1. Articles from David Prabhu

    1-13 of 13
    1. Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features

      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 ...

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    2. Optical Coherence Tomography-Based Modeling of Stent Deployment in Heavily Calcified Coronary Lesion

      Optical Coherence Tomography-Based Modeling of Stent Deployment in Heavily Calcified Coronary Lesion

      In this work, a heavily calcified coronary artery model was reconstructed from optical coherence tomography (OCT) images to investigate the impact of calcification characteristics on stenting outcomes. The calcification was quantified at various cross sections in terms of angle, maximum thickness, and area. The stent deployment procedure, including the crimping, expansion, and recoil, was implemented. The influence of calcification characteristics on stent expansion, malapposition, and lesion mechanics was characterized. Results have shown that the minimal lumen area following stenting occurred at the cross section with the greatest calcification angle. The calcification angle constricted the stretchability of the lesion and thus ...

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    3. Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images

      Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images

      Accurate identification of coronary plaque is very important for cardiologists when treating patients with advanced atherosclerosis. We developed fully-automated semantic segmentation of plaque in intravascular OCT images. We trained/tested a deep learning model on a folded, large, manually annotated clinical dataset. The sensitivities/specificities were 87.4%/89.5% and 85.1%/94.2% for pixel-wise classification of lipidous and calcified plaque, respectively. Automated clinical lesion metrics, potentially useful for treatment planning and research, compared favorably (<4%) with those derived from ground-truth labels. When we converted the results to A-line classification, they were significantly better (p < 0.05) than ...

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    4. Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets

      Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets

      We developed machine learning methods to identify fibrolipidic and fibrocalcific A-lines in intravascular optical coherence tomography (IVOCT) images using a comprehensive set of handcrafted features. We incorporated features developed in previous studies (e.g., optical attenuation and A-line peaks). In addition, we included vascular lumen morphology and three-dimensional (3-D) digital edge and texture features. Classification methods were developed using expansive datasets (∼7000  images), consisting of both clinical in-vivo images and an ex-vivo dataset, which was validated using 3-D cryo-imaging/histology. Conditional random field was used to perform 3-D classification noise cleaning of classification results. We tested various multiclass approaches, classifiers ...

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    5. Title Automated Plaque Characterization of Intravascular Optical Coherence Tomography (IVOCT) Images Using 3D Cryo-image/Histology Validation (Thesis)

      Title Automated Plaque Characterization of Intravascular Optical Coherence Tomography (IVOCT) Images Using 3D Cryo-image/Histology Validation (Thesis)

      Intravascular optical coherence tomography (IVOCT) is a high-resolution (10-20 µm), high-contrast, catheter-based imaging technology that can identify major coronary plaques types (fibrous, lipid, and calcium). However, with >500 images generated in a single 2.5 second scan, automation is needed to facilitate live-time clinical use and off-line assessment of drug therapeutics. Therefore, we developed machine learning approaches for automated plaque characterization using cryo-imaging/histology for validation. However, we confronted a challenge in that IVOCT images are obtained perpendicular to the imaging catheter, while cryo-image volumes display the true 3D vessel geometry at a higher sampling rate. Our solution was to ...

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    6. Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images

      Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images

      We develop neural-network-based methods for classifying plaque types in clinical intravascular optical coherence tomography (IVOCT) images of coronary arteries. A single IVOCT pullback can consist of >500 microscopic-resolution images, creating both a challenge for physician interpretation during an interventional procedure and an opportunity for automated analysis. In the proposed method, we classify each A-line, a datum element that better captures physics and pathophysiology than a voxel, as a fibrous layer followed by calcification (fibrocalcific), a fibrous layer followed by a lipidous deposit (fibrolipidic), or other. For A-line classification, the usefulness of a convolutional neural network (CNN) is compared with that ...

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    7. Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation

      Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation

      Evidence suggests high-resolution, high-contrast, 100    frames / s 100  frames/s intravascular optical coherence tomography (IVOCT) can distinguish plaque types, but further validation is needed, especially for automated plaque characterization. We developed experimental and three-dimensional (3-D) registration methods to provide validation of IVOCT pullback volumes using microscopic, color, and fluorescent cryo-image volumes with optional registered cryo-histology. A specialized registration method matched IVOCT pullback images acquired in the catheter reference frame to a true 3-D cryo-image volume. Briefly, an 11-parameter registration model including a polynomial virtual catheter was initialized within the cryo-image volume, and perpendicular images were extracted, mimicking IVOCT image acquisition ...

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    8. Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks

      Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks

      Analysis of intravascular optical coherence tomography (IVOCT) data has potential for real-time in vivo plaque classification. We developed a processing pipeline on a three-dimensional local region of support for estimation of optical properties of atherosclerotic plaques from coronary artery, IVOCT pullbacks. Using realistic coronary artery disease phantoms, we determined insignificant differences in mean and standard deviation estimates between our pullback analyses and more conventional processing of stationary acquisitions with frame averaging. There was no effect of tissue depth or oblique imaging on pullback parameter estimates. The method’s performance was assessed in comparison with observer-defined standards using clinical pullback data ...

