OBJECTIVE:The accurate assessment of hemiparetic gait after stroke was essential for understanding the extent of motor impairment and for guiding rehabilitation efforts. In this study, a kinematic based deep learning classifier was proposed to eliminate inter-rater variability in lower-limb Brunnstrom Recovery Stage (BRS) assessment and enable quantitative longitudinal tracking of post-stroke motor recovery. METHODS:Forty healthy adults and fifty-one hemiparetic stroke patients (BRS III-VI) were recruited, and video recorded kinematic data were collected during their standardized walking tasks. Anatomical keypoint coordinates were extracted from the video recordings via advanced pose estimation algorithms for fine-grained kinematic analysis. A novel hybrid Skeleton-Attention long short-term memory (LSTM)-Inception architecture was then developed for BRS stage classification using three-dimensional keypoint coordinate data. The architecture synergistically integrated LSTM layers for temporal sequence modeling with Inception modules for multi-scale spatial feature extraction. The proposed model's performance was systematically compared against conventional deep learning benchmarks, including convolutional neural networks (CNNs) and coupled LSTM-CNN hybrid models. RESULTS:Our experimental results revealed that the proposed framework achieved superior classification accuracy compared to alternatives (97.3% vs. 87.7-95.2%). CONCLUSION:These findings demonstrated the clinical potential of deep learning-driven motion analytics to eliminate assessment subjectivity in BRS staging. SIGNIFICANCE:This approach is expected to facilitate quantitative longitudinal monitoring of neurorehabilitation progress and support the development of data-driven personalized therapeutic strategies, potentially reducing reliance on clinician expertise.

This study aims to investigate the responses of a perovskite-based direct-conversion dual-layer flat-panel detector (DL-FPD) numerically. To this end, the X-ray sensitivity, spatial resolution quantified by the modulation transfer function (MTF), and detective quantum efficiency (DQE) of the DL-FPD are evaluated numerically using a linear cascade model. In addition, both the single-crystal (SC) and polycrystalline (PC) structures of MAPbI3 are investigated, along with various other key parameters such as the material thickness, electric field strength, X-ray beam spectrum, and electronic readout noise. The results demonstrate that SC perovskite consistently exhibits better performance than PC perovskite owing to fewer material defects. Increasing the layer thickness may decrease the MTF, but can also enhance the sensitivity and DQE. Moreover, appropriately increasing the external electric field within the material can improve the sensitivity, MTF, and DQE. Finally, reducing the electronic readout noise can significantly enhance the DQE for low-dose imaging. This study demonstrates the potential of high-quality dual-energy X-ray imaging using direct-conversion perovskite DL-FPDs.

Accurate recognition of muscle fatigue in the lower back is essential for preventing low back pain and reducing the risk of occupational injuries. However, current recognition accuracy remains unsatisfactory due to limitations in both measurement tools and recognition algorithms. This study proposes a novel recognition framework for muscle fatigue based on a 60-channel hybrid physiological sensor array and a deep learning algorithm. The sensor array, which integrated surface electromyography (sEMG) electrodes and near-infrared spectroscopy (NIRS) probes, enabled simultaneous, co-located recording of multimodal topographic signals that reflect both neuromuscular and hemodynamic activity. To effectively fuse and analyze this multimodal information, a dual-stream convolutional hybrid attention network (DCHANet) was developed and evaluated under both subject-wise and cross-subject conditions. The network comprises two modality-specific feature-extraction streams tailored to the characteristics of sEMG and NIRS, which were subsequently fused by a hierarchical attention-fusion module. Recognition performance was assessed on three-level (FAT-3) and fifteen-level (FAT-15) fatigue recognition tasks. With multimodal (sEMG-NIRS) input, DCHANet achieved high classification accuracy in the FAT-3 task (subject-wise: 97.93%, cross-subject: 96.80%) and the FAT-15 task (subject-wise: 91.06%, cross-subject: 88.53%). Compared with unimodal inputs, including sEMG and NIRS alone, the multimodal DCHANet achieved higher accuracy. It also outperformed conventional machine learning methods based on histogram of oriented gradients and standard convolutional neural network. These findings highlight the potential of combining hybrid physiological sensing with attention-based deep learning for precise and fine-grained recognition of lower back muscle fatigue, offering a promising solution for clinical monitoring and early intervention in musculoskeletal disorders.

