In Situ Growth of Wafer-Scale Patterned Graphene and Fabrication of Optoelectronic Artificial Synaptic Device Array Based on Graphene/n-AlGaN Heterojunction for Visual Learning.

The unique optical and electrical properties of graphene-based heterojunctions make them significant for artificial synaptic devices, promoting the advancement of biomimetic vision systems. However, mass production and integration of device arrays are necessary for visual imaging, which is still challenging due to the difficulty in direct growth of wafer-scale graphene patterns. Here, a novel strategy is proposed using photosensitive polymer as a solid carbon source for in situ growth of patterned graphene on diverse substrates. The growth mechanism during high-temperature annealing is elucidated, leading to wafer-scale graphene patterns with exceptional uniformity, ideal crystalline quality, and precise control over layer number by eliminating the release of volatile from oxygen-containing resin. The growth strategy enables the fabrication of two-inch optoelectronic artificial synaptic device array based on graphene/n-AlGaN heterojunction, which emulates key functionalities of biological synapses, including short-term plasticity, long-term plasticity, and spike-rate-dependent plasticity. Moreover, the mimicry of visual learning in the human brain is attributed to the regulation of excitatory and inhibitory post-synapse currents, following a learning rule that prioritizes initial recognition before memory formation. The duration of long-term memory reaches 10 min. The in situ growth strategy for patterned graphene represents the novelty for fabricating fundamental hardware of an artificial neuromorphic system.

Nonvolatile and reconfigurable two-terminal electro-optic duplex memristor based on III-nitride semiconductors.

With the fast development of artificial intelligence (AI), Internet of things (IOT), etc, there is an urgent need for the technology that can efficiently recognize, store and process a staggering amount of information. The AlScN material has unique advantages including immense remnant polarization, superior temperature stability and good lattice-match to other III-nitrides, making it easy to integrate with the existing advanced III-nitrides material and device technologies. However, due to the large band-gap, strong coercive field, and low photo-generated carrier generation and separation efficiency, it is difficult for AlScN itself to accumulate enough photo-generated carriers at the surface/interface to induce polarization inversion, limiting its application in in-memory sensing and computing. In this work, an electro-optic duplex memristor on a GaN/AlScN hetero-structure based Schottky diode has been realized. This two-terminal memristor shows good electrical and opto-electrical nonvolatility and reconfigurability. For both electrical and opto-electrical modes, the current on/off ratio can reach the magnitude of 104, and the resistance states can be effectively reset, written and long-termly stored. Based on this device, the "IMP" truth table and the logic "False" can be successfully reproduced, indicating the huge potential of the device in the field of in-memory sensing and computing.

The AlN lattice-polarity inversion in a high-temperature-annealed c-oriented AlN/sapphire originated from the diffusion of Al and O atoms from sapphire.

AlN films are widely used owing to their superior characteristics, including an ultra-wide bandgap, high breakdown field, and radiation resistance. High-temperature annealing (HTA) makes it easy to obtain high-quality AlN films, with the advantages of a simple process, good repeatability, and low cost. However, it is always found that there is a lattice-polarity inversion from a N-polarity near the sapphire to an Al-polarity in the HTA c-oriented AlN/sapphire. Currently, the formation mechanism is still unclear, which hinders its further wide applications. Therefore, the formation mechanism of the polarity inversion and its impacts on the quality and stress profile of the upper AlN in the HTA c-oriented AlN/sapphire were investigated. The results imply that the inversion originated from the diffusion of the Al and O atoms from the sapphire. Due to the presence of abundant Al vacancies (VAl) in the upper AlN, Al atoms in the sapphire diffuse into the upper AlN during the annealing to fill the VAl, resulting in the O-terminated sapphire, leading to the N-polar AlN. Meanwhile, O atoms in the sapphire also diffuse into the upper AlN during the annealing, forming an AlxOyNz layer and causing the inversion from N- to Al-polarity. The inversion has insignificant impacts on the quality and stress distribution of the upper AlN. Besides, this study predicts the presence of a two-dimensional electron gas at the inversion interface. However, the measured electron concentration is much lower than that predicted, which may be due to the defect compensation, low polarization level, and strong impurity scattering.

Plasmonic-enhanced efficiency of AlGaN-based deep ultraviolet LED by graphene/Al nanoparticles/graphene hybrid structure.

