Unveiling rupture risk and clinical outcomes in midline aneurysms: A matched cohort analysis investigating the impact of localization within the anterior or posterior circulation.

Intracranial aneurysms (IAs) located in the anterior and posterior circulations of the Circle of Willis present differential rupture risks. This study aimed to compare the rupture risk and clinical outcomes of anterior communicating artery aneurysms (AcomA) and basilar tip aneurysms (BAs); two IA types located along the midline within the Circle of Willis. We retrospectively collected data from 1026 patients presenting with saccular IAs. Only AcomA and BAs with a 3D angiography were included. Out of 186 included IAs, a cohort of 32 BAs was matched with AcomA based on the patients' pre-existing conditions and morphological parameters of IAs. Clinical outcomes, including rupture risk, hydrocephalus development, vasospasm incidence, and patients' outcome, were compared. The analysis revealed no significant difference in rupture risk, development of hydrocephalus, need for ventricular drainage, or vasospasm incidence between the matched AcomA and BA cohorts. Furthermore, the clinical outcomes post-rupture did not significantly differ between the two groups, except for a higher Fisher Grade associated with BAs. Once accounting for morphological and patient factors, the rupture risk between AcomA and BAs is comparable. These findings underscore the importance of tailored management strategies for specific IA types and suggest that further investigations should focus on the role of individual patient and aneurysm characteristics in IA rupture risk and clinical outcomes.

Enlarged tumefactive perivascular, or Virchow-Robin, spaces and hydrocephalus: do we need to treat? Illustrative cases.

BACKGROUND: Perivascular spaces (PVSs) are spaces in brain parenchyma filled with interstitial fluid surrounding small cerebral vessels. Massive enlargements of PVSs are referred to as "giant tumefactive perivascular spaces" (GTPVSs), which can be classified into three types depending on their localization. These lesions are rare, predominantly asymptomatic, and often initially misinterpreted as cystic tumor formations. However, there are several reported cases in which GTPVSs have induced neurological symptoms because of their size, mass effect, and location, ultimately leading to obstructive hydrocephalus necessitating neurosurgical intervention. Presented here are three diverse clinical presentations of GTPVS. OBSERVATIONS: Here, the authors observed an asymptomatic case of type 1 GTPVS and two symptomatic cases of type 3 GTPVS, one causing local mass effect and the other hydrocephalus. LESSONS: GTPVSs are mostly asymptomatic lesions. Patients without symptoms should be closely monitored, and biopsy is discouraged. Hydrocephalus resulting from GTPVS necessitates surgical intervention. In these cases, third ventriculostomy, shunt implantation, or direct cyst fenestration are surgical options. For patients presenting with symptoms from localized mass effect, a thorough evaluation for potential neurosurgical intervention is imperative. Follow-up in type 3 GTPVS is recommended, particularly in untreated cases. Given the infrequency of GTPVS, definitive guidelines for neurosurgical treatment and subsequent follow-up remain elusive.

Artificial intelligence in cardiac computed tomography.

Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.

4Dflow-VP-Net: A deep convolutional neural network for noninvasive estimation of relative pressures in stenotic flows from 4D flow MRI.

