Myocardial Blood Flow Quantification Using Stress Cardiac Magnetic Resonance Improves Detection of Coronary Artery Disease.

BACKGROUND: Myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) using stress cardiovascular magnetic resonance (CMR) have been shown to identify epicardial coronary artery disease. However, comparative analysis between quantitative perfusion and conventional qualitative assessment (QA) remains limited. OBJECTIVES: The aim of this multicenter study was to test the hypothesis that quantitative stress MBF (sMBF) and MPR analysis can identify obstructive coronary artery disease (obCAD) with comparable performance as QA of stress CMR performed by experienced physicians in interpretation. METHODS: The analysis included 127 individuals (mean age 62 ± 16 years, 84 men [67%]) who underwent stress CMR. obCAD was defined as the presence of stenosis ≥50% in the left main coronary artery or ≥70% in a major vessel. Each patient, coronary territory, and myocardial segment was categorized as having either obCAD or no obCAD (noCAD). Global, per coronary territory, and segmental MBF and MPR values were calculated. QA was performed by 4 CMR experts. RESULTS: At the patient level, global sMBF and MPR were significantly lower in subjects with obCAD than in those with noCAD, with median values of sMBF of 1.5 mL/g/min (Q1-Q3: 1.2-1.8 mL/g/min) vs 2.4 mL/g/min (Q1-Q3: 2.1-2.7 mL/g/min) (P < 0.001) and median values of MPR of 1.3 (Q1-Q3: 1.0-1.6) vs 2.1 (Q1-Q3: 1.6-2.7) (P < 0.001). At the coronary artery level, sMBF and MPR were also significantly lower in vessels with obCAD compared with those with noCAD. Global sMBF and MPR had areas under the curve (AUCs) of 0.90 (95% CI: 0.84-0.96) and 0.86 (95% CI: 0.80-0.93). The AUCs for QA by 4 physicians ranged between 0.69 and 0.88. The AUC for global sMBF and MPR was significantly better than the average AUC for QA. CONCLUSIONS: This study demonstrates that sMBF and MPR using dual-sequence stress CMR can identify obCAD more accurately than qualitative analysis by experienced CMR readers.

Tricuspid annular plane systolic excursion in pulmonary hypertension-Moving beyond the sector plane.

Tricuspid annular plane systolic excursion (TAPSE) is usually measured with M-mode using sector line, however, this may not align with the anatomical shortening of the right ventricular (RV). In this study, we compared the different methods to measure TAPSE using three different reference lines (sector line, anatomical line, and apico-annular line). We included 148 patients diagnosed with pulmonary arterial hypertension (PAH) who underwent TTE and right heart catheterization within 2 weeks of each other. TAPSE was measured by M-mode (sector, anatomical), 2D (sector, anatomical), or as tricuspid apico-annular displacement (TAAD). Agreement between measures was assessed using coefficient of variation (COV), Spearman's correlation, and Bland-Altman analysis. Receiver-operating characteristics and Kaplan-Meier analysis were used to explore associations with the combined outcome of death or lung transplantation at 5 years. There was a good concordance between anatomical and sector M-mode with a COV of 15.5 ± 1.6% and a bias of -0.6 ± 3.2 mm. In contrast, anatomical M-mode TAPSE and TAAD differed significantly with the mean difference of 3.3 ± 3.8 mm (COV 30.5 ± 6.1%; p < 0.0001). Among the different 2D methods, anatomical 2D agreed well with anatomical M-mode TAPSE (COV of 11.8 ± 2.0%; r = 0.89; p < 0.0001). Among the five methods, TADD had the strongest association with the combined endpoint of death or transplantation at 5 years (C-statistic 0.64, 95% confidence interval [CI] 0.57-0.71). We concluded that different measures of TAPSE are not interchangeable.

Wideband radiofrequency pulse sequence for evaluation of myocardial scar in patients with cardiac implantable devices.