The AlGaN-based deep ultraviolet light-emitting diode (DUV LED) has advantages of environmentally friendly materials, tunable emission wavelength, and easy miniaturization. However, the light extraction efficiency (LEE) of an AlGaN-based DUV LED is low, which hinders its applications. Here, we design a graphene/Al nanoparticles/graphene (Gra/Al NPs/Gra) hybrid plasmonic structure, where the strong resonant coupling of local surface plasmons (LSPs) induces a 2.9-times enhancement for the LEE of the DUV LED according to the photoluminescence (PL). The dewetting of Al NPs on a graphene layer by annealing is optimized, resulting in better formation and uniform distribution. The near-field coupling of Gra/Al NPs/Gra is enhanced via charge transfer among graphene and Al NPs. In addition, the skin depth increment results in more excitons being coupled out of multiple quantum wells (MQWs). An enhanced mechanism is proposed, revealing that the Gra/metal NPs/Gra offers a reliable strategy for improving the optoelectronic device performance, which might trigger the advances of LEDs and lasers with high brightness and power density.

Centimeter-Transferable III-Nitride Membrane Enabled by Interfacial Adhesion Control for a Flexible Photosensitive Device.

Flexible III-nitride-based optoelectronic devices are crucial for the next-generation foldable/wearable lighting sterilization and sensor working in the ultraviolet (UV) region. However, the strong bonding effect at the epitaxial interface of III-nitride and bare sapphire substrate makes it difficult for epilayer separation and flexible applications. Although the emerging van der Waals epitaxy (vdWE) with graphene insertion layer offers a feasible route for weakening the interfacial adhesion, the intact centimeter-transferable III-nitride membrane still remains challenging. The spontaneous delamination occurs due to the too weak interfacial adhesion of pure vdW force, and on the contrary, the structural damage of graphene by high-temperature hydrogen etching during the III-nitride growth might also cause separation failure. Up to now, the efficient control of vdWE interfacial adhesion is still an on-going research hotspot. Herein, we demonstrate the interfacial adhesion control of III-nitride vdWE by utilizing graded high-temperature nitridation treatment of the graphene insertion layer, which generates defects and N doping in different levels. The corresponding epitaxial modes of pure-vdWE, quasi-vdWE, and mixed epitaxy are achieved according to the interfacial adhesion difference. It reveals that the quasi-vdWE enabled by small graphene defects and proper N doping triggers the low formation energy for AlN nucleation; meanwhile, the proper interfacial adhesion ensures the growth integrality and intact separation of III-nitride membrane in the centimeter scale. The UV resin-assisted bonding technique is proposed for the successful transfer of III-nitride onto a flexible substrate. The flexible photodetector is fabricated by using a graphene monolayer as the photocarrier transport channel, and it achieves a high device yield of 90%, retaining ∼60% of its initial performance after 250 bending cycles. This work offers the promising strategy for controlling vdWE interfacial adhesion, and the separable and transferable III-nitride membrane lays the foundation for advances of future UV foldable and wearable devices.

Optimizing metal/n-AlGaN contact by recessed AlGaN heterostructure with a polarization effect.

With increasing Al mole fraction, n-contact has become an important issue limiting the development of Al-rich AlGaN-based devices. In this work, we have proposed an alternative strategy to optimize the metal/n-AlGaN contact by introducing a heterostructure with a polarization effect and by etching a recess structure through the heterostructure beneath the n-contact metal. Experimentally, we inserted an n-Al0.6Ga0.4N layer into an Al0.5Ga0.5N p-n diode on the n-Al0.5Ga0.5N layer to form a heterostructure, where a high interface electron concentration of 6 × 1018 cm-3 was achieved with the aid of a polarization effect. As a result, a quasi-vertical Al0.5Ga0.5N p-n diode with a ∼1 V reduced forward voltage was demonstrated. Numerical calculations verified that the increased electron concentration beneath the n-metal induced by the polarization effect and recess structure was the main reason for the reduced forward voltage. This strategy could simultaneously decrease the Schottky barrier height as well as provide a better carrier transport channel, enhancing both the thermionic emission and tunneling processes. This investigation provides an alternative approach to obtain a good n-contact, especially for Al-rich AlGaN-based devices, such as diodes and LEDs.

