Browsing by Author "Wang, Zhiqiang"

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    Fully parallelized Lattice Boltzmann scheme for fast extraction of biomedical geometry
    (Elsevier, 2019-06) Wang, Zhiqiang; Zhao, Ye; Yu, Huidan (Whitney); Lin, Chen; Sawchuck, Alan P.; Mechanical and Energy Engineering, School of Engineering and Technology
    We develop a fully parallel numerical method which quickly performs 2D and 3D segmentation on GPU to extract anatomical structures from medical images. The algorithm solves the level set equations completely within a Lattice Boltzmann model (LBM). Compared with existing LBM-based segmentation approaches, a parallel distance field regularization is added to the LBM computing scheme to keep computation stable with large time step iteration. This approach also avoids external regularization which has been a major impediment to direct parallelization of level set evolution with LBM. It allows the whole computing process to be efficiently executed on GPU. Moreover, the method can be incorporated with different image features to adopt in various image segmentation tasks. Therefore, our method enables fully GPU accelerated geometric extraction from medical images, leading to high computing performance which is demanded in many practical applications. This method is used to exactly accurate 2D and 3D anatomical structures from many real world CT and MRI images. The achieved results can also directly feed required boundary information to LBM-based hemodynamics simulation.
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    Using Flow Feature to Extract Pulsatile Blood Flow from 4D Flow MRI Images
    (International Society for Optics and Photonics, 2017-02) Wang, Zhiqiang; Zhao, Ye; Yu, Whitney; Chen, Xi; Lin, Chen; Kralik, Stephen F.; Hutchins, Gary D.; Surgery, School of Medicine
    4D flow MRI images make it possible to measure pulsatile blood flow inside deforming vessel, which is critical in accurate blood flow visualization, simulation, and evaluation. Such data has great potential to overcome problems in existing work, which usually does not reflect the dynamic nature of elastic vessels and blood flows in cardiac cycles. However, the 4D flow MRI data is often low-resolution and with strong noise. Due to these challenges, few efforts have been successfully conducted to extract dynamic blood flow fields and deforming artery over cardiac cycles, especially for small artery like carotid. In this paper, a robust flow feature, particularly the mean flow intensity is used to segment blood flow regions inside vessels from 4D flow MRI images in whole cardiac cycle. To estimate this flow feature more accurately, adaptive weights are added to the raw velocity vectors based on the noise strength of MRI imaging. Then, based on this feature, target arteries are tracked in at different time steps in a cardiac cycle. This method is applied to the clinical 4D flow MRI data in neck area. Dynamic vessel walls and blood flows are effectively generated in a cardiac cycle in the relatively small carotid arteries. Good image segmentation results on 2D slices are presented, together with the visualization of 3D arteries and blood flows. Evaluation of the method was performed by clinical doctors and by checking flow volume rates in the vertebral and carotid arteries.
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    Using Flow Feature to Extract Pulsatile Blood Flow from 4D Flow MRI Images
    (SPIE, 2017) Wang, Zhiqiang; Zhao, Ye; Yu, Whitney; Chen, Xi; Lin, Chen; Kralik, Stephen F.; Hutchins, Gary D.; Mechanical Engineering, School of Engineering and Technology
    4D flow MRI images make it possible to measure pulsatile blood flow inside deforming vessel, which is critical in accurate blood flow visualization, simulation, and evaluation. Such data has great potential to overcome problems in existing work, which usually does not reflect the dynamic nature of elastic vessels and blood flows in cardiac cycles. However, the 4D flow MRI data is often low-resolution and with strong noise. Due to these challenges, few efforts have been successfully conducted to extract dynamic blood flow fields and deforming artery over cardiac cycles, especially for small artery like carotid. In this paper, a robust flow feature, particularly the mean flow intensity is used to segment blood flow regions inside vessels from 4D flow MRI images in whole cardiac cycle. To estimate this flow feature more accurately, adaptive weights are added to the raw velocity vectors based on the noise strength of MRI imaging. Then, based on this feature, target arteries are tracked in at different time steps in a cardiac cycle. This method is applied to the clinical 4D flow MRI data in neck area. Dynamic vessel walls and blood flows are effectively generated in a cardiac cycle in the relatively small carotid arteries. Good image segmentation results on 2D slices are presented, together with the visualization of 3D arteries and blood flows. Evaluation of the method was performed by clinical doctors and by checking flow volume rates in the vertebral and carotid arteries.