Hybrid artificial intelligence outcome prediction using features extraction from stress perfusion cardiac magnetic resonance images and electronic health records

Background

Prediction of clinical outcomes in coronary artery disease (CAD) has been conventionally achieved using clinical risk factors. The relationship between imaging features and outcome is still not well understood. This study aims to use artificial intelligence to link image features with mortality outcome.

Methods

A retrospective study was performed on patients who had stress perfusion cardiac magnetic resonance (SP-CMR) between 2011 and 2021. The endpoint was all-cause mortality. Convolutional neural network (CNN) was used to extract features from stress perfusion images, and multilayer perceptron (MLP) to extract features from electronic health records (EHRs), both networks were concatenated in a hybrid neural network (HNN) to predict study endpoint. Image CNN was trained to predict study endpoint directly from images. HNN and image CNN were compared with a linear clinical model using area under the curve (AUC), F1 scores, and McNemar's test.

Results

Total of 1,286 cases were identified, with 201 death events (16%). The clinical model had good performance (AUC =80%, F1 score =37%). Best Image CNN model showed AUC =72% and F1 score =38%. HNN outperformed the other two models (AUC =82%, F1 score =43%). McNemar's test showed statistical difference between image CNN and both clinical model (P<0.01) and HNN (P<0.01). There was no significant difference between HNN and clinical model (P=0.15).

Conclusions

Death in patients with suspected or known CAD can be predicted directly from stress perfusion images without clinical knowledge. Prediction can be improved by HNN that combines clinical and SP-CMR images.

High-Resolution Free-Breathing Automated Quantitative Myocardial Perfusion by Cardiovascular Magnetic Resonance for the Detection of Functionally Significant Coronary Artery Disease

AIMS

Current assessment of myocardial ischaemia from stress perfusion cardiovascular magnetic resonance (SP-CMR) largely relies on visual interpretation. This study investigated the use of high-resolution free-breathing SP-CMR with automated quantitative mapping in the diagnosis of coronary artery disease (CAD). Diagnostic performance was evaluated against invasive coronary angiography (ICA) with fractional flow reserve (FFR) measurement.

METHODS & RESULTS

Seven-hundred and three patients were recruited for SP-CMR using the research sequence at 3 Tesla. Of those receiving ICA within 6 months, 80 patients either had FFR measurement, or identification of a chronic total occlusion (CTO) with inducible perfusion defects seen on SP-CMR. Myocardial blood flow (MBF) maps were automatically generated in-line on the scanner following image acquisition at hyperaemic stress and rest, allowing myocardial perfusion reserve (MPR) calculation. 75 coronary vessels assessed by FFR, and 28 vessels with CTO were evaluated at both segmental and coronary territory level. Coronary territory stress MBF and MPR were reduced in FFR-positive (≤ 0.80) regions (median stress MBF: 1.74 [0.90-2.17] ml/min/g; MPR: 1.67 [1.10-1.89]) compared with FFR-negative regions (stress MBF: 2.50 [2.15-2.95] ml/min/g; MPR 2.35 [2.06-2.54] p < 0.001 for both). Stress MBF ≤ 1.94 ml/min/g and MPR ≤ 1.97 accurately detected FFR-positive CAD on a per-vessel basis (area under the curve: 0.85 and 0.96 respectively; p < 0.001 for both).

CONCLUSIONS

A novel scanner-integrated high-resolution free-breathing SP-CMR sequence with automated in-line perfusion mapping is presented which accurately detects functionally significant CAD.

High-resolution quantification of stress perfusion defects by cardiac magnetic resonance

Aims

Quantitative stress perfusion cardiac magnetic resonance (CMR) is becoming more widely available, but it is still unclear how to integrate this information into clinical decision-making. Typically, pixel-wise perfusion maps are generated, but diagnostic and prognostic studies have summarized perfusion as just one value per patient or in 16 myocardial segments. In this study, the reporting of quantitative perfusion maps is extended from the standard 16 segments to a high-resolution bullseye. Cut-off thresholds are established for the high-resolution bullseye, and the identified perfusion defects are compared with visual assessment.

