Computer Methods and Programs in Biomedicine

BackgroundOptical Coherence Tomography Angiography (OCTA) provides high-resolution visualization of retinal microvasculature, with vascular density (VD) serving as a one of the key quantitative metrics. However, VD measurements are highly sensitive to image binarization step, and no standardized approach exists. MethodsWe analyzed 51 OCTA scans (human and porcine) using 29 binarization algorithms, including global and local thresholding techniques from ImageJ and DoxaPy, as well as Random Walker segmentation. VD was calculated for each binarization algorithm and compared against Optovue-generated values (ground truth). Results were evaluated using hierarchical clustering and agreement between them was determined by Bland-Altman analysis. ResultsWolf algorithm was found to exhibit least deviation from mean Optovue VD values for human SCP layer (46.5 {+/-} 1.2% vs. 48.3 {+/-} 1.4%; p = 3.62 x 10-5); however, there is not significant difference between VDs from Optovue and Wolf algorithms from porcine SCP layer (46.2 {+/-} 1.8 % vs 46.3 {+/-} 1.4% ; p=0.74). For DCP layer, Phansalkar algorithm exhibited least VD variability (50.7 {+/-} 2.0% vs. 51.9 {+/-} 1.7%; p=2.53 x 10-4) in the human cohort. Whereas Percentile algorithm exhibited least, non-significant variations in the porcine DCP layer VD (50.0 {+/-} 1.4 % vs. 50.3 {+/-} 1.4%; p=0.75). DiscussionEach binarization technique evaluated in this study impacts OCTA-derived VD measurements differently. Local adaptive algorithms collectively outperform global methods, particularly for SCP analysis. Standardization of image processing pipelines and layer-specific optimization are essential to improve reproducibility and clinical consistency.

Osteoporosis is a significant health and economic issue, as it predisposes patients to a higher risk of bone fracture. Measuring bone mineral density has been shown to be an accurate way to assess the risk for osteoporosis. The most common way for bone density testing is a dual-energy X-ray absorptiometry (DEXA) scan, which may be recommended for patients with increased risk of osteoporosis. Radiograph imaging is widely available in clinical settings and acquired for many reasons, such as trauma or pain. The goal of this project is to extract radiomics information from pelvic X-rays (both the hip and femoral neck regions) to assess the risk of osteoporosis (triaging patients into "normal" vs. "at-risk", or "low risk" vs. "high risk" categories). The motivation here is not to replace the DEXA scan but to proactively identify patients at risk for osteoporosis and appropriately refer them to management options. We apply machine learning-based radiomics techniques on a study cohort of 565 patients. Our preliminary results show that a correlation between the radiomics features extracted from pelvic X-rays and the level of osteoporosis risk derived from the DEXA test results.

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms both contain vital physiological information for the prevention and treatment of cardiovascular diseases. Extracted features from these waveforms have diverse clinical applications, including predicting hyper- and hypo-tension, estimating cardiac output from ABP, and monitoring blood pressure and nociception from PPG. However, the lack of standardized tools for feature extraction limits their exploration and clinical utilization. In this study, we propose an automatic feature extraction tool that first detects temporal location of landmarks within each cardiac cycle of ABP and PPG waveforms, including the systolic phase onset, systolic phase peak, dicrotic notch, and diastolic phase peak using the iterative envelope mean method. Then, based on these landmarks, extracts 852 features per cardiac cycle, encompassing time-, statistical-, and frequency-domains. The tools ability to detect landmarks was evaluated using ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. We analyzed 34,267 cardiac cycles of ABP waveforms and 33,792 cardiac cycles of PPG waveforms. Additionally, to assess the tools real-time landmark detection capability, we retrospectively analyzed 3,000 cardiac cycles of both ABP and PPG waveforms, collected from a Philips IntelliVue MX800 patient monitor. The tools detection performance was assessed against markings by an experienced researcher, achieving average F1-scores and error rates for ABP and PPG as follows: (1) On MLORD dataset: systolic phase onset (99.77 %, 0.35 % and 99.52 %, 0.75 %), systolic phase peak (99.80 %, 0.30 % and 99.56 %, 0.70 %), dicrotic notch (98.24 %, 2.63 % and 98.72 %, 1.96 %), and diastolic phase peak (98.59 %, 2.11 % and 98.88 %, 1.73 %); (2) On real time data: systolic phase onset (98.18 %, 3.03 % and 97.94 %, 3.43 %), systolic phase peak (98.22 %, 2.97 % and 97.74 %, 3.77 %), dicrotic notch (97.72 %, 3.80 % and 98.16 %, 3.07 %), and diastolic phase peak (98.04 %, 3.27 % and 98.08 %, 3.20 %). This tool has significant potential for supporting clinical utilization of ABP and PPG waveform features and for facilitating feature-based machine learning models for various clinical applications where features derived from these waveforms play a critical role.

