Dr. Jason M. Davies Profile

Dr. Jason M. Davies

at Univ at Buffalo

SPIE Involvement:

Author

Publications (23)

Proceedings Article | 10 April 2023 Presentation + Paper

Investigating angiographic injection parameters for cerebral aneurysm hemodynamic characterization using patient-specific simulated angiograms

R. White, A. Shields, S. Nagesh, E. Smith, J. Davies, D. Bednarek, S. Rudin, C. Ionita, V. Chivukula

Proceedings Volume 12468, 1246814 (2023) https://doi.org/10.1117/12.2653871

KEYWORDS: Aneurysms, Hemodynamics, Angiography, Targeting Task Performance metric, Contrast agents, Blood, Cerebral aneurysms, Simulations, Blood circulation, 3D projection, CFD analysis

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Proceedings Article | 4 April 2022 Presentation + Paper

Leveraging patient-specific simulated angiograms to characterize cerebral aneurysm hemodynamics using computational fluid dynamics

V. Chivukula, R. White, A. Shields, J. Davies, M. Mokin, D. R. Bednarek, S. Rudin, C. Ionita

Proceedings Volume 12036, 120360S (2022) https://doi.org/10.1117/12.2611473

KEYWORDS: Aneurysms, Hemodynamics, Targeting Task Performance metric, Blood circulation, Blood, Angiography, Computational fluid dynamics, CFD analysis, Cerebral aneurysms

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Proceedings Article | 4 April 2022 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 Poster + Presentation + Paper

Initial investigation of the use of angiographic parametric imaging for early prognosis of delayed cerebral ischemia in patients with subarachnoid hemorrhage

Roman Price, Mohammad Mahdi Shiraz Bhurwani, Kelsey Sommer, Andre Monteiro, Ammad Baig, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 12036, 120361Q (2022) https://doi.org/10.1117/12.2612081

KEYWORDS: Angiography, Ischemia, Targeting Task Performance metric, Visualization, Machine learning, Data modeling, Brain, Performance modeling, X-rays, Algorithm development

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Proceedings Article | 4 April 2022 Presentation + Paper

Quantitative angiography prognosis of intracranial aneurysm treatment failure using parametric imaging and distal vessel analysis

Alexander Wisniewski, Mohammad Mahdi Shiraz Bhurwani, Kelsey Sommer, Andre Monteiro, Ammad Baig, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 12036, 120360D (2022) https://doi.org/10.1117/12.2611550

KEYWORDS: Aneurysms, Angiography, Hemodynamics, Arteries, Neural networks, Targeting Task Performance metric, Blood circulation, X-rays, Failure analysis

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Proceedings Article | 4 April 2022 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 | 4 April 2022 Presentation + Paper

Prognosis of ischemia recurrence in patients with moderate intracranial atherosclerotic disease using quantitative MRA measurements

Jeff Joseph, Benjamin Weppner, Nandor Pinter, Mohammad Mahdi Shiraz Bhurwani, Andre Monteiro, Ammad Baig, Jason Davies, Adnan Siddique, Ciprian Ionita

Proceedings Volume 12036, 120360V (2022) https://doi.org/10.1117/12.2611462

KEYWORDS: Arteries, Ischemia, Phase shifts, Magnetic resonance imaging, Error analysis, Principal component analysis, Independent component analysis, Brain, Blood circulation, Hemodynamics

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Proceedings Article | 15 February 2021 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

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

Clot organization on histology is associated with radiomics features that predict treatment outcomes from mechanical thrombectomy

Tatsat R. Patel, Muhammad Waqas, Sarah Fricano, Adam Dmytriw, John Tomaszewski, Elad Levy, Kenneth Snyder, Jason Davies, Adnan Siddiqui, Vincent Tutino

Proceedings Volume 11603, 116030L (2021) https://doi.org/10.1117/12.2581130

KEYWORDS: Rutherfordium, Computed tomography, Statistical analysis, Image segmentation, Image analysis, Blood, Medical imaging, Machine vision, Ischemic stroke

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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

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 | 2 March 2020 Presentation + Paper

Use of 3D-printed patient-specific neurovascular phantoms to investigate the correlation between disease severity and quantitative angiography results

Lauren Shepard, Kelsey Sommer, Eric Paccione, Maxim Mokin, Adnan Siddiqui, Kenneth Snyder, Elad Levy, Jason Davies, Stephen Rudin, Ciprian Ionita

Proceedings Volume 11318, 1131806 (2020) https://doi.org/10.1117/12.2548213

KEYWORDS: Arteries, Angiography, 3D printing, Sensors, Image segmentation, Hemodynamics, Targeting Task Performance metric, Resistance, 3D image processing, Brain

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Proceedings Article | 28 February 2020 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 Presentation + Paper

Quantification of flow through intracranial arteriovenous malformations using Angiographic Parametric Imaging (API)

