Mr. Ryan A. Rava Profile

Ryan A. Rava

at Canon Stroke and Vascular Research Ctr

SPIE Involvement:

Author

Publications (18)

SPIE Journal Paper | 9 January 2023

Identification of infarct core and ischemic penumbra using computed tomography perfusion and deep learning

Mohammad Mahdi Shiraz Bhurwani, Timothe Boutelier, Adam Davis, Guillaume Gautier, Dennis Swetz, Ryan Rava, Dorian Raguenes, Muhammad Waqas, Kenneth Snyder, Adnan Siddiqui, Ciprian Ionita

JMI, Vol. 10, Issue 01, 014001, (January 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.1.014001

KEYWORDS: Shadows, Tunable filters, Voxels, Computed tomography, Image segmentation, Tissues, Wavelets, Diffusion weighted imaging, Optical spheres, Education and training

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Poster + Presentation + Paper

Predicting hematoma expansion after spontaneous intracranial hemorrhage through a radiomics based model

Samantha Seymour, Ryan Rava, Dennis Swetz, Andre Montiero, Ammad Baig, Kurt Schultz, Kenneth Snyder, Muhammad Waqas, Jason Davies, Elad Levy, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 12033, 120332X (2022) https://doi.org/10.1117/12.2611847

KEYWORDS: Magnetic resonance imaging, Lawrencium, Feature extraction, Medical imaging, Image segmentation, Computed tomography, Systems modeling, Process modeling, Monte Carlo methods, Data acquisition

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Purpose: Intracranial hemorrhage (ICH) is characterized as bleeding into the brain tissue, intracranial space, and ventricles and is the second most disabling form of stroke. Hematoma expansion (HE) following ICH has been correlated with significant neurological decline and death. For early detection of patients at risk, deep learning prediction models were developed to predict whether hematoma due to ICH will expand. This study aimed to explore the feasibility of HE prediction using a radiomic approach to help clinicians better stratify HE patients and tailor intensive therapies timely and effectively. Materials and Methods: Two hundred ICH patients with known hematoma evolution, were enrolled in this study. An open-source python package was utilized for the extraction of radiomic features from both non-contrast computed tomography (NCCT) and magnetic resonance imaging (MRI) scans through characterization algorithms. A total of 99 radiomic features were extracted and different features were selected for network inputs for the NCCT and MR models. Seven supervised classifiers: Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbor and Multilayer Perceptron were used to build the models. A training:testing split of 80:20 and 20 iterations of Monte Carlo cross validation were performed to prevent overfitting and assess the variability of the networks, respectively. The models were fed training datasets from which they learned to classify the data based on pre-determined radiomic categories. Results: The highest sensitivity among the NCCT classifier models was seen with the support vector machine (SVM) and logistic regression (LR) of 72 ± 0.3% and 73 ± 0.5%, respectively. The MRI classifier models had the highest sensitivity of 68 ± 0.5% and 72 ± 0.5% for the SVM and LR models, respectively. Conclusions: This study indicates that the NCCT radiomics model is a better predictor of HE and that SVM and LR classifiers are better predictors of HE due to their more cautious approach indicated by a higher sensitivity metric.

Proceedings Article | 4 April 2022 Presentation + Paper

Presentation + Paper

Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and machine learning

Dennis Swetz, Samantha Seymour, Ryan Rava, Mohammad Mahdi Shiraz Bhurwani, Andre Monteiro, Ammad Baig, Muhammad Waqas, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 12036, 120360B (2022) https://doi.org/10.1117/12.2610672

KEYWORDS: Machine learning, Brain, Computed tomography, Blood, Data modeling, Receivers, Performance modeling, Statistical analysis, Algorithm development

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Proceedings Article | 15 February 2021 Presentation + Paper

Presentation + Paper

Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters

Ryan Rava, Alexander Podgorsak, Mohammad Waqas, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 11596, 1159611 (2021) https://doi.org/10.1117/12.2579753

KEYWORDS: Convolutional neural networks, Computed tomography, Diffusion weighted imaging, Tissues, Ischemic stroke, Image segmentation, Targeting Task Performance metric, Monte Carlo methods, Hemodynamics, Diagnostics

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Proceedings Article | 15 February 2021 Presentation + Paper

Presentation + Paper

Use of biplane quantitative angiographic imaging with ensemble neural networks to assess reperfusion status during mechanical thrombectomy

Mohammad Mahdi Shiraz Bhurwani, Kenneth Snyder, Muhammad Waqas, Maxim Mokin, Ryan Rava, Alexander Podgorsak, Kelsey Sommer, Jason Davies, Elad Levy, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 11597, 115971F (2021) https://doi.org/10.1117/12.2580358

