Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images.

Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images.

It can be difficult/impossible to fully expand a coronary artery stent in a heavily calcified coronary artery lesion. Under-expanded stents are linked to later complications. Here we used machine/deep learning to analyze calcifications in pre-stent intravascular optical coherence tomography (IVOCT) images and predicted the success of vessel expansion. Pre- and post-stent IVOCT image data were obtained from 110 coronary lesions. Lumen and calcifications in pre-stent images were segmented using deep learning, and lesion features were extracted. We analyzed stent expansion along the lesion, enabling frame, segmental, and whole-lesion analyses. We trained regression models to predict the post-stent lumen area and then computed the stent expansion index (SEI). Best performance (root-mean-square-error = 0.04 ± 0.02 mm2, r = 0.94 ± 0.04, p < 0.0001) was achieved when we used features from both lumen and calcification to train a Gaussian regression model for segmental analysis of 31 frames in length. Stents with minimum SEI > 80% were classified as "well-expanded;" others were "under-expanded." Under-expansion classification results (e.g., AUC = 0.85 ± 0.02) were significantly improved over a previous, simple calculation, as well as other machine learning solutions. Promising results suggest that such methods can identify lesions at risk of under-expansion that would be candidates for intervention lesion preparation (e.g., atherectomy).

Efficacy of 3D-multidetector computed tomography and fluoroscopy fusion for percutaneous left atrial appendage occlusion procedures.

INTRODUCTION: We studied the impact of the use of three-dimensional multidetector computed tomography (3D-MDCT) and fluoroscopy fusion on percutaneous left atrial appendage occlusion (LAAO) procedures in relation to procedure time, contrast volume, fluoroscopy time, and total radiation. METHODS: This was a single-center, prospective, single-blinded, randomized control trial. Patients meeting criteria for LAAO were randomized to undergo LAAO with the WATCHMAN FLXTM device with and without 3D-MDCT-fluoroscopy fusion guidance using a prespecified protocol using computed tomography angiography for WATCHMAN FLXTM sizing, moderate sedation, and intracardiac echocardiography for procedural guidance. RESULTS: Overall, 59 participants were randomly assigned to the fusion (n = 33) or no fusion (n = 26) groups. The median (interquartile range) age was 79 (75-83) years, 24 (41%) were female, and 55 (93%) were Caucasian. The median CHA2 DS2 VASc and HASBLED scores were 5 (4-6) and 3 (3-4), respectively. At the time of the study, 51 (53%) patients were on a direct acting oral anticoagulant. There were no significant differences between the fusion and no fusion groups in procedure time (52.4 ± 15.4 vs. 56.8 ± 19.5 min, p = .36), mean contrast volume used (33.8 ± 12.0 vs. 29.6 ± 11.5 mls, p = .19), mean fluoroscopy time (31.3 ± 9.9 vs. 28.9 ± 8.7 min, p = .32), mean radiation dose (1177 ± 969 vs. 1091 ± 692 mGy, p = .70), and radiation dose product curve (23.9 ± 20.5 vs. 35.0 ± 49.1 Gy cm2 , p = .29). There was no periprosthetic leak in the two groups in the immediate 1-month postprocedure follow-up periods. CONCLUSIONS: There was no significant difference with and without 3D-MDCT-fluoroscopy fusion in procedure time, contrast volume use, radiation dose, and radiation dose product.

OCTOPUS - Optical coherence tomography plaque and stent analysis software.

