Combined connectivity and a gray-level morphological filter in magnetic resonance coronary angiography.

A connectivity algorithm combined with a new gray-level morphological filter dramatically improves the segmentation of tortuous coronary arteries from 3D MRI. Small coronary arteries are segmented from the larger ventricles with a new filter. These blood vessels are segmented from the noise background with connectivity. Coronary angiograms were computed in nine datasets acquired on volunteers with 3D stack of spirals and contrast-enhanced navigator sequences by both a maximum intensity projection and surface rendering. Surface images provided depth information needed to distinguish branching arteries from crossing veins. Magn Reson Med 43:892-895, 2000.

Fast magnetic resonance coronary angiography with a three-dimensional stack of spirals trajectory.

In this work, three-dimensional (3D) spiral imaging has been utilized for magnetic resonance coronary angiography. Spiral-based 3D techniques can dramatically reduce imaging time requirements compared with 3D Fourier Transform imaging. The method developed here utilized a "stack of spirals" trajectory, to traverse 3D k-space rapidly. Both thick-slab volumes encompassing the entire coronary tree with isotropic resolution and thin-slab volumes targeted to a particular vessel of interest were acquired. Respiratory compensation was achieved using the diminishing variance algorithm. T2-prepared contrast was also applied in some cases to improve contrast between vessel and myocardium, while off-resonance blurring was minimized by applying a linear correction to the acquired data. Images from healthy volunteers were displayed using a curved reformatting technique to view long segments of vessel in a single projection. The results demonstrate that this 3D spiral technique is capable of producing high-quality coronary magnetic resonance angiograms.

3D MR coronary artery segmentation.

Coronary arteries are segmented from the blood pool using mathematical morphology operations from a 3D magnetic resonance spiral acquisition on a continuously breathing healthy volunteer. The segmented volume is maximal intensity projected at different views to yield coronary angiograms showing the left anterior descending artery (LAD), right coronary artery (RCA), and left circumflex artery (LCX). Magnetic resonance coronary angiography provides a retrospective rotating view of the coronary artery tree that complements oblique reformatted sections.

Methods of graph searching for border detection in image sequences with applications to cardiac magnetic resonance imaging.

Automated border detection using graph searching principles has been shown useful for many biomedical imaging applications. Unfortunately, in an often unpredictable subset of images, automated border detection methods may fail. Most current edge detection methods fail to take into account the added information available in a temporal or spatial sequence of images that are commonly available in biomedical image applications. To utilize this information the authors extended their previously reported single frame graph searching method to include data from a sequence. The authors' method transforms the three-dimensional surface definition problem in a sequence of images into a two-dimensional problem so that traditional graph searching algorithms may be used. Additionally, the authors developed a more efficient method of searching the three-dimensional data set using heuristic search techniques which vastly improve execution time by relaxing the optimality criteria. The authors have applied both methods to detect myocardial borders in computer simulated images as well as in short-axis magnetic resonance images of the human heart. Preliminary results show that the new multiple image methods may be more robust in certain circumstances when compared to a single frame method and that the heuristic search techniques may reduce analysis times without compromising robustness.

Automated myocardial edge detection on MR images: accuracy in consecutive subjects.

The authors previously demonstrated the feasibility of graph-searching-based automated edge detection in cardiac magnetic resonance (MR) imaging. To further assess the clinical utility of this method, unselected images from 11 consecutive subjects undergoing clinically indicated (except for one healthy volunteer) short-axis spin-echo MR imaging were analyzed. A total of 142 images from the 11 subjects, encompassing the left ventricle from apex to outflow tract, were analyzed. The computer algorithm correctly identified complete endocardial and epicardial contours in 121 of 142 images (85%). Correlations between observer-traced and computer-derived epicardial areas for all images were good (r = .71 for epicardium, r = .83 for endocardium); they improved for a subset of higher-quality images (r = .82 for epicardium, r = .92 for endocardium). The authors conclude that the current data further support the usefulness of computer digital image processing in geometric analysis of cardiac MR image data.

Multicenter trial of automated border detection in cardiac MR imaging.

The purpose of the present study was to evaluate the robustness of a method of automated border detection in cardiac magnetic resonance (MR) imaging. Thirty-seven short-axis spin-echo cardiac images were acquired from three medical centers, each with its own image-acquisition protocol. Endo- and epicardial borders and areas were derived from these images with a graph-searching-based method of edge detection. Computer results were compared with observer-traced borders. The method accurately defined myocardial borders in 36 of 37 images (97%), with excellent agreement between computer- and observer-derived endocardial and epicardial areas (correlation coefficients, .94-.99). The algorithm worked equally well for data from all three centers, despite differences in image-acquisition protocols, MR systems, and field strengths. These data suggest that a method of computer-assisted edge detection based on graph-searching principles yields endocardial and epicardial areas that correlate well with those derived by an independent observer.

Automated identification of left ventricular borders from spin-echo magnetic resonance images. Experimental and clinical feasibility studies.

Gated cardiac magnetic resonance imaging (MRI) permits detailed evaluation of cardiac anatomy, including the calculation of left ventricular volume and mass. Current methods of deriving this information, however, require manual tracing of boundaries in several images; such manual methods are tedious, time consuming, and subjective. The purpose of this study is to apply a new computerized method to automatically identify endocardial and epicardial borders in MRIs. The authors obtained serial, short-axis, spin-echo MRIs of 13 excised animal hearts. Also obtained were selected short-axis, spin-echo ventricular images of 11 normal human volunteers. A method of automated edge detection based on graph-searching principles was applied to the ex vivo and in vivo images. Endocardial and epicardial areas were used to compute left ventricular mass and were compared with the anatomic left ventricular mass for the images of excised hearts. The endocardial and epicardial areas calculated from computer-derived borders were compared with areas from observer tracing. There was very close correspondence between computer-derived and observer tracings for excised hearts (r = 0.97 for endocardium, r = 0.99 for epicardium) and in vivo scans (r = 0.92 for endocardium, r = 0.90 for epicardium). There also was a close correspondence between computer-generated and actual left ventricular mass in the excised hearts (r = 0.99). These data suggest the feasibility of automated edge detection in MRIs. Although further validation is needed, this method may prove useful in clinical MRI.