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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

Method of precise guide-wire segmentation.(a)–(f) show a usual case where the morphological top-hat operation may not make any difference, whereas (g)–(l) exemplify such a case where the operation enhances the precision of guide-wire segmentation. (a) Set ROI from a blurred image. (b) Construct a binary image by Otsu’s method. (c) Apply the morphological erosion operation. (d) Apply the morphological top-hat operation. (e) Subtract the image in (d) from the image in (c). (f) Detect guide-wire by removing noises. (g) Set ROI, which is too wide. (h) Construct a binary image by Otsu’s method. (i) Apply the morphological erosion operation, whose resulting image includes connected guide-wire and noises denoted by a yellow circle. (j) Apply the morphological top-hat operation to detect the connected area denoted by a yellow circle. (k) Subtract the image in (j) from the image in (i) to disconnect guide-wire and noises as a yellow circle shows. (l) Detect guide-wire by removing noises.
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pone.0124192.g004: Method of precise guide-wire segmentation.(a)–(f) show a usual case where the morphological top-hat operation may not make any difference, whereas (g)–(l) exemplify such a case where the operation enhances the precision of guide-wire segmentation. (a) Set ROI from a blurred image. (b) Construct a binary image by Otsu’s method. (c) Apply the morphological erosion operation. (d) Apply the morphological top-hat operation. (e) Subtract the image in (d) from the image in (c). (f) Detect guide-wire by removing noises. (g) Set ROI, which is too wide. (h) Construct a binary image by Otsu’s method. (i) Apply the morphological erosion operation, whose resulting image includes connected guide-wire and noises denoted by a yellow circle. (j) Apply the morphological top-hat operation to detect the connected area denoted by a yellow circle. (k) Subtract the image in (j) from the image in (i) to disconnect guide-wire and noises as a yellow circle shows. (l) Detect guide-wire by removing noises.

Mentions: Secondly, unlike our previous work, we now segment guide-wire automatically. In Fig 3(b), the right (blue) arrow denotes guide-wire. We detect the location of guide-wire in two steps: the first step identifies A-lines that include guide-wire and the second step detects the exact location of guide-wire. As in Ughi et al.’s approach [29], the first step approximately segments guide-wire; we construct an en face image like Fig 2(c) from a sample data, convert it to a binary image by applying Otsu’s method [33], and detect A-lines that include guide-wire by applying the morphological operations [34]. Note that performing only the first step cannot identify the precise location of guide-wire, and 3D visualization cannot use such imprecise guide-wire information. Fig 4 exemplifies how the second step segments guide-wire precisely. To show effects of the morphological top-hat operation, Fig 4(a)–4(f) show a usual case where the operation may not have any effect, whereas Fig 4(g)–4(l) show a case where the operation enhances the precision of guide-wire segmentation. Using the A-lines of guide-wire information obtained by the first step, we set region of interest (ROI) that may include guide-wire. Not to miss any guide-wire information, we set ROI conservatively: we add ± 40 pixels on both left and right sides of the A-line candidates. Fig 4(a) shows a sample image of setting ROI with extra zero padding at the top to avoid separation of guide-wire when we apply the morphological operation later. Then, we construct a binary image by Otsu’s method as in Fig 4(b), apply the morphological erosion operation as in Fig 4(c), and apply the morphological top-hat operation as in Fig 4(d). To detect guide-wire more precisely, we subtract the resulting image of morphological top-hat from the resulting image of morphological erosion as in Fig 4(e), which does not show much differences in this case. Finally, we segment guide-wire as in Fig 4(f) by detecting objects in images by connected component labelling (CCL) [35] and then removing noises. Note that the above algorithm may find a much wider ROI than the actual guide-wire information as Fig 4(g) illustrates. In such cases, applying Otsu’s method and then morphological erosion constructs one big object that includes both guide-wire and noises as in Fig 4(i). With the observation that the connected area between guide-wire and noises is narrow, we detect the connected area by using morphological top-hat as in Fig 4(j) and eliminate it to separate guide-wire from noises. Then, we can remove noises that are now separated from guide-wire.


