4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, apparent trade-offs between acquisition time, image noise, and resolution limit clinical applicability. In particular, in regions of highly transient flow, coarse temporal resolution can hinder accurate capture of physiologically relevant flow variations. Deep learning-based post-processing techniques have shown promise in overcoming these issues using so-called super-resolution networks. However, while existing super-resolution research has primarily focused on spatial upsampling, temporal super-resolution remains largely unexplored. The aim of this study was therefore to implement and evaluate a residual data-driven network for temporal super-resolution 4D Flow MRI. To achieve this, an existing spatial network (4DFlowNet) was re-designed for temporal upsampling, adapting input dimensions, and optimizing internal layer structures. The model was trained and tested on synthetic 4D Flow MRI data derived from patient-specific in-silico models, followed by additional evaluation on clinically acquired in-vivo datasets. Overall, excellent performance was achieved with input velocities effectively denoised and temporally upsampled, with a mean absolute error (MAE) of 1.0 cm/s in an unseen in-silico setting, outperforming deterministic alternatives (linear interpolation MAE = 2.3 cm/s, sinc interpolation MAE = 2.6 cm/s). Further, the network synthesized high-resolution temporal information from unseen low-resolution in-vivo data, with strong correlation observed at peak flow frames. As such, our results highlight the potential of utilizing data-driven neural networks for temporal super-resolution 4D Flow MRI, enabling high-frame-rate flow quantification without extending acquisition times beyond clinically acceptable limits.

Coronary computed tomography angiography (CCTA) provides 3D information on obstructive coronary artery disease, but cannot fully visualize high-resolution features within the vessel wall. Intravascular imaging, in contrast, can spatially resolve atherosclerotic in cross sectional slices, but is limited in capturing 3D relationships between each slice. Co-registering CCTA and intravascular images enables a variety of clinical research applications but is time consuming and user-dependent. This is due to intravascular images suffering from non-rigid distortions arising from irregularities in the imaging catheter path. To address these issues, we present a morphology-based framework for the rigid and non-rigid matching of intravascular images to CCTA images. To do this, we find the optimal virtual catheter path that samples the coronary artery in CCTA image space to recapitulate the coronary artery morphology observed in the intravascular image. We validate our framework on a multi-center cohort of 40 patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our registration approach significantly outperforms other approaches for bifurcation alignment. By providing a differentiable framework for multi-modal vascular co-registration, our framework reduces the manual effort required to conduct large-scale multi-modal clinical studies and enables the development of machine learning-based co-registration approaches.

Objective:Carotid endarterectomy for symptomatic carotid stenosis is recommended for patients with >70% stenosis, but not in those with <50%. Because non-significant, low-degree stenoses may still cause strokes, refined risk stratification is necessary, which could be improved by assessing biological features of plaque instability. To challenge risk-stratification based on luminal narrowing, we compared biological features of carotid plaques from symptomatic patients with low-degree (<50%) vs high-degree (>70%) stenosis and explored potential mechanisms behind plaque instability in low-degree stenoses. Methods:Endarterectomy specimens were taken from symptomatic patients with high-degree (n = 204) and low-degree (n = 34) stenosis, all part of the Biobank of Karolinska Endarterectomies. Patient demographics, image-derived plaque morphology, and gene expression analyses of extracted lesions were used for comparisons. Plaque biology was assessed by transcriptomics using dimensionality reduction, differential gene expression, and gene-set enrichment analyses. Immunohistochemistry was used to study proteins corresponding to upregulated genes. Results:The demographics of the two groups were statistically similar. Calcification, lipid-rich necrotic core, intraplaque hemorrhage, plaque burden, and fibrous cap thickness were similar in both groups, whereas the sum of lipid-rich necrotic core and intraplaque hemorrhage was higher (P = .033) in the high-degree stenosis group. Dimensionality reduction analysis indicated poor clustering separation of plaque gene expression in low-compared with high-degree stenosis lesions, whereas differential gene expression showed upregulation of hypoxia-inducible factor 3A (log2 fold change, 0.7212; P = .0003), and gene-set enrichment analyses identified pathways related to tissue hypoxia and angiogenesis in low-degree stenoses. Hypoxia-inducible factor 3-alpha protein was associated with smooth muscle cells in neo-vascularized plaque regions. Conclusions:Plaques from symptomatic patients with non-significant low-degree carotid stenoses showed morphologic and biological features of atherosclerotic plaque instability that were comparable to plaques from patients with high-degree stenoses, emphasizing the need for improved stroke risk stratification for intervention in all patients with symptomatic carotid stenosis irrespective of luminal narrowing. An increased expression of hypoxia-inducible factor 3A in low-degree stenotic lesions suggested mechanisms of plaque instability associated with tissue hypoxia and plaque angiogenesis, but the exact role of hypoxia-inducible factor 3A in this process remains to be determined. Clinical relevance:Carotid plaques from symptomatic patients with <50% stenosis show morphologic and biological features of plaque instability, comparable to high-degree stenosis, which emphasizes the need for improved stroke risk stratification beyond stenosis severity.