Background and objective: Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions, assessing their outcomes, and characterizing plaque components. To aid IVOCT research studies, we developed the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software, which provides highly automated, comprehensive analysis of coronary plaques and stents in IVOCT images. Methods: User specifications for OCTOPUS were obtained from detailed, iterative discussions with IVOCT analysts in the Cardiovascular Imaging Core Laboratory at University Hospitals Cleveland Medical Center, a leading laboratory for IVOCT image analysis. To automate image analysis results, the software includes several important algorithmic steps: pre-processing, deep learning plaque segmentation, machine learning identification of stent struts, and registration of pullbacks for sequential comparisons. Intuitive, interactive visualization and manual editing of segmentations were included in the software. Quantifications include stent deployment characteristics (e.g., stent area and stent strut malapposition), strut level analysis, calcium angle, and calcium thickness measurements. Interactive visualizations include (x,y) anatomical, en face, and longitudinal views with optional overlays (e.g., segmented calcifications). To compare images over time, linked visualizations were enabled to display up to four registered vessel segments at a time. Results: OCTOPUS has been deployed for nearly 1 year and is currently being used in multiple IVOCT studies. Underlying plaque segmentation algorithm yielded excellent pixel-wise results (86.2% sensitivity and 0.781 F1 score). Using OCTOPUS on 34 new pullbacks, we determined that following automated segmentation, only 13% and 23% of frames needed any manual touch up for detailed lumen and calcification labeling, respectively. Only up to 3.8% of plaque pixels were modified, leading to an average editing time of only 7.5 s/frame, an approximately 80% reduction compared to manual analysis. Regarding stent analysis, sensitivity and precision were both greater than 90%, and each strut was successfully classified as either covered or uncovered with high sensitivity (94%) and specificity (90%). We demonstrated use cases for sequential analysis. To analyze plaque progression, we loaded multiple pullbacks acquired at different points (e.g., pre-stent, 3-month follow-up, and 18-month follow-up) and evaluated frame-level development of in-stent neo-atherosclerosis. In ex vivo cadaver experiments, the OCTOPUS software enabled visualization and quantitative evaluation of irregular stent deployment in the presence of calcifications identified in pre-stent images. Conclusions: We introduced and evaluated the clinical application of a highly automated software package, OCTOPUS, for quantitative plaque and stent analysis in IVOCT images. The software is currently used as an offline tool for research purposes; however, the software's embedded algorithms may also be useful for real-time treatment planning.

Automatic microchannel detection using deep learning in intravascular optical coherence tomography images.

Microchannel formation is known to be a significant marker of plaque vulnerability, plaque rupture, and intraplaque hemorrhage, which are responsible for plaque progression. We developed a fully-automated method for detecting microchannels in intravascular optical coherence tomography (IVOCT) images using deep learning. A total of 3,075 IVOCT image frames across 41 patients having 62 microchannel segments were analyzed. Microchannel was manually annotated by expert cardiologists, according to previously established criteria. In order to improve segmentation performance, pre-processing including guidewire detection/removal, lumen segmentation, pixel-shifting, and noise filtering was applied to the raw (r,θ) IVOCT image. We used the DeepLab-v3 plus deep learning model with the Xception backbone network for identifying microchannel candidates. After microchannel candidate detection, each candidate was classified as either microchannel or no-microchannel using a convolutional neural network (CNN) classification model. Our method provided excellent segmentation of microchannel with a Dice coefficient of 0.811, sensitivity of 92.4%, and specificity of 99.9%. We found that pre-processing and data augmentation were very important to improve results. In addition, a CNN classification step was also helpful to rule out false positives. Furthermore, automated analysis missed only 3% of frames having microchannels and showed no false positives. Our method has great potential to enable highly automated, objective, repeatable, and comprehensive evaluations of vulnerable plaques and treatments. We believe that this method is promising for both research and clinical applications.

Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries.

Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4360 IVOCT image frames of 77 lesions among 41 patients. Expert cardiologists manually labeled lipidous plaque based on established criteria. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, θ) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7° ± 17°; mean ~ 196°). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2 ± 14.6 µm; mean ~ 175 µm), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.

Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning.

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

Decision-Making During Percutaneous Coronary Intervention Guided by Optical Coherence Tomography: Insights From the LightLab Initiative.

BACKGROUND: Use of intracoronary imaging is associated with improved outcomes in patients undergoing percutaneous coronary intervention (PCI). Yet, the impact of intracoronary imaging on real-time physician decision-making during PCI is not fully known. METHODS: The LightLab Initiative is a multicenter, prospective, observational study designed to characterize the use of a standardized optical coherence tomography (OCT) workflow during PCI. Participating physicians performed pre-PCI and post-PCI OCT in accordance with this workflow and operator assessments of lesion characteristics and treatment plan were recorded for each lesion based on angiography alone and following OCT. Physicians were categorized as having low (n=15), intermediate (n=13), or high (n=14) OCT use in the year preceding participation. RESULTS: Among 925 patients with 1328 lesions undergoing PCI, the prescribed OCT workflow was followed in 773 (84%) of patients with 836 lesions. Operator lesion assessment and decision-making during PCI changed with OCT use in 86% (721/836) of lesions. Pre-PCI OCT use changed operator decision-making in 80% of lesions, including lesion assessment (45%), vessel preparation strategy (27%), stent diameter (37%), and stent length (36%). Post-PCI OCT changed stent optimization decision-making in 31% of lesions. These findings were consistent across strata of physician prior OCT experience. CONCLUSIONS: A standardized OCT workflow impacted PCI decision-making in 86% of lesions, with a predominant effect on pre-PCI lesion assessment and planning of treatment strategy. This finding was consistent regardless of operator experience level and provides insight into mechanisms by which intravascular imaging might improve PCI outcomes.

