Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification.

Purpose: Ischemic myocardial scarring (IMS) is a common outcome of coronary artery disease that potentially leads to lethal arrythmias and heart failure. Late-gadolinium-enhanced cardiac magnetic resonance (CMR) imaging scans have served as the diagnostic bedrock for IMS, with recent advancements in machine learning enabling enhanced scar classification. However, the trade-off for these improvements is intensive computational and time demands. As a solution, we propose a combination of lightweight preprocessing (LWP) and template matching (TM) to streamline IMS classification. Approach: CMR images from 279 patients (151 IMS, 128 control) were classified for IMS presence using two convolutional neural networks (CNNs) and TM, both with and without LWP. Evaluation metrics included accuracy, sensitivity, specificity, F1-score, area under the receiver operating characteristic curve (AUROC), and processing time. External testing dataset analysis encompassed patient-level classifications (PLCs) and a CNN versus TM classification comparison (CVTCC). Results: LWP enhanced the speed of both CNNs (4.9x) and TM (21.9x). Furthermore, in the absence of LWP, TM outpaced CNNs by over 10x, while with LWP, TM was more than 100x faster. Additionally, TM performed similarly to the CNNs in accuracy, sensitivity, specificity, F1-score, and AUROC, with PLCs demonstrating improvements across all five metrics. Moreover, the CVTCC revealed a substantial 90.9% agreement. Conclusions: Our results highlight the effectiveness of LWP and TM in streamlining IMS classification. Anticipated enhancements to LWP's region of interest (ROI) isolation and TM's ROI targeting are expected to boost accuracy, positioning them as a potential alternative to CNNs for IMS classification, supporting the need for further research.

Enhancing cerebral vasculature analysis with pathlength-corrected 2D angiographic parametric imaging: A feasibility study.

BACKGROUND: 2D angiographic parametric imaging (API) quantitatively extracts imaging biomarkers related to contrast flow and is conventionally applied to 2D digitally subtracted angiograms (DSA's). In the interventional suite, API is typically performed using 1-2 projection views and is limited by vessel overlap, foreshortening, and depth-integration of contrast motion. PURPOSE: This work explores the use of a pathlength-correction metric to overcome the limitations of 2D-API: the primary objective was to study the effect of converting 3D contrast flow to projected contrast flow using a simulated angiographic framework created with computational fluid dynamics (CFD) simulations, thereby removing acquisition variability. METHODS: The pathlength-correction framework was applied to in-silico angiograms, generating a reference (i.e., ground-truth) volumetric contrast distribution in four patient-specific intracranial aneurysm geometries. Biplane projections of contrast flow were created from the reference volumetric contrast distributions, assuming a cone-beam geometry. A Parker-weighted reconstruction was performed to obtain a binary representation of the vessel structure in 3D. Standard ray tracing techniques were then used to track the intersection of a ray from the focal spot with each voxel of the reconstructed vessel wall to a pixel in the detector plane. The lengths of each ray through the 3D vessel lumen were then projected along each ray-path to create a pathlength-correction map, where the pixel intensity in the detector plane corresponds to the vessel width along each source-detector ray. By dividing the projection sequences with this correction map, 2D pathlength-corrected in-silico angiograms were obtained. We then performed voxel-wise (3D) API on the ground-truth contrast distribution and compared it to pixel-wise (2D) API, both with and without pathlength correction for each biplane view. The percentage difference (PD) between the resultant API biomarkers in each dataset were calculated within the aneurysm region of interest (ROI). RESULTS: Intensity-based API parameters, such as the area under the curve (AUC) and peak height (PH), exhibited notable changes in magnitude and spatial distribution following pathlength correction: these now accurately represent conservation of mass of injected contrast media within each arterial geometry and accurately reflect regions of stagnation and recirculation in each aneurysm ROI. Improved agreement was observed between these biomarkers in the pathlength-corrected biplane maps: the maximum PD within the aneurysm ROI is 3.3% with pathlength correction and 47.7% without pathlength correction. As expected, improved agreement with ROI-averaged ground-truth 3D counterparts was observed for all aneurysm geometries, particularly large aneurysms: the maximum PD for both AUC and PH was 5.8%. Temporal parameters (mean transit time, MTT, time-to-peak, TTP, time-to-arrival, TTA) remained unaffected after pathlength correction. CONCLUSIONS: This study indicates that the values of intensity-based API parameters obtained with conventional 2D-API, without pathlength correction, are highly dependent on the projection orientation, and uncorrected API should be avoided for hemodynamic analysis. The proposed metric can standardize 2D API-derived biomarkers independent of projection orientation, potentially improving the diagnostic value of all acquired 2D-DSA's. Integration of a pathlength correction map into the imaging process can allow for improved interpretation of biomarkers in 2D space, which may lead to improved diagnostic accuracy during procedures involving the cerebral vasculature.