Background: Cardiac magnetic resonance is a useful clinical tool to identify late gadolinium enhancement in heart failure patients with implantable electronic devices. Identification of LGE in patients with CIED is limited by artifact, which can be improved with a wide band radiofrequency pulse sequence. Objective: The authors hypothesize that image quality of LGE images produced using wide-band pulse sequence in patients with devices is comparable to image quality produced using standard LGE sequences in patients without devices. Methods: Two independent readers reviewed LGE images of 16 patients with CIED and 7 patients without intracardiac devices to assess for image quality, device-related artifact, and presence of LGE using the American Society of Echocardiography/American Heart Association 17 segment model of the heart on a 4-point Likert scale. The mean and standard deviation for image quality and artifact rating were determined. Inter-observer reliability was determined by calculating Cohen's kappa coefficient. Statistical significance was determined by T-test as a p {less than or equal to} 0.05 with a 95% confidence interval. Results: All patients underwent CMR without any adverse events. Overall IQ of WB LGE images was significantly better in patients with devices compared to standard LGE in patients without devices (p = 0.001) with reduction in overall artifact rating (p = 0.05). Conclusion: Our study suggests wide-band pulse sequence for LGE can be applied safely to heart failure patients with devices in detection of LV myocardial scar while maintaining image quality, reducing artifact, and following routine imaging protocol after intravenous gadolinium contrast administration.

Novel Reference Equations for Pulmonary Artery Size and Pulsatility Using Echocardiography and Their Diagnostic Value in Pulmonary Hypertension.

BACKGROUND: According to the most recent pulmonary hypertension (PH) guidelines, a main pulmonary artery (MPA) diameter > 25 mm on transthoracic echocardiography supports the diagnosis of PH. However, the size of the pulmonary artery (PA) may vary according to body size, age, and cardiac phases. RESEARCH QUESTION: (1) What are the reference limits for PA size on transthoracic echocardiography, considering differences in body size, sex, and age? (2) What is the diagnostic value of the PA size for classifying PH? (3) How does the selection of different reference groups (healthy volunteers vs patients referred for right heart catheterization [RHC]) influence the diagnostic OR (DOR)? STUDY DESIGN AND METHODS: The study included a reference cohort of 248 healthy individuals as control patients, 693 patients with PH proven by RHC, and 156 patients without PH proven by RHC. In the PH cohort, 300 had group 1 PH, 207 had group 2 PH, and 186 had group 3 PH. MPA and right PA diameters and areas were measured in the upper sternal short-axis and suprasternal notch views. Reference limits (5th-95th percentile) were based on absolute values and height-indexed measures. Quantile regression analysis was used to derive median and 95th quantile reference equations for the PA measures. DORs and probability diagnostic plots for PH were then determined using healthy control and non-PH cohorts. RESULTS: The 95th percentile for indexed MPA diameter was 15 mm/m in diastole and 19 mm/m in systole in both sexes. Quantile regression analysis revealed a weak age effect (pseudo-R2 of 0.08-0.10 for MPA diameters). Among measures, the MPA size in diastole had the highest DOR (156.2; 95% CI, 68.3-357.5) for detection of group 1 PH. Similarly, the DORs were also high for groups 2 and 3 PH when compared with the control cohort but significantly lower compared with the non-PH cohort. INTERPRETATION: This study presents novel reference limits for MPA based on height indexing and quantile regression.

Artificial intelligence coronary computed tomography, coronary computed tomography angiography using fractional flow reserve, and physician visual interpretation in the per-vessel prediction of abnormal invasive adenosine fractional flow reserve.

