-weighted helical CB-FBP reconstruction algorithm are maintained very well in comparison to the FDK-type algorithms. Moreover, the experimental evaluation by clinical data verifies that the proposed 3D-weighted CB-FBP algorithm for image reconstruction in volumetric CT under helical source trajectory meets the challenges posed by diagnostic applications of volumetric CT imaging.

The original FDK algorithm proposed for cone beam (CB) image reconstruction under a circular source trajectory has been extensively employed in medical and industrial imaging applications. With increasing cone angle, CB artefacts in images reconstructed by the original FDK algorithm deteriorate, since the circular trajectory does not satisfy the so-called data sufficiency condition (DSC). A few 'circular plus' trajectories have been proposed in the past to help the original FDK algorithm to reduce CB artefacts by meeting the DSC. However, the circular trajectory has distinct advantages over other scanning trajectories in practical CT imaging, such as head imaging, breast imaging, cardiac, vascular and perfusion applications. In addition to looking into the DSC, another insight into the CB artefacts existing in the original FDK algorithm is the inconsistency between conjugate rays that are 180 degrees apart in view angle (namely conjugate ray inconsistency). The conjugate ray inconsistency is pixel dependent, varying dramatically over pixels within the image plane to be reconstructed. However, the original FDK algorithm treats all conjugate rays equally, resulting in CB artefacts that can be avoided if appropriate weighting strategies are exercised. Along with an experimental evaluation and verification, a three-dimensional (3D) weighted axial cone beam filtered backprojection (CB-FBP) algorithm is proposed in this paper for image reconstruction in volumetric CT under a circular source trajectory. Without extra trajectories supplemental to the circular trajectory, the proposed algorithm applies 3D weighting on projection data before 3D backprojection to reduce conjugate ray inconsistency by suppressing the contribution from one of the conjugate rays with a larger cone angle. Furthermore, the 3D weighting is dependent on the distance between the reconstruction plane and the central plane determined by the circular trajectory. The proposed 3D weighted axial CB-FBP algorithm can be implemented in either the native CB geometry or the so-called cone-parallel geometry. By taking the cone-parallel geometry as an example, the experimental evaluation shows that, up to a moderate cone angle corresponding to a detector dimension of 64 x 0.625 mm, the CB artefacts can be substantially suppressed by the proposed algorithm, while advantages of the original FDK algorithm, such as the filtered backprojection algorithm structure, 1D ramp filtering and data manipulation efficiency, are maintained.