Investigate the application of postoperative ctDNA-based molecular residual disease detection in monitoring tumor recurrence in patients with non-small cell lung cancer--A retrospective study of ctDNA.

Purpose: To evaluate whether postoperative circulating tumor DNA (ctDNA) in plasma of patients with non-small cell lung cancer (NSCLC) can be used as a biomarker for early detection of molecular residual disease (MRD) and prediction of postoperative recurrence. Methods: This study subjects were evaluated patients with surgical resected non-small cell lung cancer. All eligible patients underwent radical surgery operation followed by adjuvant therapy. Tumor tissue samples collected during operation were used to detect tumor mutation genes, and blood samples collected from peripheral veins after operation were used to collect ctDNA. Molecular residue disease (MRD) positive was defined as at least 1 true shared mutation identified in both the tumor sample and a plasma sample from the same patient was. Results: Positive postoperatively ctDNA was associated with lower recurrence-free survival (RFS).The presence of MRD was a strong predictor of disease recurrence. The relative contribution of ctDNA-based MRD to the prediction of RFS is higher than all other clinicopathological variables, even higher than traditional TNM staging. In addition, MRD-positive patients who received adjuvant therapy had improved RFS compared to those who did not, the RFS of MRD-negative patients receiving adjuvant therapy was lower than that of patients not receiving adjuvant therapy. Conclusions: Post-operative ctDNA analysis is an effective method for recurrence risk stratification of NSCLC, which is beneficial to the management of patients with NSCLC.

Van der Waals Epitaxy of c-Oriented Wurtzite AlGaN on Polycrystalline Mo Substrates for Enhanced Heat Dissipation.

The epitaxy of III-nitrides on metallic substrates is competitive due to the advantages of vertical carrier injection, enhanced heat dissipation, and flexible application in various III-nitride-based devices. However, the serious lattice mismatch, atom diffusion, and interface reaction under the rigorous growth conditions have caused enormous obstacles. Based on the thermal and chemical stability of the graphene layer, we propose the van der Waals epitaxy of c-oriented wurtzite AlGaN on the polycrystalline Mo substrate by high-temperature metal-organic chemical vapor deposition. The insertion of a graphene layer interrupts the chaotic epitaxial relationship between the polycrystalline metal and epilayers, resulting in the single-crystalline orientation along the wurtzite (0002) plane and residual stress release in AlGaN because of the weak van der Waals interaction. We also demonstrate that the epitaxy of AlGaN on Mo metal possesses enhanced heat dissipation ability, in which the epilayer temperature is controlled at only 28.7 °C by the heating of a ∼54 °C hot plate. The heat dissipation enhancement for the present epitaxial structures provides a desirable strategy for the fabrication of efficient ultraviolet devices with excellent stability and lifetime.

Point Defects in Monolayer h-AlN as Candidates for Single-Photon Emission.

A single-photon emission (SPE) system based on a solid state is one of the fundamental branches in quantum information and communication technologies. The traditional bulk semiconductors suffered limitations of difficult photon extraction and long radiative lifetime. Two-dimensional (2D) semiconductors with an entire open structure and low dielectric screening can overcome these shortcomings. In this work, we focus on monolayer h-AlN due to its wide band gap and the successful achievement of SPE compared to its bulk counterpart. We systematically investigate the properties of point defects, including vacancies, antisites, and impurities, in monolayer h-AlN by employing hybrid density functional theory calculations. The -1 charged Al vacancy (VAl-) and +1 charged nitrogen antisite (NAl+) are predicted to achieve SPE with the zero-phonon lines of 0.77 and 1.40 eV, respectively. Moreover, the charged point-defect complex CAlVN+, which is composed of vacancies and carbon substitutions, also can be used for SPE. Our results extend the avenue for realizing SPE in 2D semiconductors.

Cation Vacancy in Wide Bandgap III-Nitrides as Single-Photon Emitter: A First-Principles Investigation.

