Noise correlation and its impact on the performance of multi-material decomposition-based spectral imaging in photon-counting CT.

PURPOSE: It has been known that noise correlation plays an important role in the determination of the performance of spectral imaging based on two-material decomposition (2-MD). To further understand the basics of spectral imaging in photon-counting CT toward optimal design and implementation, we study the noise correlation in multi-MD (m-MD) and its impact on the performance of spectral imaging. METHOD: We derive the equations that characterize the noise and noise correlation in the material-specific (basis) images in m-MD, followed by a simulation study to verify the derived equations and study the noise correlation's impact on the performance of spectral imaging. Using a specially designed digital phantom, the study of noise correlation runs over the cases of two-, three-, and four-MD (2-MD, 3-MD, and 4-MD). Then, the noise correlation's impact on the performance of spectral imaging in photon-counting CT is investigated, using a modified Shepp-Logan phantom. RESULTS: The results in 2-MD show that, in-line with what has been reported in the literature, the noise correlation coefficient between the material-specific images corresponding to the basis materials approaches -1. The results in m-MD (m ≥ 3) are more complicated and interesting, as the noise correlation coefficients between a pair of the material-specific images alternate between ±1, and so do in the case of 4-MD. The m-MD data show that the noise in virtual monochromatic imaging (a form of spectral imaging) is moderate even though the noises in material-specific (basis) images vary drastically. CONCLUSIONS: The observation of noise correlation in 3-MD, 4-MD, and beyond (i.e., m-MD) is informative to the community. The relationship between noise correlation and the performance of spectral imaging revealed in this work may help clinical medical physicists understand the fundamentals of spectral imaging based on MD and optimize the performance of spectral imaging in photon-counting CT and other X-ray imaging modalities.

Tetrahydroxydiboron and Nickel Chloride Cocatalyzed Rapid Radical Cyclization toward Pyrrolizidine and Indolizidine Alkaloids.

A novel tetrahydroxydiboron and nickel chloride cocatalyzed radical cyclization cascade with a broad substrate scope and an ultrashort reaction time was developed. The mechanistic investigation indicated that the reaction might involve a homocleavage of tetrahydroxydiboron and nickel hydride intermediates. This approach enables the simple and efficient synthesis of a series of heteropolycycles.

Achieving High Efficiency at High Luminance in Fluorescent Organic Light-Emitting Diodes through Triplet-Triplet Fusion Based on Phenanthroimidazole-Benzothiadiazole Derivatives.

Achieving high efficiency at high luminance is one of the most important prerequisites towards practical application of any kind of light-emitting diode (LED). Herein, we report highly emissive organic fluorescent molecules based on phenanthroimidazole-benzothiadiazole derivatives capable of maintaining high external quantum efficiency (EQE) at high luminance enabled by triplet-triplet fusion (TTF) in doped organic LEDs. The PIBzP-, PIBzPCN-, and PIBzTPA-based devices showed EQEs of 8.27, 9.15, and 8.64 %, respectively, at luminance of higher than 1000 cd m-2 , with little efficiency roll-off.

Direct Synthesis of N-Difluoromethyl-2-pyridones from Pyridines.

A novel method for the synthesis of N-difluoromethyl-2-pyridones was described. This protocol enables the synthesis of N-difluoromethyl-2-pyridones from readily available pyridines using mild reaction conditions that are compatible with a wide range of functional groups. The preliminary mechanistic study revealed that N-difluoromethylpyridinium salts were the key intermediates to complete this conversion.

Copper-Catalyzed Oxydifluoroalkylation of Hydroxyl-Containing Alkenes.

An efficient oxydifluoroalkylation of hydroxyl-containing alkenes using a copper catalytic system has been developed. This reaction proceeded through a one-pot process of difluoroalkylation followed by nucleophilic attack of the appended hydroxyl group. This strategy has the advantages of a low-cost catalyst and broad substrate scope, which provides a facile access to various fluoroalkylated tetrahydrofurans and tetrahydropyrans.

Optimal virtual monoenergetic image in "TwinBeam" dual-energy CT for organs-at-risk delineation based on contrast-noise-ratio in head-and-neck radiotherapy.

