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Bhurwani MMS, Sommer KN, Ionita CN. Recovery of complete time density curves from incomplete angiographic data using recurrent neural networks. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12036:1203617. [PMID: 35983496 PMCID: PMC9385185 DOI: 10.1117/12.2611225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Quantitative angiography is a 2D/3D x-ray imaging modality that summarizes hemodynamic information using time density curve (TDC) based parameters. Estimation of the TDC parameters are susceptible to errors due to various factors including, patient motion, incomplete temporal data, imaging trigger errors etc. In this study, we tested the feasibility of using recurrent neural networks (RNN) to recover complete TDC temporal information from incomplete sequences and evaluate quantitative parameters generated from the corrected TDCs. Digital subtraction angiograms (DSAs) were collected from patients undergoing endovascular treatments and angiographic parametric imaging (API) parameters were calculated from each DSA. Each set of API parameters was used to simulate a TDC resulting in a dataset of 760 TDCs. One-third of each TDC was continuously masked from pseudo-random points past the peak height (PH) point to simulate missing/artifact information. An RNN was developed, trained and tested to generate completed/corrected TDCs. The RNN recovered complete TDC temporal information with an average mean squared error of 0.0086±0.002. Average mean absolute errors were calculated between each API parameter generated from the ground truth TDCs and RNN corrected TDCs, these were 11.02%±0.91 for time to peak, 10.97%±0.69 for mean transit time, 5.65%±0.76 for PH, and 15.08%±0.98 for area under the TDC. The change in API parameters was not clinically significant and the predictive power of the API parameters was retained. This study proved the feasibility of using RNNs to mitigate motion artifacts and incomplete angiographic acquisitions to extract accurate quantitative parameters.
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Affiliation(s)
- Mohammad Mahdi Shiraz Bhurwani
- Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
| | - Kelsey N Sommer
- Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- QAS.AI Incorporated, Buffalo NY 14203
| | - Ciprian N Ionita
- Department of Biomedical Engineering, University at Buffalo, Buffalo NY 14228
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203
- University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine, Buffalo NY 14228
- QAS.AI Incorporated, Buffalo NY 14203
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Bhurwani MMS, Snyder KV, Waqas M, Mokin M, Rava RA, Podgorsak AR, Sommer KN, Davies JM, Levy EI, Siddiqui AH, Ionita CN. Use of biplane quantitative angiographic imaging with ensemble neural networks to assess reperfusion status during mechanical thrombectomy. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 11597. [PMID: 33707812 DOI: 10.1117/12.2580358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Digital subtraction angiography (DSA) is the main imaging modality used to assess reperfusion during mechanical thrombectomy (MT) when treating large vessel occlusion (LVO) ischemic strokes. To improve this visual and subjective assessment, hybrid models combining angiographic parametric imaging (API) with deep learning tools have been proposed. These models use convolutional neural networks (CNN) with single view individual API maps, thus restricting use of complementary information from multiple views and maps resulting in loss of relevant clinical information. This study investigates use of ensemble networks to combine hemodynamic information from multiple bi-plane API maps to assess level of reperfusion. Three-hundred-eighty-three anteroposterior (AP) and lateral view DSAs were retrospectively collected from patients who underwent MTs of anterior circulation LVOs. API peak height (PH) and area under time density curve (AUC) maps were generated. CNNs were developed to classify maps as adequate/inadequate reperfusion as labeled by two neuro-interventionalists. Outputs from individual networks were combined by weighting each output, using a grid search algorithm. Ensembled, AP-AUC, AP-PH, lateral-AUC, and lateral-PH networks achieved accuracies of 83.0% (95% confidence-interval: 81.2%-84.8%), 74.4% (72.0%-76.7%), 74.2% (72.8%-75.7%), 74.9% (72.2%-77.7%), and 76.9% (74.4%-79.5%); area under receiver operating characteristic curves of 0.86 (0.84-0.88), 0.81 (0.79-0.83), 0.83 (0.81-0.84), 0.82 (0.8-0.84), and 0.84 (0.82-0.87); and Matthews correlation coefficients of 0.66 (0.63-0.70), 0.48 (0.43-0.53), 0.49 (0.46-0.52), 0.51 (0.45-0.56), and 0.54 (0.49-0.59) respectively. Ensembled network performance was significantly better than individual networks (McNemar's p-value<0.05). This study proved feasibility of using ensemble networks to combine hemodynamic information from multiple bi-plane API maps to assess level of reperfusion during MTs.
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Affiliation(s)
- Mohammad Mahdi Shiraz Bhurwani
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260.,Canon Stroke and Vascular Research Center, Buffalo, NY 14203
| | - Kenneth V Snyder
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203.,University Dept. of Neurosurgery, University at Buffalo, Buffalo, NY 14203
| | - Muhammad Waqas
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203.,University Dept. of Neurosurgery, University at Buffalo, Buffalo, NY 14203
| | - Maxim Mokin
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33606
| | - Ryan A Rava
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260.,Canon Stroke and Vascular Research Center, Buffalo, NY 14203
| | - Alexander R Podgorsak
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260.,Canon Stroke and Vascular Research Center, Buffalo, NY 14203
| | - Kelsey N Sommer
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260.,Canon Stroke and Vascular Research Center, Buffalo, NY 14203
| | - Jason M Davies
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203.,University Dept. of Neurosurgery, University at Buffalo, Buffalo, NY 14203.,University Dept. of Biomedical Informatics, University at Buffalo, Buffalo, NY 14214
| | - Elad I Levy
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203.,University Dept. of Neurosurgery, University at Buffalo, Buffalo, NY 14203
| | - Adnan H Siddiqui
- Canon Stroke and Vascular Research Center, Buffalo, NY 14203.,University Dept. of Neurosurgery, University at Buffalo, Buffalo, NY 14203
| | - Ciprian N Ionita
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260.,Canon Stroke and Vascular Research Center, Buffalo, NY 14203.,University Dept. of Neurosurgery, University at Buffalo, Buffalo, NY 14203
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