Heart filling exceeds emptying during late ventricular systole in patients with systolic heart failure and healthy subjects – a cardiac MRI study

Abstract

Summary Background Total heart volume (THV) within the pericardium is not constant throughout the cardiac cycle and THV would intuitively be lowest at end systole. We have, however, observed a phase shift between ventricular outflow and atrial inflow which causes the minimum THV to occur before end systole. The aims were to explain the mechanism of the late‐systolic net inflow to the heart and determine whether this net inflow is affected by increased cardiac output or systolic heart failure. Methods and Results Healthy controls (n = 21) and patients with EF<35% (n = 14) underwent magnetic resonance imaging with flow measurements in vessels to and from the heart, and this was repeated in nine controls during 140 μgram kg−1 min−1 adenosine infusion. Minimum THV occurred 78 ± 6 ms before end of systolic ejection (8 ± 1% of the cardiac cycle) in controls. The late‐systolic net inflow was 12·3 ± 1·1 ml or 6·0 ± 0·5% of total stroke volume (TSV). Cardiac output increased 66 ± 8% during adenosine but late‐systolic net inflow to the heart did not change (P = 0·73). In patients with heart failure, late‐systolic net inflow of the heart′s left side was lower (3·4 ± 0·5%) compared to healthy subjects (5·3 ± 0·6%, P = 0·03). Conclusions Heart size increases before end systole due to a late‐systolic net inflow which is unaffected by increased cardiac output. This may be explained by inertia of blood that flows into the atria generated by ventricular systole. The lower late‐systolic net inflow in patients with systolic heart failure may be a measure of decreased ventricular filling due to decreased systolic function, thus linking systolic to diastolic dysfunction.

Analysis of the sensitivity and specificity of compressed sensing magnetic resonance imaging in the diagnosis of heart failure

“…Compressed sensing refers to a signal reconstruction technology based on sparse signal characteristics that enable the signal sampling and compression process to proceed simultaneously, thus greatly reducing the sampling rate ( 14 ). Sun et al [2019] ( 15 ) combined analysis-based blind compression sensing with other non-local low-rank constraints to obtain better MRI images with fewer measurements, and simulation results showed that the performance of the proposed method was superior to previous algorithms.…”