Type B aortic dissection (TBAD) is a serious, potentially life-threatening condition which occurs when a tear develops in the inner lining (intimal layer) of the descending aorta, causing the layers of the aortic wall to separate (dissect) and creating true and false lumens. TBAD can be classified into complicated and uncomplicated types based on the presence of complications (e.g., rupture or malperfusion). For complicated TBAD, the standard treatment is thoracic endovascular aortic repair (TEVAR) with a stent graft. Uncomplicated TBAD can be managed with optimal medical therapy (OMT). Predicting growth and aneurysmal progression of uncomplicated TBAD is clinically important for timing of intervention during OMT. In this study, we extended our previously developed finite element (FE)-based tissue growth framework and applied it to predict the precise geometry and diameter growth of TBAD. Specifically, the unified-fiber-distribution (UFD) model was applied to describe aortic wall mechanics, and a novel centerline-based algorithm was developed to determine the local material coordinates of aortic tissues. A linear kinematic growth law related to local wall stress was used for tissue growth. Patient-specific aortic geometries from three serial computed tomography (CT) scans were obtained for seven patients with TBAD. Using the first two CT images and each patients blood pressure, inverse FE analysis was performed to obtain patient-specific growth parameters. These parameters were then used to simulate forward growth and predict geometry at the third time point. Predicted aortic geometries and dimensions matched well with in vivo measurements: across all patients the maximum diameter error was below 3.5% and the mean diameter error below 4%. Such accurate patient-specific growth forecasts demonstrate the potential of this computational framework to support clinical decision-making in uncomplicated TBAD.

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 technique has failure rates between 25-40%. This presents a pressing need to develop a method for determining optimal coil deployment strategies. Quantification of aneurysm hemodynamics through computational fluid dynamics (CFD) has the potential to significantly improve the understanding of the mechanics of aneurysm coiling and improve treatment outcomes, but accurately representing the coil mass in CFD simulations remains a challenge. We have used the Finite Element Method (FEM) for simulating patient-specific coil deployment based on mechanical properties and coil geometries provided by the device manufacturer 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 0.836 and the average error in porosity map is 6.3%. 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 of interest including were calculated for each of the CFD simulations. The average error across hemodynamic parameters of interest is [~]19%, a 58% reduction from the average error of the porous media simulations, demonstrating a marked improvement in the accuracy of CFD simulations using FEM generated coil geometries.

Arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms contain valuable clinical information and play a crucial role in cardiovascular health monitoring, medical research, and managing medical conditions. The features extracted from PPG waveforms have various clinical applications ranging from blood pressure monitoring to nociception monitoring, while features from ABP waveforms can be used to calculate cardiac output and predict hypertension or hypotension. In recent years, many machine learning models have been proposed to utilize both PPG and ABP waveform features for these healthcare applications. However, the lack of standardized tools for extracting features from these waveforms could potentially affect their clinical effectiveness. In this paper, we propose an automatic signal processing tool for extracting features from ABP and PPG waveforms. Additionally, we generated a PPG feature library from a large perioperative dataset comprising 17,327 patients using the proposed tool. This PPG feature library can be used to explore the potential of these extracted features to develop machine learning models for non-invasive blood pressure estimation.

