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Zhaolin Chen

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7 papers

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EAAI Journal 2026 Journal Article

An augmented physics-informed neural network approach with trainable scaling for nonlinear dynamic analysis

Zhicheng Yang
Siu-Kai Lai
Zhaolin Chen
Jiyang Fu

Nonlinear dynamic problems are ubiquitous in engineering applications, and accurately solving their governing equations is essential for understanding system behavior. Physics-informed neural networks (PINNs) have emerged as a new computing paradigm for solving partial differential equations. However, conventional PINNs struggle to predict accurate solutions for dynamic systems affected by strong nonlinearity, damping, and spatiotemporal coupling. To address this challenge, this work proposes a nonlinear vibration stepping PINN (NVS-PINN) approach for analyzing the complex nonlinear behavior of dynamic systems. This approach introduces a trainable scaling parameter within each time segment to adaptively adjust the network output. Furthermore, the hyperbolic tangent function is adopted as hard constraints to ensure that the network output consistently satisfies the initial and/or Dirichlet boundary conditions of nonlinear dynamic systems. Three illustrative examples, including a single-degree-of-freedom parametric Duffing oscillator, a two-degree-of-freedom nonlinear damped vibratory system, and a nonlinear elastic circular arch under wind load, are considered for validation. Numerical results demonstrate that the NVS-PINN approach can accurately predict long-duration nonlinear vibration responses, including complex multi-stable limit cycles, escape motions, and wind-induced vibrations. For the Duffing oscillator, the NVS-PINN approach achieves highly accurate results, with average relative errors of 5. 3444 × 10 − 3 for the strongly nonlinear system and 4. 9573 × 10 − 4 for the strongly nonlinear system with damping. For the elastic arch under wind load, incorporating a trainable scaling parameter in NVS-PINN reduces the maximum relative error by about 82. 6 %. Moreover, using smaller time intervals can improve network accuracy.

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YNIMG Journal 2025 Journal Article

Accelerating multi-directional diffusion MRI through patch-based joint reconstruction

Zhongbiao Xu
Rongli Zhang
Wei Huang
Guanhua Deng
Xiaoyun Liang
Li Guo
Junying Cheng
Yaohui Wang

Diffusion magnetic resonance imaging (dMRI) is a valuable technique for studying tissue microstructure and connectivity in the brain. However, acquiring high-resolution dMRI data is time-consuming, limiting its clinical applicability. Traditional parallel imaging techniques can accelerate the acquisition of dMRI, but they are constrained by the geometry factor. In this study, we propose a novel patch-based multiple diffusion directions joint reconstruction method that simultaneously capitalizes on the intra- and inter-image correlation across multiple diffusion directions by grouping similar 3D image patches and then enforces the sparsity of these groups in sensitivity encoding (SENSE) reconstruction, termed PB-SENSE. The simulation and in vivo experiments demonstrated that the proposed method can achieve high-quality images comparable to those obtained from fully sampled data, even with an acceleration of 5. This suggests that the proposed method has the potential to enhance the practical application of high-resolution diffusion imaging.

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YNIMG Journal 2021 Journal Article

Estimation of simultaneous BOLD and dynamic FDG metabolic brain activations using a multimodality concatenated ICA (mcICA) method

Shenpeng Li
Sharna D Jamadar
Phillip G D Ward
Gary F Egan
Zhaolin Chen

Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity.

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YNIMG Journal 2021 Journal Article

Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET

Viswanath P. Sudarshan
Shenpeng Li
Sharna D. Jamadar
Gary F. Egan
Suyash P. Awate
Zhaolin Chen

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.

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YNIMG Journal 2020 Journal Article

Analysis of continuous infusion functional PET (fPET) in the human brain

Shenpeng Li
Sharna D. Jamadar
Phillip G.D. Ward
Malin Premaratne
Gary F. Egan
Zhaolin Chen

