Myocardial perfusion in heart disease

Heart disease: Coronary heart disease is a major cause of mortality and morbidity in the UK and globally. It is managed with medical therapy and coronary revascularisation to reduce symptoms and reduce risk of major adverse cardiovascular events. When patients present with chest pain, it is important to risk stratify those that would most benefit from invasive coronary assessment and those that can be managed with medical therapy alone. Myocardial perfusion techniques have been developed in order to do this. Cardiovascular magnetic resonance (CMR) with stress perfusion: CMR allows the non-invasive assessment of coronary artery disease (CAD). Under conditions of vasodilator stress, a gadolinium based contrast agent is injected and during the first pass through the left ventricle, perfusion defects can be observed. There is a strong evidence base for perfusion CMR but the technique is qualitative, relies on experienced operators and potentially misses globally low perfusion such as in cases of “balanced” ischaemia. Quantitative perfusion CMR: In contrast, quantitative perfusion techniques allow the calculation of myocardial blood flow (MBF). It is more objective, less reliant on the expert observer and can give additional insights into microvascular disease and cardiomyopathy. As well as being less subjective, quantitative perfusion has other advantages for example it allows full assessment of ischaemic burden and may contain prognostic information that could be used to risk stratify and improve patient care. However, quantitative perfusion has been outside the realm of routine clinical practice due to difficulties in acquiring suitable data for full quantification and the laborious nature of analysing it. Perfusion mapping: Peter Kellman, Hui Xue and colleagues at the National Institutes for Health, USA developed the “perfusion mapping” technique to address these limitations. Perfusion maps are generated automatically and inline during the CMR scan and each voxel encodes myocardial blood flow. This allows the instant quantification of MBF without complex acquisition techniques and post processing. In this thesis I have taken perfusion mapping and deployed in the real-world at a scale an order of magnitude higher than prior quantitative perfusion studies, developing the evidence base for routine clinical use across a broad range of diseases and scenarios: In coronary artery disease: I have shown that perfusion mapping is accurate to detect coronary artery stenosis as defined by 3D quantitative coronary angiography in a single centre, 50 patient study. Transmural and subendocardial perfusion are particularly sensitive to detect coronary stenoses with performances similar to expert readers. There is a high sensitivity and high negative predictive value making perfusion mapping a good “rule-out” test for coronary disease. Quantitative perfusion and prognosis: I investigated whether stress MBF and myocardial perfusion reserve (MPR) calculated by perfusion mapping would encode prognostic information in a 1049 patient multi-centre study over a mean follow up time of 605 days. Both stress MBF and MPR were independently associated with death and major adverse cardiovascular events (MACE). The hazard ratio for MACE was 2.14 for each 1ml/g/min decrease in stress MBF and 1.74 for each unit decrease in MPR. This work can now be taken forward with prospective studies in order to better risk stratify patients, including those without perfusion defects on clinical read. Reference ranges and non-obstructive coronary disease: I sought to determine the factors that contribute to perfusion in a multi-centre registry study. In patients with no obstructive coronary artery disease, stress MBF was reduced with age, diabetes, left ventricular hypertrophy (LVH) and the use of beta blockers. Rest MBF was influenced by sex (higher in females) and reduced with beta blockers. This study suggests patient factors beyond coronary artery disease (and therefore likely microvascular disease) should also be considered when interpreting quantitative perfusion studies. In cardiomyopathy: I also investigated myocardial perfusion in cardiomyopathy looking at Fabry disease as an example disease. In a prospective, observational, single centre study of 44 patients and 27 controls I found Fabry patients had reduced perfusion (and therefore likely microvascular dysfunction), particularly in the subendocardium and was associated with left ventricular hypertrophy (LVH), glycophospholipid storage and scar. Perfusion was reduced even in patients without LVH suggesting it is an early disease marker. In conclusion, in this thesis, I have developed an evidence base for quantitative perfusion CMR and demonstrated how it can be integrated into routine clinical care. Perfusion mapping is accurate for detecting coronary artery stenosis and encodes prognostic information. Further work in this area could enable patients to be risk stratified based on their myocardial perfusion in order to reduce the morbidity and mortality associated with epicardial and microvascular coronary artery disease. Following on from this work, two further British Heart Foundation Clinical Research Training Fellowships have been awarded to further investigate quantitative perfusion in patients following surgical revascularisation of coronary disease and in patients with hypertrophic cardiomyopathy

Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients

Quantification of myocardial perfusion has the potential to improve detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF) extraction. Since failure here invalidates quantification, high accuracy is required. For this purpose, this study presents a robust AIF detection method using the convolutional neural net (CNN) model. CNN models were trained by assembling 25,027 scans (N=12,984 patients) from three hospitals, seven scanners. A test set of 5,721 scans (N=2,805 patients) evaluated model performance. The 2D+T AIF time series was inputted into CNN. Two variations were investigated: a) Two Classes (2CS) for background and foreground (LV mask); b) Three Classes (3CS) for background, foreground LV and RV. Final model was deployed on MR scanners via the Gadgetron InlineAI. Model loading time on MR scanner was ~340ms and applying it took ~180ms. The 3CS model successfully detect LV for 99.98% of all test cases (1 failed out of 5,721 cases). The mean Dice ratio for 3CS was 0.87+/-0.08 with 92.0% of all test cases having Dice ratio >0.75, while the 2CS model gave lower Dice of 0.82+/-0.22 (P<1e-5). Extracted AIF signals using CNN were further compared to manual ground-truth for foot-time, peak-time, first-pass duration, peak value and area-under-curve. No significant differences were found for all features (P>0.2). This study proposed, validated, and deployed a robust CNN solution to detect the LV for the extraction of the AIF signal used in fully automated perfusion flow mapping. A very large data cohort was assembled and resulting models were deployed to MR scanners for fully inline AI in clinical hospitals.Comment: Accepted by Magnetic Resonance in Medicine on March 30, 202

Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning

Recent development of quantitative myocardial blood flow (MBF) mapping allows direct evaluation of absolute myocardial perfusion, by computing pixel-wise flow maps. Clinical studies suggest quantitative evaluation would be more desirable for objectivity and efficiency. Objective assessment can be further facilitated by segmenting the myocardium and automatically generating reports following the AHA model. This will free user interaction for analysis and lead to a 'one-click' solution to improve workflow. This paper proposes a deep neural network based computational workflow for inline myocardial perfusion analysis. Adenosine stress and rest perfusion scans were acquired from three hospitals. Training set included N=1,825 perfusion series from 1,034 patients. Independent test set included 200 scans from 105 patients. Data were consecutively acquired at each site. A convolution neural net (CNN) model was trained to provide segmentation for LV cavity, myocardium and right ventricular by processing incoming 2D+T perfusion Gd series. Model outputs were compared to manual ground-truth for accuracy of segmentation and flow measures derived on global and per-sector basis. The trained models were integrated onto MR scanners for effective inference. Segmentation accuracy and myocardial flow measures were compared between CNN models and manual ground-truth. The mean Dice ratio of CNN derived myocardium was 0.93 +/- 0.04. Both global flow and per-sector values showed no significant difference, compared to manual results. The AHA 16 segment model was automatically generated and reported on the MR scanner. As a result, the fully automated analysis of perfusion flow mapping was achieved. This solution was integrated on the MR scanner, enabling 'one-click' analysis and reporting of myocardial blood flow.Comment: This work has been submitted to Radiology: Artificial Intelligence for possible publicatio