BackgroundTransesophageal echocardiography (TEE) is a critical tool in diagnosing and managing infectious endocarditis, providing detailed images of cardiac structures. However, identifying vegetations on valves and their dynamic behavior in ultrasound videos can be challenging. TEEs metadata often does not include scale enabling computation of speed. ObjectivesTo address this, we developed a simple Python-based tool that enhances the visualization of these dynamic characteristics. This tool reconstructs an optical flow from TEE images, capturing the motion of cardiac structures and offering deeper insights into their behavior. The tool also recovers scale from visual information on the TEES. MethodsBy leveraging the Marching Cubes algorithm and 2D Fast Fourier Transform (FFT) to recover scale from images, the tool efficiently processes video frames to create a 3D representation where time is the third dimension. Wit his mouse the user can select temporal slices and a view of the dynamic evolution in that slice is created together with the speeds. ResultsThis approach allows for measurement of thicknesses and speeds, aiding in the evaluation of valvular and vegetation dynamics. ConclusionsThe tools user-friendly interface, built with Dash and Plotly, enables interactive analysis and visualization, making it a valuable asset for cardiologists in clinical settings to further analyze valvular behavior.

Applications of computational fluid dynamics (CFD) techniques to aid in the diagnosis and treatment of cardiovascular disease have entered the research domain in recent years, due to their ability to provide valuable patient-specific information without risks associated with highly invasive procedures. SimVascular [1] [2] is an open-source software which allows streamlined processing and CFD blood flow analysis of medical imaging data. OpenFOAM [3] is a proven open-source software which allows for versatile modeling of various fluid dynamics phenomena. In this study, both SimVascular and OpenFOAM simulations are set up with identical computational mesh, similar numerical schemes, boundary conditions, and material properties, to model blood flow in the coronary artery of a 10 year old patient with Coarctation of the Aorta (CoA) who underwent end-to-side anastomosis. Difference in the flow fields such as flow rate, pressure, vorticity, and wall shear stress between SimVascular and OpenFOAM are analyzed. Similar results are obtained in both simulations up to a certain model time, before the results become drastically different. Both the similarities and differences are documented and discussed.

Ruptured intracranial aneurysms (IAs) are catastrophic events associated with a high mortality rate. An estimation of 6 million people in the United States have reported IAs, raising a pressing need for diagnostic tools to assess IAs rupture risks. Current population-based guidelines are imperfect, hence the need for new quantifiable variables and imaging markers. Aneurysm wall motion has been identified as a potential marker of high risk aneurysms, but conventional imaging techniques are challenged by small IAs sizes and limited spatial resolution. Recently, amplified Flow (aFlow) has been introduced as an algorithm which allows visualization and quantification of aneurysm wall motion based on amplification of 4D flow MRI data. In this work, we used aFlow to assess IAs wall motion in patients with growing aneurysms. The results were compared with a patient cohort with stable aneurysms. Among 118 patients with unruptured IAs who underwent sequential surveillance imaging, 10 patients with growing IAs who had baseline 3D TOF-MRA and 4D flow MR imaging were identified and matched with another cohort of patients with stable IAs based on IAs size and location. aFlow was then applied to the 4D flow MR data to amplify the aneurysm wall displacement. Voxel-based values of displacement were extracted for each aneurysm and normalized with respect to the reference parent artery. Following histogram analysis, the highest and lowest IAs displacements were calculated, together with their standard deviation and interquartile ranges. A paired-wise analysis was adopted to assess the differences among clinical variables, demographic data, morphological features, and aFlow parameters between patients with stable versus growing aneurysm. Results demonstrated higher wall motion and higher variability of deformation for the growing aneurysms, possibly due to inhomogeneities of the mechanical characteristics of the vessels walls or to underlying hemodynamics. Computational Fluid Dynamic simulation was also conducted for a subset of 6 stable and 6 growing aneurysms to examine the correlation between hemodynamic parameters, wall motion, and aneurysm stability. The magnitude and variance of directional wall shear stress gradient, in addition to area of colocation of elevated oscillatory shear stress and high variance in pressure, were highly correlated with both wall motion and aneurysm stability. We demonstrated here that the measurement and amplification of the aneurysm wall motion achieved with our method has the potential to differentiate stable from growing aneurysms, and potentially act as a substitute for in depth computational fluid dynamic analysis.