Methods and results

Thirty-four patients with known or suspected coronary artery disease were retrospectively analysed. Visual perfusion defects were contoured on the CMR images and pixel-wise quantitative perfusion maps were generated. Cut-off values were established on the high-resolution bullseye consisting of 1800 points and compared with the per-segment, per-coronary, and per-patient resolution thresholds. Quantitative stress perfusion was significantly lower in visually abnormal pixels, 1.11 (0.75-1.57) vs. 2.35 (1.82-2.9) mL/min/g (Mann-Whitney U test P < 0.001), with an optimal cut-off of 1.72 mL/min/g. This was lower than the segment-wise optimal threshold of 1.92 mL/min/g. The Bland-Altman analysis showed that visual assessment underestimated large perfusion defects compared with the quantification with good agreement for smaller defect burdens. A Dice overlap of 0.68 (0.57-0.78) was found.

Conclusion

This study introduces a high-resolution bullseye consisting of 1800 points, rather than 16, per patient for reporting quantitative stress perfusion, which may improve sensitivity. Using this representation, the threshold required to identify areas of reduced perfusion is lower than for segmental analysis.

Noncontact remote sensing of abnormal blood pressure using a deep neural network: a novel approach for hypertension screening

Background

As the global burden of hypertension continues to increase, early diagnosis and treatment play an increasingly important role in improving the prognosis of patients. In this study, we developed and evaluated a method for predicting abnormally high blood pressure (HBP) from infrared (upper body) remote thermograms using a deep learning (DL) model.

Methods

The data used in this cross-sectional study were drawn from a coronavirus disease 2019 (COVID-19) pilot cohort study comprising data from 252 volunteers recruited from 22 July to 4 September 2020. Original video files were cropped at 5 frame intervals to 3,800 frames per slice. Blood pressure (BP) information was measured using a Welch Allyn 71WT monitor prior to infrared imaging, and an abnormal increase in BP was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. The PanycNet DL model was developed using a deep neural network to predict abnormal BP based on infrared thermograms.

Results

A total of 252 participants were included, of which 62.70% were male and 37.30% were female. The rate of abnormally high HBP was 29.20% of the total number. In the validation group (upper body), precision, recall, and area under the receiver operating characteristic curve (AUC) values were 0.930, 0.930, and 0.983 [95% confidence interval (CI): 0.904-1.000], respectively, and the head showed the strongest predictive ability with an AUC of 0.868 (95% CI: 0.603-0.994).

Conclusions

This is the first technique that can perform screening for hypertension without contact using existing equipment and data. It is anticipated that this technique will be suitable for mass screening of the population for abnormal BP in public places and home BP monitoring.

AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance

Aims

One of the major challenges in the quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) is the estimation of the arterial input function (AIF). This is due to the non-linear relationship between the concentration of gadolinium and the MR signal, which leads to signal saturation. In this work, we show that a deep learning model can be trained to predict the unsaturated AIF from standard images, using the reference dual-sequence acquisition AIFs (DS-AIFs) for training.

Methods and results

A 1D U-Net was trained, to take the saturated AIF from the standard images as input and predict the unsaturated AIF, using the data from 201 patients from centre 1 and a test set comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 (n = 44). Fully-automated MBF was compared between the DS-AIF and AI-AIF methods using the Mann-Whitney U test and Bland-Altman analysis. There was no statistical difference between the MBF quantified with the DS-AIF [2.77 mL/min/g (1.08)] and predicted with the AI-AIF (2.79 mL/min/g (1.08), P = 0.33. Bland-Altman analysis shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF (bias of -0.11 mL/min/g). Additionally, the MBF diagnosis classification of the AI-AIF matched the DS-AIF in 669/704 (95%) of myocardial segments.

Conclusion

Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.

Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis

Background

The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging.

Methods

The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau², I² and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach.

Results

A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496).

Conclusions

Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.

AI-AIF: artificial intelligence-based arterial input function correction for quantitative stress perfusion cardiac MRI

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Wellcome Trust

Background

The quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) has the potential to facilitate the widespread clinical adoption of stress perfusion CMR. However, one of the major challenges of MBF quantification is the estimation of the arterial input function (AIF) as required for the tracer-kinetic modelling. Since high concentrations of contrast agent are recorded at the peak of the AIF, the non-linear relationship between the concentration of gadolinium and the measured MR signal leads to signal saturation. Current solutions include the use of dual-bolus or dual-sequence acquisitions but both have limited clinical adoption. Therefore, there is a clear unmet need for an AIF estimation approach that is both widely available and easy to integrate in clinical routine, with the ideal acquisition being with a single-bolus and single-saturation sequence.

