Myocardial blood flow is the dominant factor influencing cardiac magnetic resonance adenosine stress T2

Abstract

Stress imaging identifies ischemic myocardium by comparing hemodynamics during rest and hyperemic stress. Hyperemia affects multiple hemodynamic parameters in myocardium, including myocardial blood flow (MBF), myocardial blood volume (MBV), and venous blood oxygen levels (PvO2). Cardiac T2 is sensitive to these changes and therefore is a promising non‐contrast option for stress imaging; however, the impact of individual hemodynamic factors on T2 is poorly understood, making the connection from altered T2 to changes within the tissue difficult. To better understand this interplay, we performed T2 mapping and measured various hemodynamic factors independently in healthy pigs at multiple levels of hyperemic stress, induced by different doses of adenosine (0.14‐0.56 mg/kg/min). T1 mapping quantified changes in MBV. MBF was assessed with microspheres, and oxygen consumption was determined by the rate pressure product (RPP). Simulations were also run to better characterize individual contributions to T2. Myocardial T2, MBF, oxygen consumption, and MBV all changed to varying extents between each level of adenosine stress (T2 = 37.6‐41.8 ms; MBF = 0.48‐1.32 mL/min/g; RPP = 6507‐4001 bmp*mmHg; maximum percent change in MBV = 1.31%). Multivariable analyses revealed MBF as the dominant influence on T2 during hyperemia (significant β‐values >7). Myocardial oxygen consumption had almost no effect on T2 (β‐values <0.002); since PvO2 is influenced by both oxygen consumption and MBF, PvO2 changes detected by T2 during adenosine stress can be attributed to MBF. Simulations varying PvO2 and MBV confirmed that PvO2 had the strongest influence on T2, but MBV became important at high PvO2. Together, these data suggest a model where, during adenosine stress, myocardial T2 responds predominantly to changes in MBF, but at high hyperemia MBV is also influential. Thus, changes in adenosine stress T2 can now be interpreted in terms of the physiological changes that led to it, enabling T2 mapping to become a viable non‐contrast option to detect ischemic myocardial tissue.

Dynamic Cardiac Magnetic Resonance Fingerprinting During Vasoactive Breathing Maneuvers: First Results

“…However, this is in line with previous results and discussion around the lower sensitivity of stress T1 mapping to hemodynamic changes when compared to T2. 10) Initial results demonstrated a significant potential for the dynamic assessment of myocardial perfusion by stress T1 mapping. 27) However, conflicting results and potential T1 mapping sequence differences have shown that stress T1 mapping may provide a better assessment of myocardial blood volume instead of dynamic myocardial blood flow, 5) 6) and that stress T2 mapping is more sensitive to changes in myocardial blood flow.…”

“… 27) However, conflicting results and potential T1 mapping sequence differences have shown that stress T1 mapping may provide a better assessment of myocardial blood volume instead of dynamic myocardial blood flow, 5) 6) and that stress T2 mapping is more sensitive to changes in myocardial blood flow. 10) …”

“…While stress T1 mapping can detect changes in myocardial blood volume, stress T2 mapping is more sensitive to hemodynamic changes, including myocardial blood flow, oxygen consumption and venous blood oxygen levels. 10) …”

Ultra-high field cardiac MRI in large animals and humans for translational cardiovascular research

“…Pig models are an important part of both translational and imaging research., e.g., to assess post-infarction inflammation using fluorine-19 MRI ( 91 ), to understand functional and microstructural changes ( 92 ) or myocardial strain ( 93 ) following acute myocardial infarction, to understand pulmonary vasomotor control and ROS in exercising swine with multiple comorbidities ( 94 ), in resuscitation research ( 12 , 95 ), to better understand imaging contrast such as the influence of myocardial blood flow on T2 ( 96 ), to develop medical devices, and as basis for computational modelling of the heart ( 7 , 97 – 100 ).…”