Objective.X-ray diffraction (XRD) is a non-destructive technique capable of obtaining molecular structural information of materials and achieving higher sensitivity than transmission tomography (CT) for substances with similar densities. It has great potential in medical and security applications, such as rapid breast cancer screening, calculi composition analysis, and detection of drugs and explosives. Among various XRD tomography (XRDT) systems, snapshot coded aperture XRDT (SCA-XRDT) achieves the fastest scanning speed, making it well-suited for practical medical imaging and security inspection. However, SCA-XRDT suffers from poor data condition and an ill-posed reconstruction problem, leading to significant challenges in accurate image reconstruction. In this work, we explore the inherent characteristics of XRD patterns and incorporate a novel and effective prior accordingly into an iterative reconstruction algorithm, thereby improving the reconstruction performance.Approach.By analyzing the key physical factors that shape XRD patterns, we represent XRD patterns as a linear combination of basis functions, and validate the feasibility and generality of this representation using experimental data. Building upon this, we propose a novel basis-function-decomposition reconstruction (BFD-Recon) method that incorporates the basis function representation as a prior into a model-based SCA-XRDT reconstruction framework. This method transforms the optimization target from entire XRD patterns to parameters of basis functions. We further impose smoothness and sparsity constraints on the parameters to restrict the solution space. We employ the Split Bregman algorithm to iteratively solve the optimization problem. Both simulation and experimental results demonstrate the effectiveness of the proposed BFD-Recon method.Main-results.Compared with a conventional MBIR method for XRDT reconstruction, the proposed BFD-Recon method results in more accurate reconstruction of XRD patterns, especially the sharp peaks that closely match the ground truth. It substantially suppresses the noise and the impact of background signals on the reconstructed XRD patterns. Since the proposed basis function decomposition and the prior align well with the characteristics of XRD patterns, its value is well manifested along the spectral dimension of the reconstructed images. It also reduces blur along the x-ray path in the spatial dimension. Quantitatively, BFD-Recon increases the correlation coefficients between the reconstructed and ground-truth XRD patterns by up to 10% and the average PSNR by 20%.Significance.Through theoretical analysis and experiments, we propose a basis function decomposition method for XRD patterns and demonstrate its effectiveness and general applicability. Incorporating the basis-function-decomposition into the model-based iterative reconstruction can significantly enhance the XRDT reconstruction performance. The method provides prior information on XRD patterns and reduces the number of unknowns by at least one order of magnitude by transforming the optimization target to basis function parameters, which effectively alleviates the ill-posedness of the reconstruction problem.

Objective.Motion artifacts remain a major obstacle in dynamic computed tomography (CT) reconstruction, particularly for nonperiodic rapid motion such as cardiac imaging in patients with fast or irregular heart rates, where ECG-gated approaches are unreliable. The extreme limited-angle problem arising from insufficient angular coverage within a single cardiac phase makes accurate reconstruction of fine cardiac structures especially challenging. This work aims to develop a self-supervised framework that recovers high-resolution dynamic CT images from nonperiodic motion scenarios without external training datasets.Approach.We propose BIRD, an implicit neural representation (INR) framework for nonperiodic dynamic CT reconstruction, with three components: (1) a dual-feature representation decomposing the dynamic scene into topology-preserving and free-form features, enabling modeling of both deformable motion and residual variations such as contrast kinetics; (2) a backward-warping deformation model enabling direct ray-based training, overcoming the resolution limitations of prior forward-warping INR approaches at clinically relevant sub-millimeter resolution; and (3) a diffeomorphism-based regularization enforcing anatomically plausible deformation vector fields through bidirectional inverse consistency, without restricting representational capacity.Main results.BIRD was validated on digital and physical cardiac phantoms and retrospective patient data. In the XCAT phantom study under severe limited-angle conditions (0.5 s rot-1, 120 bpm), BIRD improved peak signal-to-noise ratio over the next-best method by 1.43 dB on the right coronary artery and 1.21 dB on the right ventricle. Curved planar reformation confirmed improved vessel continuity and boundary sharpness. On real projection data from a physical cardiac phantom and a retrospective patient scan, BIRD reduced motion artifacts and improved depiction of cardiac and vascular structures compared with conventional reconstructions.Significance.The proposed framework performs self-supervised reconstruction of nonperiodic dynamic CT images solely from projection data. It offers potential clinical applications including non-ECG-gated cardiac imaging for patients with arrhythmia, cinematic image sequences, and retrospective motion artifact correction in conventional CT scans.