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)

Method of precise guide-wire segmentation.(a)–(f) show a usual case where the morphological top-hat operation may not make any difference, whereas (g)–(l) exemplify such a case where the operation enhances the precision of guide-wire segmentation. (a) Set ROI from a blurred image. (b) Construct a binary image by Otsu’s method. (c) Apply the morphological erosion operation. (d) Apply the morphological top-hat operation. (e) Subtract the image in (d) from the image in (c). (f) Detect guide-wire by removing noises. (g) Set ROI, which is too wide. (h) Construct a binary image by Otsu’s method. (i) Apply the morphological erosion operation, whose resulting image includes connected guide-wire and noises denoted by a yellow circle. (j) Apply the morphological top-hat operation to detect the connected area denoted by a yellow circle. (k) Subtract the image in (j) from the image in (i) to disconnect guide-wire and noises as a yellow circle shows. (l) Detect guide-wire by removing noises.
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pone.0124192.g004: Method of precise guide-wire segmentation.(a)–(f) show a usual case where the morphological top-hat operation may not make any difference, whereas (g)–(l) exemplify such a case where the operation enhances the precision of guide-wire segmentation. (a) Set ROI from a blurred image. (b) Construct a binary image by Otsu’s method. (c) Apply the morphological erosion operation. (d) Apply the morphological top-hat operation. (e) Subtract the image in (d) from the image in (c). (f) Detect guide-wire by removing noises. (g) Set ROI, which is too wide. (h) Construct a binary image by Otsu’s method. (i) Apply the morphological erosion operation, whose resulting image includes connected guide-wire and noises denoted by a yellow circle. (j) Apply the morphological top-hat operation to detect the connected area denoted by a yellow circle. (k) Subtract the image in (j) from the image in (i) to disconnect guide-wire and noises as a yellow circle shows. (l) Detect guide-wire by removing noises.
Mentions: Secondly, unlike our previous work, we now segment guide-wire automatically. In Fig 3(b), the right (blue) arrow denotes guide-wire. We detect the location of guide-wire in two steps: the first step identifies A-lines that include guide-wire and the second step detects the exact location of guide-wire. As in Ughi et al.’s approach [29], the first step approximately segments guide-wire; we construct an en face image like Fig 2(c) from a sample data, convert it to a binary image by applying Otsu’s method [33], and detect A-lines that include guide-wire by applying the morphological operations [34]. Note that performing only the first step cannot identify the precise location of guide-wire, and 3D visualization cannot use such imprecise guide-wire information. Fig 4 exemplifies how the second step segments guide-wire precisely. To show effects of the morphological top-hat operation, Fig 4(a)–4(f) show a usual case where the operation may not have any effect, whereas Fig 4(g)–4(l) show a case where the operation enhances the precision of guide-wire segmentation. Using the A-lines of guide-wire information obtained by the first step, we set region of interest (ROI) that may include guide-wire. Not to miss any guide-wire information, we set ROI conservatively: we add ± 40 pixels on both left and right sides of the A-line candidates. Fig 4(a) shows a sample image of setting ROI with extra zero padding at the top to avoid separation of guide-wire when we apply the morphological operation later. Then, we construct a binary image by Otsu’s method as in Fig 4(b), apply the morphological erosion operation as in Fig 4(c), and apply the morphological top-hat operation as in Fig 4(d). To detect guide-wire more precisely, we subtract the resulting image of morphological top-hat from the resulting image of morphological erosion as in Fig 4(e), which does not show much differences in this case. Finally, we segment guide-wire as in Fig 4(f) by detecting objects in images by connected component labelling (CCL) [35] and then removing noises. Note that the above algorithm may find a much wider ROI than the actual guide-wire information as Fig 4(g) illustrates. In such cases, applying Otsu’s method and then morphological erosion constructs one big object that includes both guide-wire and noises as in Fig 4(i). With the observation that the connected area between guide-wire and noises is narrow, we detect the connected area by using morphological top-hat as in Fig 4(j) and eliminate it to separate guide-wire from noises. Then, we can remove noises that are now separated from guide-wire.

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