PURPOSE: To estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D flow MRI. METHODS: A novel deep learning-based approach is proposed to estimate pressure gradient across stenosis from four-dimensional flow MRI (4D flow MRI) velocities. A deep neural network 4D flow Velocity-to-Presure Network (4Dflow-VP-Net) was trained to learn the spatiotemporal relationship between velocities and pressure in stenotic vessels. Training data were simulated by computational fluid dynamics (CFD) for different pulsatile flow conditions under an aortic flow waveform. The network was tested to predict pressure from CFD-simulated velocity data, in vitro 4D flow MRI data, and in vivo 4D flow MRI data of patients with both moderate and severe aortic stenosis. TVPG derived from 4Dflow-VP-Net was compared to catheter-based pressure measurements for available flow rates, in vitro and Doppler echocardiography-based pressure measurement, in vivo. RESULTS: Relative pressures calculated by 4Dflow-VP-Net and in vitro pressure catheterization revealed strong correlation (r2 = 0.91). Correlations analysis of TVPG from reference CFD and 4Dflow-VP-Net for 450 simulated flow conditions showed strong correlation (r2 = 0.99). TVPG from in vitro MRI had a correlation coefficient of r2 = 0.98 with reference CFD. 4Dflow-VP-Net, applied to 4D flow MRI in 16 patients, showed comparable TVPG measurement with Doppler echocardiography (r2 = 0.85). Bland-Altman analysis of TVPG measurements showed mean bias and limits of agreement of -0.20 ± 2.07 mmHg and 0.19 ± 0.45 mmHg for CFD-simulated velocities and in vitro 4D flow velocities. In patients, overestimation of Doppler echocardiography relative to TVPG from 4Dflow-VP-Net (10.99 ± 6.77 mmHg) was observed. CONCLUSION: The proposed approach can predict relative pressure in both in vitro and in vivo 4D flow MRI of aortic stenotic patients with high fidelity.

Probiotics in vaginal health.

Bacterial vaginosis, a type of vaginal inflammation, can be considered the main reason for abnormal discharges of the vagina and vaginal dysbiosis during reproductive years. Epidemiological investigations of females suffering from vaginitis demonstrated that at least 30% to 50% of all women had Bacterial vaginosis (BV). One of the fields of treatment is the use of probiotics, probiotics are commonly defined as viable microorganisms (yeasts or bacteria) that can positively affect the health of their hosts. They are used in foods, notably fermented milk products, and medicine-related products. The development of new probiotic strains is aimed at more active advantageous organisms. Lactobacillus species are the dominant bacteria in a normal vagina that can decrease the pH of the vagina by the production of lactic acid. A number of lactobacilli types can produce hydrogen peroxide as well. The presence of hydrogen peroxide-induced low pH can prevent the growth of several other microorganisms. The vaginal flora of BV cases can modify by replacing the Lactobacillus species with a high density of anaerobic bacteria (i.e. Mobiluncus sp. Bacteroides sp.), Mycoplasma hominis, and Gardnerella vaginalis. More vaginal infections are treated with medications, while there is a possibility of recurrence and chronic infection because of the adverse effects on the indigenous lactobacilli. Probiotics and prebiotics have shown capacities for optimizing, maintaining, and restoring the vaginal microflora. Therefore, biotherapeutics can offer alternative approaches to reduce infections of the vagina and thus promote consumers' health.

The association between penile cancer and HIV infection: A literature review.

Penile cancer is a rare malignancy which HIV infection appears to increase the risk of. The magnitude of this risk and the pathogenesis remain unclear. A comprehensive review of the literature was undertaken using conventional search strategies. Twenty-four publications were identified by this methodology, of which nine were case reports and 15 were observational studies. These studies were highly heterogeneous, with varying study designs, populations, and objectives. The risk of penile cancer within HIV-positive individuals is significantly greater than in those without HIV (RR = 3 .7 to 5.8, 3 studies; SIR = 3.8 to 11.1, 4 studies). HIV is also shown to influence disease characteristics, with a four-fold increased risk of death from penile cancer. Moreover, progression from intraepithelial neoplasia occurs earlier in HIV, six years sooner than in HIV-negative men. HIV-positive men have a higher prevalence of HPV infection. Ethnicity is also shown to modulate the relationship between HIV and penile carcinoma, with a higher risk of cancer in Hispanic, compared with Caucasian, HIV-positive men. This review has collated data from diverse sources to improve understanding of the relationship between HIV and penile cancer. This relationship has been quantitatively and qualitatively characterised and highlights areas deserving further enquiry.

Rural vulnerability to water scarcity in Iran: an integrative methodology for evaluating exposure, sensitivity and adaptive capacity.