Background Four-dimensional (4D) phase-contrast cardiovascular magnetic resonance (CMR) allows for precise non-invasive estimation of mean pulmonary artery pressure (mPAP) by estimating the duration of pathological vortex persistence in the main pulmonary artery. This has previously been achieved with compressed sensing acceleration of a multiple two-dimensional (CS-M2D) flow sequence, but acquisition using a true time-resolved 3D excitation (CS-4D) offers theoretical advantages including spatiotemporal coherence. This study aimed to validate a state-of-the-art CS-4D sequence with a previously utilized CS-M2D sequence for estimating mPAP, and compare both to right heart catheterization (RHC).Methods The study included patients clinically referred for CMR (n=45), of which a subgroup (n=20) had prior mPAP of >16 mmHg confirmed by RHC. CMR was performed at 1.5T using CS-M2D and CS-4D sequences covering the main pulmonary artery. mPAP was estimated using a previously published linear relationship between vortex duration and mPAP. Agreement between CS-M2D and CS-4D estimates was quantified, including analysis of intra- and interobserver variabilities. The diagnostic performance of CS-M2D and CS-4D in predicting mPAP was further compared to gold-standard RHC.Results CS-M2D and CS-4D both had average scan durations under 3 minutes (175±36 and 135±34 seconds, respectively). Estimated mPAP by CS-4D and CS-M2D were strongly correlated (R2=0.93, p<0.001), with negligible mean±SD bias (0.0±2.7 mmHg) and good reproducibility. There was excellent agreement with RHC for both CS-M2D (R2=0.92, p<0.001, bias 0.6±3.1 mmHg) and CS-4D (R2=0.86, p<0.001, bias 1.1±4.5 mmHg).Conclusions CS-4D and CS-M2D sequences effectively yield interchangeable non-invasive estimations of mPAP, with excellent agreement compared to invasive RHC. They can both be acquired in a scan time applicable to clinical workflow, offering a promising tool for non-invasive mPAP estimation in clinical practice.### Competing Interest StatementD.G., N.J. and F.T. are employees of Siemens Healthineers. G.A., P.B., J.C., A.F., P.S., A.S., M.U., and D.M. are all either employed by or affiliated with Karolinska University Hospital, which has an institutional research and development agreement regarding cardiovascular magnetic resonance with Siemens Healthineers.### Funding StatementThis work was funded in part by the European Union (ERC, MultiPRESS, 101075494). Views and opinions expressed are those of the authors and do not reflect those of the European Union or the European Research Council Executive Agency. Funding was also provided in part by New South Wales Health, Heart Research Australia, University of Sydney.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:All subjects provided written informed consent, and the study was approved by the Swedish Ethical Review Authority (DNR: 2015/2106-31/1).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.* 4D : Four-dimensional CMR : cardiovascular magnetic resonance mPAP : mean pulmonary artery pressure CS : compressed sensing CS-M2D : compressed sensing multiple two-dimensional (flow sequence) CS-4D : compressed sensing time-resolved three-dimensional (flow sequence) RHC : right heart catheterization bSSFP : balanced steady-state free precession MPA : main pulmonary artery RV : right ventricle TAPSE : tricuspid annular plane excursion

