Deep Learning Synthesis of White-Blood From Dark-Blood Late Gadolinium Enhancement Cardiac Magnetic Resonance

OBJECTIVES

Dark-blood late gadolinium enhancement (DB-LGE) cardiac magnetic resonance has been proposed as an alternative to standard white-blood LGE (WB-LGE) imaging protocols to enhance scar-to-blood contrast without compromising scar-to-myocardium contrast. In practice, both DB and WB contrasts may have clinical utility, but acquiring both has the drawback of additional acquisition time. The aim of this study was to develop and evaluate a deep learning method to generate synthetic WB-LGE images from DB-LGE, allowing the assessment of both contrasts without additional scan time.

MATERIALS AND METHODS

DB-LGE and WB-LGE data from 215 patients were used to train 2 types of unpaired image-to-image translation deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation, with 5 different loss function hyperparameter settings each. Initially, the best hyperparameter setting was determined for each model type based on the Fréchet inception distance and the visual assessment of expert readers. Then, the CycleGAN and contrastive unpaired translation models with the optimal hyperparameters were directly compared. Finally, with the best model chosen, the quantification of scar based on the synthetic WB-LGE images was compared with the truly acquired WB-LGE.

RESULTS

The CycleGAN architecture for unpaired image-to-image translation was found to provide the most realistic synthetic WB-LGE images from DB-LGE images. The results showed that it was difficult for visual readers to distinguish if an image was true or synthetic (55% correctly classified). In addition, scar burden quantification with the synthetic data was highly correlated with the analysis of the truly acquired images. Bland-Altman analysis found a mean bias in percentage scar burden between the quantification of the real WB and synthetic white-blood images of 0.44% with limits of agreement from -10.85% to 11.74%. The mean image quality of the real WB images (3.53/5) was scored higher than the synthetic white-blood images (3.03), P = 0.009.

CONCLUSIONS

This study proposed a CycleGAN model to generate synthetic WB-LGE from DB-LGE images to allow assessment of both image contrasts without additional scan time. This work represents a clinically focused assessment of synthetic medical images generated by artificial intelligence, a topic with significant potential for a multitude of applications. However, further evaluation is warranted before clinical adoption.

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.

Automated inversion time selection for late gadolinium-enhanced cardiac magnetic resonance imaging

OBJECTIVES

To develop and share a deep learning method that can accurately identify optimal inversion time (TI) from multi-vendor, multi-institutional and multi-field strength inversion scout (TI scout) sequences for late gadolinium enhancement cardiac MRI.

MATERIALS AND METHODS

Retrospective multicentre study conducted on 1136 1.5-T and 3-T cardiac MRI examinations from four centres and three scanner vendors. Deep learning models, comprising a convolutional neural network (CNN) that provides input to a long short-term memory (LSTM) network, were trained on TI scout pixel data from centres 1 to 3 to identify optimal TI, using ground truth annotations by two readers. Accuracy within 50 ms, mean absolute error (MAE), Lin's concordance coefficient (LCCC) and reduced major axis regression (RMAR) were used to select the best model from validation results, and applied to holdout test data. Robustness of the best-performing model was also tested on imaging data from centre 4.

RESULTS

The best model (SE-ResNet18-LSTM) produced accuracy of 96.1%, MAE 22.9 ms and LCCC 0.47 compared to ground truth on the holdout test set and accuracy of 97.3%, MAE 15.2 ms and LCCC 0.64 when tested on unseen external (centre 4) data. Differences in vendor performance were observed, with greatest accuracy for the most commonly represented vendor in the training data.

CONCLUSION

A deep learning model was developed that can identify optimal inversion time from TI scout images on multi-vendor data with high accuracy, including on previously unseen external data. We make this model available to the scientific community for further assessment or development.

CLINICAL RELEVANCE STATEMENT

A robust automated inversion time selection tool for late gadolinium-enhanced imaging allows for reproducible and efficient cross-vendor inversion time selection.

KEY POINTS

• A model comprising convolutional and recurrent neural networks was developed to extract optimal TI from TI scout images. • Model accuracy within 50 ms of ground truth on multi-vendor holdout and external data of 96.1% and 97.3% respectively was achieved. • This model could improve workflow efficiency and standardise optimal TI selection for consistent LGE imaging.

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.

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.

Optimized automated cardiac MR scar quantification with GAN-based data augmentation

BACKGROUND

The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited by the lack of standardization, and time-consuming postprocessing. In this work, we tested the hypothesis that a cascaded deep learning pipeline trained with augmentation by synthetically generated data would improve model accuracy and robustness for automated scar quantification.

METHODS

A cascaded pipeline consisting of three consecutive neural networks is proposed, starting with a bounding box regression network to identify a region of interest around the left ventricular (LV) myocardium. Two further nnU-Net models are then used to segment the myocardium and, if present, scar. The models were trained on the data from the EMIDEC challenge, supplemented with an extensive synthetic dataset generated with a conditional GAN.

