Prof. Feng Cao Profile

Prof. Feng Cao

at Chinese PLA General Hospital

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Proceedings Article | 8 March 2019 Paper

Identification of angiogenesis and viable myocardium using hybrid cardiac imaging

Zhenzhen Xu, Bo Tao, Shenghan Ren, Feng Cao, Jimin Liang

Proceedings Volume 10951, 1095128 (2019) https://doi.org/10.1117/12.2512653

KEYWORDS: Image segmentation, Image fusion, Image registration, Heart, Single photon emission computed tomography, Positron emission tomography, Computed tomography, Cardiac imaging, Arteries, Image processing

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The existing hybrid cardiac imaging approaches focus on predicting the adverse cardiac events or disease diagnosis, yet do not offer any insight into the pathological advance in the repair process. Angiogenesis is one of the most important mechanism in the repair process after ischemic injury and has shown benefit to the prognosis of occlusive cardiovascular disorders, thus becomes a target of molecular therapies. In vivo monitoring of angiogenesis and comprehensive evaluation of cardiac function associated with angiogenesis are urgently needed in both research and clinical practice. In this paper, a multimodality image fusion strategy was proposed for angiogenesis and viable myocardium identification. Imaging approaches including coronary computed tomography angiography(CCTA), 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]DG) PET/CT, [⁶⁸Ga]-1,4,7-triazacyclononane-1,4,7-triacetic acid-(Arg-Gly-Asp)2 ([⁶⁸Ga]-NOTA-PRGD2) PET/CT and ^99mTc-sestamibi (^99mTc-MIBI) myocardial perfusion SPECT/CT scanning were performed to acquire both anatomy and three kinds of function information. All of these modality images were then fused by an automatic strategy consisting of ROI segmentation and cross modality registration. The left ventricle myocardium was categorized into 4 groups based on fusion result according to the respective relative tracer uptake. The final results intuitively reflected the extent of the [¹⁸F]DG and ^99mTc-MIBI uptake defect, the perfusion-metabolism mismatch area, as well as the location of the [⁶⁸Ga]-NOTA-PRGD2 signal. The hybrid CCTA-PET-SPECT image verified the occurrence of angiogenesis based on the in vivo noninvasive molecular imaging approaches and visualized the hibernating myocardium. The presented fusion strategy is helpful in facilitating the study of the relationship between viability, perfusion and blocked coronary arteries, as well as angiogenesis.

Proceedings Article | 12 March 2018 Paper

Automatic coronary artery lumen segmentation in computed tomography angiography using paired multi-scale 3D CNN

Fei Chen, Yu Li, Tian Tian, Feng Cao, Jimin Liang

Proceedings Volume 10578, 105782R (2018) https://doi.org/10.1117/12.2293289

KEYWORDS: Image segmentation, Arteries, Computed tomography, Angiography, Data modeling, 3D image processing, Convolutional neural networks, Image processing, Fermium, Computer aided diagnosis and therapy

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Coronary artery disease (CAD) is one of the leading causes of death worldwide. The computed tomography angiography (CTA) is increasingly used to diagnose CAD due to its non-invasive nature and high-resolution three-dimensional (3D) imaging capability of the coronary artery anatomy. CTA allows for identification and grading of stenosis by evaluating the degree of narrowing of the blood-filled coronary artery lumen. Both identification and grading rely on the precise segmentation of the coronary arteries on CTA images. In this paper, a fully automatic segmentation framework is proposed to extract the coronary arteries from the whole cardiac CTA images. The framework adopts a paired multi-scale 3D deep convolutional neural networks (CNNs) to identify which voxels belong to the vessel lumen. Voxels that may belong to coronary artery lumen are recognized by the first CNN in the pair and both artery positives and artery-like negatives are distinguished by the second one. Each CNN is assigned to a different task. They share the same architecture in common but with different weights. In order to combine local and larger contextual information, we adopt a dual pathway architecture that can process the input image simultaneously on multiple scales. The experiments were performed on a CTA dataset from 44 patients. 35 CTA scans are used for training and the rests for testing. The proposed segmentation framework achieved a mean Dice similarity coefficient (DSC) of 0.8649 and mean surface distance (MSD) of 0.5571 with reference to manual annotations. Experimental results show that the proposed framework is capable of performing complete, accurate and robust segmentation of the coronary arteries.

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