Photon-counting detector based computed tomography (PCCT) has greatly advanced in recent years. However, spectral inconsistency, referring to inter-pixel variations in detected counts per energy bin, can easily leads to ring or band artifacts and inaccuracies in CT reconstructed images. This work proposes a novel physics-model based method to correct for spectral inconsistency by modeling it through two terms: (1) a fixed spectral skew term (energy threshold-independent filtration function) determined at a given energy threshold, and (2) a variable energy-threshold bias term that can be directly calculated by using our spectral model as the threshold changes. After the two terms being computed out in the calibration stage, they will be incorporated into our spectral model to adaptively generate the spectral correction vectors as well as the material decomposition vectors if needed, pixel-by-pixel for PCCT projection data. Using a minimum set of parameters with explicit physics meaning, such an energy-threshold bias calculator (ETB-Cal) has advantages of computational efficiency, robustness in implementation, and convenience with no need of X-ray fluorescence materials in calibration. To validate our method, both numerical simulations and physical experiments using multiple phantoms were carried out on a tabletop PCCT system, with preliminary results showing a significant reduction in non-uniformity, from 29.3 to 5.8 HU for Gammex multi-energy phantom versus no correction (comparatively, 8.3 HU was achieved by a polynomial-involving model-based approach with no explicit modeling and calculating of energy threshold bias but more calibration data required), and from 27.9 to 3.2 HU for the Kyoto head phantom.

Rotational computed laminography (CL) has broad application potential in three-dimensional imaging of plate-like objects because it only requires X-rays to pass through the tested object in the thickness direction during the imaging process. In this study, a rectangular cross-section field-of-view rotational CL (RC-CL) is proposed for circuit board imaging. Compared to other rotational CL systems, the field of view is the largest and most suitable for rectangular circuit boards. Meanwhile, as the imaging geometry of RC-CL is significantly different from that of cone-beam CT, the Feldkamp-Davis-Kress (FDK) reconstruction algorithm cannot be used directly. However, transferring the projection data to fit into the CBCT geometry using two-dimensional interpolation introduces interpolation errors. Therefore, an FDK-type analytical reconstruction algorithm applicable to RC-CL was developed. The effectiveness of the method was validated through numerical experiments, and the influence of the tilt angle on the reconstruction results was analyzed. Finally, the RC-CL technique was applied to real defect detection research on circuit boards.

Multi-source stationary computed tomography (MSS-CT) offers significant advantages in medical and industrial applications due to its gantry-less scan architecture and/or capability of simultaneous multi-source emission. However, the lack of anti-scatter grid deployment in MSS-CT results in severe forward and/or cross scatter contamination, presenting a critical challenge that necessitates an accurate and efficient scatter correction. In this work, ComptoNet, an innovative end-to-end deep learning framework for scatter estimation in MSS-CT, is proposed, which integrates Compton-scattering physics with deep learning techniques to address the challenges of scatter estimation effectively. Central to ComptoNet is the Compton-map, a novel concept that captures the distribution of scatter signals outside the scan field of view, primarily consisting of large-angle Compton scatter. In ComptoNet, a reference Compton-map and/or spare detector data are used to guide the physics-driven deep estimation of scatter from simultaneous emissions by multiple sources. Additionally, a frequency attention module is employed for enhancing the low-frequency smoothness. Such a multi-source deep scatter estimation framework decouples the cross and forward scatter. It reduces network complexity and ensures a consistent low-frequency signature with different photon numbers of simulations, as evidenced by mean absolute percentage errors (MAPEs) that are less than 1.26%. Conducted by using data generated from Monte Carlo simulations with various phantoms, experiments demonstrate the effectiveness of ComptoNet, with significant improvements in scatter estimation accuracy (a MAPE of 0.84%). After scatter correction, nearly artifact-free CT images are obtained, further validating the capability of our proposed ComptoNet in mitigating scatter-induced errors.