Single-photon sources based on solid-state material are desirable in quantum technologies. However, suitable platforms for single-photon emission are currently limited. Herein, a theoretical approach to design a single-photon emitter based on defects in solid-state material is proposed. Through group theory analysis and hybrid density functional theory calculation, the charge-neutral cation vacancy in III-V compounds is found to satisfy a unique 5-electron-8-orbital electronic configuration with Td symmetry, which is possible for single-photon emission. Furthermore, it is confirmed that this type of single-photon emitter only exists in wide bandgap III-nitrides among all the III-V compounds. The corresponding photon energy in GaN, AlN, and AlGaN lies within the optimal range for transfer in optical fiber, thereby render the charge-neutral cation vacancy in wide-bandgap III-nitrides as a promising single-photon emitter for quantum information applications.

2D III-Nitride Materials: Properties, Growth, and Applications.

2D III-nitride materials have been receiving considerable attention recently due to their excellent physicochemical properties, such as high stability, wide and tunable bandgap, and magnetism. Therefore, 2D III-nitride materials can be applied in various fields, such as electronic and photoelectric devices, spin-based devices, and gas detectors. Although the developments of 2D h-BN materials have been successful, the fabrication of other 2D III-nitride materials, such as 2D h-AlN, h-GaN, and h-InN, are still far from satisfactory, which limits the practical applications of these materials. In this review, recent advances in the properties, growth methods, and potential applications of 2D III-nitride materials are summarized. The properties of the 2D III-nitride materials are mainly obtained by first-principles calculations because of the difficulties in the growth and characterizations of these materials. The discussion on the growth of 2D III-nitride materials is focused on 2D h-BN and h-AlN, as the developments of 2D h-GaN and h-InN are yet to be realized. Therefore, applications have been realized mostly based on the 2D h-BN materials; however, many potential applications are cited for the entire range of 2D III-nitride materials. Finally, future research directions and prospects in this field are also discussed.

Downsampled Imaging Geometric Modeling for Accurate CT Reconstruction via Deep Learning.

X-ray computed tomography (CT) is widely used clinically to diagnose a variety of diseases by reconstructing the tomographic images of a living subject using penetrating X-rays. For accurate CT image reconstruction, a precise imaging geometric model for the radiation attenuation process is usually required to solve the inversion problem of CT scanning, which encodes the subject into a set of intermediate representations in different angular positions. Here, we show that accurate CT image reconstruction can be subsequently achieved by downsampled imaging geometric modeling via deep-learning techniques. Specifically, we first propose a downsampled imaging geometric modeling approach for the data acquisition process and then incorporate it into a hierarchical neural network, which simultaneously combines both geometric modeling knowledge of the CT imaging system and prior knowledge gained from a data-driven training process for accurate CT image reconstruction. The proposed neural network is denoted as DSigNet, i.e., downsampled-imaging-geometry-based network for CT image reconstruction. We demonstrate the feasibility of the proposed DSigNet for accurate CT image reconstruction with clinical patient data. In addition to improving the CT image quality, the proposed DSigNet might help reduce the computational complexity and accelerate the reconstruction speed for modern CT imaging systems.

An Efficient Iterative Cerebral Perfusion CT Reconstruction via Low-Rank Tensor Decomposition With Spatial-Temporal Total Variation Regularization.

Cerebrovascular diseases, i.e., acute stroke, are a common cause of serious long-term disability. Cerebral perfusion computed tomography (CPCT) can provide rapid, high-resolution, quantitative hemodynamic maps to assess and stratify perfusion in patients with acute stroke symptoms. However, CPCT imaging typically involves a substantial radiation dose due to its repeated scanning protocol. Therefore, in this paper, we present a low-dose CPCT image reconstruction method to yield high-quality CPCT images and high-precision hemodynamic maps by utilizing the great similarity information among the repeated scanned CPCT images. Specifically, a newly developed low-rank tensor decomposition with spatial-temporal total variation (LRTD-STTV) regularization is incorporated into the reconstruction model. In the LRTD-STTV regularization, the tensor Tucker decomposition is used to describe global spatial-temporal correlations hidden in the sequential CPCT images, and it is superior to the matricization model (i.e., low-rank model) that fails to fully investigate the prior knowledge of the intrinsic structures of the CPCT images after vectorizing the CPCT images. Moreover, the spatial-temporal TV regularization is used to characterize the local piecewise smooth structure in the spatial domain and the pixels' similarity with the adjacent frames in the temporal domain, because the intensity at each pixel in CPCT images is similar to its neighbors. Therefore, the presented LRTD-STTV model can efficiently deliver faithful underlying information of the CPCT images and preserve the spatial structures. An efficient alternating direction method of multipliers algorithm is also developed to solve the presented LRTD-STTV model. Extensive experimental results on numerical phantom and patient data are clearly demonstrated that the presented model can significantly improve the quality of CPCT images and provide accurate diagnostic features in hemodynamic maps for low-dose cases compared with the existing popular algorithms.