PURPOSE: Dual-energy computed tomography (DECT) using TwinBeam CT (TBCT) is a new option for radiation oncology simulators. TBCT scanning provides virtual monoenergetic images which are attractive in treatment planning since lower energies offer better contrast for soft tissues, and higher energies reduce noise. A protocol is needed to achieve optimal performance of this feature. In this study, we investigated the TBCT scan schema with the head-and-neck radiotherapy workflow at our clinic and selected the optimal energy with best contrast-noise-ratio (CNR) in organs-at-risks (OARs) delineation for head-and-neck treatment planning. METHODS AND MATERIALS: We synthesized monochromatic images from 40 keV to 190 keV at 5 keV increments from data acquired by TBCT. We collected the Hounsfield unit (HU) numbers of OARs (brainstem, mandible, spinal cord, and parotid glands), the HU numbers of marginal regions outside OARs, and the noise levels for each monochromatic image. We then calculated the CNR for the different OARs at each energy level to generate a serial of spectral curves for each OAR. Based on these spectral curves of CNR, the mono-energy corresponding to the max CNR was identified for each OAR of each patient. RESULTS: Computed tomography scans of ten patients by TBCT were used to test the optimal monoenergetic image for the CNR of OAR. Based on the maximized CNR, the optimal energy values were 78.5 ± 5.3 keV for the brainstem, 78.0 ± 4.2 keV for the mandible, 78.5 ± 5.7 keV for the parotid glands, and 78.5 ± 5.3 keV for the spinal cord. Overall, the optimal energy for the maximum CNR of these OARs in head-and-neck cancer patients was 80 keV. CONCLUSION: We have proposed a clinically feasible protocol that selects the optimal energy level of the virtual monoenergetic image in TBCT for OAR delineation based on the CNR in head-and-neck OAR. This protocol can be applied in TBCT simulation.

Mechanisms of Rhodium(III)-Catalyzed C-H Functionalizations of Benzamides with α,α-Difluoromethylene Alkynes.

The rhodium(III)-catalyzed C-H functionalizations of benzamides with α,α-difluoromethylene alkynes have been investigated by means of density functional theory calculations. The computations show that after the formation of seven-membered rhodacycle via consecutive N-H deprotonation/C-H activation/migratory insertion, a novel redox-neutral process without the engagement of otherwise widely accepted Rh(V) nitrenoid species has been formulated in the hydroarylation reaction. It turns out that the seven-membered rhodacycle undergoes a concerted Lossen rearrangement/migration of OPiv from N to Rh to generate the isocyanate intermediate, from which the ensuing nucleophilic addition of MeOH and protodemetalation lead to the generation of final products. By preventing formation of Rh(V) nitrenoid species, the intriguing role of fluorine substituents in hydroarylation reaction has also been disclosed. Furthermore, the coordination of carbonyl oxygen of OPiv group to the Rh center proves to be crucial for both [4 + 2] annulation and hydroarylation. However, in the case of using directing group with N-OMe, the lack of such assistance would engender a considerable buildup of energy with respect to the transition state, making both [4 + 2] annulation and hydroarylation processes kinetically unfavorable. Accordingly, the alternative [4 + 1] annulation takes place as the most favored pathway via consecutive twofold ß-F eliminations.

Copper-mediated regioselective hydrodifluoroalkylation of alkynes.

A highly regioselective copper-mediated hydrodifluoroalkylation of alkynes with ethyl bromodifluoroacetate or bromodifluoroacetamides has been developed. This strategy provides straightforward access to a variety of difluoroalkylated alkenes under mild reaction conditions with low-cost cuprous bromide and metabisulfite as reduction agents. A wide range of alkynes are applicable under these reaction conditions. The excellent functional-group compatibility and good regio- and stereoselectivities are the notable features of this transformation.

An efficient AIE-active blue-emitting molecule by incorporating multifunctional groups into tetraphenylsilane.

A multifunctional AIE-active molecule, CzPySiTPE, in which carbazole (Cz) and pyridine (Py) were attached to tetraphenylsilane to facilitate carrier injection has been designed and synthesized. Tetraphenylethene (TPE) was adopted to maintain efficient blue emission. Blue electroluminescent (EL) emission of CzPySiTPE was obtained with CIE coordinates of (0.16, 0.17) and an external quantum efficiency of 1.12%.

