NeuroImage Team

NEUROIMAGE TEAM

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NeuroImage Team

NEUROIMAGE TEAM

Members

Research spotlight

Research results

Members

Alphabet order of last name.

Research spotlight

Research spotlight

The Neuroimage Team has built multiple image platforms for animal experiments and clinical trials in an effort to promote medical imaging research, including establishment of various animal models of human disease in comparison with findings of physiological and pathological changes from clinical medicine images. Via the delicate instruments and precisely designed laboratory animal models combined with imaging studies and staining techniques, the Neuroimage Team may also help medical physicians to clarify the clinical pathology and disease diagnosis by providing evidence-based imaging results.

Meanwhile, the Neuroimage Team observes the various mechanisms of neuro-signal pathways on disease regulation and pharmacology through experiments in genetics, cell biology and molecular biology. As for clinical trials, the psychophysiological assessment may serve as an important prognostic indicator of diseases by reflecting the mental state and social function of patients after the onset of an illness.

The goal of research is to develop more effective medical image markers for early diagnosis and prognosis evaluation of diseases. By utilizing the non-invasive quantitative model of medical imaging, we may build an effective screening platform used for evaluating drug efficacy and therapeutic effects. The field of research involves genetic and cellular studies, laboratory animal experiments, and research studies on human participants. The advanced technique of translational medical imaging platform is beneficial for effective biomarkers, R&D of new drugs, and translational medicine research. Apart from technical development of medical imaging, its application would support the diagnosis and treatment of four main neurological diseases.

The aim is to integrate relevant research teams in medical imaging at Taipei Medical University, establish advanced imaging facilities, provide consultation for both animal and human imaging research, develop advanced imaging and analysis techniques, and promote medical imaging while nurturing talent.

The research and development focus on translational imaging research in neurological disorders, leveraging the diverse expertise of center members. Five core laboratories have been established:

Nanomolecular Imaging Lab
7T Animal Imaging Lab
3T Clinical Brain Functional Imaging Lab
Neuroimaging Biomarker Analysis Lab
Positron Magnetic Resonance Imaging Lab

These labs span translational imaging research across different levels, from genes, cells, and animals to human studies.

Current research areas include:

1. Brain Tumor

The research topic is mainly focused on glioblastoma multiforme(GBM). We will investigate the heterogeneity of MR imaging findings between inter- and intra-tumors, and also explore the correlation between MR imaging findings and gene mutations. We have glioblastoma cell culture with specific genetic information, such as tumor formation, angiogenesis, gene clusters associated with DNA damage and repair. Furthermore, an animal model of glioblastoma multiforme will be established at 7T MRI, and the same method can be applied to MR imaging study on human participants at 3T MRI.

Meanwhile, the information of gene mutation in GBM can be obtained from DNA microarray experiment, and the molecular expressions of glioblastoma multiforme with different genomic modifications are compared with MRI-based biomarkers. With the combined application of genomic information and advanced multiparametric MRI, the tumor extent can be accurately defined, the therapeutic response can be better monitored and the prognosis can be well predicted. In brief, this bedside-to-bench translational research in the Neuroimage Team is aimed to establish a radiogenomics platform based on advanced MR imaging for the purpose of understanding the genomic modification of glioblastoma multiforme and its relation with the perspective of molecular MRI.

2. Ischemic Stroke

The therapeutic effect of treatment in ischemic stroke relies not only on the recovery of blood flow, but also the amount of possibly non-impaired neurons in the human brain after stroke. Within 4.5 hours after the cerebrovascular accident is usually a critical and valuable period of time to give treatments. Therefore, defining the onset time of CVA, locating the lesion sites as well as evaluating the preserved brain tissues precisely are all important factors to consider before any intervention or thrombolytic drugs injection are given.

There have been many MR imaging related studies in ischemic penumbra, which proposed the ischemic penumbra could be referred as the location of possibly preserved neurons. Furthermore, its area and size may be regarded as a predictor for therapeutic efficacy of pharmalogical treatments. Generally, both diffusion tensor imaging (DTI) and perfusion-weighted imaging (PWI) are advanced MRI techniques essentially for evaluating ischemic penumbra, yet the contrast agent used in PWI is not suitable for patients with certain conditions.

The result of our research illustrated that the multiple parameters calculated from DTI could be treated as an effective indicator for assessing the region of ischemic penumbra and estimating the onset time of ischemic stroke. It had been tested and verified in a stroke model of rats. Currently the application of laboratory research findings under the advanced MRI method has already been linked to studies involving human participants. The high availability of DTI will provide clinicians a new opportunity for diagnosis and treatment of ischemic stroke.

3. Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) is defined as the traumatic white matter injuries caused by impact-acceleration forces and may lead to the alteration of brain function or pathology. Most studies report results at the group average level, rendering little information on who might suffer from the prolonged neurocognitive symptoms or what might be the structural or functional underpinnings of disease progression. Due to the lack of the sensitive and quantitative measures of the axonal and myelin changes after mTBI, the progression of white matter pathological features that underlie prolonged neurocognitive symptoms and deficits experienced after mTBI is poorly understood.

Quantitative susceptibility mapping (QSM) and myelin water fraction (MWF) are the advanced quantitative magnetic resonance imaging (MRI) technique that enables us to detect myelin distribution and density for the patients with mTBI. Quantitative values derived from these quantitative MRI parametric maps are desirable as the true-quantification and vendor-independent measures which might be helpful for longitudinal evaluation of mTBI patients in the individual level.

Genetic factors may also modulate the axon and myelin degeneration and the vulnerability to secondary injury after mTBI, thus may account for some of the unexplained variation in outcome. Identifying the relationship between genetic signatures, imaging features, and outcome endophenotypes may produce the opportunities for early intervention, risk stratification and prognostication for mTBI.

4. Osteoporosis

Osteoporosis is a metabolic bone disease that is featured with low bone mineral density (BMD) and high fracture incidence. Fractures that occur as a result of underlying osteoporosis are significantly associated with elevated mortality risk and loss of disability-adjusted life years. Loss of bone mass is one of critical characteristics of osteoporosis, which is typically diagnosed using BMD measurements. Dual energy X-ray absorptiometry (DXA) method is the most widely used method of assessing BMD. In general, BMD data could be obtained at various skeletal sites such as spine, hip and forearm. Clinically, BMD obtained from lumbar vertebrae could be regarded as major diagnosis dependency for osteoporosis.

Low-dose chest computed tomography (LDCT) is popularly used for early lung cancer screening with less ionizing radiation and has been demonstrated to significantly reduce mortality from lung cancer. LDCT scans generally cover the upper thoracic vertebrae. The covering part of the spine has been suggested to detect patients with osteoporosis. The utility of vertebral CT numbers derived from LDCT for detecting osteoporosis has been confirmed.

On the other hand, CT-based texture analysis appreciates image heterogeneities that may not be discern with the human eye, and preliminary evidence has suggested its potential value in imaging characterization for diagnostic purposes. This method is based on mathematical approaches to the evaluation of gray-level intensity and position of the pixels within the image, providing the so-called “texture features” that represent a quantitative measure of heterogeneity. Patient with osteoporosis may reveal different inherent texture from normal BMD because structural integrity of trabecular bone is impaired. We hypothesized that the bone status could be related to the texture extracted from images. Therefore,we has developed a machine learning-based model based on the texture analysis, which will automatically detect osteoporosis from LDCT scans during lung cancer screening.

Research results

Projects

1. MOST Title: An Artificial Intelligence System for Precision Lung Cancer Based on Clinical Big Data

Project Period: 2020.07.01 ~ 2024.06.30

2. 經濟部科研成果價值創造計畫「胸腔深度學習:人工智慧多模影像精準健康平台計畫」

Project Period: 2023.01.01 ~ 2023.12.31

3. NHRI Title: Develop IL-19 antibody immunotherapy and unravel immunosuppressive mechanism in peritumoral region of glioblastoma by single cell transcriptome analysis

Project Period: 2023.01.01 ~ 2023.12.31

4. MOST Title: Building a national model of data hub for healthy aging

Project Period: 2021.11.01 ~ 2023.10.31

5. MOST Title: Machine Learning-Based Radiogenomics for Connecting Mr Imaging to Immune-Regulated Genes Expression in Glioblastoma

Project Period: 2019.08.01 ~ 2022.07.31

6. NHRI Title: Multi-site Radiogenomics and Radioproteomics of gliomas

Project Period: 2019.01.01 ~ 2020.12.31

7. MOST Title: Construction and Application of Medical Image Database in TMU Healthcare System

Project Period: 2017.12.01 ~ 2020.11.30

8. MOST Title: Mri Study on Trans-Neuronal Degeneration of the Thalamic Networks after Ischemic Stroke

Project Period: 2016.08.01 ~ 2019.10.01

9. MOST Title: Characterization of Thalamocortical Dysrhythmia in Mild Traumatic Brain Injury Using Simultaneous Mri and Eeg Measurements and Pre-Clinical N-Acetylcysteine Treatment Response