Background: Carotid endarterectomy (CEA) for symptomatic carotid stenosis is recommended for patients with the >70% (NASCET) stenosis but not for those with <50%. Since low-degree stenoses may still cause strokes, refined risk stratification is necessary, which could be improved by incorporating biological features of plaque instability. In order to challenge risk-stratification by the degree of stenosis, we compared biological features of carotid plaques from symptomatic patients with <50% stenosis vs. >70% and explored mechanisms behind plaque instability in low-degree stenoses. Methods: CEA specimens were retrieved from symptomatic patients with >70% (n=204) and <50% stenosis (n=34), all part of the Biobank of Karolinska Endarterectomies (BiKE). Patient demographics, plaque morphology from image analyses of preoperative computed tomography angiography (CTA), and RNA-seq analyses of lesions were used for comparisons. Plaque biology was assessed by transcriptomics using principal component analyses (PCA), differential gene expression (DGE)- and gene-set enrichment analyses (GSEA). Immunohistochemistry (IHC) was used to study proteins corresponding to upregulated genes. Results: The demographics of the two groups were statistically similar. They also showed similar plaque morphology with respect to intraplaque hemorrhage (IPH), plaque burden and fibrous cap thickness, whereas calcification was higher in the <50% group and lipid-rich necrotic core higher in the >70% group. PCA analysis indicated poor clustering, while DGE and GSEA identified enrichment of genes and pathways related to tissue hypoxia and angiogenesis in <50% lesions. Corresponding proteins were detected by IHC in neo-vascularized plaque regions. Conclusions: Plaques from symptomatic patients with <50% carotid stenoses exhibit largely similar morphological and biological features as those from >70% stenoses, emphasizing the need for improved stratification of patients for CEA of symptomatic carotid stenosis. However, pathways associating plaque burden, hypoxia and angiogenesis were particularly enriched in low-grade stenosis lesions suggesting mechanisms behind plaque instability and symptoms in patients with both low- and high-degree stenosis.

Abstract Background Rapamycin is an inhibitor of the mechanistic target of rapamycin (mTOR) protein kinase, and preclinical data demonstrate that it is a promising candidate for a general gero- and neuroprotective treatment in humans. Results from mouse models of Alzheimer’s disease have shown beneficial effects of rapamycin, including preventing or reversing cognitive deficits, reducing amyloid oligomers and tauopathies and normalizing synaptic plasticity and cerebral glucose uptake. The “Evaluating Rapamycin Treatment in Alzheimer’s Disease using Positron Emission Tomography” (ERAP) trial aims to test if these results translate to humans through evaluating the change in cerebral glucose uptake following six months of rapamycin treatment in participants with early-stage Alzheimer’s disease. Methods ERAP is a six-month-long, single-arm, open-label, phase IIa biomarker-driven study evaluating if the drug rapamycin can be repurposed to treat Alzheimer’s disease. Fifteen patients will be included and treated with a weekly dose of 7 mg rapamycin for six months. The primary endpoint will be change in cerebral glucose uptake, measured using [18F]FDG positron emission tomography. Secondary endpoints include changes in cognitive measures, markers in cerebrospinal fluid as well as cerebral blood flow measured using magnetic resonance imaging. As exploratory outcomes, the study will assess change in multiple age-related pathological processes, such as periodontal inflammation, retinal degeneration, bone mineral density loss, atherosclerosis and decreased cardiac function. Discussion The ERAP study is a clinical trial using in vivo imaging biomarkers to assess the repurposing of rapamycin for the treatment of Alzheimer’s disease. If successful, the study would provide a strong rationale for large-scale evaluation of mTOR-inhibitors as a potential disease-modifying treatment in Alzheimer’s disease. Trial registration ClinicalTrials.gov ID NCT06022068, date of registration 2023–08-30.

As modern scientific instruments generate vast amounts of data and the volume of information in the scientific literature continues to grow, machine learning (ML) has become an essential tool for organising, analysing, and interpreting these complex datasets. This paper explores the transformative role of ML in accelerating breakthroughs across a range of scientific disciplines. By presenting key examples – such as brain mapping and exoplanet detection – we demonstrate how ML is reshaping scientific research. We also explore different scenarios where different levels of knowledge of the underlying phenomenon are available, identifying strategies to overcome limitations and unlock the full potential of ML. Despite its advances, the growing reliance on ML poses challenges for research applications and rigorous validation of discoveries. We argue that even with these challenges, ML is poised to disrupt traditional methodologies and advance the boundaries of knowledge by enabling researchers to tackle increasingly complex problems. Thus, the scientific community can move beyond the necessary traditional oversimplifications to embrace the full complexity of natural systems, ultimately paving the way for interdisciplinary breakthroughs and innovative solutions to humanity's most pressing challenges.