Predictors of successful same-day discharge and 1-year outcomes after left atrial appendage closure.

BACKGROUND: Same-day discharge (SDD) following left atrial appendage closure (LAAC) is increasingly common but predictors of successful SDD and 1-year clinical outcomes have not been described. OBJECTIVE: The purpose of this study was to explore predictors of successful SDD and report 1-year outcomes in patients undergoing LAAC with SDD. METHODS: A prospective analysis was performed over a 20-month period of 225 consecutive patients that underwent LAAC in a large, academic hospital. All patients included in the study underwent a SDD protocol. Baseline characteristics and 1-year outcomes of patients discharged same day of the procedure versus those that required at least one overnight stay were compared. Adverse events, procedural success, and procedure times were evaluated. RESULTS: One hundred and sixty-one patients (72%) of patients were discharged the same day and 64 patients (28%) required at least an overnight stay (non-SDD: NSDD). NSDD patients were older and more often female. Procedure time was also longer in the NSDD group than in the SDD (63.4 vs. 55.1 min; p = 0.01). While overall procedural success rates were similar between the SDD and NSDD groups (99.4% vs. 98.4%; p = 0.39), NSDD patients had more complications (9.4% vs. 0%; p = 0.01) and higher number of devices per procedure (1.2 vs. 1.0; p = 0.01) as compared to SDD. At 1 year, there were no significant difference between the SDD and NSDD groups in stroke (1.1% vs. 0%; log-rank p = 0.44) and all-cause mortality (3.9% vs. 4.7%; log-rank p = 0.70). CONCLUSION: In this single-center LAAC experience, female sex, older age, and longer procedure duration were associated with higher likelihood for need of overnight stay. At 1-year follow-up, there were no significant differences in stroke events and death rates between SDD and NSDD groups.

Assessment of Post-Dilatation Strategies for Optimal Stent Expansion in Calcified Coronary Lesions: Ex Vivo Analysis With Optical Coherence Tomography.

INTRODUCTION: Interventional cardiologists make adjustments in the presence of coronary calcifications known to limit stent expansion, but proper balloon sizing, plaque-modification approaches, and high-pressure regimens are not well established. Intravascular optical coherence tomography (IVOCT) provides high-resolution images of coronary tissues, including detailed imaging of calcifications, and accurate measurements of stent deployment, providing a means for detailed study of stent deployment. OBJECTIVE: Evaluate stent expansion in an ex vivo model of calcified coronary arteries as a function of balloon size and high-pressure, post-dilatation strategies. METHODS: We conducted experiments on cadaver hearts with calcified coronary lesions. We assessed stent expansion as a function of size and pressure of non-compliant (NC) balloons (i.e., nominal, 0.5, 1.0, and 1.5 mm balloons at 10, 20 and 30 atm). IVOCT images were acquired pre-stent, post-stent, and at all post-dilatations. Stent expansion was calculated using minimum expansion index (MEI). RESULTS: We analyzed 134 IVOCT pullbacks from ten ex-vivo experiments. The mean distal and proximal reference lumen diameters were 2.2 ± 0.5 mm and 2.5 ± 0.7 mm, respectively, 80% of times using a 3.0 mm diameter stent. Overall, based on stent sizing, a good expansion (MEI ≥ 80%) was reached using the 1:1 NC balloon at 20 atm, and expansion > 100% was reached using the 1:1 NC balloon at 30 atm. In the subgroup analysis, comparing low-calcified and high-calcified lesions, good expansion (MEI ≥ 80%) was reached using the 1:1 NC balloon at nominal pressure (10 atm) versus using 1:1 NC balloon at 30 atm, respectively. Significant vessel rupture was identified in all the vessels mainly upon post-dilatation with larger balloons, and 60% of the experiments (6 vessels, 3 in each calcium subgroup) presented rupture with the +1.0 mm NC balloon at 20 atm. CONCLUSION: When treating calcified lesions, good stent expansion was reached using smaller balloons at higher pressures without coronary injuries, whereas bigger balloons yielded unpredictable expansion even at lower pressures and demonstrated potential harmful damages to the vessels. As these findings could help physicians with appropriate planning of stent post-dilatation for calcified lesions, it will be important to clinically evaluate the recommended protocol.

Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images.

Introduction: In-stent neoatherosclerosis has emerged as a crucial factor in post-stent complications including late in-stent restenosis and very late stent thrombosis. In this study, we investigated the ability of quantitative plaque characteristics from intravascular optical coherence tomography (IVOCT) images taken just prior to stent implantation to predict neoatherosclerosis after implantation. Methods: This was a sub-study of the TRiple Assessment of Neointima Stent FOrmation to Reabsorbable polyMer with Optical Coherence Tomography (TRANSFORM-OCT) trial. Images were obtained before and 18 months after stent implantation. Final analysis included images of 180 lesions from 90 patients; each patient had images of two lesions in different coronary arteries. A total of 17 IVOCT plaque features, including lesion length, lumen (e.g., area and diameter); calcium (e.g., angle and thickness); and fibrous cap (FC) features (e.g., thickness, surface area, and burden), were automatically extracted from the baseline IVOCT images before stenting using dedicated software developed by our group (OCTOPUS). The predictive value of baseline IVOCT plaque features for neoatherosclerosis development after stent implantation was assessed using univariate/multivariate logistic regression and receiver operating characteristic (ROC) analyses. Results: Follow-up IVOCT identified stents with (n = 19) and without (n = 161) neoatherosclerosis. Greater lesion length and maximum calcium angle and features related to FC were associated with a higher prevalence of neoatherosclerosis after stent implantation (p < 0.05). Hierarchical clustering identified six clusters with the best prediction p-values. In univariate logistic regression analysis, maximum calcium angle, minimum calcium thickness, maximum FC angle, maximum FC area, FC surface area, and FC burden were significant predictors of neoatherosclerosis. Lesion length and features related to the lumen were not significantly different between the two groups. In multivariate logistic regression analysis, only larger FC surface area was strongly associated with neoatherosclerosis (odds ratio 1.38, 95% confidence interval [CI] 1.05-1.80, p < 0.05). The area under the ROC curve was 0.901 (95% CI 0.859-0.946, p < 0.05) for FC surface area. Conclusion: Post-stent neoatherosclerosis can be predicted by quantitative IVOCT imaging of plaque characteristics prior to stent implantation. Our findings highlight the additional clinical benefits of utilizing IVOCT imaging in the catheterization laboratory to inform treatment decision-making and improve outcomes.

Hemodynamic alternations following stent deployment and post-dilation in a heavily calcified coronary artery: In silico and ex-vivo approaches.

In this work, hemodynamic alterations in a patient-specific, heavily calcified coronary artery following stent deployment and post-dilations are quantified using in silico and ex-vivo approaches. Three-dimensional artery models were reconstructed from OCT images. Stent deployment and post-dilation with various inflation pressures were performed through both the finite element method (FEM) and ex vivo experiments. Results from FEM agreed very well with the ex-vivo measurements, interms of lumen areas, stent underexpansion, and strut malapposition. In addition, computational fluid dynamics (CFD) simulations were performed to delineate the hemodynamic alterations after stent deployment and post-dilations. A pressure time history at the inlet and a lumped parameter model (LPM) at the outlet were adopted to mimic the aortic pressure and the distal arterial tree, respectively. The pressure drop across the lesion, pertaining to the clinical measure of instantaneous wave-free flow ratio (iFR), was investigated. Results have shown that post-dilations are necessary for the lumen gain as well as the hemodynamic restoration towards hemostasis. Malapposed struts induced much higher shear rate, flow disturbances and lower time-averaged wall shear stress (TAWSS) around struts. Post-dilations mitigated the strut malapposition, and thus the shear rate. Moreover, stenting induced larger area of low TAWSS (<0.4 Pa) and lager volume of high shear rate (>2000 s-1), indicating higher risks of in-stent restenosis (ISR) and stent thrombosis (ST), respectively. Oscillatory shear index (OSI) and relative residence time (RRT) indicated the wall regions more prone to ISR are located near the malapposed stent struts.

Mechanical performances of balloon post-dilation for improving stent expansion in calcified coronary artery: Computational and experimental investigations.