ObjectiveThe purpose of this study is to perform multiple (≥3) material decomposition with deep learning method for spectral cone-beam CT (CBCT) imaging based on ultra-slow kV switching.ApproachIn this work, a novel deep neural network called SkV-Net is developed to reconstruct multiple material density images from the ultra-sparse spectral CBCT projections acquired using the ultra-slow kV switching technique. In particular, the SkV-Net has a backbone structure of U-Net, and a multi-head axial attention module is adopted to enlarge the perceptual field. It takes the CT images reconstructed from each kV as input, and output the basis material images automatically based on their energy-dependent attenuation characteristics. Numerical simulations and experimental studies are carried out to evaluate the performance of this new approach.Main ResultsIt is demonstrated that the SkV-Net is able to generate four different material density images, i.e., fat, muscle, bone and iodine, from five spans of kV switched spectral projections. Physical experiments show that the decomposition errors of iodine and CaCl2 are less than 6%, indicating high precision of this novel approach in distinguishing materials.SignificanceSkV-Net provides a promising multi-material decomposition approach for spectral CBCT imaging systems implemented with the ultra-slow kV switching scheme.

The analysis and measurement of muscle fatigue are crucial in rehabilitation training assisted by robotic systems. This study proposes a method to investigate muscle fatigue by analyzing the variation patterns and interrelationships between synchronously acquired surface electromyography and blood oxygen signals. Using a self-developed synchronized sensor array and synchronized high-resolution neuromuscular electrophysiological and blood oxygen signals acquisition system, multi-channel sEMG and blood oxygen signals from the same region of forearm were recorded during rest, movement, and fatigue in three subjects. Temporal plots and heatmaps revealed the temporal evolution and spatial distribution of the sEMG median frequency (MF) and Delta HbO during fatigue. By analyzing six time-domain, frequency-domain, and entropy-domain features extracted from sEMG together with the trends of blood oxygen changes (Delta HbO, Delta HbR, and Delta HbT) as fatigue progressed, it was observed that sEMG features showed either a monotonic increase (RMS, WL) or decrease (MF, MPS, ZC, SampEn), while Delta HbO first decreased, then gradually increased, and finally decreased again. Transfer entropy analysis demonstrated stronger directional information flow from sEMG features to Delta HbO than in the reverse direction. These findings reveal synchronous variations of electrophysiological and blood oxygenation signals during fatigue induction and provide evidence of causal information flow between "neural drive" and "metabolic response," supporting the quantification of muscle fatigue and enabling intelligent, personalized rehabilitation strategies.

Laminar Optical Tomography (LOT) integrates diffuse optical tomography with microscopy to provide high-resolution, depth-resolved imaging capable of acquiring three-dimensional structural and functional information without depth scanning. Nevertheless, its imaging depth is inherently constrained by the rapid divergence of the incident Gaussian beam beyond the focal plane. Bessel beams, distinguished by their non-diffracting and self-healing properties, offer an alternative illumination strategy that can mitigate this limitation. In this study, we compared Bessel-beam LOT with conventional Gaussian-beam LOT by assessing the lateral spot size along the optical axis through angular-spectrum simulations, subsequently validating the simulated beam profiles experimentally. Simulations indicated that Gaussian beams afford a depth of focus of 50 mu m, whereas Bessel beams preserve comparable lateral resolution over 2mm. Beam-profiler measurements further revealed that at 1mm beyond the focal plane the Gaussian beam's spot diameter broadens to 817% of its focal value, whereas the Bessel beam expands to only 160%. Correspondingly, Bessel-beam illumination yielded a markedly higher signal-to-noise ratio (SNR) in deep-tissue LOT, with an average improvement of up to 10 dB relative to Gaussian illumination. Collectively, these findings demonstrate that incorporating Bessel beams substantially enhances LOT depth resolution, thereby enabling deeper structural and functional imaging in highly scattering biological tissues.