Separation of electrical and optical energy gaps: selectively adjusting the electrical and optical properties for a highly efficient blue emitter.

The design concept of separation of optical and electrical bandgap for wide bandgap materials is further developed in DCzSiPI. The HOMO/LUMO levels can be tuned by incorporation of PI and DCz substituents. The tetraphenylsilane core avoids the intramolecular charge transfer from DCz to PI (DCz = dimer carbazole, PI = phenanthro[9,10-d]imidazole). The allowed transitions are found to be from HOMO-1 to LUMO providing DCzSiPI with sufficient bandgap.

Variability of MDCT dose due to technologist performance: impact of posteroanterior versus anteroposterior localizer image and table height with use of automated tube current modulation.

OBJECTIVE: The purpose of this study was to determine MDCT dose variability due to technologist variability in performing CT studies. MATERIALS AND METHODS: Fifty consecutive adult patients who underwent two portal venous phase CT examinations of the abdomen and pelvis on the same 64-MDCT scanner between January and December 2011 were retrospectively identified. Tube voltage (kVp), tube current (mA), use of automated tube current modulation (ATCM), dose-length product (DLP), volume CT dose index (CTDIvol), table height, whether the localizer image was obtained using the posteroanterior or the anteroposterior technique, arm position, and number of overscanned slices were recorded. RESULTS: For a given patient, the total examination DLP difference comparing the two MDCT studies ranged from 0.1% to 238.0%. For the same patient, total examination DLP was always higher when the localizer image was obtained with the posteroanterior compared with the anteroposterior technique. When table position was closer to the x-ray source, patients appeared magnified in the posteroanterior localizer image (8-29%; average, 14%) and higher tube currents were selected with ATCM. Localizer technique, table height, arm position, number of overscanned slices, and technologist were all significant predictors of dose. CONCLUSION: Patient off-centering closer to the x-ray source resulted in patient magnification in the posteroanterior localizer image, leading to higher tube currents with ATCM and increased DLP. Differences in technologist, arm position, and overscanning also resulted in dose variability.

Multiscale knowledge distillation with attention based fusion for robust human activity recognition.

Knowledge distillation is an effective approach for training robust multi-modal machine learning models when synchronous multimodal data are unavailable. However, traditional knowledge distillation techniques have limitations in comprehensively transferring knowledge across modalities and models. This paper proposes a multiscale knowledge distillation framework to address these limitations. Specifically, we introduce a multiscale semantic graph mapping (SGM) loss function to enable more comprehensive knowledge transfer between teacher and student networks at multiple feature scales. We also design a fusion and tuning (FT) module to fully utilize correlations within and between different data types of the same modality when training teacher networks. Furthermore, we adopt transformer-based backbones to improve feature learning compared to traditional convolutional neural networks. We apply the proposed techniques to multimodal human activity recognition and compared with the baseline method, it improved by 2.31% and 0.29% on the MMAct and UTD-MHAD datasets. Ablation studies validate the necessity of each component.

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss.

BACKGROUND: Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. PURPOSE: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT. METHODS: The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step. RESULTS: In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion. CONCLUSIONS: Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.

Pharmacophore modeling, virtual screening, and molecular docking studies for discovery of novel Akt2 inhibitors.

Akt2 is considered as a potential target for cancer therapy. In order to find novel Akt2 inhibitors which have different scaffolds, structure-based pharmacophore model and 3D-QSAR pharmacophore model were built and validated by different methods. Then, they were used for chemical databases virtual screening. The selected compounds were further analyzed and refined using drug-like filters and ADMET analysis. Finally, seven hits with different scaffolds were picked out for docking studies. These seven hits were predicted to have high inhibitory activity and good ADMET properties, they may act as novel leads for Akt2 inhibitors designing.

Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss.

BACKGROUND: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. PURPOSE: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT.

One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT.

Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density to that of water ($\rho_e$) can also be achieved and further employed to improve stopping power ratio accuracy and reduce range uncertainty. In this work, we propose a one-step iterative estimation method, which employs multi-domain gradient $L_0$-norm minimization, for Z and $\rho_e$ maps reconstruction. The algorithm was implemented on GPU to accelerate the predictive procedure and to support potential real-time adaptive treatment planning. The performance of the proposed method is demonstrated via both phantom and patient studies.

The second-order differential phase contrast and its retrieval for imaging with x-ray Talbot interferometry.

PURPOSE: The x-ray differential phase contrast imaging implemented with the Talbot interferometry has recently been reported to be capable of providing tomographic images corresponding to attenuation-contrast, phase-contrast, and dark-field contrast, simultaneously, from a single set of projection data. The authors believe that, along with small-angle x-ray scattering, the second-order phase derivative Φ(") (s)(x) plays a role in the generation of dark-field contrast. In this paper, the authors derive the analytic formulae to characterize the contribution made by the second-order phase derivative to the dark-field contrast (namely, second-order differential phase contrast) and validate them via computer simulation study. By proposing a practical retrieval method, the authors investigate the potential of second-order differential phase contrast imaging for extensive applications. METHODS: The theoretical derivation starts at assuming that the refractive index decrement of an object can be decomposed into δ = δ(s) + δ(f), where δ(f) corresponds to the object's fine structures and manifests itself in the dark-field contrast via small-angle scattering. Based on the paraxial Fresnel-Kirchhoff theory, the analytic formulae to characterize the contribution made by δ(s), which corresponds to the object's smooth structures, to the dark-field contrast are derived. Through computer simulation with specially designed numerical phantoms, an x-ray differential phase contrast imaging system implemented with the Talbot interferometry is utilized to evaluate and validate the derived formulae. The same imaging system is also utilized to evaluate and verify the capability of the proposed method to retrieve the second-order differential phase contrast for imaging, as well as its robustness over the dimension of detector cell and the number of steps in grating shifting. RESULTS: Both analytic formulae and computer simulations show that, in addition to small-angle scattering, the contrast generated by the second-order derivative is magnified substantially by the ratio of detector cell dimension over grating period, which plays a significant role in dark-field imaging implemented with the Talbot interferometry. CONCLUSIONS: The analytic formulae derived in this work to characterize the second-order differential phase contrast in the dark-field imaging implemented with the Talbot interferometry are of significance, which may initiate more activities in the research and development of x-ray differential phase contrast imaging for extensive preclinical and eventually clinical applications.

Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain.

PURPOSES: The suppression of noise in x-ray computed tomography (CT) imaging is of clinical relevance for diagnostic image quality and the potential for radiation dose saving. Toward this purpose, statistical noise reduction methods in either the image or projection domain have been proposed, which employ a multiscale decomposition to enhance the performance of noise suppression while maintaining image sharpness. Recognizing the advantages of noise suppression in the projection domain, the authors propose a projection domain multiscale penalized weighted least squares (PWLS) method, in which the angular sampling rate is explicitly taken into consideration to account for the possible variation of interview sampling rate in advanced clinical or preclinical applications. METHODS: The projection domain multiscale PWLS method is derived by converting an isotropic diffusion partial differential equation in the image domain into the projection domain, wherein a multiscale decomposition is carried out. With adoption of the Markov random field or soft thresholding objective function, the projection domain multiscale PWLS method deals with noise at each scale. To compensate for the degradation in image sharpness caused by the projection domain multiscale PWLS method, an edge enhancement is carried out following the noise reduction. The performance of the proposed method is experimentally evaluated and verified using the projection data simulated by computer and acquired by a CT scanner. RESULTS: The preliminary results show that the proposed projection domain multiscale PWLS method outperforms the projection domain single-scale PWLS method and the image domain multiscale anisotropic diffusion method in noise reduction. In addition, the proposed method can preserve image sharpness very well while the occurrence of "salt-and-pepper" noise and mosaic artifacts can be avoided. CONCLUSIONS: Since the interview sampling rate is taken into account in the projection domain multiscale decomposition, the proposed method is anticipated to be useful in advanced clinical and preclinical applications where the interview sampling rate varies.

Characterization of imaging performance in differential phase contrast CT compared with the conventional CT: spectrum of noise equivalent quanta NEQ(k).