Purpose

In this work, we propose the artificial intelligence-based AIF (AI-AIF). In particular, we show that a deep learning model can be trained to predict the unsaturated AIF from a saturated single-bolus, single-sequence AIF, using the reference standard dual-sequence AIFs (DS-AIFs) for training.

Methods

This was a multicentre retrospective study, including data from two UK centres with institutional ethical approval. The training dataset was a retrospective sample of patients exclusively from centre 1 (n=218), and a test set was comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 (n=44) to test the generalisation of the model. A 1D U-Net convolutional network was used with 5 resolution steps of two 1D convolutional blocks with batch normalisation, ReLU activations, and dropout (probability=0.2). 1D max-pooling and transposed convolutions are used for down and upsampling respectively. The model was trained for 200,000 iterations (including data augmentation), with a batch size of 10 using the ADAM optimization algorithm (learning rate, 0.001) to minimise the mean squared error between the predicted and reference standard unsaturated AIFs. MBF was quantified in a fully-automated manner (1,2) and compared between the DS-AIF and AI-AIF methods using the Mann-Whitney U test and Bland-Altman analysis.

Results

There was no statistical difference between the median MBF quantified with the DS-AIF (2.77 ml/min/g (1.08)) and quantified with the AI-AIF (2.79 ml/min/g (1.08), p = 0.33. Figure 1 shows an example patient’s predicted AI-AIF compared with the DS-AIF (a) and pixelwise MBF maps with both the DS-AIF and AI-AIF (b). Additionally, the Bland-Altman analysis (shown in Figure 2) shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF with a mean bias of -0.11 ml/min/g.

Conclusion

Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.

Implementation of a CT-derived correction factor to refine the measurement of aortic valve area and stroke volume using Doppler echocardiography improves grading of severity and prediction of prognosis in patients with severe aortic stenosis

AIMS

To assess rates of reclassification of severity and associated 5-year survival in patients with severe aortic stenosis (AS) and preserved left ventricular ejection fraction (LVEF) after application of a CT-derived correction factor (CF) to refine the measurement of aortic valve area (AVA) and stroke volume index (SVi) using Doppler echocardiography.

METHODS AND RESULTS

We enrolled 1450 patients with severe AS and preserved LVEF from a French registry. Multiplication of echocardiographic LV outflow tract diameter by a CT-derived CF of 1.13 to calculate the AVA and SVi using the continuity equation resulted in reclassification of 39% of patients from severe to moderate AS (AVA > 1 cm²) and 77% from low flow (LF, SVi < 35 ml/m²) to normal flow (NF, SVi ≥ 35 ml/m²). After application of the CF, 5-year survival with conservative management was 50 ± 4% for severe AS compared to 62 ± 4% for moderate AS (p < 0.001). A strategy of medical management followed by intervention for severe AS was associated with higher risk of mortality over 5-year follow-up after adjustment for covariates and application of the CF (HR 1.35 [1.10-1.55], p = 0.015). Five-year survival was also poorer in patients remaining in the LF group after application of the CF, even after valve intervention (72%, 66% and 47% for NF to NF, LF to NF and LF to LF, respectively). After adjustment for covariates (including intervention), risk of mortality was higher in LF to LF patients compared to NF to NF (HR 1.78 [1.25-2.56]), but similar for NF to NF and LF to NF (HR 1.20 [0.90-1.60]).

CONCLUSION

Refined accuracy of echocardiographic LV outflow tract diameter measurement using a CF of 1.13 before derivation of AVA and SVi in patients with severe AS and preserved LVEF allows improved grading of severity, and prediction of prognosis. We recommend implementation of the CF during routine echocardiography when using the continuity equation for Doppler haemodynamic measurements.

Brief Research Report: Quantitative Analysis of Potential Coronary Microvascular Disease in Suspected Long-COVID Syndrome

Background

Case series have reported persistent cardiopulmonary symptoms, often termed long-COVID or post-COVID syndrome, in more than half of patients recovering from Coronavirus Disease 19 (COVID-19). Recently, alterations in microvascular perfusion have been proposed as a possible pathomechanism in long-COVID syndrome. We examined whether microvascular perfusion, measured by quantitative stress perfusion cardiac magnetic resonance (CMR), is impaired in patients with persistent cardiac symptoms post-COVID-19.