3D Gaussian splatting (3DGS) has shown impressive performance in 3D scene reconstruction. However, it suffers from severe degradation when the number of training views is limited, resulting in blur and floaters. Many works have been devoted to standardize the optimization process of 3DGS through regularization techniques. However, we identify that inadequate initialization is a critical issue overlooked by current studies. To address this, we propose EAP-GS, a method to enhance initialization for fast, accurate, and stable few-shot scene reconstruction. Specifically, we introduce an Attentional Pointcloud Augmentation (APA) technique, which retains two-view tracks as an option for pointcloud generation. Additionally, the scene complexity is used to determine the required density distribution, thereby constructing a better pointcloud. We implemented APA by extending Structure-From-Motion (SFM) to focus on pointcloud generation in regions with complex structure but sparse pointcloud distribution, which significantly increases the number of valuable points and effectively harmonizes the density distribution. A better pointcloud leads to more accurate scene geometry and mitigates local overfitting during reconstruction stage. Furthermore, our APA can be framed as a modular augmentation to existing methods with minimal overhead. Experimental results from various indoor and outdoor scenes demonstrate that the proposed EAP-GS achieves outstanding scene reconstruction performance and surpasses state-of-the-art methods. Project page: https://osierddr.github.io/eapgs/

X-ray diffraction (XRD) has been considered to be a potential non-destructive detection technology in medicine and security inspection. It could discriminate different materials with similar attenuation coefficients, such as fat and carcinoma, cola and gasoline. However, the lack of clear knowledge regarding the XRD spectra of prevalent amorphous substances pose significant impediments to the advancement of this technology. In this work, we scanned over 100 materials to establish a valuable dataset resource of XRD patterns for further in-depth study on XRD phenomena. Using a pencil beam polychromatic XRD laboratory system, we conducted scans of thin material samples and collected diffraction signals with a pixelated energy-dispersive photon counting detector. XRD patterns were extracted from the signals. The dataset contains XRD spectra of a diverse materials, including crystal powders, metals, tissues, botanical specimens, various types of polymers, geological stones, and common household items. Among them, the XRD patterns of amorphous materials such as water, adipose, and beef exhibit excellent congruence with the Geant4 dataset. These patterns serve as strong evidences to our data precision. Moreover, we presented the XRD patterns of 20 representative materials. As an example of applying our dataset, we conducted a simple simulation. In future works, we believe the dataset will facilitate in-depth researches on XRD technology.

Multi-segment static computed tomography (MS-staticCT) is a generalized and efficient configuration of static CT systems, achieving high temporal resolution imaging by sequentially firing x-ray sources, instead of rotation. However, it contains numerous geometric parameters. Due to the dense arrangement of both the x-ray sources and detectors within their respective configurations, there are some coupled illumination relationships where some x-ray sources simultaneously illuminate multiple detectors. To address these calibration challenges, we propose a geometric calibration method based on ordered subsets. We categorize two types of ordered subsets of sources and detectors: source subsets and detector subsets. Each source subset includes a group of sources that illuminate the same detectors, along with the illuminated detectors. Similarly, each detector subset includes a group of detectors illuminated by the same sources, along with the sources that illuminate them. The calibration of the sources in source subsets and the detectors in detector subsets is performed alternately until convergence, ensuring that the calibrated geometry to accurately describe all the illumination relationships. These calibration steps are detailed in a workflow. During each step, the estimations for different ordered subsets are independent and parallelizable to significantly improving computational efficiency. A calibration phantom is involved in our method. During the calibration, we iteratively estimate the parameters by minimizing the average re-projection error (aRPE) of the balls in the calibration phantom. We evaluated the proposed method by simulation and actual experiments. The aRPE was reduced to 0.0087 mm and the reconstructed images were clear without obvious misalignment in simulation. Compared to estimating all parameters together, our method improved computational efficiency by a factor of 2.20. The targeted spatial resolution (2.5 lp·mm-1) of an actual MS-staticCT system was obtained. These results verified the efficiency and accuracy of the proposed method.