Clinical situations for which 3D Printing is considered an appropriate representation or extension of data contained in a medical imaging examination: vascular conditions.

BACKGROUND: Medical three-dimensional (3D) printing has demonstrated utility and value in anatomic models for vascular conditions. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (3DPSIG) provides appropriateness recommendations for vascular 3D printing indications. METHODS: A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with vascular indications. Each study was vetted by the authors and strength of evidence was assessed according to published appropriateness ratings. RESULTS: Evidence-based recommendations for when 3D printing is appropriate are provided for the following areas: aneurysm, dissection, extremity vascular disease, other arterial diseases, acute venous thromboembolic disease, venous disorders, lymphedema, congenital vascular malformations, vascular trauma, vascular tumors, visceral vasculature for surgical planning, dialysis access, vascular research/development and modeling, and other vasculopathy. Recommendations are provided in accordance with strength of evidence of publications corresponding to each vascular condition combined with expert opinion from members of the 3DPSIG. CONCLUSION: This consensus appropriateness ratings document, created by the members of the 3DPSIG, provides an updated reference for clinical standards of 3D printing for the care of patients with vascular conditions.

Improving the accuracy of computational fluid dynamics simulations of coiled cerebral aneurysms using finite element modeling.

Cerebral aneurysms are a serious clinical challenge, with ∼half resulting in death or disability. Treatment via endovascular coiling significantly reduces the chances of rupture, but the techniquehas failure rates of ∼20 %. This presents a pressing need to develop a method fordetermining optimal coildeploymentstrategies. Quantification of the hemodynamics of coiled aneurysms using computational fluid dynamics (CFD) has the potential to predict post-treatment outcomes, but representing the coil mass in CFD simulations remains a challenge. We use the Finite Element Method (FEM) for simulating patient-specific coil deployment for n = 4 ICA aneurysms for which 3D printed in vitro models were also generated, coiled, and scanned using ultra-high resolution synchrotron micro-CT. The physical and virtual coil geometries were voxelized onto a binary structured grid and porosity maps were generated for geometric comparison. The average binary accuracy score is 0.8623 and the average error in porosity map is 4.94 %. We then conduct patient-specific CFD simulations of the aneurysm hemodynamics using virtual coils geometries, micro-CT generated oil geometries, and using the porous medium method to represent the coil mass. Hemodynamic parameters including Neck Inflow Rate (Qneck) and Wall Shear Stress (WSS) were calculated for each of the CFD simulations. The average relative error in Qneck and WSS from CFD using FEM geometry were 6.6 % and 21.8 % respectively, while the error from CFD using a porous media approximation resulted in errors of 55.1 % and 36.3 % respectively; demonstrating a marked improvement in the accuracy of CFD simulations using FEM generated coil geometries.

Use of high-speed angiography HSA-derived boundary conditions and Physics Informed Neural Networks (PINNs) for comprehensive estimation of neurovascular hemodynamics.