Iterative reconstruction for low dose dual energy CT using information-divergence constrained spectral redundancy information.

Dual energy computed tomography (DECT) can improve the capability of differentiating different materials compared with conventional CT. However, due to non-negligible radiation exposure to patients, dose reduction has recently become a critical concern in CT imaging field. In this work, to reduce noise at the same time maintain DECT images quality, we present an iterative reconstruction algorithm for low-dose DECT images where in the objective function of the algorithm consists of a data-fidelity term and a regularization term. The former term is based on alpha-divergence to describe the statistical distribution of the DE sinogram data. And the latter term is based on the redundant information to reflect the prior information of the desired DECT images. For simplicity, the presented algorithm is termed as "AlphaD-aviNLM". To minimize the associative objective function, a modified proximal forward-backward splitting algorithm is proposed. Digital phantom, physical phantom, and patient data were utilized to validate and evaluate the presented AlphaD-aviNLM algorithm. The experimental results characterize the performance of the presented AlphaD-aviNLM algorithm. Speficically, in the digital phantom study, the presented AlphaD-aviNLM algorithm performs better than the PWLS-TV, PWLS-aviNLM, and AlphaD-TV with more than 49%, 34%, and 40% gains for the RMSE metric, 1.3%, 0.4%, and 0.7% gains for the FSIM metric and 13%, 8%, and 11% gains for the PSNR metric. In the physical phantom study, the presented AlphaD-aviNLM algorithm performs better than the PWLS-TV, PWLS-aviNLM, and AlphaD-TV with more than 0.55%, 0.07%, and 0.16% gains for the FSIM metric.

Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization.

Dynamic cerebral perfusion computed tomography (DCPCT) has the ability to evaluate the hemodynamic information throughout the brain. However, due to multiple 3-D image volume acquisitions protocol, DCPCT scanning imposes high radiation dose on the patients with growing concerns. To address this issue, in this paper, based on the robust principal component analysis (RPCA, or equivalently the low-rank and sparsity decomposition) model and the DCPCT imaging procedure, we propose a new DCPCT image reconstruction algorithm to improve low-dose DCPCT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor. For simplicity, the first proposed model is termed tensor-based RPCA (T-RPCA). Specifically, the T-RPCA model views the DCPCT sequential images as a mixture of low-rank, sparse, and noise components to describe the maximum temporal coherence of spatial structure among phases in a tensor framework intrinsically. Moreover, the low-rank component corresponds to the "background" part with spatial-temporal correlations, e.g., static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial-temporal continuity, e.g., dynamic perfusion enhanced information, which is approximately sparse over time. Furthermore, an improved nonlocal patch-based T-RPCA (NL-T-RPCA) model which describes the 3-D block groups of the "background" in a tensor is also proposed. The NL-T-RPCA model utilizes the intrinsic characteristics underlying the DCPCT images, i.e., nonlocal self-similarity and global correlation. Two efficient algorithms using alternating direction method of multipliers are developed to solve the proposed T-RPCA and NL-T-RPCA models, respectively. Extensive experiments with a digital brain perfusion phantom, preclinical monkey data, and clinical patient data clearly demonstrate that the two proposed models can achieve more gains than the existing popular algorithms in terms of both quantitative and visual quality evaluations from low-dose acquisitions, especially as low as 20 mAs.

Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization.