Monte Carlo simulation is a powerful tool for research in photon counting detector spectral CT and X-ray diffraction imaging ( XRDI). We developed an efficient GPU-accelerated Monte Carlo simulation program specialized for X-ray diffraction imaging (XRDI). We implemented the coherent scattering process with the molecular interference on GPU to enable the proper simulation of X-ray diffraction. Besides, multiple non-ideal factors such as charge sharing, charge collection efficiency and ballistic deficit are incorporated in single-carrier detectors' photon detection and spectrum measurement process. The current induction, pulse shaping and signal readout concerning the depth of photon interaction is pre-calculated at the initialization stage and stored in a lookup table for fast simulation on GPU. We carried out simulations of pencil beam and coded aperture XRDI to prove the effectiveness of our program. The form factor extracted from the pencil beam XRDI data shows a good agreement with the experiment data. The coded aperture XRDI simulation demonstrates our program's ability to simulate complex imaging scenarios. We also compared the simulation of the energy response of a pixelated CdZnTe spectroscopic detector with the experimental calibrated energy response of a realistic detector, and they matched well. The computation speed shows more than 500 times acceleration compared to the CPU simulation code. To our knowledge, this is the first work to integrate the simulations of photon transportation, detection process and signal generation into one stage on GPU. The program also has the potential for photon counting spectral CT simulation.

Computed laminography (CL) is widely used in imaging plate-like object. Due to lacking projection data along non-thickness direction, images reconstructed from CL contain severe interlayer aliasing artifacts. These artifacts can greatly affect later object identification and information extraction from the CL images, restricting their utility value. To reduce aliasing artifact in CL, we develop a deep learning method to post-process the FDKreconstructed image. Firstly, we analyze the characteristics of aliasing artifacts in CL images. Based on that, a modified U-Net convolutional neural network (CNN), which takes 2.5D radial slice as input, is proposed. Then, the effectiveness of the proposed method is tested and compared with other strategies, including the methods using 2D (z direction, x direction, and radical direction) slice and 2.5D (z direction, x direction) slice as input. Experimental results on ball grid array (BGA) specimens shows that the proposed method give the best performance in the CL aliasing artifact reduction in all comparison strategies.

Limited-view computed tomography (CT) presents significant potential for reducing radiation exposure and expediting the scanning process. While deep learning (DL) methods have exhibited promising results in mitigating streaking artifacts caused by a reduced number of projection views, their generalization remains challenging. In this work, we proposed a DL-driven alternative Bayesian reconstruction method (DLBayesian) that efficiently integrates data-driven priors and data consistency constraints. DLBayesian comprises three stages: group-level embedding, significance evaluation, and individual-level consistency adaptation. Firstly, DL network parameters are optimized to learn how to eliminate the general limited-view artifacts on a large-scale paired dataset. Then, we introduced a significance score to quantitatively evaluate the contribution of parameters in DL models as a guide for the subsequent individual-level adaptation. Finally, in the Bayesian adaptation stage, an alternative Bayesian reconstruction further optimizes the DL network parameters precisely according to the projection data of the target case. We validated DLBayesian with sparse-view (90 views) projections from a circular trajectory CT and a special data missing case from a multi-segment linear trajectory CT. The results underscore DLBayesian's superior generalization capabilities across variations in patients, anatomic structures, and data distribution, as well as excelling in contextual structure recovery compared to networks solely trained via supervised loss. Real experiments on a dead rat demonstrate its capability in practical CT scans.

In this work, we investigate the feature of projection sampling and analytical reconstruction algorithms for a Static CT with sources and detectors distributed in a Multi-Segment manner (MS-StaticCT). MS-StaticCT is a generalized configuration of previous static linear CT systems offering enhanced design flexibility and utilization efficiency in both X-ray source and detector components. By analyzing the imaging geometry of single-segment source and detector pairs, we delved into the Radon space properties of MS-StaticCT and proposed a data sufficiency condition for system design. To explore the impact of the unique sampling characteristics of MS-StaticCT on reconstruction quality, we derived analytical algorithms under two popular pipelines filtered-backprojection (MS-FBP) and differentiated backprojection filtration (MS-DBF), and assessed their performance. Due to the non-uniform sampling and singular points between segments, the global filtration process of MS-FBP requires local rebinning. The local nature of differentiation enables convenient filtration without rebinning. Besides, to address insufficient data caused by optical obstruction by sources and detectors, we incorporated multiple imaging planes and designed a generalized weighting function that efficiently utilizes conjugate projections. Simulation studies on numerical phantoms and clinical CT data demonstrate the feasibility of MS-StaticCT and the proposed reconstruction algorithms. The results highlighted MS-DBF's superiority in accuracy and spatial resolution for multi-segment geometries without compromising noise performance compared to MS-FBP whose performance depends on the number of detector segments involved for each focal spot. Our study provides a comprehensive understanding of the essential data structure and basic reconstruction tailored for systems characterized by linear source trajectories and detectors.