Purpose: Physics-informed neural networks (PINNs) and computational fluid dynamics (CFD) have both demonstrated an ability to derive accurate hemodynamics if boundary conditions (BCs) are known. Unfortunately, patient-specific BCs are often unknown, and assumptions based upon previous investigations are used instead. High speed angiography (HSA) may allow extraction of these BCs due to the high temporal fidelity of the modality. We propose to investigate whether PINNs using convection and Navier-Stokes equations with BCs derived from HSA data may allow for extraction of accurate hemodynamics in the vasculature. Materials and Methods: Imaging data generated from in vitro 1000 fps HSA, as well as simulated 1000 fps angiograms generated using CFD were utilized for this study. Calculations were performed on a 3D lattice comprised of 2D projections temporally stacked over the angiographic sequence. A PINN based on an objective function comprised of the Navier-Stokes equation, the convection equation, and angiography-based BCs was used for estimation of velocity, pressure and contrast flow at every point in the lattice. Results: Imaging-based PINNs show an ability to capture such hemodynamic phenomena as vortices in aneurysms and regions of rapid transience, such as outlet vessel blood flow within a carotid artery bifurcation phantom. These networks work best with small solution spaces and high temporal resolution of the input angiographic data, meaning HSA image sequences represent an ideal medium for such solution spaces. Conclusions: The study shows the feasibility of obtaining patient-specific velocity and pressure fields using an assumption-free data driven approach based purely on governing physical equations and imaging data.

Geometrically independent contrast dilution gradient (CDG) velocimetry using photon-counting 1000 fps High Speed Angiography (HSA) for 2D velocity distribution estimation.

Purpose: Previous studies have demonstrated the efficacy of contrast dilution gradient (CDG) analysis in determining large vessel velocity distributions from 1000 fps high-speed angiography (HSA). However, the method required vessel centerline extraction, which made it applicable only to non-tortuous geometries using a highly specific contrast injection technique. This study seeks to remove the need for a priori knowledge regarding the direction of flow and modify the vessel sampling method to make the algorithm more robust to non-linear geometries. Materials and Methods: 1000 fps HSA acquisitions were obtained in vitro with a benchtop flow loop using the XC-Actaeon (Varex Inc.) photon-counting detector, and in silico using a passive-scalar transport model within a computational fluid dynamics (CFD) simulation. CDG analyses were obtained using gridline sampling across the vessel, and subsequent 1D velocity measurement in both the x- and y-directions. The velocity magnitudes derived from the component CDG velocity vectors were aligned with CFD results via co-registration of the resulting velocity maps and compared using mean absolute percent error (MAPE) between pixels values in each method after temporal averaging of the 1-ms velocity distributions. Results: Regions well-saturated with contrast throughout the acquisition showed agreement when compared to CFD (MAPE of 18% for the carotid bifurcation inlet and MAPE of 27% for the internal carotid aneurysm), with respective completion times of 137 seconds and 5.8 seconds. Conclusions: CDG may be used to obtain velocity distributions in and surrounding vascular pathologies provided the contrast injection is sufficient to provide a gradient, and diffusion of contrast through the system is negligible.

Radiological Society of North America (RSNA) 3D Printing Special Interest Group (SIG) clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: breast conditions.

The use of medical 3D printing has expanded dramatically for breast diseases. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides updated appropriateness criteria for breast 3D printing in various clinical scenarios. Evidence-based appropriateness criteria are provided for the following clinical scenarios: benign breast lesions and high-risk breast lesions, breast cancer, breast reconstruction, and breast radiation (treatment planning and radiation delivery).

Barium sulfate and pigment admixture for photoacoustic and x-ray contrast imaging of the gut.

Significance: X-ray imaging is frequently used for gastrointestinal imaging. Photoacoustic imaging (PAI) of the gastrointestinal tract is an emerging approach that has been demonstrated for preclinical imaging of small animals. A contrast agent active in both modalities could be useful for imaging applications. Aim: We aimed to develop a dual-modality contrast agent comprising an admixture of barium sulfate with pigments that absorb light in the second near-infrared region (NIR-II), for preclinical imaging with both x-ray and PAI modalities. Approach: Eleven different NIR-II dyes were evaluated after admixture with a 40% w/v barium sulfate mixture. The resulting NIR-II absorption in the soluble fraction and in the total mixture was characterized. Proof-of-principle imaging studies in mice were carried out. Results: Pigments that produced more uniform suspensions were assessed further for photoacoustic contrast signal at a wavelength of 1064 nm that corresponds to the output of the Nd:YAG laser used. Phantom imaging studies demonstrated that the pigment-barium sulfate mixture generated imaging contrast in both x-ray and PAI modalities. The optimal pigment selected for further study was a cyanine tetrafluoroborate salt. Ex-vivo and whole-body mouse imaging demonstrated that photoacoustic and x-ray contrast signals co-localized in the intestines for both imaging modalities. Conclusion: These data demonstrate that commercially-available NIR-II pigments can simply be admixed with barium sulfate to generate a dual-modality contrast agent appropriate for small animal gastrointestinal imaging.