KEYWORDS: Angiography, Neural networks, Hemodynamics, Visualization, Visual process modeling, Receivers, Optimization (mathematics), Ischemic stroke, Convolutional neural networks, Brain mapping

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Proceedings Article | 15 February 2021 Presentation + Paper

Presentation + Paper

The Aneurysm Occlusion Assistant, an AI platform for real time surgical guidance of intracranial aneurysms

Kyle Williams, Alexander Podgorsak, Mohammad Mahdi Shiraz Bhurwani, Ryan Rava, Kelsey Sommer, Ciprian Ionita

Proceedings Volume 11601, 116010V (2021) https://doi.org/10.1117/12.2581003

KEYWORDS: Aneurysms, Angiography, Artificial intelligence, Algorithm development, Telecommunications, Evolutionary algorithms, Surgery, Machine learning, Image segmentation

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SPIE Journal Paper | 10 February 2021

Investigation of convolutional neural networks using multiple computed tomography perfusion maps to identify infarct core in acute ischemic stroke patients

Ryan Rava, Alexander Podgorsak, Muhammad Waqas, Kenneth Snyder, Maxim Mokin, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

JMI, Vol. 8, Issue 01, 014505, (February 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.1.014505

KEYWORDS: Image segmentation, Diffusion weighted imaging, Targeting Task Performance metric, Computed tomography, Optical spheres, Convolutional neural networks, Tissues, Ischemic stroke, Arteries, Brain

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Proceedings Article | 16 March 2020 Paper

Paper

Predicting treatment outcome of intracranial aneurysms using angiographic parametric imaging and recurrent neural networks

Mohammad Mahdi Shiraz Bhurwani, Mohammad Waqas, Kyle Williams, Ryan Rava, Alexander Podgorsak, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 11314, 113142O (2020) https://doi.org/10.1117/12.2548635

KEYWORDS: Aneurysms, Angiography, Neural networks, Targeting Task Performance metric, Receivers, X-rays, Monte Carlo methods, Blood circulation, Binary data, Hemodynamics

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Proceedings Article | 16 March 2020 Paper

Paper

Effect of truncated singular value decomposition on digital subtraction angiography derived angiographic parametric imaging maps

Ryan Rava, Ariana Allman, Stephen Rudin, Ciprian Ionita

Proceedings Volume 11312, 1131235 (2020) https://doi.org/10.1117/12.2545994

KEYWORDS: Arteries, Angiography, Statistical modeling, Blood, Aneurysms, Hemodynamics, Ischemic stroke, Artificial intelligence, Software development, Tissues

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Proceedings Article | 2 March 2020 Presentation + Paper

Presentation + Paper

Use of 3D printed intracranial aneurysm phantoms to test the effect of flow diverters geometry on hemodynamics

Ariana Allman, Mohammad Mahdi Shiraz Bhurwani, Jillian Senko, Ryan Rava, Alexander Podgorsak, Stephan Rudin, Ciprian Ionita

Proceedings Volume 11318, 1131803 (2020) https://doi.org/10.1117/12.2549575

KEYWORDS: Resistance, Aneurysms, Arteries, Hemodynamics, 3D modeling, 3D printing

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Proceedings Article | 2 March 2020 Presentation + Paper

Presentation + Paper

Challenges and limitations of patient specific mitral valve 3D-printing

Jillian Senko, Alexander Podgorsak, Ariana Allman, Ryan Rava, Mohammad Mahdi Shiraz Bhurwani, Vijay Iyer, Stephen Rudin, Ciprian Ionita

Proceedings Volume 11318, 1131805 (2020) https://doi.org/10.1117/12.2549140

KEYWORDS: 3D modeling, Image segmentation, 3D printing, Computed tomography, 3D image processing, Tissues, Manufacturing, Blood, Electrocardiography, Heart

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Proceedings Article | 28 February 2020 Presentation + Paper

Presentation + Paper

Co-registration of infarct core location with angiography during mechanical thrombectomy procedures for treatment progression monitoring

Nicholas Pham, Ryan Rava, Mohammad Mahdi Shiraz Bhurwani, Alexander Podgorsak, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 11317, 113170E (2020) https://doi.org/10.1117/12.2549039

KEYWORDS: Angiography, Image registration, Targeting Task Performance metric, Tissues, Arteries, Image segmentation, Brain, Blood circulation, Computed tomography, Blood

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Proceedings Article | 28 February 2020 Paper

Paper

Optimization of DSA image data input to a machine learning aneurysm identifier

Alexander Podgorsak, Mohammad Mahdi Shiraz Bhurwani, Ryan Rava, Jason Davies, Ciprian Ionita

Proceedings Volume 11317, 113171Y (2020) https://doi.org/10.1117/12.2549727

KEYWORDS: Aneurysms, Image segmentation, Content addressable memory, Image processing, Angiography, Machine learning, Visualization, Arteries, Convolutional neural networks, Image visualization

