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Hongxia Yin

Publications and source records attributed to Hongxia Yin.

4 recordsLinked to original sources

Coupling Spectrum Estimation and Single Energy Material Decomposition via X-ray Grating Interferometry

Dark-field imaging based on grating interferometry is an emerging X-ray modality in medical imaging, which is particularly capable of providing complementary diagnostic information by visualizing the microstructural properties of lung tissue. However, quantitative dark-field imaging remains fundamentally challenged by beam hardening, which arises from the energy-dependent fringe visibility under polychromatic illumination. The resulting artifacts substantially degrade the quantitative accuracy of dark-field images. In this work, motivated by our key observation of an intrinsic similarity between the X-ray energy spectrum and the system-related coupling spectrum, we propose a unified framework to simultaneously and independently estimate both spectra. By measuring the transmission associated with the zeroth- and first-order components of the phase-stepping curve using solid step-wedge phantoms, the two spectra are robustly estimated via an expectation-maximization algorithm. The recovered spectra are subsequently incorporated into a physics-based correction model to mitigate beam-hardening-induced artifacts in dark-field imaging effectively. Furthermore, leveraging the inherent availability of two independent spectra within X-ray grating interferometry, we introduce a single-energy material decomposition method that achieves basis material imaging without dual-energy scans. Wave-optical simulations and experiments demonstrate accurate spectrum estimation, effective dark-field signal correction, and reliable material decomposition. Consequently, the proposed framework extends the diagnostic potential of X-ray grating interferometry beyond pulmonary imaging, facilitating broader applications in medical imaging.

physics.med-ph

UltraEar: a multicentric, large-scale database combining ultra-high-resolution computed tomography and clinical data for ear diseases

Ear diseases affect billions of people worldwide, leading to substantial health and socioeconomic burdens. Computed tomography (CT) plays a pivotal role in accurate diagnosis, treatment planning, and outcome evaluation. The objective of this study is to present the establishment and design of UltraEar Database, a large-scale, multicentric repository of isotropic 0.1 mm ultra-high-resolution CT (U-HRCT) images and associated clinical data dedicated to ear diseases. UltraEar recruits patients from 11 tertiary hospitals between October 2020 and October 2035, integrating U-HRCT images, structured CT reports, and comprehensive clinical information, including demographics, audiometric profiles, surgical records, and pathological findings. A broad spectrum of otologic disorders is covered, such as otitis media, cholesteatoma, ossicular chain malformation, temporal bone fracture, inner ear malformation, cochlear aperture stenosis, enlarged vestibular aqueduct, and sigmoid sinus bony deficiency. Standardized preprocessing pipelines have been developed for geometric calibration, image annotation, and multi-structure segmentation. All personal identifiers in DICOM headers and metadata are removed or anonymized to ensure compliance with data privacy regulation. Data collection and curation are coordinated through monthly expert panel meetings, with secure storage on an offline cloud system. UltraEar provides an unprecedented ultra-high-resolution reference atlas with both technical fidelity and clinical relevance. This resource has significant potential to advance radiological research, enable development and validation of AI algorithms, serve as an educational tool for training in otologic imaging, and support multi-institutional collaborative studies. UltraEar will be continuously updated and expanded, ensuring long-term accessibility and usability for the global otologic research community.

eess.IV

A lateral semicircular canal segmentation based geometric calibration for human temporal bone CT Image

Computed Tomography (CT) of the temporal bone has become an important method for diagnosing ear diseases. Due to the different posture of the subject and the settings of CT scanners, the CT image of the human temporal bone should be geometrically calibrated to ensure the symmetry of the bilateral anatomical structure. Manual calibration is a time-consuming task for radiologists and an important pre-processing step for further computer-aided CT analysis. We propose an automatic calibration algorithm for temporal bone CT images. The lateral semicircular canals (LSCs) are segmented as anchors at first. Then, we define a standard 3D coordinate system. The key step is the LSC segmentation. We design a novel 3D LSC segmentation encoder-decoder network, which introduces a 3D dilated convolution and a multi-pooling scheme for feature fusion in the encoding stage. The experimental results show that our LSC segmentation network achieved a higher segmentation accuracy. Our proposed method can help to perform calibration of temporal bone CT images efficiently.

eess.IV

A homotopy method for computing the largest eigenvalue of an irreducible nonnegative tensor

In this paper we propose a homotopy method to compute the largest eigenvalue and a corresponding eigenvector of a nonnegative tensor. We prove that it converges to the desired eigenpair when the tensor is irreducible. We also implement the method using an prediction-correction approach for path following. Some numerical results are provided to illustrate the efficiency of the method.

math.NA