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Chao Chai

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4 papers

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YNIMG Journal 2023 Journal Article

The iron burden of cerebral microbleeds contributes to brain atrophy through the mediating effect of white matter hyperintensity

Ke Lv
Yanzhen Liu
Yongsheng Chen
Sagar Buch
Ying Wang
Zhuo Yu
Huiying Wang
Chenxi Zhao

The goal of this work was to explore the total iron burden of cerebral microbleeds (CMBs) using a semi-automatic quantitative susceptibility mapping and to establish its effect on brain atrophy through the mediating effect of white matter hyperintensities (WMH). A total of 95 community-dwelling people were enrolled. Quantitative susceptibility mapping (QSM) combined with a dynamic programming algorithm (DPA) was used to measure the characteristics of 1309 CMBs. WMH were evaluated according to the Fazekas scale, and brain atrophy was assessed using a 2D linear measurement method. Histogram analysis was used to explore the distribution of CMBs susceptibility, volume, and total iron burden, while a correlation analysis was used to explore the relationship between volume and susceptibility. Stepwise regression analysis was used to analyze the risk factors for CMBs and their contribution to brain atrophy. Mediation analysis was used to explore the interrelationship between CMBs and brain atrophy. We found that the frequency distribution of susceptibility of the CMBs was Gaussian in nature with a mean of 201 ppb and a standard deviation of 84 ppb; however, the volume and total iron burden of CMBs were more Rician in nature. A weak but significant correlation between the susceptibility and volume of CMBs was found (r = -0.113, P < 0.001). The periventricular WMH (PVWMH) was a risk factor for the presence of CMBs (number: β = 0.251, P = 0.014; volume: β = 0.237, P = 0.042; total iron burden: β = 0.238, P = 0.020) and was a risk factor for brain atrophy (third ventricle width: β = 0.325, P = 0.001; Evans's index: β = 0.323, P = 0.001). PVWMH had a significant mediating effect on the correlation between CMBs and brain atrophy. In conclusion, QSM along with the DPA can measure the total iron burden of CMBs. PVWMH might be a risk factor for CMBs and may mediate the effect of CMBs on brain atrophy.

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AIIM Journal 2022 Journal Article

Brain gray matter nuclei segmentation on quantitative susceptibility mapping using dual-branch convolutional neural network

Chao Chai
Pengchong Qiao
Bin Zhao
Huiying Wang
Guohua Liu
Hong Wu
Wen Shen
Chen Cao

Abnormal iron accumulation in the brain subcortical nuclei has been reported to be correlated to various neurodegenerative diseases, which can be measured through the magnetic susceptibility from the quantitative susceptibility mapping (QSM). To quantitatively measure the magnetic susceptibility, the nuclei should be accurately segmented, which is a tedious task for clinicians. In this paper, we proposed a dual-branch residual-structured U-Net (DB-ResUNet) based on 3D convolutional neural network (CNN) to automatically segment such brain gray matter nuclei. Due to memory limit, 3D-CNN-based methods typically adopted image patches, instead of the whole volumetric image, which, however, ignored the spatial contextual information of the neighboring patches, and therefore led to the accuracy loss. To better tradeoff segmentation accuracy and the memory efficiency, the proposed DB-ResUNet incorporated patches with different resolutions. By jointly using QSM and 3D T1 weighted imaging (T1WI) as inputs, the proposed method was able to achieve better segmentation accuracy over its single-branch counterpart, as well as the conventional atlas-based method and the classical 3D CNN structures. The susceptibility values and the volumes were also measured, which indicated that the measurements from the proposed DB-ResUNet was able to present high correlation with values from the manually annotated regions of interest.

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YNIMG Journal 2019 Journal Article

Cerebral microbleed detection using Susceptibility Weighted Imaging and deep learning

Saifeng Liu
David Utriainen
Chao Chai
Yongsheng Chen
Lin Wang
Sean K. Sethi
Shuang Xia
E. Mark Haacke

Detecting cerebral microbleeds (CMBs) is important in diagnosing a variety of diseases including dementia, stroke and traumatic brain injury. However, manual detection of CMBs can be time-consuming and prone to errors, whereas the current automatic algorithms for CMB detection are usually limited by large number of false positives. In this study, we present a two-stage CMB detection framework which contains a candidate detection stage based on a 3D fast radial symmetry transform of the composite images from Susceptibility Weighted Imaging (SWI), and a false positive reduction stage based on deep residual neural networks using both the SWI and the high-pass filtered phase images. While the SWI images provide exquisite sensitivity to the presence of blood products, the high-pass filtered phase images enable the differentiation of diamagnetic calcifications from paramagnetic microbleeds. The deep learning model was trained using 154 data sets, and the best models were selected using 25 validation data sets. Finally, the models were tested using 41 cases, including 13 hemodialysis cases, 9 traumatic brain injury cases, 9 stroke cases and 10 healthy controls. Using 3D SWI and high-pass filtered phase images as input, the best model led to a sensitivity of 95. 8%, a precision of 70. 9%, and 1. 6 false positives per case. This model achieved similar performance to the most experienced human rater and outperformed recently reported CMB detection methods. This study demonstrates the potential of applying deep learning techniques to medical imaging for improving efficiency and accuracy in diagnosis.

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YNICL Journal 2017 Journal Article

Decreased susceptibility of major veins in mild traumatic brain injury is correlated with post-concussive symptoms: A quantitative susceptibility mapping study

Chao Chai
Rui Guo
Chao Zuo
Linlin Fan
Saifeng Liu
Tianyi Qian
E. Mark Haacke
Shuang Xia

Cerebral venous oxygen saturation (SvO2) is an important biomarker of brain function. In this study, we aimed to explore the relative changes of regional cerebral SvO2 among axonal injury (AI) patients, non-AI patients and healthy controls (HCs) using quantitative susceptibility mapping (QSM). 48 patients and 32 HCs were enrolled. The patients were divided into two groups depending on the imaging based evidence of AI. QSM was used to measure the susceptibility of major cerebral veins. Nonparametric testing was performed for susceptibility differences among the non-AI patient group, AI patient group and healthy control group. Correlation was performed between the susceptibility of major cerebral veins, elapsed time post trauma (ETPT) and post-concussive symptom scores. The ROC analysis was performed for the diagnostic efficiency of susceptibility to discriminate mTBI patients from HCs. The susceptibility of the straight sinus in non-AI and AI patients was significantly lower than that in HCs (P <0. 001, P =0. 004, respectively, Bonferroni corrected), which may indicate an increased regional cerebral SvO2 in patients. The susceptibility of the straight sinus in non-AI patients positively correlated with ETPT (r =0. 573, P =0. 003, FDR corrected) while that in AI patients negatively correlated with the Rivermead Post Concussion Symptoms Questionnaire scores (r =−0. 582, P =0. 018, FDR corrected). The sensitivity, specificity and AUC values of susceptibility for the discrimination between mTBI patients and HCs were 88%, 69% and 0. 84. In conclusion, the susceptibility of the straight sinus can be used as a biomarker to monitor the progress of mild TBI and to differentiate mTBI patients from healthy controls.

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