Apical Ischemia Is a Universal Feature of Apical Hypertrophic Cardiomyopathy

BACKGROUND: Apical hypertrophic cardiomyopathy (ApHCM) accounts for ≈10% of hypertrophic cardiomyopathy cases and is characterized by apical hypertrophy, apical cavity obliteration, and tall ECG R waves with ischemic-looking deep T-wave inversion. These may be present even with <15 mm apical hypertrophy (relative ApHCM). Microvascular dysfunction is well described in hypertrophic cardiomyopathy. We hypothesized that apical perfusion defects would be common in ApHCM. METHODS: A 2-center study using cardiovascular magnetic resonance short- and long-axis quantitative adenosine vasodilator stress perfusion mapping. One hundred patients with ApHCM (68 overt hypertrophy [≥15 mm] and 32 relative ApHCM) were compared with 50 patients with asymmetrical septal hypertrophy hypertrophic cardiomyopathy and 40 healthy volunteer controls. Perfusion was assessed visually and quantitatively as myocardial blood flow and myocardial perfusion reserve. RESULTS: Apical perfusion defects were present in all overt ApHCM patients (100%), all relative ApHCM patients (100%), 36% of asymmetrical septal hypertrophy hypertrophic cardiomyopathy, and 0% of healthy volunteers (P<0.001). In 10% of patients with ApHCM, perfusion defects were sufficiently apical that conventional short-axis views missed them. In 29%, stress myocardial blood flow fell below rest values. Stress myocardial blood flow was most impaired subendocardially, with greater hypertrophy or scar, and with apical aneurysms. Impaired apical myocardial blood flow was most strongly predicted by thicker apical segments (β-coefficient, -0.031 mL/g per min [CI, -0.06 to -0.01]; P=0.013), higher ejection fraction (-0.025 mL/g per min [CI, -0.04 to -0.01]; P<0.005), and ECG maximum R-wave height (-0.023 mL/g per min [CI, -0.04 to -0.01]; P<0.005). CONCLUSIONS: Apical perfusion defects are universally present in ApHCM at all stages. Its ubiquitous presence along with characteristic ECG suggests ischemia may play a disease-defining role in ApHCM

Fully automated, inline quantification of myocardial blood flow with cardiovascular magnetic resonance: repeatability of measurements in healthy subjects

Background: Non-invasive assessment of myocardial ischaemia is a cornerstone of the diagnosis of coronary artery disease. Measurement of myocardial blood flow (MBF) using positron emission tomography (PET) is the current reference standard for non-invasive quantification of myocardial ischaemia. Dynamic myocardial perfusion cardiovascular magnetic resonance (CMR) offers an alternative to PET and a recently developed method with automated inline perfusion mapping has shown good correlation of MBF values between CMR and PET. This study assessed the repeatability of myocardial perfusion mapping by CMR in healthy subjects. Methods: Forty-two healthy subjects were recruited and underwent adenosine stress and rest perfusion CMR on two visits. Scans were repeated with a minimum interval of 7 days. Intrastudy rest and stress MBF repeatability were assessed with a 15-min interval between acquisitions. Interstudy rest and stress MBF and myocardial perfusion reserve (MPR) were measured for global myocardium and regionally for coronary territories and slices. Results: There was no significant difference in intrastudy repeated global rest MBF (0.65 ± 0.13 ml/g/min vs 0.62 ± 0.12 ml/g/min, p = 0.24, repeatability coefficient (RC) =24%) or stress (2.89 ± 0.56 ml/g/min vs 2.83 ± 0.64 ml/g/min, p = 0.41, RC = 29%) MBF. No significant difference was seen in interstudy repeatability for global rest MBF (0.64 ± 0.13 ml/g/min vs 0.64 ± 0.15 ml/g/min, p = 0.80, RC = 32%), stress MBF (2.71 ± 0.61 ml/g/min vs 2.55 ± 0.57 ml/g/min, p = 0.12, RC = 33%) or MPR (4.24 ± 0.69 vs 3.73 ± 0.76, p = 0.25, RC = 36%). Regional repeatability was good for stress (RC = 30–37%) and rest MBF (RC = 32–36%) but poorer for MPR (RC = 35–43%). Within subject coefficient of variation was 8% for rest and 11% for stress within the same study, and 11% for rest and 12% for stress between studies. Conclusions: Fully automated, inline, myocardial perfusion mapping by CMR shows good repeatability that is similar to the published PET literature. Both rest and stress MBF show better repeatability than MPR, particularly in regional analysis