Aims: A comparison of diagnostic performance comparing AI-QCTISCHEMIA, coronary computed tomography angiography using fractional flow reserve (CT-FFR), and physician visual interpretation on the prediction of invasive adenosine FFR have not been evaluated. Furthermore, the coronary plaque characteristics impacting these tests have not been assessed. Methods and results: In a single centre, 43-month retrospective review of 442 patients referred for coronary computed tomography angiography and CT-FFR, 44 patients with CT-FFR had 54 vessels assessed using intracoronary adenosine FFR within 60 days. A comparison of the diagnostic performance among these three techniques for the prediction of FFR ≤ 0.80 was reported. The mean age of the study population was 65 years, 76.9% were male, and the median coronary artery calcium was 654. When analysing the per-vessel ischaemia prediction, AI-QCTISCHEMIA had greater specificity, positive predictive value (PPV), diagnostic accuracy, and area under the curve (AUC) vs. CT-FFR and physician visual interpretation CAD-RADS. The AUC for AI-QCTISCHEMIA was 0.91 vs. 0.76 for CT-FFR and 0.62 for CAD-RADS ≥ 3. Plaque characteristics that were different in false positive vs. true positive cases for AI-QCTISCHEMIA were max stenosis diameter % (54% vs. 67%, P < 0.01); for CT-FFR were maximum stenosis diameter % (40% vs. 65%, P < 0.001), total non-calcified plaque (9% vs. 13%, P < 0.01); and for physician visual interpretation CAD-RADS ≥ 3 were total non-calcified plaque (8% vs. 12%, P < 0.01), lumen volume (681 vs. 510 mm3, P = 0.02), maximum stenosis diameter % (40% vs. 62%, P < 0.001), total plaque (19% vs. 33%, P = 0.002), and total calcified plaque (11% vs. 22%, P = 0.003). Conclusion: Regarding per-vessel prediction of FFR ≤ 0.8, AI-QCTISCHEMIA revealed greater specificity, PPV, accuracy, and AUC vs. CT-FFR and physician visual interpretation CAD-RADS ≥ 3.

Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram.

Aims: Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models. Methods and results: Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites. Conclusion: Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.

Identifying consistent echocardiographic thresholds for risk stratification in pulmonary arterial hypertension.

Several indices of right heart remodeling and function have been associated with survival in pulmonary arterial hypertension (PAH). Outcome analysis and physiological relationships between variables may help develop a consistent grading system. Patients with Group 1 PAH followed at Stanford Hospital who underwent right heart catheterization and echocardiography within 2 weeks were considered for inclusion. Echocardiographic variables included tricuspid annular plane systolic excursion (TAPSE), right ventricular (RV) fractional area change (RVFAC), free wall strain (RVFWS), RV dimensions, and right atrial volumes. The main outcome consisted of death or lung transplantation at 5 years. Mathematical relationships between variables were determined using weighted linear regression and severity thresholds for were calibrated to a 20% 1-year mortality risk. PAH patients (n = 223) had mean (SD) age of 48.1 (14.1) years, most were female (78%), with a mean pulmonary arterial pressure of 51.6 (13.8) mmHg and pulmonary vascular resistance index of 22.5(6.3) WU/m2. Measures of right heart size and function were strongly related to each other particularly RVFWS and RVFAC (R 2 = 0.82, p < 0.001), whereas the relationship between TAPSE and RVFWS was weaker (R 2 = 0.28, p < 0.001). Death or lung transplantation at 5 years occurred in 78 patients (35%). Guided by outcome analysis, we ascertained a uniform set of parameter thresholds for grading the severity of right heart adaptation in PAH. Using these quantitative thresholds, we, then, validated the recently reported REVEAL-echo score (AUC 0.68, p < 0.001). This study proposes a consistent echocardiographic grading system for right heart adaptation in PAH guided by outcome analysis.

Deep learning-based rapid image reconstruction and motion correction for high-resolution cartesian first-pass myocardial perfusion imaging at 3T.