Kyle Williams, Mohammed Mahdi Shiraz Bhurwani, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 11317, 113170S (2020) https://doi.org/10.1117/12.2548636

KEYWORDS: Angiography, Targeting Task Performance metric, Arteries, Brain, Veins, Quantitative analysis, Analytical research, Medical research

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Purpose: Intracranial arteriovenous malformations (AVMs) are severe neurovascular diseases in which the arterial branches of an area of the brain communicate directly with venous circulation through a network of dilated vasculature (nidus) which significantly increases the risk of hemorrhage. Treatment plans typically incorporate direct embolization with liquid materials delivered via micro-catheters under fluoroscopy. Currently, the progression and success of this procedure are qualitatively evaluated using digitally subtracted angiographic (DSA) sequences. This study sought to validate the use of Angiographic Parametric Imaging (API) for quantitative analysis of the hemodynamic changes caused by embolization treatment using imaging biomarkers. Materials and Methods: 36 patients with AVMs were selected randomly from a list of patients with known symptoms at presentation. For each, at least one set of frontal and lateral angiograms were analyzed using API. Parametric maps were calculated for five imaging biomarkers, including time to peak (TTP), mean transit time (MTT), time to arrival (TTA), peak height (PH), and area under the curve (AUC). Regions of interest (ROIs) were selected over the feeding arteries, AVM nidus, and draining veins. Average ROI parameters were calculated and changes in flow due to embolization were quantified through a percent change analysis. Results were verified using correlation coefficients across AVM vasculature at multiple sites following normalization. Results: Frontal to lateral correlation coefficients; TTP, 0.54±0.07; MTT, 0.24±0.09; TTA 0.60±0.06; PH, 0.33±0.08; AUC, 0.22±0.09. Nidus to principle draining vein (PDV) correlation coefficients; TTP, 0.75±0.03; MTT, 0.64±0.04; TTA, 0.80±0.02; PH, 0.32±0.06; AUC, 0.68±0.04. PH and AUC values affected by DSA inversion. Conclusions: The study concludes that the API software is reliable in determining the flow parameters throughout the AVM, provided that the selected ROI is consistent between frontal and lateral scans and DSA selection is optimal.

Proceedings Article | 28 February 2020 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

Initial assessment of neuro pressure gradients in carotid stenosis using 3D printed patient-specific phantoms

Lauren Shepard, Adnan Siddiqui, Kenneth Snyder, Elad Levy, Jason Davies, Ciprian Ionita

Proceedings Volume 10953, 1095314 (2019) https://doi.org/10.1117/12.2510279

KEYWORDS: Arteries, Image segmentation, 3D printing, Blood circulation, Diagnostics, Computed tomography, Cerebral blood flow

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Proceedings Article | 13 March 2019 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 Presentation + Paper

Initial study of the radiomics of intracranial aneurysms using Angiographic Parametric Imaging (API) to evaluate contrast flow changes

Anusha Ramesh Chandra, Alexander Podgorsak, Mohammad Waqas, Mohammad Mahdi Shiraz Bhurwani, Hussain Shallwani, Jordan Marshall, Adnan Siddiqui, Jason Davies, Stephen Rudin, Ciprian Ionita

Proceedings Volume 10948, 1094805 (2019) https://doi.org/10.1117/12.2512457

KEYWORDS: Aneurysms, Angiography, Machine learning, Targeting Task Performance metric, Arteries, Blood circulation, Neural networks, Receivers, Neck, Statistical analysis

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Proceedings Article | 1 March 2019 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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Proceedings Article | 3 March 2017 Presentation + Paper

Computer-assisted adjuncts for aneurysmal morphologic assessment: toward more precise and accurate approaches

Hamidreza Rajabzadeh-Oghaz, Nicole Varble, Jason Davies, Ashkan Mowla, Hakeem Shakir, Ashish Sonig, Hussain Shallwani, Kenneth Snyder, Elad Levy, Adnan Siddiqui, Hui Meng

Proceedings Volume 10134, 101341C (2017) https://doi.org/10.1117/12.2255553

KEYWORDS: Neck, 3D modeling, Image segmentation, 3D image processing, Independent component analysis, Error analysis, Software development, Precision measurement, Cerebral aneurysms, Angiography, Morphological analysis

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Proceedings Article | 3 March 2017 Paper

Association between hemodynamic modifications and clinical outcome of intracranial aneurysms treated using flow diverters

Nikhil Paliwal, Robert Damiano, Jason Davies, Adnan Siddiqui, Hui Meng

Proceedings Volume 10135, 101352F (2017) https://doi.org/10.1117/12.2254584

KEYWORDS: Hemodynamics, Computational fluid dynamics, CFD analysis, Blood, Cerebral aneurysms, Cerebral blood flow, Electroluminescence, Computer simulations, Process modeling, Angiography, 3D modeling

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Showing 5 of 23 publications

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