Methods

Our population consisted of 33 patients post-COVID-19 examined in Berlin and London, 11 (33%) of which complained of persistent chest pain and 13 (39%) of dyspnea. The scan protocol included standard cardiac imaging and dual-sequence quantitative stress perfusion. Standard parameters were compared to 17 healthy controls from our institution. Quantitative perfusion was compared to published values of healthy controls.

Results

The stress myocardial blood flow (MBF) was significantly lower [31.8 ± 5.1 vs. 37.8 ± 6.0 (μl/g/beat), P < 0.001] and the T2 relaxation time was significantly higher (46.2 ± 3.6 vs. 42.7 ± 2.8 ms, P = 0.002) post-COVID-19 compared to healthy controls. Stress MBF and T1 and T2 relaxation times were not correlated to the COVID-19 severity (Spearman r = −0.302, −0.070, and −0.297, respectively) or the presence of symptoms. The stress MBF showed a U-shaped relation to time from PCR to CMR, no correlation to T1 relaxation time, and a negative correlation to T2 relaxation time (Pearson r = −0.446, P = 0.029).

Conclusion

While we found a significantly reduced microvascular perfusion post-COVID-19 compared to healthy controls, this reduction was not related to symptoms or COVID-19 severity.

High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration

Introduction

To develop and test the feasibility of free-breathing (FB), high-resolution quantitative first-pass perfusion cardiac MR (FPP-CMR) using dual-echo Dixon (FOSTERS; Fat-water separation for mOtion-corrected Spatio-TEmporally accelerated myocardial peRfuSion).

Materials and Methods

FOSTERS was performed in FB using a dual-saturation single-bolus acquisition with dual-echo Dixon and a dynamically variable Cartesian k-t undersampling (8-fold) approach, with low-rank and sparsity constrained reconstruction, to achieve high-resolution FPP-CMR images. FOSTERS also included automatic in-plane motion estimation and T2* correction to obtain quantitative myocardial blood flow (MBF) maps. High-resolution (1.6 x 1.6 mm²) FB FOSTERS was evaluated in eleven patients, during rest, against standard-resolution (2.6 x 2.6 mm²) 2-fold SENSE-accelerated breath-hold (BH) FPP-CMR. In addition, MBF was computed for FOSTERS and spatial wavelet-based compressed sensing (CS) reconstruction. Two cardiologists scored the image quality (IQ) of FOSTERS, CS, and standard BH FPP-CMR images using a 4-point scale (1–4, non-diagnostic – fully diagnostic).

Results

FOSTERS produced high-quality images without dark-rim and with reduced motion-related artifacts, using an 8x accelerated FB acquisition. FOSTERS and standard BH FPP-CMR exhibited excellent IQ with an average score of 3.5 ± 0.6 and 3.4 ± 0.6 (no statistical difference, p > 0.05), respectively. CS images exhibited severe artifacts and high levels of noise, resulting in an average IQ score of 2.9 ± 0.5. MBF values obtained with FOSTERS presented a lower variance than those obtained with CS.

Discussion

FOSTERS enabled high-resolution FB FPP-CMR with MBF quantification. Combining motion correction with a low-rank and sparsity-constrained reconstruction results in excellent image quality.

The impact of dark-blood versus conventional bright-blood late gadolinium enhancement on the myocardial ischemic burden

PURPOSE

In perfusion cardiovascular magnetic resonance (CMR), ischemic burden predicts adverse prognosis and is often used to guide revascularization. Ischemic scar tissue can cause stress perfusion defects that do not represent myocardial ischemia. Dark-blood late gadolinium enhancement (LGE) methods detect more scar than conventional bright-blood LGE, however, the impact on the myocardial ischemic burden estimation is unknown and evaluated in this study.

METHODS

Forty patients with CMR stress perfusion defects and ischemic scar on both dark-blood and bright-blood LGE were included. For dark-blood LGE, phase sensitive inversion recovery imaging with left ventricular blood pool nulling was used. Ischemic scar burden was quantified for both methods using >5 standard deviations above remote myocardium. Perfusion defects were manually contoured, and the myocardial ischemic burden was calculated by subtracting the ischemic scar burden from the perfusion defect burden.