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

Purpose: The size and location of infarct and penumbra are key to decision-making for acute ischemic stroke (AIS) management. CT perfusion (CTP) software estimate infarct and penumbra volume using contralateral hemisphere relative thresholding. This approach is not robust and widely contested by the scientific community. In this study, we investigate the use of deep learning-based algorithms to efficiently locate infarct and penumbra tissue on CTP hemodynamic maps. Approach: CTP scans were retrospectively collected for 60 and 59 patients in the infarct only and infarct + penumbra substudies respectively. Commercial CTP software was used to generate cerebral blood flow, cerebral blood volume, mean transit time, time to peak, and delay time maps. U-Net-shaped architectures were trained to segment infarct or infarct + penumbra. Test-time-augmentation, ensembling, and watershed segmentation were used as postprocessing techniques. Segmentation performance was evaluated using Dice coefficients (DC) and mean absolute volume errors (MAVE). Results: The algorithm segmented infarct tissue resulted in DC of 0.64 ± 0.03 (0.63, 0.65), and MAVE of 4.91 ± 0.94 (4.5, 5.32) mL. In comparison, the commercial software predicted infarct with a DC of 0.31 ± 0.17 (0.26, 0.36) and MAVE of 9.77 ± 8.35 (7.12, 12.42) mL. The algorithm was able to segment infarct + penumbra with a DC of 0.61 ± 0.04 (0.6, 0.63), and MAVE of 6.51 ± 1.37 (5.91, 7.11) mL. In comparison, the commercial software predicted infarct + penumbra with a DC of 0.3 ± 0.19 (0.25, 0.35) and MAVE of 9.18 ± 7.55 (7.25, 11.11) mL. Conclusions: Use of deep learning algorithms to assess severity of AIS in terms of infarct and penumbra volume is precise and outperforms current relative thresholding methods. Such an algorithm would enhance the reliability of CTP in guiding treatment decisions.

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

Purpose: Intracerebral Hemorrhage (ICH) is one of the most devastating types of strokes with mortality and morbidity rates ranging from about 51%-65% one year after diagnosis. Early hematoma expansion (HE) is a known cause of worsening neurological status of ICH patients. The goal of this study was to investigate whether non-contrast computed tomography imaging biomarkers (NCCT-IB) acquired at initial presentation can predict ICH growth in the acute stage. Materials and Methods: We retrospectively collected NCCT data from 200 patients with acute (<6 hours) ICH. Four NCCT-IBs (blending region, dark hole, island, and edema) were identified for each hematoma, respectively. HE status was recorded based on the clinical observation reported in the patient chart. Supervised machine learning models were developed, trained, and tested for 15 different input combinations of the NCCT-IBs to predict HE. Model performance was assessed using area under the receiver operating characteristic curve and probability for accurate diagnosis (PAD) was calculated. A 20-fold Monte-Carlo cross validation was implemented to ensure model reliability on a limited sample size of data, by running a myriad of random training/testing splits. Results: The developed algorithm was able to predict expansion utilizing all four inputs with an accuracy of 70.17%. Further testing of all biomarker combinations yielded P AD ranging from 0.57, to 0.70. Conclusion: Specific attributes of ICHs may influence the likelihood of HE and can be evaluated via a machine learning algorithm. However, certain parameters may differ in importance to reach accurate conclusions about potential expansion.

Predicting Hematoma Expansion after Spontaneous Intracranial Hemorrhage Through a Radiomics Based Model.

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.