PURPOSE: Differential phase contrast CT (DPC-CT) is emerging as a new technology to improve the contrast sensitivity of conventional attenuation-based CT. The noise equivalent quanta as a function over spatial frequency, i.e., the spectrum of noise equivalent quanta NEQ(k), is a decisive indicator of the signal and noise transfer properties of an imaging system. In this work, we derive the functional form of NEQ(k) in DPC-CT. Via system modeling, analysis, and computer simulation, we evaluate and verify the derived NEQ(k) and compare it with that of the conventional attenuation-based CT. METHODS: The DPC-CT is implemented with x-ray tube and gratings. The x-ray propagation and data acquisition are modeled and simulated through Fresnel and Fourier analysis. A monochromatic x-ray source (30 keV) is assumed to exclude any system imperfection and interference caused by scatter and beam hardening, while a 360° full scan is carried out in data acquisition to avoid any weighting scheme that may disrupt noise randomness. Adequate upsampling is implemented to simulate the x-ray beam's propagation through the gratings G(1) and G(2) with periods 8 and 4 µm, respectively, while the intergrating distance is 193.6 mm (1∕16 of the Talbot distance). The dimensions of the detector cell for data acquisition are 32 × 32, 64 × 64, 96 × 96, and 128 × 128 µm(2), respectively, corresponding to a 40.96 × 40.96 mm(2) field of view in data acquisition. An air phantom is employed to obtain the noise power spectrum NPS(k), spectrum of noise equivalent quanta NEQ(k), and detective quantum efficiency DQE(k). A cylindrical water phantom at 5.1 mm diameter and complex refraction coefficient n = 1 - δ + iß = 1 -2.5604 × 10(-7) + i1.2353 × 10(-10) is placed in air to measure the edge transfer function, line spread function and then modulation transfer function MTF(k), of both DPC-CT and the conventional attenuation-based CT. The x-ray flux is set at 5 × 10(6) photon∕cm(2) per projection and observes the Poisson distribution, which is consistent with that of a micro-CT for preclinical applications. Approximately 360 regions, each at 128 × 128 matrix, are used to calculate the NPS(k) via 2D Fourier transform, in which adequate zero padding is carried out to avoid aliasing in noise. RESULTS: The preliminary data show that the DPC-CT possesses a signal transfer property [MTF(k)] comparable to that of the conventional attenuation-based CT. Meanwhile, though there exists a radical difference in their noise power spectrum NPS(k) (trait 1∕|k| in DPC-CT but |k| in the conventional attenuation-based CT) the NEQ(k) and DQE(k) of DPC-CT and the conventional attenuation-based CT are in principle identical. CONCLUSIONS: Under the framework of ideal observer study, the joint signal and noise transfer property NEQ(k) and detective quantum efficiency DQE(k) of DPC-CT are essentially the same as those of the conventional attenuation-based CT. The findings reported in this paper may provide insightful guidelines on the research, development, and performance optimization of DPC-CT for extensive preclinical and clinical applications in the future.

The mathematical equivalence of consistency conditions in the divergent-beam computed tomography.

In this paper, we discuss the mathematical equivalence among four consistency conditions in the divergent-beam computed tomography (CT). The first is the consistency condition derived by Levine et al. by degenerating the John's equation; the second is the integral invariant derived by Wei et al. using the symmetric group theory; the third is the so-called parallel-fan-beam Hilbert projection equality derived by Hamaker et al.; and the fourth is the fan-beam data consistency condition (FDCC) derived by Chen et al. using the complex analysis theory. Historically, most of these consistency conditions were derived by their corresponding authors using complicated mathematical strategies, which are usually not easy to be precisely understood by researchers with only a general engineering mathematical background. In this paper, we symmetrically re-derive all these consistency conditions using a friendly mathematical language. Based on theoretical derivation, it has been found that all these consistency conditions can be viewed as a necessary condition for the specific solution to John's equation. From the physical point of view, all these consistency conditions have been essentially expressed as a similar constraint on the projection data acquired with arbitrary two x-ray source points. Numerical simulations have been carried out to experimentally evaluate and verify their merits.