Functional positron emission tomography (fPET) is a neuroimaging method involving continuous infusion of 18-F-fluorodeoxyglucose (FDG) radiotracer during the course of a PET examination. Compared with the conventional bolus administration of FDG in a static PET scan, which provides an average glucose uptake into the brain over an extended period of up to 30 min, fPET offers a significantly higher temporal resolution to study the dynamics of glucose uptake. Several earlier studies have applied fPET to investigate brain FDG uptake and study its relationship with functional magnetic resonance imaging (fMRI). However, due to the unique characteristics of fPET signals, modelling of the fPET signal is a complex task and poses challenges for accurate interpretation of the results from fPET experiments. This study applied independent component analysis (ICA) to analyse resting state fPET data, and to compare the performance of ICA and the general linear model (GLM) for estimation of brain activation in response to tasks. The fPET signal characteristics were compared using GLM and ICA methods to model fPET data from a visual activation experiment. Our aim was to evaluate GLM and ICA methods for analysing task fPET datasets, and to apply ICA methods to the analysis of resting state fPET datasets. Using both simulation and in-vivo experimental datasets, we show that both ICA and GLM methods can successfully identify task related brain activation. We report fPET metabolic resting state brain networks revealed by application of the fPET ICA method to a cohort of 28 healthy subjects. Functional PET provides a unique method to map dynamic changes of glucose uptake in the resting human brain and in response to extrinsic stimulation.

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YNIMG Journal 2019 Journal Article

Simultaneous task-based BOLD-fMRI and [18-F] FDG functional PET for measurement of neuronal metabolism in the human visual cortex

Sharna D. Jamadar
Phillip GD. Ward
Shenpeng Li
Francesco Sforazzini
Jakub Baran
Zhaolin Chen
Gary F. Egan

Studies of task-evoked brain activity are the cornerstone of cognitive neuroscience, and unravel the spatial and temporal brain dynamics of cognition in health and disease. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI) is one of the most common methods of studying brain function in humans. BOLD-fMRI indirectly infers neuronal activity from regional changes in blood oxygenation and is not a quantitative metric of brain function. Regional variation in glucose metabolism, measured using [18-F] fluorodeoxyglucose positron emission tomography (FDG-PET), provides a more direct and interpretable measure of neuronal activity. However, while the temporal resolution of BOLD-fMRI is in the order of seconds, standard FDG-PET protocols provide a static snapshot of glucose metabolism. Here, we develop a novel experimental design for measurement of task-evoked changes in regional blood oxygenation and glucose metabolism with high temporal resolution. Over a 90-min simultaneous BOLD-fMRI/FDG-PET scan, [18F] FDG was constantly infused to 10 healthy volunteers, who viewed a flickering checkerboard presented in a hierarchical block design. Dynamic task-related changes in blood oxygenation and glucose metabolism were examined with temporal resolution of 2. 5sec and 1-min, respectively. Task-related, temporally coherent brain networks of haemodynamic and metabolic connectivity were jointly coupled in the visual cortex, as expected. Results demonstrate that the hierarchical block design, together with the infusion FDG-PET technique, enabled both modalities to track task-related neural responses with high temporal resolution. The simultaneous MR-PET approach has the potential to provide unique insights into the dynamic haemodynamic and metabolic interactions that underlie cognition in health and disease.

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YNIMG Journal 2010 Journal Article

An optimised framework for reconstructing and processing MR phase images

Zhaolin Chen
Leigh A. Johnston
Dae Hyuk Kwon
Se Hong Oh
Zang-Hee Cho
Gary F. Egan

Phase contrast imaging holds great potential for in vivo biodistribution studies of paramagnetic molecules and materials. However, in vivo quantification of iron storage and other paramagnetic materials requires improvements in reconstruction and processing of MR complex images. To achieve this, we have developed a framework including (i) an optimal coil sensitivity smoothing filter for phase imaging determined at the maximal signal to noise ratio, (ii) a phase optimised and a complex image optimised reconstruction approach, and (iii) a magnitude and phase correlation test criterion to determine the low pass filter parameter for background phase removal. The method has been evaluated using 3T and 7T MRI data containing cortical regions, the basal ganglia including the caudate, and the midbrain including the substantia nigra. The optimised reconstruction improves phase image contrast and noise suppression compared with conventional reconstruction approaches, and the correlation test criterion provides an objective method for separation of the local phase signal from the background phase measurements. Phase values of several brain regions of interest have been calculated, including gray matter (−1. 23 Hz at 7T and −0. 55 Hz at 3T), caudate (−3. 8 Hz at 7T), and the substantia nigra (−6. 2 Hz at 7T).

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