PURPOSE: To develop and evaluate a deep learning (DL) -based rapid image reconstruction and motion correction technique for high-resolution Cartesian first-pass myocardial perfusion imaging at 3T with whole-heart coverage for both single-slice (SS) and simultaneous multi-slice (SMS) acquisitions. METHODS: 3D physics-driven unrolled network architectures were utilized for the reconstruction of high-resolution Cartesian perfusion imaging. The SS and SMS multiband (MB) = 2 networks were trained from 135 slices from 20 subjects. Structural similarity index (SSIM), peak SNR (PSNR), and normalized RMS error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5, excellent; 1, poor). For respiratory motion correction, a 2D U-Net based motion corrected network was proposed, and the temporal fidelity and second-order derivative were calculated to assess the performance of the motion correction. RESULTS: Excellent performance was demonstrated in the proposed technique with high SSIM and PSNR, and low NRMSE. Image quality scores were (4.3 [4.3, 4.4], 4.5 [4.4, 4.6], 4.3 [4.3, 4.4], and 4.5 [4.3, 4.5]) for SS DL and SS L1-SENSE, MB = 2 DL and MB = 2 SMS-L1-SENSE, respectively, showing no statistically significant difference (p > 0.05 for SS and SMS) between (SMS)-L1-SENSE and the proposed DL technique. The network inference time was around 4 s per dynamic perfusion series with 40 frames while the time of (SMS)-L1-SENSE with GPU acceleration was approximately 30 min. CONCLUSION: The proposed DL-based image reconstruction and motion correction technique enabled rapid and high-quality reconstruction for SS and SMS MB = 2 high-resolution Cartesian first-pass perfusion imaging at 3T.

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

ACR benchmark testing of a novel high-speed ring-gantry linac kV-CBCT system.

A new generation cone-beam computed tomography (CBCT) system with new hardware design and advanced image reconstruction algorithms is available for radiation treatment simulation or adaptive radiotherapy (HyperSight CBCT imaging solution, Varian Medical Systems-a Siemens Healthineers company). This study assesses the CBCT image quality metrics using the criteria routinely used for diagnostic CT scanner accreditation as a first step towards the future use of HyperSight CBCT images for treatment planning and target/organ delineations. Image performance was evaluated using American College of Radiology (ACR) Program accreditation phantom tests for diagnostic computed tomography systems (CTs) and compared HyperSight images with a standard treatment planning diagnostic CT scanner (Siemens SOMATOM Edge) and with existing CBCT systems (Varian TrueBeam version 2.7 and Varian Halcyon version 2.0).  Image quality performance for all Varian HyperSight CBCT vendor-provided imaging protocols were assessed using ACR head and body ring CT phantoms, then compared to existing imaging modalities. Image quality analysis metrics included contrast-to-noise (CNR), spatial resolution, Hounsfield number (HU) accuracy, image scaling, and uniformity. All image quality assessments were made following the recommendations and passing criteria provided by the ACR. The Varian HyperSight CBCT imaging system demonstrated excellent image quality, with the majority of vendor-provided imaging protocols capable of passing all ACR CT accreditation standards. Nearly all (8/11) vendor-provided protocols passed ACR criteria using the ACR head phantom, with the Abdomen Large, Pelvis Large, and H&N vendor-provided protocols produced HU uniformity values slightly exceeding passing criteria but remained within the allowable minor deviation levels (5-7 HU maximum differences). Compared to other existing CT and CBCT imaging modalities, both HyperSight Head and Pelvis imaging protocols matched the performance of the SOMATOM CT scanner, and both the HyperSight and SOMATOM CT substantially surpassed the performance of the Halcyon 2.0 and TrueBeam version 2.7 systems. Varian HyperSight CBCT imaging system could pass almost all tests for all vendor-provided protocols using ACR accreditation criteria, with image quality similar to those produced by diagnostic CT scanners and significantly better than existing linac-based CBCT imaging systems.

Cardiometabolic biomarker patterns associated with cardiac MRI defined fibrosis and microvascular dysfunction in patients with heart failure with preserved ejection fraction.

