3D Deep Learning Angiography (3D-DLA) from C-arm Conebeam CT

Montoya, Juan; Li, Y.; Strother, Charles M.; Chen, Guang‐Hong

doi:10.3174/ajnr.a5597

Cited by 12 publications

(18 citation statements)

References 35 publications

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“…The qualitative and quantitative analysis demonstrated reliable visualization of the anterior and posterior circulation and excellent agreement with the current criterion standard 3D DSA. The results are in accordance with the recent literature: Montoya et al [15] were the first to describe the feasibility of an AI-based method to reconstruct DSA-like 3D volumes from a cerebral rotational angiography without using a mask run. The authors observed in a quantitative analysis a nearly 100% correspondence between their AIbased and the conventional method and stated that an AIbased 3D-angiography approach accurately recreates vascular anatomy.…”

Section: Discussionsupporting

confidence: 91%

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Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature

et al. 2019

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Purpose The three-dimensional digital subtraction angiography (3D DSA) technique is the current standard and is based on both mask and fill runs to enable the subtraction technique. Artificial intelligence (AI)-based 3D angiography (3DA) was developed to reduce radiation dosage because only one contrast-enhanced run of the C-arm system is required for reconstruction of DSA-like 3D volumes. The aim was the evaluation of this algorithm regarding its diagnostic information. Methods 3D DSA datasets without pathologic findings were reconstructed both with subtraction technique and with the AI-based algorithm. Corresponding reconstructions were evaluated by 2 neuroradiologists with respect to image quality (IQ), visualization of major segments of the circle of Willis (ICA = C4-C7; OphA; ACA = A1-A2, MCA = M1-M2; VA = V4; BA; AICA; SUCA; PCA = P1-P2), identifiability of perforators (lenticulostriate/thalamoperforating arteries) and vessel diameters (ICA = C4; MCA = M1; BA; PCA = P1). Results In total 15 datasets were successfully reconstructed as 3D DSA and 3DA with diagnostic image quality. All major segments of the circle of Willis and perforators were comparably visualized with 3DA. Quantitative analysis of vessel diameters in 3D DSA and 3DA datasets was equivalent and did not show relevant differences (rICA = 0.901, p = 0.001; rM1 = 0.951, p = 0.001; rBA = 0.906, p = 0.001; rP1 = 0.991, p = 0.001). Conclusions The use of 3DA demonstrated reliable visualization of cerebral vasculature with respect to quantitative and qualitative parameters. Therefore, 3DA is a promising method that might help to reduce patient radiation. Keywords 3D angiography • Artificial intelligence • Deep learning • Flat-detector computed tomography • Dose reductionDisclaimer The concepts and results presented in this paper are based on research and are not commercially available.

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Section: Discussionsupporting

confidence: 91%

“…Recently, Montoya et al described a novel alternative method based on artificial intelligence (AI) to generate DSA-like 3D volumes of cerebral vasculature [15]. The authors demonstrated the general feasibility of the method and concluded in the quantitative analysis that it accurately recreates vascular anatomy.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature

et al. 2019

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“…Four ML algorithms were selected for model building since these have shown good performance in clinical healthcare classification studies. 20,30,31,35,36 The models include the standard statistical logistic regression (LR), support vector machine (SVM, with linear [L-SVM] and Gaussian [G-SVM] kernels), k-nearest neighbor (k-NN), and neural network (NN). An illustration of the concept of each algorithm is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning

Paliwal

Jaiswal²,

Tutino

et al. 2018

Neurosurgical Focus

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OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment hemodynamics, and FD-device characteristics and then used these parameters to train machine learning algorithms to predict 6-month clinical outcomes after FD treatment.METHODSData were retrospectively collected for 84 FD-treated sidewall aneurysms in 80 patients. Based on 6-month angiographic outcomes, IAs were classified as occluded (n = 63) or residual (incomplete occlusion, n = 21). For each case, the authors modeled FD deployment using a fast virtual stenting algorithm and hemodynamics using image-based computational fluid dynamics. Sixteen morphological, hemodynamic, and FD-based parameters were calculated for each aneurysm. Aneurysms were randomly assigned to a training or testing cohort in approximately a 3:1 ratio. The Student t-test and Mann-Whitney U-test were performed on data from the training cohort to identify significant parameters distinguishing the occluded from residual groups. Predictive models were trained using 4 types of supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM; linear and Gaussian kernels), K-nearest neighbor, and neural network (NN). In the testing cohort, the authors compared outcome prediction by each model trained using all parameters versus only the significant parameters.RESULTSThe training cohort (n = 64) consisted of 48 occluded and 16 residual aneurysms and the testing cohort (n = 20) consisted of 15 occluded and 5 residual aneurysms. Significance tests yielded 2 morphological (ostium ratio and neck ratio) and 3 hemodynamic (pre-treatment inflow rate, post-treatment inflow rate, and post-treatment aneurysm averaged velocity) discriminants between the occluded (good-outcome) and the residual (bad-outcome) group. In both training and testing, all the models trained using all 16 parameters performed better than all the models trained using only the 5 significant parameters. Among the all-parameter models, NN (AUC = 0.967) performed the best during training, followed by LR and linear SVM (AUC = 0.941 and 0.914, respectively). During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome.CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.

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“…We note that it typically takes a couple of years for biomedical graduate students to reach the level of expertise required to conduct reliable CFD simulations of cerebral aneurysm hemodynamics. While rapid progress in machine learning algorithms is posed to automate image segmentation (136,137) and dramatically reduce modeling time and cost, knowledge of flow physics and cardiovascular mechanics will likely remain necessary for patient-specific hemodynamic analysis. Since it is impractical to train medical practitioners in advanced CFD modeling and implausible that each medical center will keep on staff a modeling expert, the remaining alternative is to use cloud-based computing to conduct flow analysis at dedicated centers.…”

Section: Conducting Modeling Studies In a Clinical Settingmentioning

confidence: 99%

“…Cartesian grids allowed these authors to avoid meshing luminal surfaces and could be easily refined to improve the resolution in smaller vessels. Adopting deep learning algorithms for image segmentation will allow further reduction of the modeling time and automated elimination of image artifacts, as evidenced by innovative studies applying neural networks to CTA data and 3D angiograms of cerebral arteries (136,137,140).…”

Section: Conducting Modeling Studies In a Clinical Settingmentioning

confidence: 99%

Hemodynamics of Cerebral Aneurysms: Connecting Medical Imaging and Biomechanical Analysis

Rayz

Cohen-Gadol

2020

Annu. Rev. Biomed. Eng.

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In the last two decades, numerous studies have conducted patient-specific computations of blood flow dynamics in cerebral aneurysms and reported correlations between various hemodynamic metrics and aneurysmal disease progression or treatment outcomes. Nevertheless, intra-aneurysmal flow analysis has not been adopted in current clinical practice, and hemodynamic factors usually are not considered in clinical decision making. This review presents the state of the art in cerebral aneurysm imaging and image-based modeling, discussing the advantages and limitations of each approach and focusing on the translational value of hemodynamic analysis. Combining imaging and modeling data obtained from different flow modalities can improve the accuracy and fidelity of resulting velocity fields and flow-derived factors that are thought to affect aneurysmal disease progression. It is expected that predictive models utilizing hemodynamic factors in combination with patient medical history and morphological data will outperform current risk scores and treatment guidelines. Possible future directions include novel approaches enabling data assimilation and multimodality analysis of cerebral aneurysm hemodynamics.

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3D Deep Learning Angiography (3D-DLA) from C-arm Conebeam CT

Cited by 12 publications

References 35 publications

Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature

Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning

Hemodynamics of Cerebral Aneurysms: Connecting Medical Imaging and Biomechanical Analysis

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