BACKGROUND:Recently, the popularity of dual-layer flat-panel detector (DL-FPD) based dual-energy cone-beam CT (CBCT) imaging has been increasing. However, the image quality of dual-energy CBCT remains constrained by the Compton scattered x-ray photons. PURPOSE:The objective of this study is to develop a novel scatter correction method, named e-Grid, for DL-FPD based CBCT imaging. METHODS:In DL-FPD, a certain portion of the x-ray photons (mainly low-energy [LE] primary and scattered photons) passing through the object are captured by the top detector layer, while the remaining x-ray photons (mainly high-energy [HE] primary and scattered photons) are collected by the bottom detector layer. A linear signal model was approximated between the HE primary and scatter signals and the LE primary and scatter signals. Physical calibration experiments were performed on cone beam and fan beam to validate the aforementioned signal model via linear fittings. Monte Carlo (MC) simulations of a 10 cm diameter water phantom were conducted on GATE at first to verify this newly developed scatter estimation method. In addition, physical validation experiments of water phantom, head phantom, and abdominal phantom were carried out on a DL-FPD based benchtop CBCT imaging system. The image non-uniformity (NU), which represents the relative difference between the center and the edges of CT images, was measured to quantify the reduction of image shading artifacts. Finally, multi-material decomposition was conducted. RESULTS:The MC results, CBCT images and line profiles, showed that the newly proposed e-Grid approach was able to accurately predict the scatter distributions in both shape and intensity. As a result, uniform CBCT images that are close to the scatter artifact-free reference images can be obtained. Moreover, the physical experiments demonstrated that the e-Grid method can greatly reduce the shading artifacts in both LE and HE CBCT images acquired from DL-FPD. Results also demonstrated that the e-Grid method is effective for varied objects that having different diameters (from 10 to 28 cm). Quantitatively, the NU value was reduced by over 77% in the LE CBCT image and by over 66% in the HE CBCT image on average. As a consequence, the accuracy of the decomposed multi-material bases, iodine and gadolinium, was substantially improved. CONCLUSIONS:The Compton scattered x-ray signals could be significantly reduced using the proposed e-Grid method for DL-FPD based dual-energy CBCT imaging systems.

Background: Recently, deep learning techniques have been widely used in low-dose computed tomography (LDCT) imaging applications for quickly generating high quality computed tomography (CT) images at lower radiation dose levels. The purpose of this study is to validate the reproducibility of the denoising performance of a given network that has been trained in advance across varied LDCT image datasets that are acquired from different imaging systems with different spatial resolutions. Methods: Specifically, LDCT images with comparable noise levels but having different spatial resolutions were prepared to train the U-Net. The number of CT images used for the network training, validation and test was 2,400, 300 and 300, respectively. Afterwards, self- and cross-validations among six selected spatial resolutions (62.5, 125, 250, 375, 500, 625 µm) were studied and compared side by side. The residual variance, peak signal to noise ratio (PSNR), normalized root mean square error (NRMSE) and structural similarity (SSIM) were measured and compared. In addition, network retraining on a small number of image set was performed to fine tune the performance of transfer learning among LDCT tasks with varied spatial resolutions. Results: Results demonstrated that the U-Net trained upon LDCT images having a certain spatial resolution can effectively reduce the noise of the other LDCT images having different spatial resolutions. Regardless, results showed that image artifacts would be generated during the above cross validations. For instance, noticeable residual artifacts were presented at the margin and central areas of the object as the resolution inconsistency increased. The retraining results showed that the artifacts caused by the resolution mismatch can be greatly reduced by utilizing about only 20% of the original training data size. This quantitative improvement led to a reduction in the NRMSE from 0.1898 to 0.1263 and an increase in the SSIM from 0.7558 to 0.8036. Conclusions: In conclusion, artifacts would be generated when transferring the U-Net to a LDCT denoising task with different spatial resolution. To maintain the denoising performance, it is recommended to retrain the U-Net with a small amount of datasets having the same target spatial resolution.

Background: Recently, the popularity of dual-layer flat-panel detector (DL-FPD) based dual-energy cone-beam CT (DE-CBCT) imaging has been increasing. However, the image quality of DE-CBCT remains constrained by the Compton scattered X-ray photons. Purpose: The objective of this study is to develop an energy-modulated scatter correction method for DL-FPD based CBCT imaging. Methods: The DLFPD can measure primary and Compton scattered X-ray photons having dfferent energies: X-ray photons with lower energies are predominantly captured by the top detector layer, while X-ray photons with higher energies are primarily collected by the bottom detector layer. Afterwards, the scattered X-ray signals acquired on both detector layers can be analytically retrieved via a simple model along with several pre-calibrated parameters. Both Monte Carlo simulations and phantom experiments are performed to verify this energy-modulated scatter correction method utilizing DL-FPD. Results: Results demonstrate that the proposed energy-modulated scatter correction method can signficantly reduce the shading artifacts of both low-energy and high-energy CBCT images acquired from DL-FPD. On average, the image non-uniformity is reduce by over 77 over 66 decomposed multi-material results is also substantially improved. Conclusion: In the future, Compton scattered X-ray signals can be easily corrected for CBCT systems using DL-FPDs.