RESULTS

Ischemic scar burden by dark-blood LGE was higher than bright-blood LGE (13.3 ± 7.4% vs. 10.3 ± 7.1%, p < 0.001). Dark-blood LGE derived myocardial ischemic burden was lower compared with bright-blood LGE (15.6% (IQR: 10.3 to 22.0) vs. 19.3 (10.9 to 25.5), median difference -2.0%, p < 0.001) with a mean bias of -2.8% (95% confidence intervals: -4.0 to -1.6%) and a large effect size (r = 0.62).

CONCLUSION

Stress perfusion defects are associated with higher ischemic scar burden using dark-blood LGE compared with bright-blood LGE, which leads to a lower estimation of the myocardial ischemic burden. The prognostic value of using a dark-blood LGE derived ischemic burden to guide revascularization is unknown and warrants further investigation.

Stress myocardial blood flow reduced after severe COVID-19, not related to symptoms

Introduction

Persistent cardiopulmonary symptoms after COVID-19 are reported in a large number of patients and the underlying pathology is still poorly understood. (1) Histopathologic studies revealed myocardial macrophage infiltrates in deceased patients, likely an unspecific finding of severe illness, and increased prevalence of micro- and macrovascular thrombi. (2) We examined whether microvascular perfusion, measured by quantitative cardiac magnetic resonance under vasodilator stress, was altered post COVID-19.

Methods

Our population consisted of 12 patients from the Pa-COVID-19-Study of the Charité Berlin, which received a cardiac MRI as part of a systematic follow up post discharge, 10 patients that presented at the German Heart Center Berlin with persistent cardiac symptoms post COVID-19 and 12 patients from the Kings College London referred for stress MRI and previous COVID-19.

The scan protocol included standard functional, edema and scar imaging and quantitative stress and rest perfusion to assess both macro- and microvascular coronary artery disease. The pharmacological stress agent was regadenosone in 20 and adenosine in 13 of the patients. To control for the higher heart rate increase under regadenosone compared to adenosine, we calculated the myocardial blood flow per heartbeat (MBF_HRi) under stress.

Results

The median time between first positive PCR for COVID-19 and the CMR exam was 2 months (Range 0 to 12). None of the 33 patients exhibited signs of myocardial edema. One patient with a previous history of myocarditis had focal fibrosis. Three patients with known coronary artery disease showed ischemic Late Enhancement. Five patients had a small pericardial effusion; one of these four patients showed slight focal pericardial edema and LGE, consistent with mild focal pericarditis. Five Patients had a stress-induced focal perfusion deficit.

Mean Stress MBF_HRi was 32.5±6.5 μl/beat/g. Stress MBF_HRi was negatively correlated with COVID-19 severity (rho=−0.361, P=0.039) and age (r=−0.452, P=0.009). The correlation with COVID-19 severity remained significant after controlling for age (rho=−0.390, P=0.027). There was no apparent difference in stress MBF_HRi between patients with and without persistent chest pain (34.5 vs. 31.5 μl/beat/g, P=0.229)

Conclusion

While vasodilator-stress myocardial blood flow after COVID-19 was negatively correlated to COVID-19 severity, it was not correlated to the presence of chest pain. The etiology of persistent cardiac symptoms after COVID-19 remains unclear.

Funding Acknowledgement

Type of funding sources: Private company. Main funding source(s): Philips Figure 1. A) Quantitative regadenosone stress myocardial blood flow (MBF) map, medial short axis slice, in a patient with persistent cardiac symptoms after COVID-19. B) Boxplot of stress MBF per heart beat by COVID-19 severity, showing decreasing MBF with increasing COVID-19 severity.

High-resolution CINE MRI allows estimation of 3D regional atrial strains

Introduction

It is increasingly evident that atrial function is an important marker of cardiovascular health. Impaired global left atrial strain has been associated with risk of thromboembolic events, atrial fibrillation and heart failure. When performed at high spatial resolution, CINE MRI allows the estimation of regional atrial strains, which may facilitate earlier identification of atrial disease and improved (non-contrast) characterisation of atrial fibrosis. Nevertheless, to date, high resolution regional atrial strains has not been assessed using CINE MRI.

Purpose

We introduce a novel rapid 2.2-mm isotropic atrial CINE MRI protocol used to image healthy subjects and patients with cardiovascular disease (CVD). We additionally present a dedicated image analysis pipeline to estimate regional 3D atrial strains from these images.

