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GPU-accelerated framework for intracoronary optical coherence tomography imaging at the push of a button.

Han M, Kim K, Jang SJ, Cho HS, Bouma BE, Oh WY, Ryu S - PLoS ONE (2015)

Bottom Line: To help more accurate diagnosis and monitoring of the disease, many researchers have recently worked on visualization of various coronary microscopic features including stent struts by constructing three-dimensional (3D) volumetric rendering from series of cross-sectional intracoronary FD-OCT images.In this paper, we present the first, to our knowledge, "push-of-a-button" graphics processing unit (GPU)-accelerated framework for intracoronary OCT imaging.Our framework visualizes 3D microstructures of the vessel wall with stent struts from raw binary OCT data acquired by the system digitizer as one seamless process.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

ABSTRACT
Frequency domain optical coherence tomography (FD-OCT) has become one of the important clinical tools for intracoronary imaging to diagnose and monitor coronary artery disease, which has been one of the leading causes of death. To help more accurate diagnosis and monitoring of the disease, many researchers have recently worked on visualization of various coronary microscopic features including stent struts by constructing three-dimensional (3D) volumetric rendering from series of cross-sectional intracoronary FD-OCT images. In this paper, we present the first, to our knowledge, "push-of-a-button" graphics processing unit (GPU)-accelerated framework for intracoronary OCT imaging. Our framework visualizes 3D microstructures of the vessel wall with stent struts from raw binary OCT data acquired by the system digitizer as one seamless process. The framework reports the state-of-the-art performance; from raw OCT data, it takes 4.7 seconds to provide 3D visualization of a 5-cm-long coronary artery (of size 1600 samples x 1024 A-lines x 260 frames) with stent struts and detection of malapposition automatically at the single push of a button.

No MeSH data available.


Related in: MedlinePlus

Cross-sectional images and their corresponding en face images.(a) FD-OCT images in polar coordinate. (b) FD-OCT images in Cartesian coordinate. (c) Sample en face image constructed from a series of FD-OCT images from a single pullback. (d) The en face image after applying the Laplacian filter to (c) and selecting only the pixel values greater than 240 from the resulting image, which shows consecutive 150 images.
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pone.0124192.g002: Cross-sectional images and their corresponding en face images.(a) FD-OCT images in polar coordinate. (b) FD-OCT images in Cartesian coordinate. (c) Sample en face image constructed from a series of FD-OCT images from a single pullback. (d) The en face image after applying the Laplacian filter to (c) and selecting only the pixel values greater than 240 from the resulting image, which shows consecutive 150 images.

Mentions: First, we build an en face image from the entire cross-sectional images corresponding to a single pullback. From the cross-sectional images presented in polar coordinate (Fig 2(a)), we build an en face image as shown in Fig 2(c), where the height of the en face image is the number of A-lines in each cross-sectional image and the width is the number of all the cross-sectional images in a single pullback. We calculate the “pixel” value at the (i, j) coordinate in the en face image by computing the intensity average of all depth points at the jth A-line of the ith cross-sectional image.


GPU-accelerated framework for intracoronary optical coherence tomography imaging at the push of a button.

Han M, Kim K, Jang SJ, Cho HS, Bouma BE, Oh WY, Ryu S - PLoS ONE (2015)

Cross-sectional images and their corresponding en face images.(a) FD-OCT images in polar coordinate. (b) FD-OCT images in Cartesian coordinate. (c) Sample en face image constructed from a series of FD-OCT images from a single pullback. (d) The en face image after applying the Laplacian filter to (c) and selecting only the pixel values greater than 240 from the resulting image, which shows consecutive 150 images.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4400174&req=5

pone.0124192.g002: Cross-sectional images and their corresponding en face images.(a) FD-OCT images in polar coordinate. (b) FD-OCT images in Cartesian coordinate. (c) Sample en face image constructed from a series of FD-OCT images from a single pullback. (d) The en face image after applying the Laplacian filter to (c) and selecting only the pixel values greater than 240 from the resulting image, which shows consecutive 150 images.
Mentions: First, we build an en face image from the entire cross-sectional images corresponding to a single pullback. From the cross-sectional images presented in polar coordinate (Fig 2(a)), we build an en face image as shown in Fig 2(c), where the height of the en face image is the number of A-lines in each cross-sectional image and the width is the number of all the cross-sectional images in a single pullback. We calculate the “pixel” value at the (i, j) coordinate in the en face image by computing the intensity average of all depth points at the jth A-line of the ith cross-sectional image.

Bottom Line: To help more accurate diagnosis and monitoring of the disease, many researchers have recently worked on visualization of various coronary microscopic features including stent struts by constructing three-dimensional (3D) volumetric rendering from series of cross-sectional intracoronary FD-OCT images.In this paper, we present the first, to our knowledge, "push-of-a-button" graphics processing unit (GPU)-accelerated framework for intracoronary OCT imaging.Our framework visualizes 3D microstructures of the vessel wall with stent struts from raw binary OCT data acquired by the system digitizer as one seamless process.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

ABSTRACT
Frequency domain optical coherence tomography (FD-OCT) has become one of the important clinical tools for intracoronary imaging to diagnose and monitor coronary artery disease, which has been one of the leading causes of death. To help more accurate diagnosis and monitoring of the disease, many researchers have recently worked on visualization of various coronary microscopic features including stent struts by constructing three-dimensional (3D) volumetric rendering from series of cross-sectional intracoronary FD-OCT images. In this paper, we present the first, to our knowledge, "push-of-a-button" graphics processing unit (GPU)-accelerated framework for intracoronary OCT imaging. Our framework visualizes 3D microstructures of the vessel wall with stent struts from raw binary OCT data acquired by the system digitizer as one seamless process. The framework reports the state-of-the-art performance; from raw OCT data, it takes 4.7 seconds to provide 3D visualization of a 5-cm-long coronary artery (of size 1600 samples x 1024 A-lines x 260 frames) with stent struts and detection of malapposition automatically at the single push of a button.

No MeSH data available.


Related in: MedlinePlus