Central venous pressure (CVP), a key component of hemodynamic monitoring, is widely used to guide fluid resuscitation in critically ill patients. It is typically measured using central venous line catheterization, which is the gold standard, but this method is invasive, time-consuming, and associated with complications. This study aims to investigate whether machine learning (ML)-based analysis of features extracted from a non-invasive, standard-of-care waveform--the photoplethysmography (PPG) signal--can identify patients with elevated CVP. We trained Light Gradient-Boosting Machine (LightGBM) model using a large perioperative dataset (MLORD), containing 17,327 surgical patients from 2019 to 2022 at UCLA. For this study, we selected 1665 patients with both PPG and CVP waveforms available. A total of 843 PPG features per cardiac cycle (CC) were extracted from the PPG waveforms using a signal processing-based feature extraction tool, along with the simultaneous maximum value calculated from the corresponding CCs in the CVP waveform. Additionally, for each patient, the average and standard deviation of each PPG feature, as well as the mean of the maximum CVP values, were calculated across all cardiac cycles, resulting in 843 averaged PPG features, 843 PPG feature standard deviations, and one averaged maximum CVP value per patient. The average maximum CVP value was used as the ground truth to classify patients as either normal (5 [≤] CVP [≤] 15 mmHg) or elevated (CVP > 15 mmHg). Of the 1,665 patients, 1,182 were normal and 483 were elevated. The dataset was split into 90% for training (1,063 normal and 435 elevated) and 10% for testing (119 normal and 48 elevated). From the 1686 PPG features (843 averaged and 843 standard deviation), 246 were selected for model development using the Recursive Feature Elimination with Cross-Validation (RFECV) approach. To further enhance performance, hyperparameters were tuned through 5-fold cross-validation on the training set. Finally, the best-performing configuration was retrained on the full training data, and its performance was evaluated on the held-out test set. To provide a robust estimate and confidence interval, a bootstrapping procedure with 100 iterations was performed on the test set. The LightGBM classifier achieved a mean area under the receiver operating characteristic curve (AUC) of 0.79 (95% CI: 0.71-0.84) and mean accuracy of 0.71 (95% CI: 0.65-0.77), demonstrating good discriminatory power in distinguishing between patients with normal and elevated CVP. This study highlights the ability of PPG-derived features to discriminate between patients with normal and elevated CVP using ML. These early findings lay the groundwork for future research aimed at developing non-invasive approaches to CVP assessment.

Osteoporotic hip fractures (HFs) in the elderly are a pertinent issue in healthcare, particularly in developed countries such as Australia. Estimating prognosis following admission remains a key challenge. Current predictive tools require numerous patient input features including those unavailable early in admission. Moreover, attempts to explain machine learning [ML]-based predictions are lacking. We developed 7 ML prognostication models to predict in-hospital mortality following minimal trauma HF in those aged [≥] 65 years of age, requiring only sociodemographic and comorbidity data as input. Hyperparameter tuning was performed via fractional factorial design of experiments combined with grid search; models were evaluated with 5-fold cross-validation and area under the receiver operating characteristic curve (AUROC). For explainability, ML models were directly interpreted as well as analyzed with SHAP values. Top performing models were random forests, naive Bayes [NB], extreme gradient boosting, and logistic regression (AUROCs ranging 0.682 - 0.696, p>0.05). Interpretation of models found the most important features were chronic kidney disease, cardiovascular comorbidities and markers of bone metabolism; NB also offers direct intuitive interpretation. Overall, we conclude that NB has much potential as an algorithm, due to its simplicity and interpretability whilst maintaining competitive predictive performance. Author SummaryOsteoporotic hip fractures are a critical health issue in developed countries. Preventative measures have ameliorated this issue somewhat, but the problem is expected to remain in main due to the aging population. Moreover, the mortality rate of patients in-hospital remains unacceptably high, with estimates ranging from 5-10%. Thus, a risk stratification tool would play a critical in optimizing care by facilitating the identification of the susceptible elderly in the community for prevention measures and the prioritisation of such patients early during their hospital admission. Unfortunately, such a tool has thus far remained elusive, despite forays into relatively exotic algorithms in machine learning. There are three major drawbacks (1) most tools all rely on information typically unavailable in the community and early during admission (for example, intra-operative data), limiting their potential use in practice, (2) few studies compare their trained models with other potential algorithms and (3) machine learning models are commonly cited as being black boxes and uninterpretable. Here we show that a Naive Bayes model, trained using only sociodemographic and comorbidity data of patients, performs on par with the more popular methods lauded in literature. The model is interpretable through direct analysis; the comorbidities of chronic kidney disease, cardiovascular, and bone metabolism were identified as being important features contributing to the likelihood of deaths. We also showcase an algorithm-agnostic approach to machine learning model interpretation. Our study shows the potential for Naive Bayes in predicting elderly patients at risk of death during an admission for hip fracture.