Methods

We imaged 10 healthy subjects (5 female, 24–36 years old) and 6 patients referred for cardiac MRI due to known or suspected CVD (2 female, 25–80 years old). All subjects were scanned in a 1.5T Philips Ingenia MRI scanner in a single breath-hold (&lt;25 s), using a short-axis 3D bSSFP protocol (flip angle: 60°, TE/TR: 1.6/3.3 ms) with retrospective cardiac gating, SENSE = 2.3 (along both phase encode directions), typical FOV: 400 x 270 x 70 mm3, isotropic acquisition resolution of 2.2 mm3. Images were reconstructed to 20 cardiac phases with 55% view sharing.

The left atrium (LA) was manually segmented in atrial diastole. We tracked the position of evenly spaced points along the LA contour across all phases of the cardiac cycle using the Medical Image Tracking Toolbox. This was used to create a series of deforming smooth triangular meshes, from which Lagrange strain tensors were estimated.

Results

Figs a-c show 3 orthogonal views of the proposed high-resolution atrial CINE MRI scans for a representative CVD patient, with the LA segmentation overlaid in red. Representative LA principal strain directions (as arrows) with the colour indicating the amount of strain observed along this direction are shown in Fig d for active atrial contraction (posterior view). The calculated strain directions varied smoothly in space and time, as expected, and were largest in amplitude in the regions closest to the mitral valve.

Overall, principal strains were larger in healthy subjects (AC strains: 0.12±0.06) than in the CVD cohort (AC strains: 0.04±0.01). This difference was statistically significant during AC (p-value: 0.02), but not during atrial diastole (p-value: 0.06).

Conclusions

We present a novel high-resolution CINE-MRI protocol for estimating regional atrial strains in 3D, with pilot data from 10 healthy subjects and 6 cardiovascular patients. Future studies will compare regions of abnormal atrial strain with fibrosis identified in late gadolinium enhanced MRI to assess whether regional strains can provide a better characterisation of atrial tissue and improved stratification of patients at risk.

Figure 1

Funding Acknowledgement

Type of funding source: Foundation. Main funding source(s): British Heart Foundation, EPSRC/Wellcome Trust Centre for Medical Engineering

Aortic valve area using computed tomography-derived correction factor to improve the validity of left ventricular outflow tract measurements

AIMS

Given the inherent inaccuracies stemming from the assumption that the left ventricular outflow tract (LVOT) is circular, this study aimed to improve the accuracy of transthoracic echocardiography (TTE)-based aortic valve area (AVA) calculation using continuity equation (CE) by introducing a correction factor (CF) derived from multidetector computed tomography angiography (MDCTA) images and validate it in aortic stenosis (AS) patients.

METHODS AND RESULTS

This retrospective study used MDCTA images of 400 patients for modeling and 403 TTE dataset for validation. Echocardiographic parasternal long-axis view was modeled using MDCTA, and LVOT diameter (D1) was measured. Direct planimetry of LVOT area was performed and subsequently converted into a theoretical circle. The assumed circle (D2) diameter was derived, and D2/D1 was calculated and termed as the CF. The CF was 1.13, and it improved the agreement between MDCTA- and TTE-derived LVOT areas and correlation between AVA and peak velocity, mean pressure gradient, and velocity ratio. In discordant subgroups of severe AS, the CF reclassified patients to moderate AS in 40% in the low flow (LF), low gradient (LG), and low ejection fraction (EF) group; 53% in the LF, LG, and normal EF group; and 68% in the LF, high gradient, and normal EF group.

CONCLUSIONS

CF of 1.13 derived from MDCTA improved the accuracy of TTE-derived LVOT area and AVA and improved correlation with hemodynamic variables in AS patients. Reclassification of AS patients using CF may have clinical applicability for patient selection for early intervention.

Pannus Formation Leads to Valve Malfunction in the Tricuspid Position 19 Years after Triple Valve Replacement

The Medtronic ATS Open Pivot mechanical valve has been successfully used in heart valve surgery for more than two decades. We present the case of a patient who, 19 years following a tricuspid valve replacement with an ATS prosthesis as part of a triple valve operation following infective endocarditis, developed severe tricuspid regurgitation due to pannus formation.