Microvascular dysfunction in HCM has been associated with adverse clinical outcomes. Advances in quantitative cardiovascular magnetic resonance (CMR) perfusion imaging now allow myocardial blood flow to be quantified at the pixel level. We applied these techniques to investigate the spectrum of microvascular dysfunction in hypertrophic cardiomyopathy (HCM) and to explore its relationship with fibrosis and wall thickness.

Regadenoson, dipyridamole and adenosine are commonly used vasodilators in myocardial perfusion imaging for the detection of obstructive coronary artery disease. There are few comparative studies of the vasodilator properties of regadenoson, adenosine and dipyridamole in humans. The specific aim of this study was to determine the relative potency of these three vasodilators by quantifying stress and rest myocardial perfusion in humans using cardiovascular magnetic resonance (CMR). Fifteen healthy normal volunteers, with Framingham score less than 1% underwent vasodilator stress testing with regadenoson (400 μg bolus), dipyridamole (0.56 mg/kg) and adenosine (140 μg /kg/min) on separate days. Rest perfusion imaging was performed initially. Twenty minutes later, stress imaging was performed at peak vasodilation, i.e. 70 seconds after regadenoson, 4 minutes after dipyridamole infusion and between 3–4 minutes of the adenosine infusion. Myocardial blood flow (MBF) in ml/min/g and myocardial perfusion reserve (MPR) were quantified using a fully quantitative model constrained deconvolution. Regadenoson produced higher stress MBF than dipyridamole and adenosine (3.58 ± 0.58 vs. 2.81 ± 0.67 vs. 2.78 ± 0.61 ml/min/g, p = 0.0009 and p = 0.0008 respectively). Regadenoson had a much higher heart rate response than adenosine and dipyridamole respectively (95 ± 11 vs. 76 ± 13 vs. 86 ± 12 beats/ minute) When stress MBF was adjusted for heart rate, there were no differences between regadenoson and adenosine (37.8 ± 6 vs. 36.6 ± 4 μl/sec/g, p = NS), but differences between regadenoson and dipyridamole persisted (37.8 ± 6 vs. 32.6 ± 5 μl/sec/g, p = 0.03). The unadjusted MPR was higher with regadenoson (3.11 ± 0.63) when compared with adenosine (2.7 ± 0.61, p = 0.02) and when compared with dipyridamole (2.61 ± 0.57, p = 0.04). Similar to stress MBF, these differences in MPR between regadenoson and adenosine were abolished when adjusted for heart rate (2.04 ± 0.34 vs. 2.12 ± 0.27, p = NS), but persisted between regadenoson and dipyridamole (2.04 ± 0.34 vs. 1.77 ± 0.33, p = 0.07) and between adenosine and dipyridamole (2.12 ± 0.27 vs. 1.77 ± 0.33, p = 0.01). Based on fully quantitative perfusion using CMR, regadenoson and adenosine have similar vasodilator efficacy and are superior to dipyridamole.

Purpose: To study the nonlinearity of myocardial signal intensity and gadolinium contrast concentration during first‐pass perfusion MRI, and to compare quantitative perfusion estimates using nonlinear myocardial signal intensity correction.Materials and Methods: The nonlinearity of signal intensity and contrast concentration was simulated by magnetization modeling and evaluated in phantom measurements. A total of 10 healthy volunteers underwent rest and stress dual‐bolus perfusion studies using an echo‐planar imaging sequence at both short and long saturation‐recovery delay times (TD70 and TD150). Perfusion estimates were compared before and after the correction.Results: The phantom data showed a linear relationship (R² = 1.00 and 0.99) of corrected signal intensity vs. contrast concentrations. Peak myocardial contrast concentration averaged 0.64 ± 0.10 mmol · L⁻¹ at rest and 0.91 ± 0.21 mmol · L⁻¹ during stress for TD70 and were similar for TD150 (P = not significant [NS]). The corrections were larger for stress than rest perfusion and larger for TD150 than TD70 studies (both P < 0.01). Perfusion estimates of TD70 and TD150 stress studies were significantly different before the correction (P < 0.01) but equivalent after the correction (P = NS).Conclusion: The nonlinearity between signal intensity and myocardial contrast concentration in perfusion MRI can be corrected through magnetization modeling. A nonlinear correction of myocardial signal intensity is feasible and improves quantitative perfusion analysis. J. Magn. Reson. Imaging 2008.

Quantitative assessment of myocardial blood flow (MBF) with first-pass perfusion cardiovascular magnetic resonance (CMR) requires a measurement of the arterial input function (AIF). This study presents an automated method to improve the objectivity and reduce processing time for measuring the AIF from first-pass perfusion CMR images. This automated method is used to compare the impact of different AIF measurements on MBF quantification. Gadolinium-enhanced perfusion CMR was performed on a 1.5 T scanner using a saturation recovery dual-sequence technique. Rest and stress perfusion series from 270 clinical studies were analyzed. Automated image processing steps included motion correction, intensity correction, detection of the left ventricle (LV), independent component analysis, and LV pixel thresholding to calculate the AIF signal. The results were compared with manual reference measurements using several quality metrics based on the contrast enhancement and timing characteristics of the AIF. The median and 95 % confidence interval (CI) of the median were reported. Finally, MBF was calculated and compared in a subset of 21 clinical studies using the automated and manual AIF measurements. Two clinical studies were excluded from the comparison due to a congenital heart defect present in one and a contrast administration issue in the other. The proposed method successfully processed 99.63 % of the remaining image series. Manual and automatic AIF time-signal intensity curves were strongly correlated with median correlation coefficient of 0.999 (95 % CI [0.999, 0.999]). The automated method effectively selected bright LV pixels, excluded papillary muscles, and required less processing time than the manual approach. There was no significant difference in MBF estimates between manually and automatically measured AIFs (p = NS). However, different sizes of regions of interest selection in the LV cavity could change the AIF measurement and affect MBF calculation (p = NS to p = 0.03). The proposed automatic method produced AIFs similar to the reference manual method but required less processing time and was more objective. The automated algorithm may improve AIF measurement from the first-pass perfusion CMR images and make quantitative myocardial perfusion analysis more robust and readily available.

Echocardiography (echo) is a critical tool in diagnosing various cardiovascular diseases. Despite its diagnostic and prognostic value, interpretation and analysis of echo images are still widely performed manually by echocardiographers. A plethora of algorithms has been proposed to analyze medical ultrasound data using signal processing and machine learning techniques. These algorithms provided opportunities for developing automated echo analysis and interpretation systems. The automated approach can significantly assist in decreasing the variability and burden associated with manual image measurements. In this paper, we review the state-of-the-art automatic methods for analyzing echocardiography data. Particularly, we comprehensively and systematically review existing methods of four major tasks: echo quality assessment, view classification, boundary segmentation, and disease diagnosis. Our review covers three echo imaging modes, which are B-mode, M-mode, and Doppler. We also discuss the challenges and limitations of current methods and outline the most pressing directions for future research. In summary, this review presents the current status of automatic echo analysis and discusses the challenges that need to be addressed to obtain robust systems suitable for efficient use in clinical settings or point-of-care testing.