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SPIE Journal Paper | 11 February 2020

Performance of angiographic parametric imaging in locating infarct core in large vessel occlusion acute ischemic stroke patients

Ryan Rava, Maxim Mokin, Kenneth Snyder, Muhammad Waqas, Adnan Siddiqui, Jason Davies, Elad Levy, Ciprian Ionita

JMI, Vol. 7, Issue 01, 016001, (February 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.1.016001

KEYWORDS: Tissues, Angiography, Magnetic resonance imaging, Targeting Task Performance metric, Image registration, Arteries, Artificial intelligence, Ischemic stroke, Hemodynamics, Image segmentation

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Proceedings Article | 15 March 2019 Presentation + Paper

Presentation + Paper

Controlled compliancy of 3D printed vascular patient specific phantoms

Ariana Allman, Lauren Shepard, Alexander Podgorsak, Ryan Rava, Ciprian Ionita

Proceedings Volume 10954, 109540C (2019) https://doi.org/10.1117/12.2512528

KEYWORDS: Arteries, Liquids, 3D printing, Angiography, 3D modeling, Blood, Image segmentation, Blood circulation, Control systems, X-rays

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Purpose: 3D printed (3DP) patient specific vascular phantoms provide the ability to improve device testing and to aid in the course of treatment of vascular disease, while reducing the need for in-vivo experiments. In addition to accurate vascular geometric reproducibility, such phantoms could allow simulation of certain vascular mechanical properties. We investigated various 3DP designs to allow simulation of physiological transmural pressure on phantom vasculature. Materials and Methods: A transparent compliance chamber was created using an Eden260V printer (Stratasys) with VeroClear and acrylic to accommodate 3DP patient specific vascular phantoms. The patient vascular geometries were acquired from a CT angiogram (Aquilion ONE, Canon Medical) and segmented using Vitrea workstation (Vital Images). The segmented geometry was manipulated in Autodesk Meshmixer and 3D printed using Agilus. The phantom was integrated in the compliance chamber and connected to a pump which simulated physiologic pulsatile flow waveforms. Compliance of the vessels was varied by filling the chamber with various levels of liquid and air. This setup allowed controlled expansion of the 3DP arteries, as a function of the liquid level while a programmable pump simulated the blood flow through the vascular network. The pressure within the vessels was measured for various compliancy settings while physiological flow rates were simulated through the arteries. Results: A neurovascular phantom was placed in the chamber and amount of artery expansion diameter was controlled by changing the liquid level in the compliance chamber. Artery patency and contrast flow were demonstrated using x-ray angiography. The pressures in the left and the right internal carotid artery increased from 98mmHg to 104mmHg and from 96mmHg to 102mmHg, respectively, while maintaining the same flow rates. Conclusions: 3D printed patient specific neurovascular phantoms can be manipulated through using of a compliance chamber in order to establish physiologically relevant hemodynamic conditions.

Proceedings Article | 13 March 2019 Paper

Paper

Use of a convolutional neural network for aneurysm identification in digital subtraction angiography

Alexander Podgoršak, Mohammad Mahdi Bhurwani, Ryan Rava, Anusha Chandra, Ciprian Ionita

Proceedings Volume 10950, 109504E (2019) https://doi.org/10.1117/12.2512810

KEYWORDS: Aneurysms, Image segmentation, Angiography, Network architectures, Machine learning, Binary data, Convolutional neural networks, Digital filtering, Brain, Neural networks

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Proceedings Article | 13 March 2019 Paper

Paper

Feasibility study of deep neural networks to classify intracranial aneurysms using angiographic parametric imaging

Mohammad Mahdi Shiraz Bhurwani, Alexander Podgorsak, Anusha Ramesh Chandra, Ryan Rava, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 10950, 109502A (2019) https://doi.org/10.1117/12.2512643

KEYWORDS: Aneurysms, Data modeling, Machine learning, Neural networks, Angiography, Blood circulation, Brain mapping

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Proceedings Article | 1 March 2019 Paper

Paper

Feasibility of locating infarct core with 2D angiographic parametric imaging (API) using computed tomography perfusion data

Ryan Rava, Ariana Allman, Mohammad Mahdi Shiraz Bhurwani, Kenneth Snyder, Elad Levy, Adnan Siddiqui, Jason Davies, Ciprian Ionita

Proceedings Volume 10948, 109485T (2019) https://doi.org/10.1117/12.2511980

KEYWORDS: Tissues, Brain, Targeting Task Performance metric, Angiography, Computed tomography, Ischemic stroke, LabVIEW, Brain mapping, Software development, Blood circulation

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Showing 5 of 18 publications

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