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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

Results of stent segmentation and malapposition detection.(a) Result of manual stent segmentation. (b) Result of automatic stent segmentation. (c) Result of manual malapposition detection. (d) Result of automatic malapposition detection.
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pone.0124192.g007: Results of stent segmentation and malapposition detection.(a) Result of manual stent segmentation. (b) Result of automatic stent segmentation. (c) Result of manual malapposition detection. (d) Result of automatic malapposition detection.

Mentions: Stent Segmentation and Malapposition DetectionFig 7 shows the results of both manual and automatic stent segmentation and malapposition detection. Using the manual inspection results by two independent observers as the ground truth, we analyzed the accuracy of our automated stent segmentation and malapposition detection using 3 pullback data, which amounts to 360 FD-OCT images. One trained cardiologist manually inspected all the images and identified stent struts in them. To obtain the inter-observer reliability, another independent observer analyzed the same images for stent segmentation and malapposition detection. For the intra-observer reliability, each observer analyzed the same images twice. Table 4 shows some statistical measurement of the inter- and intra-observer reliability of our manual stent segmentation and malapposition detection. For stent strut assessment, we used Kendall’s rank correlation which is a nonparametric method used for ordinal variables. For malapposition detection, we calculated overall agreement and kappa value which is a chance-corrected indicator of agreement. The results showed that both measurements are in high correlation and excellent agreement.


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)

Results of stent segmentation and malapposition detection.(a) Result of manual stent segmentation. (b) Result of automatic stent segmentation. (c) Result of manual malapposition detection. (d) Result of automatic malapposition detection.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4400174&req=5

pone.0124192.g007: Results of stent segmentation and malapposition detection.(a) Result of manual stent segmentation. (b) Result of automatic stent segmentation. (c) Result of manual malapposition detection. (d) Result of automatic malapposition detection.
Mentions: Stent Segmentation and Malapposition DetectionFig 7 shows the results of both manual and automatic stent segmentation and malapposition detection. Using the manual inspection results by two independent observers as the ground truth, we analyzed the accuracy of our automated stent segmentation and malapposition detection using 3 pullback data, which amounts to 360 FD-OCT images. One trained cardiologist manually inspected all the images and identified stent struts in them. To obtain the inter-observer reliability, another independent observer analyzed the same images for stent segmentation and malapposition detection. For the intra-observer reliability, each observer analyzed the same images twice. Table 4 shows some statistical measurement of the inter- and intra-observer reliability of our manual stent segmentation and malapposition detection. For stent strut assessment, we used Kendall’s rank correlation which is a nonparametric method used for ordinal variables. For malapposition detection, we calculated overall agreement and kappa value which is a chance-corrected indicator of agreement. The results showed that both measurements are in high correlation and excellent agreement.

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