In flat-panel detector (FPD) based cone-beam computed tomography (CBCT) imaging, the native receptor array is usually binned into a smaller matrix size. By doing so, the signal readout speed could be increased by 4-9 times at the expense of a spatial resolution loss of 50%-67%. Clearly, such manipulation poses a key bottleneck in generating high spatial and high temporal resolution CBCT images at the same time. In addition, the conventional FPD is also difficult in generating dual-energy CBCT images. In this paper, we propose an innovative super resolution dual-energy CBCT imaging method, named as suRi, based on dual-layer FPD (DL-FPD) to overcome these aforementioned difficulties at once. With suRi, specifically, a 1D or 2D sub-pixel (half pixel in this study) shifted binning is applied instead of the conventionally aligned binning to double the spatial sampling rate during the dual-energy data acquisition. As a result, the suRi approach provides a new strategy to enable high spatial resolution CBCT imaging while at high readout speed. Moreover, a penalized likelihood material decomposition algorithm is developed to directly reconstruct the high resolution bases from these dual-energy CBCT projections containing sub-pixel shifts. Numerical and physical experiments are performed to validate this newly developed suRi method with phantoms and biological specimen. Results demonstrate that suRi can significantly improve the spatial resolution of the CBCT image. We believe this developed suRi method would greatly enhance the imaging performance of the DL-FPD based dual-energy CBCT systems in future.

Laminar Optical Tomography (LOT) is a promising non-invasive technique for three-dimensional imaging of complex biological structures, combining high resolution and deep penetration. In this study, a LOT system was assembled using a 520nm continuous-wave laser, exploiting the contrast mechanism provided by the unique optical absorption coefficient of hemoglobin at this wavelength for microvascular imaging. The system utilizes raster scanning to illuminate the target tissue's surface, capturing backscattered light intensity with a multi-channel Photomultiplier Tubes. The inverse problem of light propagation within the tissue is solved to reconstruct a three-dimensional image of microvasculature. Phantom and in-vivo rat ear results demonstrate the system's ability to image micro vessels with diameters of several hundred micrometers within the tissue. The implemented LOT system, known for its simplicity and cost-effectiveness, showcases its potential for applications in microvascular physiological and pathological research, as well as clinical diagnostics.

BACKGROUND:Multi-material decomposition is an interesting topic in dual-energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms. PURPOSE:In this work, a novel multi-material decomposition network (MMD-Net) is proposed to improve the multi-material decomposition performance of DECT imaging. METHODS:To achieve dual-energy multi-material decomposition, a deep neural network, named as MMD-Net, is proposed in this work. In MMD-Net, two specific convolutional neural network modules, Net-I and Net-II, are developed. Specifically, Net-I is used to distinguish the material triangles, while Net-II predicts the effective attenuation coefficients corresponding to the vertices of the material triangles. Subsequently, the material-specific density maps are calculated analytically through matrix inversion. The new method is validated using in-house benchtop DECT imaging experiments with a solution phantom and a pig leg specimen, as well as commercial medical DECT imaging experiments with a human patient. The decomposition accuracy, edge spreading function, and noise power spectrum are quantitatively evaluated. RESULTS:Compared to the conventional multiple material decomposition (MMD) algorithm, the proposed MMD-Net method is more effective at suppressing image noise. Additionally, MMD-Net outperforms the iterative MMD approach in maintaining decomposition accuracy, image sharpness, and high-frequency content. Consequently, MMD-Net is capable of generating high-quality material decomposition images. CONCLUSION:A high performance multi-material decomposition network is developed for dual-energy CT imaging.