RESULTS

The cascaded pipeline significantly outperformed a single nnU-Net directly segmenting both the myocardium (mean Dice similarity coefficient (DSC) (standard deviation (SD)): 0.84 (0.09) vs 0.63 (0.20), p < 0.01) and scar (DSC: 0.72 (0.34) vs 0.46 (0.39), p < 0.01) on a per-slice level. The inclusion of the synthetic data as data augmentation during training improved the scar segmentation DSC by 0.06 (p < 0.01). The mean DSC per-subject on the challenge test set, for the cascaded pipeline augmented by synthetic generated data, was 0.86 (0.03) and 0.67 (0.29) for myocardium and scar, respectively.

CONCLUSION

A cascaded deep learning-based pipeline trained with augmentation by synthetically generated data leads to myocardium and scar segmentations that are similar to the manual operator, and outperforms direct segmentation without the synthetic images.

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.

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.

Physics-informed neural networks for myocardial perfusion MRI quantification

Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters. These neural networks can be trained to fit the observed perfusion MR data while respecting the underlying physical conservation laws described by a multi-compartment exchange model. Here, we provide a framework for the implementation of PINNs in myocardial perfusion MR. The approach is validated both in silico and in vivo. In the in silico study, an overall decrease in mean-squared error with the ground-truth parameters was observed compared to a standard non-linear least squares fitting approach. The in vivo study demonstrates that the method produces parameter values comparable to those previously found in literature, as well as providing parameter maps which match the clinical diagnosis of patients.

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.

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.

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.

Automated Quantitative Stress Perfusion Cardiac Magnetic Resonance in Pediatric Patients

Background: Myocardial ischemia occurs in pediatrics, as a result of both congenital and acquired heart diseases, and can lead to further adverse cardiac events if untreated. The aim of this work is to assess the feasibility of fully automated, high resolution, quantitative stress myocardial perfusion cardiac magnetic resonance (CMR) in a cohort of pediatric patients and to evaluate its agreement with the coronary anatomical status of the patients. Methods: Fourteen pediatric patients, with 16 scans, who underwent dual-bolus stress perfusion CMR were retrospectively analyzed. All patients also had anatomical coronary assessment with either CMR, CT, or X-ray angiography. The perfusion CMR images were automatically processed and quantified using an analysis pipeline previously developed in adults. Results: Automated perfusion quantification was successful in 15/16 cases. The coronary perfusion territories supplied by vessels affected by a medium/large aneurysm or stenosis (according to the AHA guidelines), induced by Kawasaki disease, an anomalous origin, or interarterial course had significantly reduced myocardial blood flow (MBF) (median (interquartile range), 1.26 (1.05, 1.67) ml/min/g) as compared to territories supplied by unaffected coronaries [2.57 (2.02, 2.69) ml/min/g, p < 0.001] and territories supplied by vessels with a small aneurysm [2.52 (2.45, 2.83) ml/min/g, p = 0.002]. Conclusion: Automatic CMR-derived MBF quantification is feasible in pediatric patients, and the technology could be potentially used for objective non-invasive assessment of ischemia in children with congenital and acquired heart diseases.

High-Resolution Cardiac Magnetic Resonance Imaging Techniques for the Identification of Coronary Microvascular Dysfunction

OBJECTIVES

This study assessed the ability to identify coronary microvascular dysfunction (CMD) in patients with angina and nonobstructive coronary artery disease (NOCAD) using high-resolution cardiac magnetic resonance (CMR) and hypothesized that quantitative perfusion techniques would have greater accuracy than visual analysis.

BACKGROUND

Half of all patients with angina are found to have NOCAD, while the presence of CMD portends greater morbidity and mortality, it now represents a modifiable therapeutic target. Diagnosis currently requires invasive assessment of coronary blood flow during angiography. With greater reliance on computed tomography coronary angiography as a first-line tool to investigate angina, noninvasive tests for diagnosing CMD warrant validation.

METHODS

Consecutive patients with angina and NOCAD were enrolled. Intracoronary pressure and flow measurements were acquired during rest and vasodilator-mediated hyperemia. CMR (3-T) was performed and analyzed by visual and quantitative techniques, including calculation of myocardial blood flow (MBF) during hyperemia (stress MBF), transmural myocardial perfusion reserve (MPR: MBF_HYPEREMIA / MBF_REST), and subendocardial MPR (MPR_ENDO). CMD was defined dichotomously as an invasive coronary flow reserve <2.5, with CMR readers blinded to this classification.

RESULTS

A total of 75 patients were enrolled (57 ± 10 years of age, 81% women). Among the quantitative perfusion indices, MPR_ENDO and MPR had the highest accuracy (area under the curve [AUC]: 0.90 and 0.88) with high sensitivity and specificity, respectively, both superior to visual assessment (both p < 0.001). Visual assessment identified CMD with 58% accuracy (41% sensitivity and 83% specificity). Quantitative stress MBF performed similarly to visual analysis (AUC: 0.64 vs. 0.60; p = 0.69).