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

Purpose: To investigate the relation between delayed ischemic stroke and the intracranial atherosclerotic disease (ICAD) hemodynamics as determined by Non-invasive Optimal Vessel Analysis (NOVA) MRI measurements. Materials and Methods: Thirty-three patients with ICAD were enrolled in this study. All patients underwent clinically indicated angioplasty followed by 2-dimensional phase contrast MR (2D PCMR) performed on a 3.0 Tesla MRI scanner using either a 16-channel neurovascular coil or 32-channel head coil. The volumetric flow rate measurements were calculated from 2D PCMR with Non-invasive Optimal Vessel Analysis (NOVA) software (VasSol, Chicago, IL, USA). Flow rate measurements were obtained in 20 major arteries distal, proximal and within the Circle of Willis. Patients were followed up for six month, and ischemia reoccurrence and location were recorded. Receiver operating characteristic (ROC) analysis was performed using flow rates measurements in the ipsilateral side of the ischemic event occurrence. Results: Complete set of measurements was achieved in n=34. Left and right hemisphere ischemia recurrence was observed in seven and three cases respectively. Best predictor of ischemic event reoccurrence was flow rate in the middle cerebral artery with area under the ROC of 0.821±0.109. Conclusions: This is an effectiveness study to determine whether blood flow measurements in the intracranial vasculature may be predictive of future ischemic events. Our results demonstrated significant correlation between the blood flow measurements using 2D PCMR processed with the NOVA software and the reoccurrence of ischemia. These results support further investigation for using this method for risk stratification of ICAD patients.

Recovery of complete time density curves from incomplete angiographic data using recurrent neural networks.

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.

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

Purpose: Data-driven methods based on x-ray angiographic parametric imaging (API) have been successfully used to provide prognosis for intracranial aneurysm (IA) treatment outcome. Previous studies have mainly focused on embolization devices where the flow pattern visualization is in the aneurysm dome; however, this is not possible in IAs treated with endovascular coils due to high x-ray attenuation of the devices. To circumvent this challenge, we propose to investigate whether flow changes in the parent artery distal to the coil-embolized IAs could be used to achieve the same accuracy of surgical outcome prognosis. Methods: Eighty digital subtraction angiography sequences were acquired from patients with IA embolized with coils. Five API parameters were recorded from a region of interest (ROI) placed distal to the IA neck in the main artery. Average API values were recorded and pre-treatment values. A supervised machine learning algorithm was trained to provide a six-month post procedure binary outcome (occluded/not occluded). Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the method. Results: Use of API parameters with data driven methods yielded an area under the ROC curve of 0.77 ±0.11 and accuracy of 78.6%. Single parameter-based analysis yielded accuracies which were suboptimal for clinical acceptance. Conclusions: We determined that data-driven method based on API analysis of flow in the parent artery of IA treated with coils provide clinically acceptable accuracy for the prognosis of six months occlusion outcome.

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

Purpose: Subarachnoid Hemorrhage (SAH) is a lethal hemorrhagic stroke that account for 25% of cerebrovascular deaths. As a result of the initial bleed, a chain of physiological events are initiated which may lead to Delayed Cerebral Ischemia (DCI). As of now we have no diagnostic capability to identify patients which may present DCI a few weeks after initial presentation. We propose to investigate whether a data driven approach using angiographic parametric imaging (API) may predict occurrence of the DCI. Materials and Methods: Digital Subtraction Angiographic (DSA) sequences from 125 SAH patients were used retrospectively to perform API assessment of the entire brain hemisphere where the hemorrhage was detected. Four Regions of Interests (ROIs) were placed to extract five average API biomarkers in the lateral and AP DSAs. Data driven analysis using Logistic Regression was performed for various API parameters and ROIs to find the optimal configuration to maximize the prognosis accuracy. Each model performance was evaluated using area under the curve of the receiver operator characteristic (AUROC). Results: Data driven approach with API has a 60% accuracy predicting DCI occurrence. We determined that location of the ROI for extraction of the API parameters is very important for the data driven model performance. Normalizing the values using the inlet velocities for each patient yield higher and more consistent results. Single API biomarkers models had poor prediction accuracies, barely better than chance. Conclusions: This effectiveness exploratory study demonstrates for the first time, that prognosis of the DCI in SAH patients, is feasible and warrants a more in-depth investigation.

Automation of ischemic myocardial scar detection in cardiac magnetic resonance imaging of the left ventricle using machine learning.