Objective. This study aims at developing a simple and rapid Compton scatter correction approach for cone-beam CT (CBCT) imaging. Approach. In this work, a new Compton scatter estimation model is established based on two distinct CBCT scans: one measures the full primary and scatter signals without anti-scatter grid (ASG), and the other measures a portion of primary and scatter signals with ASG. To accelerate the entire data acquisition speed, a half anti-scatter grid (h-ASG) that covers half of the full detector surface is proposed. As a result, the distribution of scattered x-ray photons could be estimated from a single CBCT scan. Physical phantom experiments are conducted to validate the performance of the newly proposed scatter correction approach. Main results. Results demonstrate that the proposed half grid approach can quickly and precisely estimate the distribution of scattered x-ray photons from only one single CBCT scan, resulting in a significant reduction of shading artifacts. In addition, it is found that the h-ASG approach is less sensitive to the grid transmission fractions, grid ratio and object size, indicating a robust performance of the new method. Significance. In the future, the Compton scatter artifacts can be quickly corrected using a half grid in CBCT imaging.

Background The widespread application of X-ray computed tomography (CT) imaging in medical screening makes radiation safety a major concern for public health. Sparse-view CT is a promising solution to reduce the radiation dose. However, the reconstructed CT images obtained using sparse-view CT may suffer severe streaking artifacts and structural information loss. Methods In this study, a novel attention-based dual-branch network (ADB-Net) is proposed to solve the ill-posed problem of sparse-view CT image reconstruction. In this network, downsampled sinogram input is processed through 2 parallel branches (CT branch and signogram branch) of the ADB-Net to independently extract the distinct, high-level feature maps. These feature maps are fused in a specified attention module from 3 perspectives (channel, plane, and spatial) to allow complementary optimizations that can mitigate the streaking artifacts and the structure loss in sparse-view CT imaging. Results Numerical simulations, an anthropomorphic thorax phantom, and in vivo preclinical experiments were conducted to verify the sparse-view CT imaging performance of the ADB-Net. The proposed network achieved a root-mean-square error (RMSE) of 20.6160, a structural similarity (SSIM) of 0.9257, and a peak signal-to-noise ratio (PSNR) of 38.8246 on numerical data. The visualization results demonstrate that this newly developed network can consistently remove the streaking artifacts while maintaining the fine structures. Conclusions The proposed attention-based dual-branch deep network, ADB-Net, provides a promising alternative to reconstruct high-quality sparse-view CT images for low-dose CT imaging.

The low gain avalanche detectors (LGADs) are thin sensors with fast charge collection which in combination with internal gain deliver an outstanding time resolution of about 30 ps for Minimum Ionizing Particles (MIP). High collision rates and consequent large particle rates crossing the detectors at the upgraded Large Hadron Collider (LHC) in 2028 will lead to radiation damage and deteriorated performance of the LGADs. The main consequence of radiation damage is loss of gain layer doping (acceptor removal) which requires an increase of bias voltage to compensate for the loss of charge collection efficiency and consequently time resolution. The Institute of High Energy Physics (IHEP), Chinese Academy of Sciences (CAS) has developed a process based on the Institute of Microelectronics (IME), CAS capability to enrich the gain layer with carbon to reduce the acceptor removal effect by radiation. After 1 MeV neutron equivalent fluence of 2.5 x 10(15) n(eq)/cm(2), which is the maximum fluence to which sensors will be exposed at ATLAS High Granularity Timing Detector (HGTD), the IHEP-IME second version (IHEP-IMEv2) 50 mu m LGAD sensors already deliver adequate charge collection >4 fC and time resolution <50 ps at voltages <400 V. The operation voltages of these 50 mu m devices are well below those at which single event burnout may occur.

Rehabilitation robots play an important role in the motor function rehabilitation for stroke survivors with hemiplegia. However, the rehabilitation effect of current robots is still limited partly because a single training of motor function can be strongly affected by the decreased blood supply function of the bedridden patients. This work proposed an approach to study the coupling relationship between the motor and blood supply functions by combining the synchronously recorded EEG and cerebral blood oxygen information, where the cerebrations in different movement paradigms were analyzed from an aspect of "functional coupling". The results show that the information of oxyhemoglobin concentration change (ΔHbO) can effectively indicate the cortex activation, and a stronger blood supply is needed in cortexes to perform body movements. The correlations within motor cortexes are significantly stronger than the ones between motor and prefrontal cortexes, and a higher resistance level of extremity training will cause stronger correlations. Calculation of transfer entropy (TE) shows that there exists a bidirectional information transmission between the electrophysiological and blood supply signals, and more information is transmitted always in the direction from ΔHbO to EEG than in the opposite direction. The information transmission or the coupling relationship can be significantly enhanced by extremity movements, and large TE values are achieved in the Theta, Beta and Gamma frequency bands of EEG that correspond to motor functions. This work has demonstrated the functional coupling between nerve and blood microcirculation, which would provide a technical guidance to improve the rehabilitation effect for current robot systems and have great application potentials.