CONCLUSIONS

High-resolution CMR has good accuracy at detecting CMD but only when analyzed quantitatively. Although omission of rest imaging and stress-only protocols make for quicker scans, this is at the cost of accuracy compared with integrating rest and stress perfusion. Quantitative perfusion CMR has an increasingly important role in the management of patients frequently encountered with angina and NOCAD.

Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging

Dynamic contrast-enhanced quantitative first-pass perfusion using magnetic resonance imaging enables non-invasive objective assessment of myocardial ischemia without ionizing radiation. However, quantification of perfusion is challenging due to the non-linearity between the magnetic resonance signal intensity and contrast agent concentration. Furthermore, respiratory motion during data acquisition precludes quantification of perfusion. While motion correction techniques have been proposed, they have been hampered by the challenge of accounting for dramatic contrast changes during the bolus and long execution times. In this work we investigate the use of a novel free-breathing multi-echo Dixon technique for quantitative myocardial perfusion. The Dixon fat images, unaffected by the dynamic contrast-enhancement, are used to efficiently estimate rigid-body respiratory motion and the computed transformations are applied to the corresponding diagnostic water images. This is followed by a second non-linear correction step using the Dixon water images to remove residual motion. The proposed Dixon motion correction technique was compared to the state-of-the-art technique (spatiotemporal based registration). We demonstrate that the proposed method performs comparably to the state-of-the-art but is significantly faster to execute. Furthermore, the proposed technique can be used to correct for the decay of signal due to T2* effects to improve quantification and additionally, yields fat-free diagnostic images.

Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI

BACKGROUND

Quantitative myocardial perfusion cardiac MRI can provide a fast and robust assessment of myocardial perfusion status for the noninvasive diagnosis of myocardial ischemia while being more objective than visual assessment. However, it currently has limited use in clinical practice due to the challenging postprocessing required, particularly the segmentation.

PURPOSE

To evaluate the efficacy of an automated deep learning (DL) pipeline for image processing prior to quantitative analysis.

STUDY TYPE

Retrospective.

POPULATION

In all, 175 (350 MRI scans; 1050 image series) clinical patients under both rest and stress conditions (135/10/30 training/validation/test).

FIELD STRENGTH/SEQUENCE

3.0T/2D multislice saturation recovery T1 -weighted gradient echo sequence.

ASSESSMENT

Accuracy was assessed, as compared to the manual operator, through the mean square error of the distance between landmarks and the Dice similarity coefficient of the segmentation and bounding box detection. Quantitative perfusion maps obtained using the automated DL-based processing were compared to the results obtained with the manually processed images.

STATISTICAL TESTS

Bland-Altman plots and intraclass correlation coefficient (ICC) were used to assess the myocardial blood flow (MBF) obtained using the automated DL pipeline, as compared to values obtained by a manual operator.

RESULTS

The mean (SD) error in the detection of the time of peak signal enhancement in the left ventricle was 1.49 (1.4) timeframes. The mean (SD) Dice similarity coefficients for the bounding box and myocardial segmentation were 0.93 (0.03) and 0.80 (0.06), respectively. The mean (SD) error in the RV insertion point was 2.8 (1.8) mm. The Bland-Altman plots showed a bias of 2.6% of the mean MBF between the automated and manually processed MBF values on a per-myocardial segment basis. The ICC was 0.89, 95% confidence interval = [0.87, 0.90].

DATA CONCLUSION

We showed high accuracy, compared to manual processing, for the DL-based processing of myocardial perfusion data leading to quantitative values that are similar to those achieved with manual processing.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1689-1696.

Hierarchical Bayesian myocardial perfusion quantification

Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

Robust Non-Rigid Motion Compensation of Free-Breathing Myocardial Perfusion MRI Data

Kinetic parameter values, such as myocardial perfusion, can be quantified from dynamic contrast-enhanced magnetic resonance imaging data using tracer-kinetic modeling. However, respiratory motion affects the accuracy of this process. Motion compensation of the image series is difficult due to the rapid local signal enhancement caused by the passing of the gadolinium-based contrast agent. This contrast enhancement invalidates the assumptions of the (global) cost functions traditionally used in intensity-based registrations. The algorithms are unable to distinguish whether the differences in signal intensity between frames are caused by the spatial motion artifacts or the local contrast enhancement. In order to address this problem, a fully automated motion compensation scheme is proposed, which consists of two stages. The first of which uses robust principal component analysis (PCA) to separate the local signal enhancement from the baseline signal, before a refinement stage which uses the traditional PCA to construct a synthetic reference series that is free from motion but preserves the signal enhancement. Validation is performed on 18 subjects acquired in free-breathing and 5 clinical subjects acquired with a breath-hold. The validation assesses the visual quality, the temporal smoothness of tissue curves, and the clinically relevant quantitative perfusion values. The expert observers score the visual quality increased by a mean of 1.58/5 after motion compensation and improvement over the previously published methods. The proposed motion compensation scheme also leads to the improved quantitative performance of motion compensated free-breathing image series [30% reduction in the coefficient of variation across quantitative perfusion maps and 53% reduction in temporal variations (p < 0.001)].