Stent deployment in a calcified coronary artery is often associated with suboptimal outcomes such as stent underexpansion and malapposition. Post-dilation after stent deployment is commonly used for optimal stent implantation. There is no guideline for choosing the post-dilation balloon diameter and inflation pressure. In this work, ex-vivo/in-silico experiments were performed to investigate the efficacy of post-dilation balloon diameter and inflation pressure in improving the stent expansion in a calcified lesion. Post-dilations with three balloon diameters (3 mm, 3.5 mm, and 4 mm) were performed. For each balloon diameter, three inflation pressures (10 atm, 20 atm, and 30 atm) were sequentially applied. In ex-vivo experiments, optical coherence tomography images were acquired during the stenting procedure, i.e., pre- and post-deployment of 3 mm diameter stent, as well as after each post-dilation. The results from in-silico experiments were compared with ex-vivo experiments in terms of lumen area. In addition, stretch ratio analysis was developed to predict the stent-induced lumen area, along with the strain analysis and the in-silico experiments. Results have shown that target lumen area could be achieved with an oversized nominal balloon diameter of +0.5 mm (i.e., 0.5 mm greater than reference lumen diameter) at an inflation pressure of 20 atm. After each post-dilation, fibrotic tissue demonstrated a larger strain, contributing to improved lumen gain. However, minimal changes were observed in calcification. Moreover, a strong correlation (R2 = 0.95) between the stretch ratio of fibrotic tissue and lumen area after each post-dilation was observed. This indicated that the morphology of the fibrotic tissue could be a potential marker to predict the lumen gain. The detailed mechanistic quantifications of a single lesion cannot be generalized to all clinical cases. However, this work could be used to provide a fundamental understanding of the post-dilations, to develop experimental protocols for producing generalized guidelines, and to exploit their potential for optimal pre- and post-stent strategies.

Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features.

For intravascular OCT (IVOCT) images, we developed an automated atherosclerotic plaque characterization method that used a hybrid learning approach, which combined deep-learning convolutional and hand-crafted, lumen morphological features. Processing was done on innate A-line units with labels fibrolipidic (fibrous tissue followed by lipidous tissue), fibrocalcific (fibrous tissue followed by calcification), or other. We trained/tested on an expansive data set (6,556 images), and performed an active learning, relabeling step to improve noisy ground truth labels. Conditional random field was an important post-processing step to reduce classification errors. Sensitivities/specificities were 84.8%/97.8% and 91.4%/95.7% for fibrolipidic and fibrocalcific plaques, respectively. Over lesions, en face classification maps showed automated results that agreed favorably to manually labeled counterparts. Adding lumen morphological features gave statistically significant improvement (p < 0.05), as compared to classification with convolutional features alone. Automated assessments of clinically relevant plaque attributes (arc angle and length), compared favorably to those from manual labels. Our hybrid approach gave statistically improved results as compared to previous A-line classification methods using deep learning or hand-crafted features alone. This plaque characterization approach is fully automated, robust, and promising for live-time treatment planning and research applications.

Análise direcional do fluxo sanguíneo miocárdico após revascularização transmiocárdica com laser de CO2: estudo através de ressonância magnética com imagens de gradiente ultra-rápido / Analize of the miocardial blood flow direction after CO2 transmyocardial laser revascularization, by first - pass magnect ressonance imaging

Objetivo: Avaliar a direção do fluxo sangüíneo miocárdico de pacientes submetidos à revascularização transmiocárdica com laser de CO2 (RTML), através de estudos de imagem por ressonância magnética. Casuística e Métodos: Dez pacientes submetidos a RTML com laser de C02 (potência de 800 W) foram estudados através da ressonância magnética de gradiente ultra-rápido (Gradiente eco-EPI de seqüência híbrida), visando avaliar o direcionamento da perfusão miocárdica após o procedimento. Gadolínio - DTPA (0,1 mmol/kg) foi injetado "em bolus" através de veia periférica em velocidade de 5 ml/seg em repouso durante o pico de "stress" induzido por dipiridamol. Foi avaliada sua distribuição miocárdica através da obtenção de curvas de intensidade de sinal no tempo para as diversas subregiões do mìocárdio, em modelo de 24 segmentos.Resultados: Após período médio de 14,7 meses, pudemos detectar isquemia em ao menos uma das paredes ventriculares em 6 (60por cento) pacientes. Em 1 (10por cento) paciente pode-se notar que o fluxo sangüíneo miocárdico dirigia-se do subendocárdio para o subepicárdio, ao contrário dos demais. Conclusão: A ressonância magnética, usando a técnica de perfusão miocárdica de primeira passagem, permitiu observar o direcionamento do fluxo sangüíneo miocárdico. Em um dos pacientes, a presença de fluxo miocárdico invertido (do endocárdio para o epicárdio) sugeriu a patência dos canais realizados através da RTML(AU)#S#a