The water crisis is the main stress in arid and semi-arid areas, especially in rural areas where agriculture is the main livelihood. This study assessed vulnerability to water scarcity in six rural regions of Isfahan, Iran. These areas have lost their primary water source of agriculture, the Zayandeh Rud River, since 2006. They have confronted many socio-ecological problems which threatened their existence. A mixed methodology was used to assess vulnerability as a function of exposure, sensitivity, and adaptive capacity. Structured questionnaires and in-depth interviews were conducted with key informants and 266 households. The method of Multidimensional Poverty Index was applied to calculate the sensitivity index, which has not been used for sensitivity assessment yet. The results showed that the leading cause of water scarcity is poor water governance. The three districts that had direct access to the Zayandeh Rud river were more vulnerable to water scarcity (scores of 0.35, 0.39, and 0.44) than those that had never had direct access to the river (scores of 0.19, 0.21, and 0.23) due to the more exposure and less adaption to water shortage. Inappropriate financial resilience (from 0.24 to 0.41) and living standards (from 0.19 to 0.36) have made more contributions to creating sensitivity than socioeconomic factors (from 0.14 to 0.28). Different natural capitals have mainly created differences in adaptive capacity across rural areas. Villages located downstream have lost their natural capital due to water-quality degradation caused by river drying up and groundwater overexploitation. Supplementary Information: The online version contains supplementary material available at 10.1007/s10708-022-10726-0.

Overcoming Barriers in Neurosurgical Education: A Novel Approach to Practical Ventriculostomy Simulation.

BACKGROUND: In the high-risk, high-stakes specialty of neurosurgery, traditional teaching methods often fail to provide young residents with the proficiency needed to perform complex procedures in stressful situations, with direct effects on patient outcomes. Physical simulators provide the freedom of focused, hands-on training in a more controlled environment. However, the adoption of simulators in neurosurgical training remains a challenge because of high acquisition costs, complex production processes, and lack of realism. OBJECTIVE: To introduce an easily reproducible, cost-effective simulator for external ventricular drain placements through various ventriculostomy approaches with life-like tactile brain characteristics based on real patients' data. METHODS: Whole brain and skull reconstruction from patient's computed tomography and MRI data were achieved using freeware and a desktop 3-dimensional printer. Subsequently, a negative brain silicone mold was created. Based on neurosurgical expertise and rheological measurements of brain tissue, gelatin in various concentrations was tested to cast tactilely realistic brain simulants. A sample group of 16 neurosurgeons and medical students tested and evaluated the simulator in respect to realism, haptics, and general usage, scored on a 5-point Likert scale. RESULTS: We saw a rapid and significant improvement of accuracy among novice medical students. All participants deemed the simulator as highly realistic, effective, and superior to conventional training methods. CONCLUSION: We were able to demonstrate that building and implementing a high-fidelity simulator for one of the most important neurosurgical procedures as an effective educational and training tool is achievable in a timely manner and without extensive investments.

FlowRAU-Net: Accelerated 4D Flow MRI of Aortic Valvular Flows With a Deep 2D Residual Attention Network.

In this work, we propose a novel deep learning reconstruction framework for rapid and accurate reconstruction of 4D flow MRI data. Reconstruction is performed on a slice-by-slice basis by reducing artifacts in zero-filled reconstructed complex images obtained from undersampled k-space. A deep residual attention network FlowRAU-Net is proposed, trained separately for each encoding direction with 2D complex image slices extracted from complex 4D images at each temporal frame and slice position. The network was trained and tested on 4D flow MRI data of aortic valvular flow in 18 human subjects. Performance of the reconstructions was measured in terms of image quality, 3-D velocity vector accuracy, and accuracy in hemodynamic parameters. Reconstruction performance was measured for three different k-space undersamplings and compared with one state of the art compressed sensing reconstruction method and three deep learning-based reconstruction methods. The proposed method outperforms state of the art methods in all performance measures for all three different k-space undersamplings. Hemodynamic parameters such as blood flow rate and peak velocity from the proposed technique show good agreement with reference flow parameters. Visualization of the reconstructed image and velocity magnitude also shows excellent agreement with the fully sampled reference dataset. Moreover, the proposed method is computationally fast. Total 4D flow data (including all slices in space and time) for a subject can be reconstructed in 69 seconds on a single GPU. Although the proposed method has been applied to 4D flow MRI of aortic valvular flows, given a sufficient number of training samples, it should be applicable to other arterial flows.

