Efficient blood flow visualization using flowline extraction and opacity modulation based on vascular structure analysis.
With the recent advances regarding the acquisition and simulation of blood flow data, blood flow visualization has been widely used in medical imaging for the diagnosis and treatment of pathological vessels. In this paper, we present a novel method for the visualization of the blood flow in vascular structures. The vessel inlet or outlet is first identified using the orthogonality metric between the normal vectors of the flow velocity and vessel surface. Then, seed points are generated on the identified inlet or outlet by Poisson disk sampling. Therefore, it is possible to achieve the automatic seeding that leads to a consistent and faster flow depiction by skipping the manual location of a seeding plane for the initiation of the line integration. In addition, the early terminated line integration in the thin curved vessels is resolved through the adaptive application of the tracing direction that is based on the flow direction at each seed point. Based on the observation that blood flow usually follows the vessel track, the representative flowline for each branch is defined by the vessel centerline. Then, the flowlines are rendered through an opacity assignment according to the similarity between their shape and the vessel centerline. Therefore, the flowlines that are similar to the vessel centerline are shown transparently, while the different ones are shown opaquely. Accordingly, the opacity modulation method enables the flowlines with an unusual flow pattern to appear more noticeable, while the visual clutter and line occlusion are minimized. Finally, Hue-Saturation-Value color coding is employed for the simultaneous exhibition of flow attributes such as local speed and residence time. The experiment results show that the proposed technique is suitable for the depiction of the blood flow in vascular structures. The proposed approach is applicable to many kinds of tubular structures with embedded flow information.
School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea. Electronic address: ohjae.kwon@samsung.com.
Ohjae
O
Kwon
School of Computer Science and Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 156-743, Republic of Korea. Electronic address: leejeongjin@ssu.ac.kr.
Jeongjin
J
Lee
Division of Biomedical Engineering, Hankuk University of Foreign Studies, 81 Mohyeon-myeon, Oedae-ro, Cheoin-gu, Yongin-si, Gyeonggi-do 17035, Republic of Korea. Electronic address: bkim@hufs.ac.kr.
Bohyoung
B
Kim
Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeong gi-do 440-746, Republic of Korea. Electronic address: jsshin@skku.edu.
Juneseuk
J
Shin
School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea. Electronic address: yshin@snu.ac.kr.
Yeong-Gil
YG
Shin
D000818:Animals; D001783:Blood Flow Velocity / Q000502:physiology*; D001808:Blood Vessels / Q000502:physiology*; D003196:Computer Graphics; D006801:Humans; D008955:Models, Cardiovascular; D012212:Rheology / Q000379:methods*; D014584:User-Computer Interface
Automatic seeding; Blood flow visualization; Flowline extraction; Integration based flow visualization; Opacity modulation; Vascular structure