Publications

Our study aimed to validate the agreement and diagnostic performance of a deep-learning-based coronary artery calcium scoring (DL-CACS) system for ECG-gated and non-gated low-dose chest CT (LDCT) across multivendor datasets

We highlighted that one-fits-all strategy using phantom-based dose optimization may not be appropriate for clinical CT, and proposed a patient-specific IQA method.

We aimed to evaluate long-term prognostic implications of TAC assessed using AI-based quantification on routine chest CT in a screening population.

This paper explores automated strategy for auditing seg-hallucination in medical image segmentation task.

This study evaluated the predictive value of myosteatosis based on an AI-driven CT analysis in patients with localized RCC who underwent partial nephrectom.

This study proposed a multi-planar UNet with an augmented contrast-boosting technique.

This study proposed overscan decision algorithm enables the retrospective scan range monitoring in LDCT for lung cancer screening program.

This study proposed the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.

This study evaluated the nanotribological properties of MoS2 bilayer on a SiO2/Si substrate using both Friction Force Microscopy (FFM) and Kelvin Probe Force Microscopy (KPFM) methods.

This study automatically discriminates homogeneous and structure edge regions on computed tomography (CT) images, and it evaluates the noise level and edge preservation ratio (EPR) according to the different types of iterative reconstruction (IR).

This study proposed an approach to synthesize panoramic images from the CBCT images.