Automated segmentation and registration of myocardium for quantitative assessment of first pass perfusion MRI

INTRODUCTION Myocardial perfusion assessment from cardiac magnetic resonance (CMR) images has proven to be feasible and useful. Extraction of quantitative perfusion information relies on the definition of myocardial regions of interest (ROI). This is usually achieved by manually drawing ROIs in one frame and then adjusting their position on subsequent frames. Unfortunately this tecnique requires a large amount of time and is potentially inaccurate. We recently developed a method based on image segmentation and registration for automated dynamic endocardial and epicardial borders detection for quantification of left ventricular perfusion. METHODS Data Acquisition: LV short-axis images (Philips 1.5T) were obtained at three levels of the left ventricle during first pass of a Gadolinium-DTPA bolus (0.10mmol/kg @5ml/ sec). Images were acquired using a hybrid gradient echo/echo planar imaging sequence (3 slices, thickness 10mm, pixel size 2.5x2.5mm, nonselective 90° saturation pulse followed by 80 ms delay, flip angle 20°, TR=5.9 ms, TE=2.7 ms, EPI factor 5, SENSE factor 2). Image segmentation: For each slice, the only manual action required was to place a seed point inside the LV cavity on a single frame. Starting from that point, an automated algorithm was developed to detect the two best frames for endocardial and epicardial boundaries segmentation, respectively. These frames were compulsory chosen among the first part of the image sequence, in which no myocardium movement took place. Hence the endocardial boundary was automatically computed using a modified region-based model based on the noise probability distribution of gray levels in MRI. Then epicardial boundary was detected using a classical edge based level-set model with curvature, advection and expansion driving terms. Both of the boundaries underwent a slight regularization motion in order to get a more physiological shape. Image registration: this step was necessary to compensate for respiratory motion and consisted in the rigid movement of the boundaries computed during the segmentation step. The goal was achieved by the implementation of two-dimensional cross-correlation. This algorithm was modified in order to look for correlation between the gradient of the images, in order to seek shape rather than intensity correspondence. Contrast enhancement curves extraction: Six standard myocardial ROIs were automatically defined and contrast enhancement curves were constructed throughout the image sequence. This approach was tested on 24 slices during first pass perfusion by: (1) visually judging frame-byframe the accuracy of endo- and epicardial boundary positions, and (2) calculating the ratio between the amplitude of the contrast enhancement curve and the SD of the plateau phase (SNR), which should be a good performance indicator of the algorithm as a whole. All the code was written in MATLAB® (The MathWorks, Natick, MA, USA). RESULTS Time needed to complete the automated analysis of one perfusion slice was less than 1 minute on a personal computer. The outcome of endo- and epicardial boundaries segmentation and registration was judged accurate in all image sequences. The computed contrast enhancement curves clearly showed the typical pattern of first-pass perfusion, with its wash-in slope and subsequent plateau phases. SNR averaged over 24 image sequences was 32±10. CONCLUSION Detection of myocardial boundaries and quantification of intra-myocardial contrast can be done in a fast, automated and dynamic fashion. The final outcome consists in regional contrast enhancement curves with excellent noise levels, which can be used to extract parameters of clinical value. The described approach provides a user-friendly and potentially more accurate technique, which may fulfill the strong need for clinical quantitative evaluation of myocardial perfusion from contrastenhanced CMR images.