This retrospective cohort study aimed to assess whether basal septal wall thickness (BSWT), anterior (AML) and posterior (PML) mitral leaflet length, or sex were associated with remaining left ventricular outflow tract obstruction (LVOTO) in patients with hypertrophic obstructive cardiomyopathy (HOCM) undergoing alcohol septal ablation (ASA). One hundred fifty-four patients who underwent ASA at the Karolinska University Hospital in Stockholm, Sweden, between 2009 and 2021, were included retrospectively. Anatomical and hemodynamic parameters were collected from invasive catheterization before and during ASA, and from echocardiography (ECHO) examinations before, during, and at 1-year follow-up after ASA. Linear and logistic regression models were used to assess the association between sex, BSWT, AML, PML, and outcome, which was defined as the remaining LVOTO (≥ 30 mmHg) after ASA. The median follow-up was 364 days (interquartile range 334–385 days). BSWT ≥ 23 mm (n = 13, 12

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is limited by spatial resolution and image noise, incorporation of trained super-resolution (SR) networks has potential to enhance image quality post-scan. However, these efforts have predominantly been restricted to narrowly defined cardiovascular domains, with limited exploration of how SR performance extends across the cardiovascular system; a task aggravated by contrasting hemodynamic conditions apparent across the cardiovasculature. The aim of our study was to explore the generalizability of SR 4D Flow MRI using a combination of heterogeneous training sets and dedicated ensemble learning. With synthetic training data generated across three disparate domains (cardiac, aortic, cerebrovascular), varying convolutional base and ensemble learners were evaluated as a function of domain and architecture, quantifying performance on both in-silico and acquired in-vivo data from the same three domains. Results show that both bagging and stacking ensembling enhance SR performance across domains, accurately predicting high-resolution velocities from low-resolution input data in-silico. Likewise, optimized networks successfully recover native resolution velocities from downsampled in-vivo data, as well as show qualitative potential in generating denoised SR-images from clinicallevel input data. In conclusion, our work presents a viable approach for generalized SR 4D Flow MRI, with ensemble learning extending utility across various clinical areas of interest.

Decisions in the management of aortic stenosis are based on the peak pressure drop, captured by Doppler echocardiography, whereas gold standard catheterization measurements assess the net pressure drop but are limited by associated risks. The relationship between these two measurements, peak and net pressure drop, is dictated by the pressure recovery along the ascending aorta which is mainly caused by turbulence energy dissipation. Currently, pressure recovery is considered to occur within the first 40–50 mm distally from the aortic valve, albeit there is inconsistency across interventionist centers on where/how to position the catheter to capture the net pressure drop. We developed a non-invasive method to assess the pressure recovery distance based on blood flow momentum via 4D Flow cardiovascular magnetic resonance (CMR). Multi-center acquisitions included physical flow phantoms with different stenotic valve configurations to validate this method, first against reference measurements and then against turbulent energy dissipation (respectively n = 8 and n = 28 acquisitions) and to investigate the relationship between peak and net pressure drops. Finally, we explored the potential errors of cardiac catheterisation pressure recordings as a result of neglecting the pressure recovery distance in a clinical bicuspid aortic valve (BAV) cohort of n = 32 patients. In-vitro assessment of pressure recovery distance based on flow momentum achieved an average error of 1.8 ± 8.4 mm when compared to reference pressure sensors in the first phantom workbench. The momentum pressure recovery distance and the turbulent energy dissipation distance showed no statistical difference (mean difference of 2.8 ± 5.4 mm, R2 = 0.93) in the second phantom workbench. A linear correlation was observed between peak and net pressure drops, however, with strong dependences on the valvular morphology. Finally, in the BAV cohort the pressure recovery distance was 78.8 ± 34.3 mm from vena contracta, which is significantly longer than currently accepted in clinical practise (40–50 mm), and 37.5

The development of cerebrovascular disease is tightly coupled to regional changes in intracranial flow and relative pressure. Image-based assessment using phase contrast magnetic resonance imaging has particular promise for non-invasive full-field mapping of cerebrovascular hemodynamics. However, estimations are complicated by the narrow and tortuous intracranial vasculature, with accurate image-based quantification directly dependent on sufficient spatial resolution. Further, extended scan times are required for high-resolution acquisitions, and most clinical acquisitions are performed at comparably low resolution (>1 mm) where biases have been observed with regard to the quantification of both flow and relative pressure. The aim of our study was to develop an approach for quantitative intracranial super-resolution 4D Flow MRI, with effective resolution enhancement achieved by a dedicated deep residual network, and with accurate quantification of functional relative pressures achieved by subsequent physics-informed image processing. To achieve this, our two-step approach was trained and validated in a patient-specific in-silico cohort, showing good accuracy in estimating velocity (relative error: 15.0 ± 0.1%, mean absolute error (MAE): 0.07 ± 0.06 m/s, and cosine similarity: 0.99 ± 0.06 at peak velocity) and flow (relative error: 6.6 ± 4.7%, root mean square error (RMSE): 0.56 mL/s at peak flow), and with the coupled physics-informed image analysis allowing for maintained recovery of functional relative pressure throughout the circle of Willis (relative error: 11.0 ± 7.3%, RMSE: 0.3 ± 0.2 mmHg). Furthermore, the quantitative super-resolution approach is applied to an in-vivo volunteer cohort, effectively generating intracranial flow images at <0.5 mm resolution and showing reduced low-resolution bias in relative pressure estimation. Our work thus presents a promising two-step approach to non-invasively quantify cerebrovascular hemodynamics, being applicable to dedicated clinical cohorts in the future.

