MR physicists and radiologists interested in quantification of myocardial blood flow and extracellular volume fractionRecently there have been several studies of estimation of extracellular volume fraction from myocardial perfusion images using a distributed parameter (DP) model [1,2]. The DP model has been validated in normal volunteers and patients with acute myocardial infarction with dynamic contrast enhanced MR perfusion protocols (protocol 1: 0.1 mmol/kg Gd-DTPA bolus, 90 cardiac cycles [1]; protocol 2: dual bolus, 0.01 mmol/kg pre-bolus, 0.1 mmol/kg bolus, Gd-DO3A-butrol, 210 cardiac cycles [2]). We use a similar perfusion protocol (saturation recovery preparation, FLASH readout, 0.1 mmol/kg bolus, Gadovist, 80 cardiac cycles) and a DP model fitting, and demonstrate extracellular volume fraction (ECV) measurements in patients with aortic stenosis (AS) and patients with hypertrophic cardiomyopathy (HCM). Cardiac MRI data were acquired on a Siemens 1.5T scanner. Conventional ECG-gated saturation recovery FLASH (pre-pulse delay=110ms; TR/TE=2.2/1.08 ms; flip angle=12°; slice thickness=8mm; in-plane resolution=2.4mm x 2.9mm) was used to acquire dynamic contrast enhanced (DCE) myocardial perfusion data. A bolus of gadolinium-based contrast agent was administrated into the subject’s antecubital vein. Images were acquired every R-R interval for subjects’ 80 heartbeats. The subject was instructed to perform breath-hold as long as possible during imaging. Adenosine stress perfusion data in basal and mid slices from 13 severe AS patients with no indications of obstructive coronary artery disease, 11 HCM patients with positive late gadolinium enhancement (LGE), and 10 normal volunteers were considered for analysis. Methods for image analysis were implemented in Matlab. The myocardium was divided into six angular segments. Motion correction was performed using a non-rigid registration method. Myocardial signal intensity correction was performed using the information of pre-contrast baseline signals. MR signal model based on pulse sequence parameters used in the perfusion imaging was applied to correct for signal saturation in arterial input function [3,6]. Blood hematocrit (hct) values were individually obtained from the subjects. DP model fitting was performed in Fourier space [4] to estimate plasma flow (Fp), mean capillary transit time (Tc), mean interstitial transit time (Te), and mean overall transit time (T) [3,5]. Plasma volume fraction denoted by Vp was computed as Fp*Tc/(1-hct), and extravascular extracellular volume fraction denoted by Ve was computed as Fp*(T-Tc)/(1-hct). ECV was computed as (Vp+Ve)*100(%) [7]. Myocardial T1 mapping data were acquired with MOLLI sequences (flip angle=35°; slice thickness=6mm; in-plane resolution=1.4mm x 2.2mm). Native T1 maps and 5 or 10 min post-contrast T1 maps were used to compute ECV, which was computed as (∆R1_myo/∆R1_blood)*(1-hct)*100 (%) and served as reference. Myocardial segment with fibrosis showed slower decay of myocardial Gd concentration after first pass (~25 sec) than normal myocardium (compare Fig 1d and 1b). Mean capillary transit time (Tc) was estimated to be longer in segment with myocardial fibrosis (compare the widths of the rectangular pulses in Fig 1c and 1a). ECV measurements estimated by DP model (Fig 2c, 2d) correlated well with those estimated by T1 mapping (Fig 2a, 2b), but the distribution pattern of high ECV is not as focal as Fig 2a. Although not shown in this abstract, when patient specific native T1 of LV blood was used in DP model rather than the assumed blood T1 of 1435 ms as in [5], DP model derived ECV estimates increased as patient specific native T1 of LV blood increased. Figure 3a indicates that mean ECVs of the normal volunteer group range from 33 to 40%, which are higher than normal individual’s ECVs of 20-30% in the literature [8]. The AS patient group showed mean ECV values similar to those from the normal volunteer group. This may be attributed to the fact that the extent of fibrosis in the AS patient group was typically small in size and spatial resolution of our perfusion imaging protocol was lower than that of T1 mapping. We have demonstrated that ECV measurement with DP model fitting of myocardial perfusion data is feasible with a standard DCE myocardial perfusion imaging especially in the HCM patient group with large extent of myocardial fibrosis. Validation of DP model derived ECV measurement with higher spatial resolution perfusion imaging remains as future work.