Objective. While deep learning (DL) methods have exhibited promising results in mitigating streaking artifacts caused by limited-view computed tomography (CT), their generalization to practical applications remains challenging. To address this challenge, we aim to develop a novel approach that integrates DL priors with targeted-case data consistency for improved artifact suppression and robust reconstruction. Approach. We propose an alternative penalized weighted least squares reconstruction framework by strategic optimization of a DL model (PWLS-SOM). This framework combines data-driven DL priors with data consistency constraints in a three-stage process: (1) Group-level embedding: DL network parameters are optimized on a large-scale paired dataset to learn general artifact elimination. (2) Significance evaluation: A novel significance score quantifies the contribution of DL model parameters, guiding the subsequent strategic adaptation. (3) Individual-level consistency adaptation: PWLS-driven strategic optimization further adapts DL parameters for target-specific projection data. Main results. Experiments were conducted on sparse-view (90 views) circular trajectory CT data and a multi-segment linear trajectory CT scan with a mixed data missing problem. PWLS-SOM reconstruction demonstrated superior generalization across variations in patients, anatomical structures, and data distributions. It outperformed supervised DL methods in recovering contextual structures and adapting to practical CT scenarios. The method was validated with real experiments on a dead rat, showcasing its applicability to real-world CT scans. Significance. PWLS-SOM reconstruction advances the field of limited-view CT reconstruction by uniting DL priors with PWLS adaptation. This approach facilitates robust and personalized imaging. The introduction of the significance score provides an efficient metric to evaluate generalization and guide the strategic optimization of DL parameters, enhancing adaptability across diverse data and practical imaging conditions.

With advancements in modern industry, computed tomography (CT) has played a significant role in nondestructive testing. However, owing to the limitations in the imaging field of view and X-ray penetration capability, CT faces challenges in the scanning and imaging of plate-like objects. To address the inspection plate-like objects, computed laminography (CL) has been developed by modifying the scanning geometry and reconstruction algorithms. CL reconstruction methods are inspired by CT reconstruction techniques and include analytical and iterative reconstruction methods. Filtered backprojection algorithms are fast, but often result in significant discrepancies from the true values; iterative methods, although more accurate than analytical reconstruction, are usually time-consuming. This study draws on the differentiated backprojection (DBP) reconstruction method of circular trajectory fan-beam CT to derive a DBP reconstruction method for a square cross-section field of view (FOV) rotational CL. Using both simulated data and actual printed circuit board scanning data for reconstruction, the image quality of the DBP method was found to be similar to that of the filtered backprojection algorithm, with certain advantages in addressing projection truncation issues.

OBJECTIVES:The objective of this study was to evaluate the efficacy and safety of tofacitinib in the treatment of active dermatomyositis (DM) and anti-synthetase syndrome (ASS). METHODS:Tofacitinib was administered at a dose of 5 mg twice daily to patients who exhibited inadequate response to conventional treatments. The primary end point was the reduction in T follicular helper (Tfh) cells at week 24. Key secondary end points included clinical scores. Moreover, we analysed the immunological profiles and conducted RNA sequencing (RNAseq) on peripheral blood samples from four patients. RESULTS:A total of 26 patients were enrolled, with 21 completing the study. Both DM and ASS patients demonstrated significant improvements in disease activity. Among these patients, the percentage of Tfh cells in peripheral blood decreased in 81.0% (17/21) of them (P = 0.003). Significant reductions in Th17 cells were observed in vivo in the peripheral blood mononuclear cells (PBMCs) of these patients (P = 0.017). In vitro, Tfh cells (2.88 ± 1.13 vs 2.28 ± 0.92, P< 0.001), Th17 cells (1.42 ± 0.92 vs 1.01 ± 0.74, P = 0.016), Treg cells (2.06 ± 1.26 vs 0.98 ± 0.65, P = 0.019) and Tfh17 cells (33.38 ± 15.14 vs 30.28 ± 4.89, P = 0.014) were inhibited. RNAseq analysis revealed significant downregulation of genes associated with the 'herpes simplex virus 1 infection' and 'IL-17 signalling' pathway. Myositis Disease Activity Assessment Tool (MDAAT) scores improved in 21 out of 24 patients. Fifteen (62.5%) patients met the criteria for International Myositis Assessment and Clinical Studies (IMACS) definition of improvement (DOI). Importantly, no severe adverse events necessitated treatment discontinuation. CONCLUSION:Tofacitinib demonstrated significant immunologic and clinical effectiveness in DM and ASS patients, reducing key immune cell populations and downregulating immune activation pathways.