Introduction: Heart failure with preserved ejection fraction (HFpEF) is a complex disease process influenced by metabolic disorders, systemic inflammation, myocardial fibrosis, and microvascular dysfunction. The goal of our study is to identify potential relationships between plasma biomarkers and cardiac magnetic resonance (CMR) imaging markers in patients with HFpEF. Methods: Nineteen subjects with HFpEF and 15 age-matched healthy controls were enrolled and underwent multiparametric CMR and plasma biomarker analysis using the Olink® Cardiometabolic Panel (Olink Proteomics, Uppsala, Sweden). Partial least squares discriminant analysis (PLS-DA) was used to characterize CMR and biomarker variables that differentiate the subject groups into two principal components. Orthogonal projection to latent structures by partial least squares (OPLS) analysis was used to identify biomarker patterns that correlate with myocardial perfusion reserve (MPR) and extracellular volume (ECV) mapping. Results: A PLS-DA could differentiate between HFpEF and normal controls with two significant components explaining 79% (Q2 = 0.47) of the differences. For OPLS, there were 7 biomarkers that significantly correlated with ECV (R2 = 0.85, Q = 0.53) and 6 biomarkers that significantly correlated with MPR (R2 = 0.92, Q2 = 0.32). Only 1 biomarker significantly correlated with both ECV and MPR. Discussion: Patients with HFpEF have unique imaging and biomarker patterns that suggest mechanisms associated with metabolic disease, inflammation, fibrosis and microvascular dysfunction.

Quantitative functional imaging of the pigeon brain: implications for the evolution of avian powered flight.

The evolution of flight is a rare event in vertebrate history, and one that demands functional integration across multiple anatomical/physiological systems. The neuroanatomical basis for such integration and the role that brain evolution assumes in behavioural transformations remain poorly understood. We make progress by (i) generating a positron emission tomography (PET)-based map of brain activity for pigeons during rest and flight, (ii) using these maps in a functional analysis of the brain during flight, and (iii) interpreting these data within a macroevolutionary context shaped by non-avian dinosaurs. Although neural activity is generally conserved from rest to flight, we found significant increases in the cerebellum as a whole and optic flow pathways. Conserved activity suggests processing of self-movement and image stabilization are critical when a bird takes to the air, while increased visual and cerebellar activity reflects the importance of integrating multimodal sensory information for flight-related movements. A derived cerebellar capability likely arose at the base of maniraptoran dinosaurs, where volumetric expansion and possible folding directly preceded paravian flight. These data represent an important step toward establishing how the brain of modern birds supports their unique behavioural repertoire and provide novel insights into the neurobiology of the bird-like dinosaurs that first achieved powered flight.

High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.

The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)-based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high-resolution spiral real-time cine imaging, including 2D balanced steady-state free precession imaging at 1.5 T and gradient echo (GRE) imaging at 1.5 and 3 T. A 3D U-Net-based image reconstruction network and 2D U-Net-based image segmentation network were proposed and evaluated. Low-rank plus sparse (L+S) served as the reference for the image reconstruction network and manual contouring of the left ventricle was the reference of the segmentation network. To assess the image reconstruction quality, structural similarity index, peak signal-to-noise ratio, normalized root-mean-square error, and blind grading by two experienced cardiologists (5: excellent; 1: poor) were performed. To assess the segmentation performance, quantification of the LVEF on GRE imaging at 3 T was compared with the quantification from manual contouring. Excellent performance was demonstrated by the proposed technique. In terms of image quality, there was no difference between L+S and the proposed DESIRE technique. For quantification analysis, the proposed DL method was not different to the manual segmentation method (p > 0.05) in terms of quantification of LVEF. The reconstruction time for DESIRE was ~32 s (including nonuniform fast Fourier transform [NUFFT]) per dynamic series (40 frames), while the reconstruction time of L+S with GPU acceleration was approximately 3 min. The DL segmentation takes less than 5 s. In conclusion, the proposed DL-based image reconstruction and quantification techniques enabled 1-min image reconstruction for the whole heart and quantification with automatic reconstruction and quantification of the left ventricle function for high-resolution spiral real-time cine imaging with excellent performance.

Genetic architecture of cardiac dynamic flow volumes.

Cardiac blood flow is a critical determinant of human health. However, the definition of its genetic architecture is limited by the technical challenge of capturing dynamic flow volumes from cardiac imaging at scale. We present DeepFlow, a deep-learning system to extract cardiac flow and volumes from phase-contrast cardiac magnetic resonance imaging. A mixed-linear model applied to 37,653 individuals from the UK Biobank reveals genome-wide significant associations across cardiac dynamic flow volumes spanning from aortic forward velocity to aortic regurgitation fraction. Mendelian randomization reveals a causal role for aortic root size in aortic valve regurgitation. Among the most significant contributing variants, localizing genes (near ELN, PRDM6 and ADAMTS7) are implicated in connective tissue and blood pressure pathways. Here we show that DeepFlow cardiac flow phenotyping at scale, combined with genotyping data, reinforces the contribution of connective tissue genes, blood pressure and root size to aortic valve function.