A method to estimate myocardial action potentials (APs) from electrocardiograms (ECGs) would be an advance in ECG-based diagnosis, utilised for clinical diagnosis, assessment of potential cardiac disease risk and prediction of lethal arrhythmias. However, the ECG inverse problem, which estimates the spatial distribution of AP signals from the ECG, has been considered difficult electromagnetically. For clinical ECG analysis, timescales of collective APs, synchrony and the duration of depolarisation and repolarisation is informative. Thus, we attempted to obtain the time distribution of collective AP transitions from the ECG rather than the spatial distribution. To analyse the variance of the collective myocardial APs from the ECG, we designed a model equation using the probability densities of the Gaussian function of time-series point processes in the cardiac cycle and dipoles of collective APs in the myocardium. The equation to calculate the difference between the two cumulative distribution functions (CDFs) as the positive- and negative-epicardium potential fits well with the R and T waves. The mean, standard deviation, weights, and level of each CDFs are metrics for the variance of the AP transition state of the collective myocardial AP transition states. Clinical ECGs of myocardial ischaemia during coronary intervention showed abnormalities in the aforementioned specific elements of the tensor associated with repolarisation transition variance earlier than in conventional indicators of ischaemia. The tensor could evaluate the beat-to-beat dynamic repolarisation changes between the ventricular epi and endocardium using the Mahalanobis distance (MD). Tensor Cardiography, a method that uses CDF differences CDF as the transition of a collective myocardial AP transition, has the potential to be a new analysis tool for ECGs. Authors SummaryMyocardial action potentials (APs) which indicate electric excitation of the cells can provide important information to suggest the mechanisms of cardiac disease such as myocardial ischemia and arrhythmias. However, it has been challenging to estimate APs from electrocardiograms (ECGs). Unlike other imaging techniques like CT or MRI, the electrocardiographic inverse problem requires estimating the geometric distribution of APs from the ECG, has been considered difficult. Our approach, known as Tensor Cardiography, uses a model equation based on cumulative distribution functions (CDFs) to analyze the time series variance of collective myocardial APs from the ECG. By fitting this equation to the R and T waves, we have obtained a set of metrics that represent beat-to-beat dynamic variance of polarization and repolarization of the epi and endocardium. Our study of ECGs from myocardial ischemia during coronary intervention has demonstrated abnormalities in the tensor elements associated with repolarization, which appeared earlier and more prominently than conventional ST changes. Tensor Cardiography provides a revolutionary analysis tool for ECGs that holds enormous potential for clinical diagnosis, risk assessment, and prediction of lethal arrhythmias. Our approach shows promise as a new frontier in cardiac disease management and has significant implications for patient care.