Reducing radiation dose in dual energy computed tomography (DECT) is highly desirable but it may lead to excessive noise in the filtered backprojection (FBP) reconstructed DECT images, which can inevitably increase the diagnostic uncertainty. To obtain clinically acceptable DECT images from low-mAs acquisitions, in this work we develop a novel scheme based on measurement of DECT data. In this scheme, inspired by the success of edge-preserving non-local means (NLM) filtering in CT imaging and the intrinsic characteristics underlying DECT images, i.e. global correlation and non-local similarity, an averaged image induced NLM-based (aviNLM) regularization is incorporated into the penalized weighted least-squares (PWLS) framework. Specifically, the presented NLM-based regularization is designed by averaging the acquired DECT images, which takes the image similarity within the two energies into consideration. In addition, the weighted least-squares term takes into account DECT data-dependent variance. For simplicity, the presented scheme was termed as 'PWLS-aviNLM'. The performance of the presented PWLS-aviNLM algorithm was validated and evaluated on digital phantom, physical phantom and patient data. The extensive experiments validated that the presented PWLS-aviNLM algorithm outperforms the FBP, PWLS-TV and PWLS-NLM algorithms quantitatively. More importantly, it delivers the best qualitative results with the finest details and the fewest noise-induced artifacts, due to the aviNLM regularization learned from DECT images. This study demonstrated the feasibility and efficacy of the presented PWLS-aviNLM algorithm to improve the DECT reconstruction and resulting material decomposition.

Robust low-dose dynamic cerebral perfusion CT image restoration via coupled dictionary learning scheme.

Dynamic cerebral perfusion x-ray computed tomography (PCT) imaging has been advocated to quantitatively and qualitatively assess hemodynamic parameters in the diagnosis of acute stroke or chronic cerebrovascular diseases. However, the associated radiation dose is a significant concern to patients due to its dynamic scan protocol. To address this issue, in this paper we propose an image restoration method by utilizing coupled dictionary learning (CDL) scheme to yield clinically acceptable PCT images with low-dose data acquisition. Specifically, in the present CDL scheme, the 2D background information from the average of the baseline time frames of low-dose unenhanced CT images and the 3D enhancement information from normal-dose sequential cerebral PCT images are exploited to train the dictionary atoms respectively. After getting the two trained dictionaries, we couple them to represent the desired PCT images as spatio-temporal prior in objective function construction. Finally, the low-dose dynamic cerebral PCT images are restored by using a general DL image processing. To get a robust solution, the objective function is solved by using a modified dictionary learning based image restoration algorithm. The experimental results on clinical data show that the present method can yield more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps than the state-of-the-art methods.

Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations.

Cerebral perfusion x-ray computed tomography (PCT) is an important functional imaging modality for evaluating cerebrovascular diseases and has been widely used in clinics over the past decades. However, due to the protocol of PCT imaging with repeated dynamic sequential scans, the associative radiation dose unavoidably increases as compared with that used in conventional CT examinations. Minimizing the radiation exposure in PCT examination is a major task in the CT field. In this paper, considering the rich similarity redundancy information among enhanced sequential PCT images, we propose a low-dose PCT image restoration model by incorporating the low-rank and sparse matrix characteristic of sequential PCT images. Specifically, the sequential PCT images were first stacked into a matrix (i.e., low-rank matrix), and then a non-convex spectral norm/regularization and a spatio-temporal total variation norm/regularization were then built on the low-rank matrix to describe the low rank and sparsity of the sequential PCT images, respectively. Subsequently, an improved split Bregman method was adopted to minimize the associative objective function with a reasonable convergence rate. Both qualitative and quantitative studies were conducted using a digital phantom and clinical cerebral PCT datasets to evaluate the present method. Experimental results show that the presented method can achieve images with several noticeable advantages over the existing methods in terms of noise reduction and universal quality index. More importantly, the present method can produce more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps.

[Edge-detecting operator-based selection of Huber regularization threshold for low-dose computed tomography imaging].

OBJECTIVE: To compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging. METHODS: Huber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators. RESULTS: The experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal. CONCLUSION: Both of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.

Threshold choices of Huber regularization using global- and local-edge-detecting operators for X-ray computed tomographic reconstruction.

Statistical iterative reconstruction (SIR) approaches have shown great potential in x-ray computed tomographic (CT) reconstruction in the case of low-dose protocol. For yielding high quality image, an edge-preserving regularization should be incorporated into the objective function of SIR approaches. A typical example is the Huber regularization with an edge-preserving non-quadratic potential function which increases less rapidly than the quadratic potential function for sufficiently large arguments. However, a major drawback of the Huber regularization is the determining the threshold, which precludes its extensive applications. In this paper, we investigate both global- and local- edge-detecting operators for threshold choices of Huber regularization and apply them to SIR CT image reconstruction with low-dose scan protocol. Experiments were performed on XCAT phantom by using a CT simulator to obtain the low-dose projection data.