Purpose: Machine learning techniques can be applied to cardiac magnetic resonance imaging (CMR) scans in order to differentiate patients with and without ischemic myocardial scarring (IMS). However, processing the image data in the CMR scans requires manual work that takes a significant amount of time and expertise. We propose to develop and test an AI method to automatically identify IMS in CMR scans to streamline processing and reduce time costs. Materials and Methods: CMR scans from 170 patients (138 IMS & 32 without IMS as identified by a clinical expert) were processed using a multistep automatic image data selection algorithm. This algorithm consisted of cropping, circle detection, and supervised machine learning to isolate focused left ventricle image data. We used a ResNet-50 convolutional neural network to evaluate manual vs. automatic selection of left ventricle image data through calculating accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUROC). Results: The algorithm accuracy, sensitivity, specificity, F1 score, and AUROC were 80.6%, 85.6%, 73.7%, 83.0%, and 0.837, respectively, when identifying IMS using manually selected left ventricle image data. With automatic selection of left ventricle image data, the same parameters were 78.5%, 86.0%, 70.7%, 79.7%, and 0.848, respectively. Conclusion: Our proposed automatic image data selection algorithm provides a promising alternative to manual selection when there are time and expertise limitations. Automatic image data selection may also prove to be an important and necessary step toward integration of machine learning diagnosis and prognosis in clinical workflows.

2D vessel contrast dilution gradient (CDG) analysis using 1000 fps high speed angiography (HSA) for velocity distribution estimation.

Purpose: Contrast dilution gradient (CDG) analysis is a technique used to extract velocimetric 2D information from digitally subtracted angiographic (DSA) acquisitions. This information may then be used by clinicians to quantitatively assess the effects of endovascular treatment on flow conditions surrounding pathologies of interest. The method assumes negligible diffusion conditions, making 1000 fps high speed angiography (HSA), in which diffusion between 1 ms frames may be neglected, a strong candidate for velocimetric analysis using CDG. Previous studies have demonstrated the success of CDG analysis in obtaining velocimetric one-dimensional data at the arterial centerline of simple vasculature. This study seeks to resolve velocity distributions across the entire vessel using 2D-CDG analysis with HSA acquisitions. Materials and Methods: HSA acquisitions for this study were obtained in vitro with a benchtop flow loop at 1000 fps using the XC-Actaeon (Direct Conversion Inc.) photon counting detector. 2D-CDG analyses were compared with computational fluid dynamics (CFD) via automatic co-registration of the results from each velocimetry method. This comparison was performed using mean absolute error between pixel values in each method (after temporal averaging). Results: CDG velocity magnitudes were slightly under approximated relative to CFD results (mean velocity: 27 cm/s, mean absolute error: 4.3 cm/s) as a result of incomplete contrast filling. Relative 2D spatial velocity distributions in CDG analysis agreed well with CFD distributions qualitatively. Conclusions: CDG may be used to obtain velocity distributions in and surrounding vascular pathologies provided diffusion is negligible relative to convection in the flow, given a continuous gradient of contrast.

Semi-automatic Co-Registration of 3D CFD Vascular Geometry to 1000 FPS High-Speed Angiographic (HSA) Projection Images for Flow Determination Comparisons.

Image co-registration is an important tool that is commonly used to quantitatively or qualitatively compare information from images or data sets that vary in time, origin, etc. This research proposes a method for the semi-automatic co-registration of the 3D vascular geometry of an intracranial aneurysm to novel high-speed angiographic (HSA) 1000 fps projection images. Using the software Tecplot 360, 3D velocimetry data generated from computational fluid dynamics (CFD) for patient-specific vasculature models can be extracted and uploaded into Python. Dilation, translation, and angular rotation of the 3D velocimetry data can then be performed in order to co-register its geometry to corresponding 2D HSA projection images of the 3D printed vascular model. Once the 3D CFD velocimetry data is geometrically aligned, a 2D velocimetry plot can be generated and the Sørensen-Dice coefficient can be calculated in order to determine the success of the co-registration process. The co-registration process was performed ten times for two different vascular models and had an average Sørensen-Dice coefficient of 0.84 ± 0.02. The method presented in this research allows for a direct comparison between 3D CFD velocimetry data and in-vitro 2D velocimetry methods. From the 3D CFD, we can compare various flow characteristics in addition to velocimetry data with HSA-derived flow metrics. The method is robust to other vascular geometries as well.