The Jugular Venous Pulse (JVP) is a vital gauge of proper heart health, reflecting the venous pressure via the Jugular Vein observation. It offers crucial insights for discerning numerous cardiac and pulmonary conditions. Yet, its evaluation is often over-shadowed by the challenges in its process, especially in patients with neck obesity obstructing visibility. Although central venous catheterization provides an alternative, it is invasive and typically reserved for critical cases. Traditional JVP monitoring methods, both visual and via catheterization, present significant hurdles, limiting their frequent application despite their clinical significance. Therefore, there is a pressing need for a non-invasive, efficient JVP monitoring method accessible for home-based and hospitalized patients. Such a method could preempt numerous hospital admissions by offering early indicators. We introduce a non-invasive method using a frequency-modulated continuous wave (FMCW) radar for JVP estimation directly from the skin surface. Our signal processing technique involves an eigen beamforming method to enhance the signal-to-noise ratio for better estimation of JVP. By meticulously fine-tuning various parameters, we identified the optimal settings to enhance the JVP signal quality. In addition, we performed a detailed morphological analysis comparing the JVP and photoplethysmography signals. Our investigation effectively achieved signal localization within a Direction of Arrival (DoA) range from -20{degrees} to 20{degrees}. This initial study validates the effectiveness of using a 60 GHz far-field radar in measuring JVP. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/24308313v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@d95a7dorg.highwire.dtl.DTLVardef@1c3e6d4org.highwire.dtl.DTLVardef@678f28org.highwire.dtl.DTLVardef@e7a0d7_HPS_FORMAT_FIGEXP M_FIG C_FIG

ObjectiveTo present Motiro, an unified framework for non-supervised statistical analysis endomicroscopy videos of the colorectal mucosa. Materials and MethodsWe wrote an open-source Python wrapper using ImageJ software with OpenCV, Seaborn and NumPy libraries. It generates a mosaic from the video of the mucosa, evaluates morphometric properties of the crypts, their distribution, and return their statistics. Shannon entropy (and Hellinger distance) are used for quantifying variability (and comparing different mucosa). ResultsThe segmentation process applied to normal mucosa of pre(post)- neoadjuvant patient is presented along with the corresponding statistical analysis of morphometric parameters. DiscussionOur analysis provides estimation of morphometric parameters consistent with available methods, is faster, and, additionally, provides statistical characterization of the mucosa morphometry. Motiro enables the analysis of large amounts of endomicroscopy videos for building a normal rectum features dataset to help on: detection of small variability; classification of post-neoadjuvant recovery; decision about surgical intervention necessity.

The present study presents a systematic approach for generating data-driven synthetic cerebral aneurysm geometries and evaluating their hemodynamics through computational fluid dynamics. Seven patient-specific aneurysm geometries from the right internal carotid artery were reconstructed from time-of-flight magnetic resonance angiography images and standardized through orientation alignment, followed by non-rigid registration onto a common spherical point cloud as a template. Principal component analysis (PCA) was then applied to the aligned point-cloud data to quantify morphological variability and parameterize shape deformation. The first four principal components captured over 90% of the total variance; however, higher-order components were required to capture the detailed geometrical features of the original geometries. Computational fluid dynamic simulations were performed on the PCA-based synthetic geometries under pulsatile flow conditions to investigate the influence of shape variations on intra-aneurysmal flow patterns, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI). The first principal component score (PCS1), which was associated with changes in aneurysm height and dome width, had the strongest effects on TAWSS and OSI levels. Lower PCS1 values, which corresponded to taller and more oblique domes, produced slower adjacent flow and elevated OSI, whereas higher PCS1 values increased TAWSS. The second principal component score primarily modulated lateral geometric asymmetry and further influenced OSI distribution for the lower PCS1 values. Collectively, these findings indicate that PCA-based shape parameterization provides a practical approach for generating synthetic aneurysm datasets and systematically assessing how specific morphological features govern hemodynamic behavior. The proposed approach is expected to contribute to the future development of surrogate modeling and data-driven hemodynamic prediction.