Relative pressure estimation from 4D flow MRI using generalized Bernoulli equation in a phantom model of arterial stenosis.

OBJECTIVE: Arterial stenosis is a significant cardiovascular disease requiring accurate estimation of the pressure gradients for determining hemodynamic significance. In this paper, we propose Generalized Bernoulli Equation (GBE) utilizing interpolated-based method to estimate relative pressures using streamlines and pathlines from 4D Flow MRI. METHODS: 4D Flow MRI data in a stenotic phantom model and computational fluid dynamics simulated velocities generated under identical flow conditions were processed by Generalized Bernoulli Equation (GBE), Reduced Bernoulli Equations (RBE), as well as the Simple Bernoulli Equation (SBE) which is clinically prevalent. Pressures derived from 4D flow MRI and noise corrupted CFD velocities were compared with pressures generated directly with CFD as well as pressures obtained using Millar catheters under identical flow conditions. RESULTS: It was found that SBE and RBE methods underestimated the relative pressure for lower flow rates while overestimating the relative pressure at higher flow rates. Specifically, compared to the reference pressure, SBE underestimated the maximum relative pressure by 22[Formula: see text] for a pulsatile flow data with peak flow rate [Formula: see text] and overestimated by around 40[Formula: see text] when [Formula: see text]. In contrast, for GBE method the relative pressure values were overestimated by 15[Formula: see text] with [Formula: see text]and around 10[Formula: see text] with [Formula: see text]. CONCLUSION: GBE methods showed robust performance to additive image noise compared to other methods. Our findings indicate that GBE pressure estimation over pathlines attains the highest level of accuracy compared to GBE over streamlines, and the SBE and RBE methods.

VR-based training of craniotomy for intracranial aneurysm surgery.

PURPOSE: Intracranial aneurysms can be treated micro-surgically. This procedure involves an appropriate head position of the patient and a proper craniotomy. These steps enable a proper access, facilitating the subsequent steps. To train the access planning process, we propose a VR-based training system. METHOD: We designed and implemented an immersive VR access simulation, where the user is surrounded by a virtual operating room, including medical equipment and virtual staff. The patient's head can be positioned via hand rotation and an arbitrary craniotomy contour can be drawn. The chosen access can be evaluated by exposing the aneurysm using a microscopic view. RESULTS: The evaluation of the simulation took place in three stages: testing the simulation using the think-aloud method, conducting a survey and examining the precision of drawing the contour. Although there are differences between the virtual interactions and their counterparts in reality, the participants liked the immersion and felt present in the operating room. The calculated surface dice similarity coefficient, Hausdorff distance and feedback of the participants show that the difficulty of drawing the craniotomy is appropriate. CONCLUSION: The presented training simulation for head positioning and access planning benefits from the immersive environment. Thus, it is an appropriate training for novice neurosurgeons and medical students with the goal to improve anatomical understanding and to become aware of the importance of the right craniotomy hole.

A Unified Optimization for Resilient Dynamic Event-Triggering Consensus Under Denial of Service.

This article proposes a resilient framework for optimized consensus using a dynamic event-triggering (DET) scheme, where the multiagent system (MAS) is subject to denial-of-service (DoS) attacks. When initiated by an adversary, DoS blocks the local and neighboring communication channels in the network. A distributed DET scheme is utilized to limit transmissions between the neighboring agents. A novel convex optimization approach is proposed that simultaneously co-designs all unknown control and DET parameters. The optimization is based on the weighted sum approach and increases the interevent interval for a predefined consensus convergence rate. In the presence of DoS, the proposed co-design framework is beneficial in two ways: 1) the desired level of resilience to DoS is included as a given (desired) input and 2) the upper bound for guaranteed resilience associated with the proposed co-design approach is less conservative (larger) compared to those obtained from other analytical solutions. A structured tradeoff between relevant features of the MAS, namely, the consensus convergence rate, frequency of event triggerings, and level of resilience to DoS attacks, is established. Simulations based on nonholonomic mobile robots quantify the effectiveness of the proposed implementation.

Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 - 0.91. The team's predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm's correction).

Chamberless NDIR CO2 Sensor Robust against Environmental Fluctuations.

Accuracy of CO2 measurement is affected by ambient air fluctuations, making the compensation of such variations in drift-like sensor response essential for concentration level assessment. Here, a series of experiments were carried out with a chamberless approach in a nondispersive infrared (NDIR) gas sensor to examine the combined effect of environmental temperature and relative humidity fluctuations on sensor responses at different concentrations of CO2. To eliminate the drift-like terms caused by environmental fluctuations, the behavior of the sensor was modeled to include ambient temperature, relative humidity, the measured responses as the inputs, and the concentration level as the output. The sensor was fabricated by a light source with an embedded parabolic reflector, a thermopile detector, and two reflective walls that are exposed to the applicable range of CO2 gas. The predicted concentration level was determined by analyzing the system and acquiring a heuristic function based on an ensemble regression model. The created model's reliability and sensor's performance were evaluated by the test and validation data, and the respective accuracies of 99.83 and 98.90% demonstrated the model effectiveness. The chamberless structure of the sensor provides reduction in diffusion time, improves the linearity of responses accompanied by eliminating drift-like variation of responses in varying ambient conditions, and prepares the sensor for industrial applications.

AI in Medical Imaging Informatics: Current Challenges and Future Directions.

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.

Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans.

PURPOSE: Multiview two-dimensional (2D) convolutional neural networks (CNNs) and three-dimensional (3D) CNNs have been successfully used for analyzing volumetric data in many state-of-the-art medical imaging applications. We propose an alternative modular framework that analyzes volumetric data with an approach that is analogous to radiologists' interpretation, and apply the framework to reduce false positives that are generated in computer-aided detection (CADe) systems for pulmonary nodules in thoracic computed tomography (CT) scans. METHODS: In our approach, a deep network consisting of 2D CNNs first processes slices individually. The features extracted in this stage are then passed to a recurrent neural network (RNN), thereby modeling consecutive slices as a sequence of temporal data and capturing the contextual information across all three dimensions in the volume of interest. Outputs of the RNN layer are weighed before the final fully connected layer, enabling the network to scale the importance of different slices within a volume of interest in an end-to-end training framework. RESULTS: We validated the proposed architecture on the false positive reduction track of the lung nodule analysis (LUNA) challenge for pulmonary nodule detection in chest CT scans, and obtained competitive results compared to 3D CNNs. Our results show that the proposed approach can encode the 3D information in volumetric data effectively by achieving a sensitivity >0.8 with just 1/8 false positives per scan. CONCLUSIONS: Our experimental results demonstrate the effectiveness of temporal analysis of volumetric images for the application of false positive reduction in chest CT scans and show that state-of-the-art 2D architectures from the literature can be directly applied to analyzing volumetric medical data. As newer and better 2D architectures are being developed at a much faster rate compared to 3D architectures, our approach makes it easy to obtain state-of-the-art performance on volumetric data using new 2D architectures.

ECG Gating Is More Precise Than Peripheral Pulse Gating When Quantifying Spinal CSF Pulsations Using Phase Contrast Cine MRI.