Objective.This study aims at investigating a novel super resolution CBCT imaging approach with a dual-layer flat panel detector (DL-FPD).Approach.With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampled spatial information, from which super-resolution CT images can be reconstructed. A simple mathematical model is proposed to explain the signal formation procedure in DL-FPD, and a dedicated recurrent neural network, named suRi-Net, is developed based upon the above imaging model to nonlinearly retrieve the high-resolution dual-energy information. Physical benchtop experiments are conducted to validate the performance of this newly developed super-resolution CBCT imaging method.Main Results.The results demonstrate that the proposed suRi-Net can accurately retrieve high spatial resolution information from the low-energy and high-energy projections of low spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images from the top and bottom detector layers is increased by about 45% and 54%, respectively.Significance.In the future, suRi-Net will provide a new approach to perform high spatial resolution dual-energy imaging in DL-FPD-based CBCT systems.

Low Gain Avalanche Detectors (LGAD) for the High-Granularity Timing Detector (HGTD) are crucial in reducing pileups in the High-Luminosity Large Hadron Collider. Numerous studies have been conducted on the bulk irradiation damage of LGADs. However, few studies have been carried out on the surface irradiation damage of LGAD sensors with shallow carbon implantation. In this paper, the IHEP-IME LGADs with shallow carbon implantation were irradiated up to 2 MGy using gamma irradiation to investigate surface damage. Important characteristic parameters, including leakage currents, breakdown voltage (BV), inter-pad resistances, and capacitances, were tested before and after irradiation. The results showed that the leakage current and BV increased after irradiation, whereas overall inter-pad resistance exhibited minimal change and remained above 10 9 Ω before and after irradiation. Capacitance was found to be less than 4.5 pF with a slight decrease in the gain layer depletion voltage (V gl ) after irradiation. No parameter affected by the inter-pad separation was observed before and after irradiation. All characteristic parameters meet the requirements of HGTD, and this design can be used to further optimization.

We report precise TCAD simulations of IHEP-IME-v1 Low Gain Avalanche Diode (LGAD) calibrated by secondary ion mass spectroscopy (SIMS). Our setup allows us to evaluate the leakage current, capacitance, and breakdown voltage of LGAD, which agree with measurements' results before irradiation. And we propose an improved LGAD Radiation Damage Model (LRDM) which combines local acceptor removal with global deep energy levels. The LRDM is applied to the IHEP-IME-v1 LGAD and able to predict the leakage current well at -30 degrees C after an irradiation fluence of Phi(eq) = 2.5 x 10(15) n(eq)/cm(2). The charge collection efficiency (CCE) is under development.

Low-gain avalanche diode (LGAD) is the chosen technology for the ATLAS high-granularity timing detector (HGTD). According to previous studies, the acceptor removal effect due to the radiation and the single-event burnout (SEB) at high bias voltages are still a challenge for the LGAD. The Institute of High Energy Physics (IHEP), Beijing, China, cooperated with the Institute of Microelectronics (IME), Beijing, China, for the design and fabrication of the IHEP-IME LGAD sensors with shallow carbon and deep N++ layer to improve the radiation hardness of LGAD. After neutron irradiation up to $2.5 \times 10^{15}\,\,{\mathrm{ n}}_{\mathrm{ eq}}$ /cm 2 , the leakage current, the collected charge, and timing resolution of the three IHEP-IME sensors measured with a beta telescope setup meet the HGTD requirements ( $< 125~\mu \text{A}$ /cm 2 , >4 fC, and <70 ps). The LGAD sensor with shallow carbon had the lowest operation voltage after irradiation and is very promising to avoid the SEB effect. A sensor with a deep N++ layer increased the breakdown voltage of the LGAD with a high dopant concentration, which could alleviate the problem of the early breakdown of radiation-hard LGAD before irradiation.