According to the guidelines of the European Society of Hypertension International Protocol revision 2010, the requirements for long time blood pressure measurement (BPM) are: simple handling, robustness against movements, accuracy of better than {+/-} 5 mmHg, and, above all, that the patients motion should not be restricted during measurement. These requirements are in particular important for a reliable interpretation of the blood pressure (BP) of hypertensive patients, because such a diagnosis will usually be confirmed by long-term measurement. Moreover, to be able to correlate the patients BP with his normal daily activity, non-obstructive non-invasive methods are desired to reduce the patients load. The main concern of this paper is to present a novel method for estimating non-invasive continuous blood pressure (CBP) from a single photoplethysmography (PPG) signal. In contrast to the pulse transit time (PTT) method, our approach is based on the assumption that the phase-velocities of the fundamental and higher harmonics depend on the (non-linear) elastic properties of the arteries. Consequently, phase velocity varies as a function of a vessels instantaneous dilation and can be effectively utilised for CBP estimation. In addition to its numerous advantages for a simplified measurement setup, we could show that the method achieves a high degree of correlation for a reliable BP estimation from PPG data. Comparison with state-of-the-art PTT methods was carried out using a dataset from the PhysioBank Database comprising a reference invasive blood pressure (IBP) signal measured at the radial artery, a PPG signal measured at the fingertip and a standard ECG signal. The correlation values obtained from the long-time estimation of the systolic blood pressure (SBP) were as high as r = 0.8945, while the value for the diastolic blood pressure (DBP) was found to be r = 0.9082 and the correlation of the mean blood pressure (MBP) was r = 0.9322. These results were achieved by analysing the dataset in a beat-to-beat manner and regarding several post-processing procedures like coherent averaging (CA) and zero padding with quasi-continuous frequency domain estimation and artificially refined frequency resolution.

PurposeBlood pressure gradient ({Delta}P) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of{Delta} P estimates derived non-invasively using patient-specific 0D and 3D deformable wall simulations. MethodsMedical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N=17). 0D simulations were performed first and used to tune boundary conditions and initialize 3D simulations.{Delta} P across the CoA estimated using both 0D and 3D simulations were compared to invasive catheter-based pressure measurements for validation. ResultsThe 0D simulations were extremely efficient ([~]15 secs computation time) compared to 3D simulations ([~]30 hrs computation time on a cluster). However, the 0D{Delta} P estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3D estimates (12.1 {+/-} 9.9 mmHg vs 5.3 {+/-} 5.4 mmHg). In particular, the 0D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0D model classified patients with severe CoA requiring intervention (defined as{Delta} P[≥] 20 mmHg) with 76% accuracy and 3D simulations improved this to 88%. ConclusionOverall, a combined approach, using 0D models to efficiently tune and launch 3D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.

PurposeThis study aims to quantify the patient-specific hemodynamics of complex conduit routing configurations of coronary artery bypass grafting (CABG) operation which are specifically suitable for off-pump surgeries. Coronary perfusion efficacy and local hemodynamics of multiple left internal mammary artery (LIMA) with sequential and end-to-side anastomosis are investigated. Using a full anatomical model comprised of aortic arch and coronary artery branches the optimum perfusion configuration in multi-vessel coronary artery stenosis is desired. MethodologyTwo clinically relevant CABG configurations are created using a virtual surgical planning tool where for each configuration set, the stenosis level, anastomosis distance and angle were varied. A non-Newtonian computational fluid dynamics solver in OpenFOAM incorporated with resistance boundary conditions representing the coronary perfusion physiology was developed. The numerical accuracy is verified and results agreed well with a validated commercial cardiovascular flow solver and experiments. For segmental performance analysis, new coronary perfusion indices to quantify deviation from the healthy scenario were introduced. ResultsThe first simulation configuration set; - a CABG targeting two stenos sites on the left anterior descending artery (LAD), the LIMA graft was capable of 31 mL/min blood supply for all the parametric cases and uphold the healthy LAD perfusion in agreement with the clinical experience. In the second end-to-side anastomosed graft configuration set; -the radial artery graft anastomosed to LIMA, a maximum of 64 ml/min flow rate in LIMA was observed. However, except LAD, the obtuse marginal (OM) and second marginal artery (m2) suffered poor perfusion. In the first set, average wall shear stress (WSS) were in the range of 4 to 35 dyns/cm2 for in LAD. Nevertheless, for second configuration sets the WSS values were higher as the LIMA could not supply enough blood to OM and m2. ConclusionThe virtual surgical configurations have the potential to improve the quality of operation by providing quantitative surgical insight. The degree of stenosis is a critical factor in terms of coronary perfusion and WSS. The sequential anastomosis can be done safely if the anastomosis angle is less than 90 degrees regardless of degree of stenosis. The smaller proposed perfusion index value, O(0.04-0)x102, enable us to quantify the post-op hemodynamic performance by comparing with the ideal healthy physiological flow.