PURPOSE: To compare accuracy of spinal cerebrospinal fluid (CSF) pulsatile flow measurements at cervical, thoracic, and lumbar levels using Phase Contrast Cine MRI (PCCMRI) with retrospective electrocardiogram (recg) vs. retrospective peripheral pulse gating (rppg) gating. METHODS: We scanned 10 healthy volunteers, ages 23-46 years, using external recg-gated or rppg-gated 2D PCCCMRI at 3T. Transverse scans of CSF, arteries and veins scans were at C1/C4/T1/T7/L1-L3. Data were analyzed with custom Matlab-based software, measuring CSF, arterial (descending aorta, abdominal aorta, common carotid artery, ICA, and vertebral artery) and venous (internal jugular vein and inferior vena cava) flow, velocity and region of interest area. RESULTS: recgPCCMRI produced less quantitative and temporal statistical variation than pcgPCCMRI when analyzing CSF flow. The instantaneous recgPCCMRI CSF flows consistently decreased craniocaudally, while the results with rppgPCCMRI were less consistent. The recgPCCMRI root mean square error values were 6.04, 6.94, 4.81, 4.49, and 4.16 for C1, C4, T1, T7, and L2, compared with 7.24, 8.97, 7.9, 7.82, and 6.68 for rppgPCCMRI. Results were independent of analysts. Summations of standard errors produced similar results. RppgPCCMRI also showed increase variability of CSF flow correlations with arteries and veins compared to recgPCCMRI. None-the-less, when recgPCCMRI is considered the reference standard, there is good correlations between rppgPCCMRI and recgPCCMRIdata sets, when averaged over cohorts of at least five subjects. CONCLUSION: Our results indicated that recgPCCMRI is more quantitatively and temporally precise than rppgPCCMRI in CSF quantitative flow analysis. Pulse-gating CSF flow results are reasonable when averaged over cohorts of at least five subjects, but subtle conclusions should be interpreted with caution.

Lung Nodule Malignancy Prediction From Longitudinal CT Scans With Siamese Convolutional Attention Networks.

Goal: We propose a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-time-point classification in a Siamese structure. Methods: Our proposed framework is evaluated for single- and multi-time-point classification to explore the value that temporal information, such as nodule growth, adds to malignancy prediction. Results: Our results show that the proposed method outperforms a comparable 3-D network with less than half the parameters on single-time-point classification and further achieves performance gains on multi-time-point classification. Conclusions: Attention-based, Siamese 2-D pre-trained CNNs lead to fast training times and are effective for malignancy prediction from single-time-point or multiple-time-point imaging data.

Dual-Venc acquisition for 4D flow MRI in aortic stenosis with spiral readouts.

BACKGROUND: Single Venc 4D flow MRI with Cartesian readout is hampered by poor velocity resolution and noise when imaging during diastole. Dual Venc acquisitions typically require the acquisition of two distinct datasets, which leads to longer scan times. PURPOSE/HYPOTHESIS: To design and develop a 4D Spiral Dual Venc sequence. The sequence allows for separate systolic and diastolic Venc s as part of a single acquisition with a prescribed switch time. The implemented sequence was hypothesized to be comparable to Cartesian 4D flow, but with increased velocity resolution in the diastolic phase and with better scan efficiency and reduced noise. STUDY TYPE: Prospective. POPULATION: The studied populations were two phantoms-a straight pipe with a stenotic narrowing and a phantom of the aortic arch which included a calcific polymeric valve-under both steady and pulsatile flows, six healthy volunteers, and eight patients with severe aortic stenosis (AS). FIELD STRENGTH/SEQUENCE: 1.5T, Dual Venc 4D flow with spiral readouts. ASSESSMENT: Data from the proposed sequence were compared with data from 4D Cartesian Dual Venc and Single Venc acquisitions. Noise was assessed from the acquired velocity data with the pump turned off and by varying Venc . Steady acquisitions were compared to the proximal slice of the lowest Single Venc acquisition. STATISTICAL TESTS: Steady flows were compared using relative-root-mean-squared-error (RRMSE). For in vivo flows and pulsatile in vitro flows, net flow for corresponding timepoints were compared with the Pearson correlation test (P < 0.01). RESULTS: For steady flows, RRMSEs for Single Venc s ranged from 17.6% to 19.4%, and 9.6% to 16.5% for Dual Venc s. The net flow correlation coefficient for the aortic arch phantom was 0.975, and 0.995 for the stenotic phantom. Normal volunteer and patient comparisons yielded a correlation of 0.970 and 0.952, respectively. in vitro and in vivo pulsatile flow waveforms closely matched. DATA CONCLUSION: The Dual Venc offers improved noise properties and velocity resolution, while the spiral trajectory offers a scan efficient acquisition with short echo time yielding reduced flow artifacts. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;52:117-128.