Purpose: In minimally invasive spring-assisted craniectomy, surgeons plan the surgery by manually locating the cranial sutures. However, this approach is prone to error. Augmented reality (AR) could be used to visualize the cranial sutures and assist in the surgery planning. The purpose of our work is to develop an AR-based system to visualize cranial sutures, and to assess the accuracy and usability of using AR-based navigation for surgical guidance in minimally invasive spring-assisted craniectomy. Methods: An AR system was developed that consists of an electromagnetic tracking system linked with a Microsoft HoloLens. The system was used to conduct a study with two skull phantoms. For each phantom, five sutures were annotated and visualized on the skull surface. Twelve participants assessed the system. For each participant, model alignment using six anatomical landmarks was performed, followed by the participant delineation of the visualized sutures. At the end, the participants filled a system usability scale (SUS) questionnaire. For evaluation, an independent optical tracking system was used and the delineated sutures were digitized and compared to the CT-annotated sutures. Results: For a total of 120 delineated sutures, the distance of the annotated sutures to the planning reference was 2.4 ± 1.2 mm. The average delineation time per suture was 13 ± 5 s. For the system usability questionnaire, an average SUS score of 73 was obtained. Conclusion: The developed AR-system has good accuracy (average 2.4 mm distance) and could be used in the OR. The system can assist in the pre-planning of minimally invasive craniosynostosis surgeries to locate cranial sutures accurately instead of the traditional approach of manual palpation. Although the conducted phantom study was designed to closely reflect the clinical setup in the OR, further clinical validation of the developed system is needed and will be addressed in a future work.@en

Background: X-ray digital subtraction angiography (DSA) is the imaging modality for peri-procedural guidance and treatment evaluation in (neuro-) vascular interventions. Perfusion image construction from DSA, as a means of quantitatively depicting cerebral hemodynamics, has been shown feasible. However, the quantitative property of perfusion DSA has not been well studied. Purpose: To comparatively study the independence of deconvolution-based perfusion DSA with respect to varying injection protocols, as well as its sensitivity to alterations in brain conditions. Methods: We developed a deconvolution-based algorithm to compute perfusion parametric images from DSA, including cerebral blood volume (CBV (Figure presented.)), cerebral blood flow (CBF (Figure presented.)), time to maximum (Tmax), and mean transit time (MTT (Figure presented.)) and applied it to DSA sequences obtained from two swine models. We also extracted the time intensity curve (TIC)-derived parameters, that is, area under the curve (AUC), peak concentration of the curve, and the time to peak (TTP) from these sequences. Deconvolution-based parameters were quantitatively compared to TIC-derived parameters in terms of consistency upon variations in injection profile and time resolution of DSA, as well as sensitivity to alterations of cerebral condition. Results: Comparing to TIC-derived parameters, the standard deviation (SD) of deconvolution-based parameters (normalized with respect to the mean) are two to five times smaller, indicating that they are more consistent across different injection protocols and time resolutions. Upon ischemic stroke induced in a swine model, the sensitivities of deconvolution-based parameters are equal to, if not higher than, those of TIC-derived parameters. Conclusions: In comparison to TIC-derived parameters, deconvolution-based perfusion imaging in DSA shows significantly higher quantitative reliability against variations in injection protocols across different time resolutions, and is sensitive to alterations in cerebral hemodynamics. The quantitative nature of perfusion angiography may allow for objective treatment assessment in neurovascular interventions.@en

Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases.@en

Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases.@en

Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases.@en

Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases.@en

Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases.@en

Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases.@en

Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases.@en

Retinal vascular diseases are a leading cause for blindness and partial sight certifications. By applying adaptive optics (AO) to conventional imaging modalities, the microstructures of the retinal vasculature can be observed with high spatial resolution, hence offering a unique opportunity for the exploration of the human microcirculation. The objective of this systematic review is to describe the current state of retinal vascular biomarkers imaged by AO flood illumination ophthalmoscopy (FIO) and AO scanning laser ophthalmoscopy (SLO). A literature research was conducted in the PubMed and Scopus databases on July 9, 2020. From 217 screened studies, 42 were eligible for this review. All studies underwent a quality check regarding their content. A meta-analysis was performed for the biomarkers reported for the same pathology in at least three studies using the same modality. The most frequently studied vascular biomarkers were the inner diameter (ID), outer diameter (OD), parietal thickness (PT), wall cross-sectional area (WCSA), and wall-to-lumen ratio (WLR). The applicability of AO vascular biomarkers has been mostly explored in systemic hypertension using AO FIO and in diabetes using AO SLO. The result of the meta-analysis for hypertensive patients showed that WLR, PT, and ID were significantly different when compared to healthy controls, while WCSA was not (P < 0.001, P = 0.002, P < 0.001, and P = 0.070, respectively). The presented review shows that, although a substantial number of retinal vascular biomarkers have been explored in AO en face imaging, further clinical research and standardization of procedures is needed to validate such biomarkers for the longitudinal monitoring of arterial hypertension and other diseases.@en

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter-and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.@en

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter-and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.@en

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter-and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.@en

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter-and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.@en

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter-and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.@en

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter-and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.@en

The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome measure after endovascular treatment (EVT). Existing TICI scores are defined in coarse ordinal grades based on visual inspection, leading to inter-and intra-observer variation. In this work, we present autoTICI, an automatic and quantitative TICI scoring method. First, each digital subtraction angiography (DSA) acquisition is separated into four phases (non-contrast, arterial, parenchymal and venous phase) using a multi-path convolutional neural network (CNN), which exploits spatio-temporal features. The network also incorporates sequence level label dependencies in the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is computed using the motion corrected arterial and parenchymal frames. On the MINIP image, vessel, perfusion and background pixels are segmented. Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a routinely acquired multi-center dataset, the proposed autoTICI shows good correlation with the extended TICI (eTICI) reference with an average area under the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate that autoTICI is overall comparable to eTICI.@en

Purpose: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. Methods: First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. Results: The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were 29±46μm for the intima–media interface, 30±50μm for the media–adventitia interface, and 50±64μm for the adventitia–periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78–0.98). Conclusion: The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.@en

Purpose: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena. Methods: First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic. Results: The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were 29±46μm for the intima–media interface, 30±50μm for the media–adventitia interface, and 50±64μm for the adventitia–periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78–0.98). Conclusion: The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.@en

Purpose: Identification of rupture-prone plaques in coronary arteries is a major clinical challenge. Fibrous cap thickness and wall shear stress are two relevant image-based risk factors, but these two parameters are generally computed and analyzed separately. Accordingly, combining these two parameters can potentially improve the identification of at-risk regions. Therefore, the purpose of this study is to investigate the feasibility of the fusion of wall shear stress and fibrous cap thickness of coronary arteries in patient data. Methods: Fourteen patients were included in this pilot study. Imaging of the coronary arteries was performed with optical coherence tomography and with angiography. Fibrous cap thickness was automatically quantified from optical coherence tomography pullbacks using a contour segmentation approach based on fast marching. Wall shear stress was computed by applying computational fluid dynamics on the 3D volume reconstructed from two angiograms. The two parameters then were co-registered using anatomical landmarks such as side branches. Results: The two image modalities were successfully co-registered, with a mean (±SD) error corresponding to 8.6±6.7% of the length of the analyzed region. For all the analyzed participants, the average thinnest portion of each fibrous cap was 129±69μm, and the average WSS value at the location of the fibrous cap was 1.46±1.16Pa. A unique index was finally generated for each patient via the fusion of fibrous cap thickness and wall shear stress measurements, to translate all the measured parameters into a single risk map. Conclusion: The introduced risk map integrates two complementary parameters and has potential to provide valuable information about plaque vulnerability.@en