This is the first study of the intravascular iron oxide particle contrast agent, NC100150 Injection (Nycomed Imaging AS, Oslo, Norway, a part of Nycomed Amersham) in magnetic resonance imaging of the human heart. Eighteen healthy male volunteers were studied at both 0.5 and 1.5 T before and after the administration of NC100150 Injection. Transaxial spin-echo images were acquired at both field strengths, conventional gradient-echo cine images at 0.5 T, and breathhold Turbo-FLASH cine images at 1.5 T. Optimized cine imaging sequences were used postcontrast, with a high flip angle of 60-70 degrees. In the spin-echo images there was a significant reduction in the blood pool flow artifact at the level of the right atrium (0.5 T, 57%, p < 0.01; 1.5 T, 41%, p = 0.01) and the left ventricle (LV) (0.5 T, 45%, p = 0.01; 1.5 T, 45%, p < 0.01). In the conventional gradient-echo cines at 0.5 T, there was a significant increase in the LV blood pool and myocardial signal difference-to-noise ratio (SDNR) in the diastolic (56%, p = 0.01) and systolic (141%, p < 0.001) frames. There was also a significant increase in the signal intensity (SI) gradient at the LV blood pool-myocardial border in the diastolic and systolic frames (both p < 0.001). At higher doses of NC100150 Injection (3 and 4 mg/kg), a rim of signal void around the LV blood pool was observed, perfectly defining the LV blood pool-myocardial border. In the Turbo-FLASH breathhold cines at 1.5 T, there was a significant increase in the LV blood pool-myocardial SDNR in the diastolic (221%, p < 0.001) and systolic (916%, p < 0.001) frames. Again, there was also a significant increase in the SI gradient at the LV blood pool-myocardial border in the diastolic and systolic frames (both p = 0.003). In conclusion, NC100150 Injection was given safely to 18 healthy subjects. Image quality and LV blood pool-myocardial definition were improved after the administration of NC100150 Injection. These improvements enable better spin-echo anatomical definition, better definition of myocardial wall motion, and should improve the capability of automated edge detection algorithms.