Multi-source stationary computed tomography (MSS-CT) offers significant advantages in medical and industrial applications due to its gantryless scan architecture and capability of simultaneous multi-source emission. However, the lack of anti-scatter grid deployment in MSS-CT leads to severe forward and cross scatter contamination, necessitating accurate and efficient scatter correction. In this work, we propose ComptoNet, an innovative decoupled deep learning framework that integrates Compton-scattering physics with deep learning for scatter estimation in MSS-CT. The core innovation lies in the Compton-map, a representation of large-angle Compton scatter signals outside the scan field of view. ComptoNet employs a dual-network architecture: a conditional encoder-decoder network guided by reference Compton-maps and spare detector data for cross scatter estimation, and a frequency U-Net with attention mechanisms for forward scatter correction. Experiments on Monte Carlo-simulated data demonstrate ComptoNet's superior performance, achieving a mean absolute percentage error of 0.84% on scatter estimation. After correction, CT images show nearly artifact-free quality for all test phantoms, validating ComptoNet's robustness in mitigating scatter-induced errors across diverse photon counts and phantoms comparing with other methods.

Dynamic computed tomography (CT) reconstruction faces significant challenges in addressing motion artifacts, particularly for nonperiodic rapid movements such as cardiac imaging with fast heart rates. Traditional methods struggle with the extreme limited-angle problems inherent in nonperiodic cases. Deep learning methods have improved performance but face generalization challenges. Recent implicit neural representation (INR) techniques show promise through self-supervised deep learning, but have critical limitations: computational inefficiency due to forward-warping modeling, difficulty balancing DVF complexity with anatomical plausibility, and challenges in preserving fine details without additional patient-specific pre-scans. This paper presents a novel INR-based framework, BIRD, for nonperiodic dynamic CT reconstruction. It addresses these challenges through four key contributions: (1) backward-warping deformation that enables direct computation of each dynamic voxel with significantly reduced computational cost, (2) diffeomorphism-based DVF regularization that ensures anatomically plausible deformations while maintaining representational capacity, (3) motion-compensated analytical reconstruction that enhances fine details without requiring additional pre-scans, and (4) dimensional-reduction design for efficient 4D coordinate encoding. Through various simulations and practical studies, including digital and physical phantoms and retrospective patient data, we demonstrate the effectiveness of our approach for nonperiodic dynamic CT reconstruction with enhanced details and reduced motion artifacts. The proposed framework enables more accurate dynamic CT reconstruction with potential clinical applications, such as one-beat cardiac reconstruction, cinematic image sequences for functional imaging, and motion artifact reduction in conventional CT scans.