Dynamic cardiac MRI with high spatiotemporal resolution using accelerated spiral-out and spiral-in/out bSSFP pulse sequences at 1.5 T.

OBJECTIVE: To develop two spiral-based bSSFP pulse sequences combined with L + S reconstruction for accelerated ungated, free-breathing dynamic cardiac imaging at 1.5 T. MATERIALS AND METHODS: Tiny golden angle rotated spiral-out and spiral-in/out bSSFP sequences combined with view-sharing (VS), compressed sensing (CS), and low-rank plus sparse (L + S) reconstruction were evaluated and compared via simulation and in vivo dynamic cardiac imaging studies. The proposed methods were then validated against the standard cine, in terms of quantitative image assessment and qualitative quality rating. RESULTS: The L + S method yielded the least residual artifacts and the best image sharpness among the three methods. Both spiral cine techniques showed clinically diagnostic images (score > 3). Compared to standard cine, there were significant differences in global image quality and edge sharpness for spiral cine techniques, while there was significant difference in image contrast for the spiral-out cine but no significant difference for the spiral-in/out cine. There was good agreement in left ventricular ejection fraction for both the spiral-out cine (- 1.6 [Formula: see text] 3.1%) and spiral-in/out cine (- 1.5 [Formula: see text] 2.8%) against standard cine. DISCUSSION: Compared to the time-consuming standard cine (~ 5 min) which requires ECG-gating and breath-holds, the proposed spiral bSSFP sequences achieved ungated, free-breathing cardiac movies at a similar spatial (1.5 × 1.5 × 8 mm3) and temporal resolution (36 ms) per slice for whole heart coverage (10-15 slices) within 45 s, suggesting the clinical potential for improved patient comfort or for imaging patients with arrhythmias or who cannot hold their breath.

Novel left ventricular mechanical index in pulmonary arterial hypertension.

Ventricular interdependence plays an important role in pulmonary arterial hypertension (PAH). It can decrease left ventricular (LV) longitudinal strain (LVLS) and lead to a leftward displacement ("transverse shortening") of the interventricular septum (sTS). For this study, we hypothesized the ratio of LVLS/sTS would be a sensitive marker of systolic ventricular interactions in PAH. In a cross-sectional cohort of patients with PAH (n = 57) and matched controls (n = 57), we quantified LVLS and septal TS in the amplitude and time domain. We then characterized LV phenotypes using upset plots, ventricular interactions using network analysis, and longitudinal analysis in a representative cohort of 45 patients. We also measured LV metrics in mice subjected to pulmonary arterial banding (PAB) using a 7 T magnetic resonance imaging at baseline, Week 1, and Week 7 post-PAB (N = 9). Patients with PAH had significantly reduced absolute LVLS (15.4 ± 3.4 vs. 20.1 ± 2.3%, p < 0.0001), higher sTS (53.0 ± 12.2 vs. 28.0 ± 6.2%, p < 0.0001) and lower LVLS/sTS (0.30 ± 0.09 vs. 0.75 ± 0.16, p < 0.0001) compared to controls. Reduced LVLS/sTS was observed in 89.5% of patients, while diastolic dysfunction, impaired LVLS (<16%), and LV atrophy were observed in 73.7%, 52.6%, and 15.8%, respectively. In the longitudinal cohort, changes in LVLS/sTS were closely associated with changes in N-terminal pro B-type natriuretic peptide (r = 0.73, p < 0.0001) as well as survival. Mice subjected to PAB showed significant RV systolic dysfunction and decreased LVLS/sTS compared to sham animals. We conclude that in PAH, LVLV/sTS is a simple ratio that can reflect ventricular systolic interactions.