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    9. 3D registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation

      3D registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation

      High resolution, 100 frames/sec intravascular optical coherence tomography (IVOCT) can distinguish plaque types, but further validation is needed, especially for automated plaque characterization. We developed experimental and 3D registration methods, to provide validation of IVOCT pullback volumes using microscopic, brightfield and fluorescent cryoimage volumes, with optional, exactly registered cryo-histology. The innovation was a method to match an IVOCT pullback images, acquired in the catheter reference frame, to a true 3D cryo-image volume. Briefly, an 11-parameter, polynomial virtual catheter was initialized within the cryo-image volume, and perpendicular images were extracted, mimicking IVOCT image acquisition. Virtual catheter parameters were optimized to ...

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    10. Classification of calcium in intravascular OCT images for the purpose of intervention planning

      Classification of calcium in intravascular OCT images for the purpose of intervention planning

      The presence of extensive calcification is a primary concern when planning and implementing a vascular percutaneous intervention such as stenting. If the balloon does not expand, the interventionalist must blindly apply high balloon pressure, use an atherectomy device, or abort the procedure. As part of a project to determine the ability of Intravascular Optical Coherence Tomography (IVOCT) to aid intervention planning, we developed a method for automatic classification of calcium in coronary IVOCT images. We developed an approach where plaque texture is modeled by the joint probability distribution of a bank of filter responses where the filter bank was chosen ...

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    11. Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography

      Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography

      We developed robust, three-dimensional methods, as opposed to traditional A-line analysis, for estimating the optical properties of calcified, fibrotic, and lipid atherosclerotic plaques from in vivo coronary artery intravascular optical coherence tomography clinical pullbacks. We estimated attenuation μ t and backscattered intensity I 0 from small volumes of interest annotated by experts in 35 pullbacks. Some results were as follows: noise reduction filtering was desirable, parallel line (PL) methods outperformed individual line methods, root mean square error was the best goodness-of-fit, and α -trimmed PL ( α -T-PL) was the best overall method. Estimates of μ t were calcified ( 3.84 ± 0.95     mm − 1 ...

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    12. Volumetric Characterization of Human Coronary Calcification by Frequency-Domain Optical Coherence Tomography.

      Volumetric Characterization of Human Coronary Calcification by Frequency-Domain Optical Coherence Tomography.

      Background: Coronary artery calcification (CAC) presents unique challenges for percutaneous coronary intervention. Calcium appears as a signal-poor region with well-defined borders by frequency-domain optical coherence tomography (FD-OCT). The objective of this study was to demonstrate the accuracy of intravascular FD-OCT to determine the distribution of CAC. Methods and Results: Cadaveric coronary arteries were imaged using FD-OCT at 100-μm frame interval. Arteries were subsequently frozen, sectioned and imaged at 20-μm intervals using the Case Cryo-Imaging automated system TM . Full volumetric co-registration between FD-OCT and cryo-imaging was performed. Calcium area, calcium-lumen distance (depth) and calcium angle were traced on every ...

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    13. Three-Dimensional Fourier-Domain Optical Coherence Tomography Imaging: Advantages and Future Development

      Three-Dimensional Fourier-Domain Optical Coherence Tomography Imaging: Advantages and Future Development

      Traditionally, intravascular imaging methods display the coronary anatomy in two dimensions, through a series of consecutive cross-sectional tomographic images. The physician is then required to mentally reassemble these images in order to visualize the vascular anatomy and all its complex interactions. The ability to depict the vascular structure with its actual spatial appearance, in three dimensions, is a powerful way to provide an easy, objective, and comprehensive overview of its complex and dynamic anatomy. However, three-dimensional (3D) application of intravascular imaging has been plagued by lack of enough resolution, frequent presence of imaging and motion artifacts and need for extensive ...

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    1-13 of 13
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  2. Topics in the News

    1. (13 articles) Case Western Reserve University
    2. (12 articles) Hiram G. Bezerra
    3. (11 articles) David L. Wilson
    4. (5 articles) Marco A. Costa
    5. (5 articles) Madhusudhana Gargesha
    6. (3 articles) Yusuke Fujino
    7. (3 articles) Andrew M. Rollins
    8. (1 articles) Cleveland Clinic
    9. (1 articles) Zhao Wang
    10. (1 articles) Abbot
    11. (1 articles) Academic Medical Center at the University of Amsterdam
    12. (1 articles) Jichi Medical University
    13. (1 articles) University of Milan
    14. (1 articles) Bern University Hospital
    15. (1 articles) Capital Medical University
    16. (1 articles) Nanjing University of Science and Technology
    17. (1 articles) Martin S. Zinkernagel
    18. (1 articles) Junya Ako
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    Three-Dimensional Fourier-Domain Optical Coherence Tomography Imaging: Advantages and Future Development Volumetric Characterization of Human Coronary Calcification by Frequency-Domain Optical Coherence Tomography. Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography Classification of calcium in intravascular OCT images for the purpose of intervention planning 3D registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation Processing to determine optical parameters of atherosclerotic disease from phantom and clinical intravascular optical coherence tomography three-dimensional pullbacks Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images Title Automated Plaque Characterization of Intravascular Optical Coherence Tomography (IVOCT) Images Using 3D Cryo-image/Histology Validation (Thesis) Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images Correlation between optical coherence tomography, multifocal electroretinogram findings and visual acuity in diabetic macular edema Ciliary body length revisited by anterior segment optical coherence tomography: implications for safe access to the pars plana for intravitreal injections