Quantification of left and right atrial kinetic energy using four-dimensional intracardiac magnetic resonance imaging flow measurements

Kinetic energy (KE) of atrial blood has been postulated as a possible contributor to ventricular filling. Therefore, we aimed to quantify the left and right atrial blood KE using cardiac magnetic resonance (CMR). Fifteen healthy volunteers underwent CMR at 3T, including a four-dimensional phase contrast flow sequence. Mean left atrial (LA) KE was lower than right atrial (RA) KE (1.1±0.1 mJ vs 1.7±0.1 mJ, P<0.01). Three KE peaks were seen in both atria; one in ventricular systole, one during early ventricular diastole, and one during atrial contraction. The systolic LA peak was significantly smaller than the RA peak (P<0.001), and the early diastolic LA peak was larger than the RA peak (P<0.05). Rotational flow contained 46 ± 7% of total KE, and conserved energy better than non-rotational flow did. The KE increase in early diastole was higher in the LA (P<0.001). Systolic KE correlated with the combination of atrial volume and systolic velocity of the atrioventricular plane displacement (R2=0.57 for LA and R2=0.64 for RA). Early diastolic KE of the LA correlated with LV mass (R2=0.28), however no such correlation was found in the right heart. This suggests that LA KE increases during early ventricular diastole due to LV elastic recoil, indicating that LV filling is dependent on diastolic suction. RV relaxation does not seem to contribute to atrial KE. Instead, atrial KE generated during ventricular systole may be conserved in a hydraulic "flywheel" and transferred to the RV through helical flow, which may contribute to RV filling

Validation of an automated method to quantify stress-induced ischemia and infarction in rest-stress myocardial perfusion SPECT.

Myocardial perfusion SPECT (MPS) is one of the frequently used methods for quantification of perfusion defects in patients with known or suspected coronary artery disease. This article describes open access software for automated quantification in MPS of stress-induced ischemia and infarction and provides phantom and in vivo validation

Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance

Background: T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Methods: Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. Results: MaR was 32.9 +/- 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 +/- 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 +/- 6.4% of LVM, R = 0.81 (p < 0.001) for Segment MaR, -2.3 +/- 4.9%, R = 0.91 (p < 0.001) for inter observer variability of manual delineation, -7.7 +/- 11.4%, R = 0.38 (p = 0.008) for 2SD, -21.0 +/- 9.9%, R = 0.41 (p = 0.004) for FWHM, and 5.3 +/- 9.6%, R = 0.47 (p < 0.001) for Otsu. Conclusions: There is a good agreement between automatic Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a promising, objective method for standardized MaR quantification in T2-weighted CMR