Comparison of fluid dynamics changes due to physical activity in 3D printed patient specific coronary phantoms with the Windkessel equivalent model of coronary flow.

BACKGROUND: 3D printing (3DP) used to replicate the geometry of normal and abnormal vascular pathologies has been demonstrated in many publications; however, reproduction of hemodynamic changes due to physical activities, such as rest versus moderate exercise, need to be investigated. We developed a new design for patient specific coronary phantoms, which allow adjustable physiological variables such as coronary distal resistance and coronary compliance in patients with coronary artery disease. The new design was tested in precise benchtop experiments and compared with a theoretical Windkessel electrical circuit equivalent, that models coronary flow and pressure using arterial resistance and compliance. METHODS: Five phantoms from patients who underwent clinically indicated elective invasive coronary angiography were built from CCTA scans using multi-material 3D printing. Each phantom was used in a controlled flow system where patient specific flow conditions were simulated by a programmable cardiac pump. To simulate the arteriole and capillary beds flow resistance and the compliance for various physical activities, we designed a three-chamber outlet system which controls the outflow dynamics of each coronary tree. Benchtop pressure measurements were recorded using sensors embedded in each of the main coronary arteries. Using the Windkessel model, patient specific flow equivalent electrical circuit models were designed for each coronary tree branch, and flow in each artery was determined for known inflow conditions. Local flow resistances were calculated through Poiseuille's Law derived from the radii and lengths of the coronary arteries using CT angiography based multi-planar reconstructions. The coronary stenosis flow rates from the benchtop and the electrical models were compared to the localized flow rates calculated from invasive pressure measurements recorded in the angio-suites. RESULTS: The average Pearson correlations of the localized flow rates at the location of the stenosis between each of the models (Benchtop/Electrical, Benchtop/Angio, Electrical/Angio) are 0.970, 0.981, and 0.958 respectively. CONCLUSIONS: 3D printed coronary phantoms can be used to replicate the human arterial anatomy as well as blood flow conditions. It displays high levels of correlation when compared to hemodynamics calculated in electrically-equivalent coronary Windkessel models as well as invasive angio-suite pressure measurements.

Clot imaging characteristics predict first pass effect of aspiration-first approach to thrombectomy.

BACKGROUND: The value of clot imaging in patients with emergent large vessel occlusion (ELVO) treated with thrombectomy is unknown. METHODS: We performed retrospective analysis of clot imaging (clot density, perviousness, length, diameter, distance to the internal carotid artery (ICA) terminus and angle of interaction (AOI) between clot and the aspiration catheter) of consecutive cases of middle cerebral artery (MCA) occlusion and its association with first pass effect (FPE, TICI 2c-3 after a first attempt). RESULTS: Patients (n = 90 total) with FPE had shorter clot length (9.9 ± 4.5 mm vs. 11.7 ± 4.6 mm, P = 0.07), shorter distance from ICA terminus (11.0 ± 7.1 mm vs. 14.7 ± 9.8 mm, P = 0.048), higher perviousness (39.39 ± 29.5 vs 25.43 ± 17.6, P = 0.006) and larger AOI (153.6 ± 17.6 vs 140.3 ± 23.5, P = 0.004) compared to no-FPE patients. In multivariate analysis, distance from ICA terminus to clot ≤13.5 mm (odds ratio (OR) 11.05, 95% confidence interval (CI) 2.65-46.15, P = 0.001), clot length ≤9.9 mm (OR 7.34; 95% CI 1.8-29.96, P = 0.005), perviousness ≥ 19.9 (OR 2.54, 95% CI 0.84-7.6, P = 0.09) and AOI ≥ 137°^ (OR 6.8, 95% CI 1.55-29.8, P = 0.011) were independent predictors of FPE. The optimal cut off derived using Youden's index was 6.5. The area under the curve of a score predictive of FPE success was 0.816 (0.728-0.904, P < 0.001). In a validation cohort (n = 30), sensitivity, specificity, positive and negative predictive value of a score of 6-10 were 72.7%, 73.6%, 61.5% and 82.3%. CONCLUSIONS: Clot imaging predicts the likelihood of achieving FPE in patients with MCA ELVO treated with the aspiration-first approach.