Zhiqiang Chen

Carbon nanotube (CNT) cathodes offer compact size, rapid response, and simplified thermal management, making them promising for stationary tomosynthesis and computed tomography (CT). In this study, we improved the high-purity production process for small-diameter multi-walled CNTs and developed a novel five-state cathode fabrication process based on electrophoretic deposition. This process increased the field-emission current while enabling the reproducible fabrication of cathodes with high consistency. The resulting multi-beam X-ray source, integrating eight cathodes, operated stably at an anode voltage of 110 kV, a pulse width of 5 ms, and a set cathode current of 40 mA (anode current: 25 mA), with an actual current deviation of less than 0.5% among the cathodes and a gate-control-voltage deviation of less than 3.7%. An eight-angle imaging test and 200 h operation (5.76 × 10⁶ pulses) demonstrated that the tube can provide high-resolution imaging, rapid multi-angle acquisition, and stable long-term operation, thereby demonstrating potential for commercial applications. To the best of our knowledge, this study is the first to demonstrate both highly consistent fabrication of high-current CNT cathodes and sustained high-current operation of a multi-beam X-ray tube, providing a key hardware platform for development on stationary tomosynthesis and CT, and may facilitate further advances in medical imaging technology.

Digital chest tomosynthesis (DCT) has been clinically validated to offer significant advantages in diagnostic efficiency for pulmonary diseases and radiation dose reduction. Emerging stationary DCT (sDCT) systems can further shorten acquisition time and eliminate motion artifacts caused by X-ray source movement and patient respiration. This work focuses on the development of a multi-beam X-ray source for mobile sDCT systems by specification definition, source design, and experimental validation. The developed X-ray tube integrates 63 focal spots arranged linearly over a length of 816 mm. X-rays are emitted through seven segmented windows, achieving an angular span of 36° at a source image distance (SID) of 120 cm, with full coverage of a detector area of 35.6 cm × 43.2 cm. The tube operates at a maximum anode voltage of 140 kV, maximum anode current of 20 mA, and 24 mAs per scan, with a focal spot size of IEC 0.6. The developed multi-beam X-ray source achieves multiple key performance breakthroughs and provides an alternative source architecture for future sDCT implementation, with the potential to facilitate further system performance optimization and engineering development.

This work aims to achieve both high current emission density and high emission current of carbon nanotube (CNT) cathodes for high-power X-ray generation applications. High-purity small-diameter CNT materials were obtained, and a novel “five-state” electrophoretic deposition method was proposed to fabricate CNT cathodes. For an emission area of 10 mm × 0.45 mm, a high and stable cathode emission current of 350 mA was achieved, corresponding to an emission current density of 7.8 A/cm². An X-ray dose rate of 39.49 mGy/s@50 cm was measured under a tube potential of 120 kV, cathode current of 100 mA, and pulse width of 10 ms. The focal spot size of the X-ray source, measured using a slit camera, was 0.98 mm (width) × 1.05 mm (length) at 15% max intensity, and the pulse width range was 100 µs–100 ms. Through continuous testing at 200 mA emission current, 100 µs pulse width, and 0.3% duty cycle for 400 h, the CNT cathode is estimated to exhibit a lifetime of approximately 5085 h, demonstrating stable and reliable durability. This study, for the first time, simultaneously realizes multi-A/cm²-level emission current density, hundreds-of-milliampere emission current, and hundreds-of-millisecond operating pulse width for CNT cathodes.

Diffusion bridge models have demonstrated promising performance in conditional image generation tasks, such as image restoration and translation, by initializing the generative process from corrupted images instead of pure Gaussian noise. However, existing diffusion bridge models often rely on Stochastic Differential Equation (SDE) samplers, which result in slower inference speed compared to diffusion models that employ high-order Ordinary Differential Equation (ODE) solvers for acceleration. To mitigate this gap, we propose a high-order ODE sampler with a stochastic start for diffusion bridge models. To overcome the singular behavior of the probability flow ODE (PF-ODE) at the beginning of the reverse process, a posterior sampling approach is introduced at the first reverse step. The sampling is designed to ensure a smooth transition from corrupted images to the generative trajectory while reducing discretization errors. Following this stochastic start, Heun's second-order solver is applied to solve the PF-ODE, achieving high perceptual quality with significantly reduced neural function evaluations (NFEs). Our method is fully compatible with pretrained diffusion bridge models and requires no additional training. Extensive experiments on image restoration and translation tasks, including super-resolution, JPEG restoration, Edges-to-Handbags, and DIODE-Outdoor, demonstrate that our sampler outperforms state-of-the-art methods in both visual quality and Frechet Inception Distance (FID).

X-ray scatter has been a serious concern in computed tomography (CT), leading to image artifacts and distortion of CT values. The linear Boltzmann transport equation (LBTE) is recognized as a fast and accurate approach for scatter estimation. However, for multi-spectral CT, it is cumbersome to compute multiple scattering components for different spectra separately when applying LBTE-based scatter correction. In this work, we propose a Matrixed-Spectrum Decomposition accelerated LBTE solver (MSD-LBTE) that can be used to compute X-ray scatter distributions from CT acquisitions at two or more different spectra simultaneously, in a unified framework with no sacrifice in accuracy and significant reduction in computation compared with conventional solvers in theory. First, a matrixed-spectrum solver of the LBTE is obtained by introducing an additional label dimension to expand the phase space. Then, we propose a "spectrum basis" for the LBTE and a principle of selection of basis using the QR decomposition, along with the above solver to construct the MSD-LBTE. Based on MSD-LBTE, a unified scatter correction method can be established for multi-spectral CT.We validate the effectiveness and accuracy of our method by comparing it with the Monte Carlo method. We also evaluate the scatter correction performance using two different phantoms for kV-switching dual-energy spectral CT, and using an elliptical phantom in a numerical simulation for kV-modulation enabled CT scans, validating that our proposed method can significantly reduce the computational cost at multiple spectra and effectively reduce scatter artifact in reconstructed CT images.

Reconstructing CT images from incomplete projection data remains challenging due to the ill-posed nature of the problem. Diffusion bridge models have recently shown promise in restoring clean images from their corresponding Filtered Back Projection (FBP) reconstructions, but incorporating data consistency into these models remains largely underexplored. Incorporating data consistency can improve reconstruction fidelity by aligning the reconstructed image with the observed projection data, and can enhance detail recovery by integrating structural information contained in the projections. In this work, we propose the Projection Embedded Diffusion Bridge (PEDB). PEDB introduces a novel reverse stochastic differential equation (SDE) to sample from the distribution of clean images conditioned on both the FBP reconstruction and the incomplete projection data. By explicitly conditioning on the projection data in sampling the clean images, PEDB naturally incorporates data consistency. We embed the projection data into the score function of the reverse SDE. Under certain assumptions, we derive a tractable expression for the posterior score. In addition, we introduce a free parameter to control the level of stochasticity in the reverse process. We also design a discretization scheme for the reverse SDE to mitigate discretization error. Extensive experiments demonstrate that PEDB achieves strong performance in CT reconstruction from three types of incomplete data, including sparse-view, limited-angle, and truncated projections. For each of these types, PEDB outperforms evaluated state-of-the-art diffusion bridge models across standard, noisy, and domain-shift evaluations.

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.