Objective: Guidance for preventing myocardial infarction and ischemic stroke by tailoring treatment for indi-vidual patients with atherosclerosis is an unmet need. Such development may be possible with computational modeling. Given the multifactorial biology of atherosclerosis, modeling must be based on complete biological networks that capture protein-protein interactions estimated to drive disease progression. Here, we aimed to develop a clinically relevant scale model of atherosclerosis, calibrate it with individual patient data, and use it to simulate optimized pharmacotherapy for individual patients. Approach and results: The study used a uniquely constituted plaque proteomic dataset to create a comprehensive systems biology disease model for simulating individualized responses to pharmacotherapy. Plaque tissue was collected from 18 patients with 6735 proteins at two locations per patient. 113 pathways were identified and included in the systems biology model of endothelial cells, vascular smooth muscle cells, macrophages, lym-phocytes, and the integrated intima, altogether spanning 4411 proteins, demonstrating a range of 39-96% plaque instability. After calibrating the systems biology models for individual patients, we simulated intensive lipid-lowering, anti-inflammatory, and anti-diabetic drugs. We also simulated a combination therapy. Drug response was evaluated as the degree of change in plaque stability, where an improvement was defined as a reduction of plaque instability. In patients with initially unstable lesions, simulated responses varied from high (20%, on combination therapy) to marginal improvement, whereas patients with initially stable plaques showed generally less improvement. Conclusion: In this pilot study, proteomics-based system biology modeling was shown to simulate drug response based on atherosclerotic plaque instability with a power of 90%, providing a potential strategy for improved personalized management of patients with cardiovascular disease.

Background: Intraplaque hemorrhage (IPH) is a hallmark of atherosclerotic plaque instability. Biliverdin reductase B (BLVRB) is enriched in plasma and plaques from patients with symptomatic carotid atherosclerosis and functionally associated with IPH. Objective: We explored the biomarker potential of plasma BLVRB through (1) its correlation with IPH in carotid plaques assessed by magnetic resonance imaging (MRI), and with recurrent ischemic stroke, and (2) its use for monitoring pharmacotherapy targeting IPH in a preclinical setting. Methods: Plasma BLVRB levels were measured in patients with symptomatic carotid atherosclerosis from the PARISK study (n = 177, 5 year follow-up) with and without IPH as indicated by MRI. Plasma BLVRB levels were also measured in a mouse vein graft model of IPH at baseline and following antiangiogenic therapy targeting vascular endothelial growth factor receptor 2 (VEGFR-2). Results: Plasma BLVRB levels were significantly higher in patients with IPH (737.32 ± 693.21 vs. 520.94 ± 499.43 mean fluorescent intensity (MFI), p = 0.033), but had no association with baseline clinical and biological parameters. Plasma BLVRB levels were also significantly higher in patients who developed recurrent ischemic stroke (1099.34 ± 928.49 vs. 582.07 ± 545.34 MFI, HR = 1.600, CI [1.092–2.344]; p = 0.016). Plasma BLVRB levels were significantly reduced following prevention of IPH by anti-VEGFR-2 therapy in mouse vein grafts (1189 ± 258.73 vs. 1752 ± 366.84 MFI; p = 0.004). Conclusions: Plasma BLVRB was associated with IPH and increased risk of recurrent ischemic stroke in patients with symptomatic low- to moderate-grade carotid stenosis, indicating the capacity to monitor the efficacy of IPH-preventive pharmacotherapy in an animal model. Together, these results suggest the utility of plasma BLVRB as a biomarker for atherosclerotic plaque instability.