Objective. Low-dose interior tomography integrates low-dose CT (LDCT) with region-of-interest (ROI) imaging which finds wide application in radiation dose reduction and high-resolution imaging. However, the combined effects of noise and data truncation pose great challenges for accurate tomographic reconstruction. This study aims to develop a novel reconstruction framework that achieves high-quality ROI reconstruction and efficient extension of recoverable region to provide innovative solutions to address coupled ill-posed problems. Approach. We conducted a comprehensive analysis of projection data composition and angular sampling patterns in low-dose interior tomography. Based on this analysis, we proposed two novel deep learning-based reconstruction pipelines: (1) deep projection extraction-based reconstruction (DPER) that focuses on ROI reconstruction by disentangling and extracting noise and background projection contributions using a dual-domain deep neural network; and (2) DPER with progressive extension (DPER-Pro) that enhances DPER by a progressive 'coarse-to-fine' strategy for missing data compensation, enabling simultaneous ROI reconstruction and extension of recoverable regions. The proposed methods were rigorously evaluated through extensive experiments on simulated torso datasets and real CT scans of a torso phantom. Main results. The experimental results demonstrated that DPER effectively handles the coupled ill-posed problem and achieves high-quality ROI reconstructions by accurately extracting noise and background projections. DPER-Pro extends the recoverable region while preserving ROI image quality by leveraging disentangled projection components and angular sampling patterns. Both methods outperform competing approaches in reconstructing reliable structures, enhancing generalization, and mitigating noise and truncation artifacts. Significance. This work presents a novel decoupled deep learning framework for low-dose interior tomography that provides a robust and effective solution to the challenges posed by noise and truncated projections. The proposed methods significantly improve ROI reconstruction quality while efficiently recovering structural information in exterior regions, offering a promising pathway for advancing low-dose ROI imaging across a wide range of applications.

This study aimed to investigate and analyze the clinical and immunological features of patients with anti-melanoma differentiation-associated gene-5 antibody-positive dermatomyositis (MDA5 + DM) complicated with clinical liver dysfunction. A cohort of 85 patients diagnosed with MDA5 + DM admitted into Peking University People’s Hospital from 2006 to 2023 were retrospectively enrolled in this study. Clinical characteristics and survival status were collected and analyzed. Clinical liver dysfunction occurred in 28

Objective. Photon counting detectors (PCDs) have well-acknowledged advantages in computed tomography (CT) imaging, such as decreasing noise, relieving beam hardening, and increasing material discrimination. However, charge sharing and other problems prevent PCDs from fully realizing the anticipated potential in diagnostic CT. PCDs with multi-energy inter-pixel coincidence counters (MEICC) have been proposed to provide particular information about charge sharing, thereby achieving lower Cram & eacute;r-Rao lower bound than conventional PCDs when assessing its performance by estimating material thickness or virtual monochromatic attenuation integrals (VMAIs). This work explores charge sharing compensation using local spatial coincidence counter information for MEICC detectors through a deep-learning method. Approach. By analyzing the impact of charge sharing on photon count detection, we designed our network with a focus on individual pixels. Employing MEICC data of patches centered on pixel of interests as input, we utilized local information for effective charge sharing compensation. The output was VMAI at different energies to address real detector issues without knowledge of primary counts. To achieve data diversity, a fast and online data generation method was proposed to provide adequate training data. A new loss function was introduced to reduce bias for training with high-noise data. The proposed method was validated by Monte Carlo simulation data for MEICC detectors that were compared with conventional PCDs. For both MEICC and conventional detectors, networks were trained with high-noise data and low-noise data. Additionally, the network method was also compared with a polynomial fitting (PF) method. Main-Results. For conventional data as a reference, networks trained on low-noise data yielded results with a minimal bias (about 0.7%) compared with >3% for the PF method. The results of networks trained on high-noise data exhibited a slightly increased bias (about 1.3%) but a significantly reduced standard deviation (STD) and normalized root mean square error. The simulation study of the MEICC detector demonstrated superior compared to the conventional detector across all the metrics. Specifically, for both networks trained on high-noise and low-noise data, their biases were reduced to about 1% and 0.6%, respectively. Meanwhile, the results from a MEICC detector were of about 10% lower noise than a conventional detector. Moreover, an ablation study showed that the additional loss function on bias was beneficial for training on high-noise data. Significance. We demonstrated that a network-based method could utilize local information in PCDs effectively by patch-based learning to reduce the impact of charge sharing. MEICC detectors provide very valuable local spatial information by additional coincidence counters. Rather accurate estimations of VMAIs at virtual energies can be obtained by patch-based learning, which provides a solution for charge sharing compensation for a real detector scenario. Compared with MEICC detectors, conventional PCDs only have limited local spatial information for charge sharing compensation, resulting in higher bias and STD in VMAI estimation with the same patch strategy.