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

Myocardial Storage, Inflammation, and Cardiac Phenotype in Fabry Disease After One Year of Enzyme Replacement Therapy.

BACKGROUND Cardiac response to enzyme replacement therapy (ERT) in Fabry disease is typically assessed by measuring left ventricular mass index using echocardiography or cardiovascular magnetic resonance, but neither quantifies myocardial biology. Low native T1 in Fabry disease represents sphingolipid accumulation; late gadolinium enhancement with high T2 and troponin elevation reflects inflammation. We evaluated the effect of ERT on myocardial storage, inflammation, and hypertrophy. METHODS Twenty patients starting ERT (60% left ventricular hypertrophy-positive) were compared with 18 patients with early disease and 18 with advanced disease over 1 year at 3 centers. Cardiovascular magnetic resonance (left ventricular mass index, T1, T2, global longitudinal strain, and late gadolinium enhancement) and biomarkers (high-sensitive troponin-T and NT-proBNP [N-terminal Pro-B-type natriuretic peptide]) at baseline (pre-ERT) and 12 months were performed. Early disease controls were stable, treatment-naïve patients (mainly left ventricular hypertrophy-negative); advanced disease controls were stable, established ERT patients (mainly left ventricular hypertrophy-positive). RESULTS Over 1 year, early disease controls increased maximum wall thickness and left ventricular mass index (9.8±2.7 versus 10.2±2.6 mm; =0.010; 65±15 versus 67±16 g/m; =0.005) and native T1 fell (981±58 versus 959±61 ms; =0.002). Advanced disease controls increased T2 in the late gadolinium enhancement area (57±6 versus 60±7 ms; =0.023) with worsening global longitudinal strain (-13.2±3.4 versus -12.1±4.8; =0.039). Newly treated patients had a small reduction in maximum wall thickness (14.8±5.9 versus 14.4±5.7 mm; =0.028), stable left ventricular mass index (93±42 versus 92±40 g/m; =0.186) and a reduction in T1 lowering (917±49 versus 931±54 ms; =0.017). CONCLUSIONS Fabry myocardial phenotype development is different at different disease stages. After 1 year of ERT initiation, left ventricular hypertrophy-positive patients have a detectable, small reduction in left ventricular mass and storage