Aims:Non-invasive estimation of mean pulmonary artery pressure (mPAP) by cardiovascular magnetic resonance (CMR) four-dimensional (4D) flow analysis has shown excellent agreement with invasive right heart catheterization. However, clinical application is limited by relatively long scan times. Therefore, the aim of this study was to evaluate the accuracy and time reduction of compressed sensing (CS) accelerated acquisition for mPAP estimation. Methods and results:Patients (n = 51) referred for clinical CMR at 1.5 T or 3 T underwent imaging with both a prototype CS-accelerated and a non-CS-accelerated flow sequence acquiring time-resolved multiple 2D slice phase-contrast three-directional velocity-encoded images covering the pulmonary artery. Prototype software was used for the blinded analysis of pulmonary artery (PA) vortex duration to estimate mPAP as previously validated. CS-accelerated and non-CS-accelerated acquisition showed increased mPAP in 22/51 (43%) and 24/51 (47%) patients, respectively. The mean bias for estimating mPAP between the two methods was 0.1 ± 1.9 mmHg and the intraclass correlation coefficient was 0.97 (95% confidence interval 0.94-0.98). Effective scan time was lower for the CS-accelerated acquisition (1 min 55 s ± 27 s vs. 9 min 6 s ± 2 min 20 s, P < 0.001, 79% reduction). Conclusions:CS-accelerated CMR acquisition enables preserved accuracy for estimating mPAP compared to a non-CS-accelerated sequence, allowing for an average scan time of less than 2 min. CS-acceleration thereby increases the clinical utility of CMR 4D flow analysis to estimate mPAP.

Changes in regional hemodynamics are indicative of cerebrovascular disease. However, image-based monitoring is complicated by the unique flow and anatomies found in the brain, with accurate estimates requiring beyond state-of-the-art image resolutions. To address this, we combine a deep residual network, 4D Flow MRI, and physics-informed image processing to provide super-resolution flow images and coupled accurate quantification of intracranial relative pressure. The method is trained and validated on patient-specific in-silico data, highlighting how low resolution-biases are mitigated by super-resolution conversion. Data were also effectively generated at <0.5 mm in a representative in-vivo cohort, highlighting the potential of our presented approach.

The gold-standard for pulmonary hypertension (PH) diagnosis is invasive right-heart catheterisation, a technique ill-suited for general patient screening. Noninvasive markers of PH are thus of direct clinical value. Using 4D Flow Magnetic Resonance Imaging (MRI), a recent empirical correlation has been observed between the duration of a blood vortex in the main pulmonary artery (MPA), and the mean pulmonary arterial pressure. The mechanism underlying this relationship, however, remains unknown. In this context, our aim was to replicate this correlation using computational fluid dynamics simulations. Retrospective MRI data from six subjects with suspected PH were used to generate patient-specific models of the pulmonary arteries. Vortex durations in the MPA were derived from the simulations using four methods: visual assessment, Q-criterion, Lambda2 criterion, and backward flow. Results were compared to reference durations predicted by the empirical correlation. The method most closely reproducing values from the relationship was the backward-flow approach (overall error of 7

Background: Non-invasive estimation of mean pulmonary artery pressure (mPAP) by cardiovascular magnetic resonance (CMR) four-dimensional (4D) flow analysis has shown excellent agreement with invasive right heart catheterization. However, clinical application is limited by relatively long scan times. Objectives: The aim of this study was to evaluate the accuracy and time reduction of compressed sensing (CS) accelerated acquisition for mPAP estimation. Methods: Patients (n=51) referred for clinical CMR at 1.5T or 3T underwent imaging with both a prototype CS-accelerated and a non-CS-accelerated flow sequence acquiring time-resolved multiple 2D slice phase contrast three-directional velocity-encoded images covering the pulmonary artery. Prototype software was used for blinded analysis of pulmonary artery (PA) vortex duration to estimate mPAP as previously validated. Results: CS-accelerated and non-CS-accelerated acquisition showed increased mPAP in 22/51 (43%) and 24/51 (47%) patients, respectively. Mean bias for estimating mPAP between the two methods was 0.1+-1.9 mmHg and the intraclass correlation coefficient was 0.97 [95% confidence interval 0.94-0.98]. Effective scan time was lower for the CS-accelerated acquisition (1 min 55 sec +- 27 sec vs 9 min 6 sec +- 2 min 20 sec, p<0.001, 79% reduction). Conclusions: CS-accelerated CMR acquisition enables preserved accuracy for estimating mPAP compared to a non-CS-accelerated sequence, allowing for an average scan time of less than 2 minutes. CS-acceleration thereby increases the clinical utility of CMR 4D flow analysis to estimate mPAP.