IntroductionThe increased demand for mechanical ventilation caused by the SARS-CoV-2 pandemic could generate a critical situation where patients may lose access to mechanical ventilators. Combined ventilation, in which two patients are ventilated simultaneously but independently with a single ventilator has been proposed as a life-saving bridge while waiting for new ventilators availability. New devices have emerged to facilitate this task and allow individualization of ventilatory parameters in combined ventilation. In this work we run computer-based electrical simulations of combined ventilation. We introduce an electrical model of a proposed mechanical device which is designed to individualize ventilatory parameters, and tested it under different circumstances. Materials and MethodsWith an electronic circuit simulator applet, an electrical model of combined ventilation is created using resistor-capacitor circuits. A device is added to the electrical model which is capable of individualizing the ventilatory parameters of two patients connected to the same ventilator. Through computational simulation, the model is tested in different scenarios with the aim of achieving adequate ventilation of two subjects under different circumstances: 1) two identical subjects; 2) two subjects with the same size but different lung compliance; and 3) two subjects with different sizes and compliances. The goal is to achieve the established charge per unit of size on each capacitor under different levels of end-expiratory voltage (as an end-expiratory pressure analog). Data collected included capacitor charge, voltage, and charge normalized to the weight of the simulated patient. ResultsSimulations show that it is possible to provide the proper charge to each capacitor under different circumstances using an array of electrical components as equivalents to a proposed mechanical device for combined ventilation. If the pair of connected capacitors have different capacitances, adjustments must be made to the source voltage and/or the resistance of the device to provide the appropriate charge for each capacitor under initial conditions. In pressure control simulation, increasing the end-expiratory voltage on one capacitor requires increasing the source voltage and the device resistance associated with the other simulated patient. On the other hand, in the volume control simulation, it is only required to intervene in the device resistance. ConclusionsUnder simulated conditions, this electrical model allows individualization of combined mechanical ventilation.

ObjectivesThe rising global burden of cardiovascular diseases (CV) highlights the critical need for efficiency in disease diagnosis and management. An important area for such improvement is utilization of artificial intelligence (AI) for streamlining time and resources in CV imaging workflow. We evaluate the performance of artificial intelligence (AI) segmentation for aortic segmentation on clinical computed tomography angiography (CTA) images and compare accuracy to manual methods. Such automation would markedly improve efficiency and accuracy of aortic surveillance. MethodsThis retrospective study included 27 scans from 20 patients who underwent thoracic endovascular aortic repair (TEVAR) between January 2020 and March 2022. An open-source AI model was applied to segment the aorta, and its performance was assessed by comparing AI-generated segmentations with manual segmentations using Dice similarity coefficients, volumetric analysis, and aortic dimensions. Centerline reconstructed images of thoracoabdominal aorta were processed to extract radiomic features, including maximum diameter and cross-sectional area, for analysis. ResultsThe AI tool achieved a median Dice coefficient of 0.96 (0.02), indicating a high degree of concordance with manual segmentation. Multiplanar reconstruction was performed to visualize the aorta and extract measurements along its length using the automated centerline, and radiomic features, including maximum diameter and cross-sectional area, were subsequently extracted for analysis. ConclusionsAI segmentation demonstrates strong potential for improving efficiency and consistency in thoracoabdominal aortic segmentation, achieving high accuracy compared to manual methods. These findings highlight the feasibility of AI integration into clinical practice for diagnosis and surveillance of aortopathies, warranting further validation on larger datasets to enable clinical translation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/25320502v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@d00e80org.highwire.dtl.DTLVardef@167fd18org.highwire.dtl.DTLVardef@1972faorg.highwire.dtl.DTLVardef@cb96d5_HPS_FORMAT_FIGEXP M_FIG C_FIG