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

Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study

Background

Pancreas segmentation is of great significance for pancreatic cancer radiotherapy positioning, pancreatic structure, and function evaluation.

Purpose

To investigate the feasibility of computer-aided pancreas segmentation based on optimized three-dimensional (3D) Dixon magnetic resonance imaging (MRI).

Material and Methods

Seventeen healthy volunteers (13 men, 4 women; mean age = 53.4 ± 13.2 years; age range = 28–76 years) underwent routine and optimized 3D gradient echo (GRE) Dixon MRI at 3.0 T. The computer-aided segmentation of the pancreas was executed by the Medical Imaging Interaction ToolKit (MITK) with the traditional segmentation algorithm pipeline (a threshold method and a morphological method) on the opposed-phase and water images of Dixon. The performances of our proposed computer segmentation method were evaluated by Dice coefficients and two-dimensional (2D)/3D visualization figures, which were compared for the opposed-phase and water images of routine and optimized Dixon sequences.

Results

The dice coefficients of the computer-aided pancreas segmentation were 0.633 ± 0.080 and 0.716 ± 0.033 for opposed-phase and water images of routine Dixon MRI, respectively, while they were 0.415 ± 0.143 and 0.779 ± 0.048 for the optimized Dixon MRI, respectively. The Dice index was significantly higher based on the water images of optimized Dixon than those in the other three groups (all P values < 0.001), including water images of routine Dixon MRI and both of the opposed-phase images of routine and optimized Dixon sequences.

Conclusion

Computer-aided pancreas segmentation based on Dixon MRI is feasible. The water images of optimized Dixon obtained the best similarity with a good stability.

Region-growing segmentation of brain vessels: an atlas-based automatic approach

PURPOSE

To propose an atlas-based method that uses both phase and magnitude images to integrate anatomical information in order to improve the segmentation of blood vessels in cerebral phase-contrast magnetic resonance angiography (PC-MRA).

MATERIAL AND METHODS

An atlas of the whole head was developed to store the anatomical information. The atlas divides a magnitude image into several vascular areas, each of which has specific vessel properties. It can be applied to any magnitude image of an entire or nearly entire head by deformable matching, which helps to segment blood vessels from the associated phase image. The segmentation method used afterwards consists of a topology-preserving, region-growing algorithm that uses adaptive threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels that are selected according to their grayscale value and the variation of values in their neighborhood. The topology preservation is guaranteed because only simple points are selected during the growing process.

RESULTS

The method was performed on 40 PC-MRA images of the brain. The results were validated using maximum-intensity projection (MIP) and three-dimensional surface rendering visualization, and compared with results obtained with two non-atlas-based methods.

CONCLUSION

The results show that the proposed method significantly improves the segmentation of cerebral vascular structures from PC-MRA. These experiments tend to prove that the use of vascular atlases is an effective way to optimize vessel segmentation of cerebral images.

"Soap-Bubble" visualization and quantitative analysis of 3D coronary magnetic resonance angiograms

In order to compare coronary magnetic resonance angiography (MRA) data obtained with different scanning methodologies, adequate visualization and presentation of the coronary MRA data need to be ensured. Furthermore, an objective quantitative comparison between images acquired with different scanning methods is desirable. To address this need, a software tool ("Soap-Bubble") that facilitates visualization and quantitative comparison of 3D volume targeted coronary MRA data was developed. In the present implementation, the user interactively specifies a curved subvolume (enclosed in the 3D coronary MRA data set) that closely encompasses the coronary arterial segments. With a 3D Delaunay triangulation and a parallel projection, this enables the simultaneous display of multiple coronary segments in one 2D representation. For objective quantitative analysis, frequently explored quantitative parameters such as signal-to-noise ratio (SNR); contrast-to-noise ratio (CNR); and vessel length, sharpness, and diameter can be assessed. The present tool supports visualization and objective, quantitative comparisons of coronary MRA data obtained with different scanning methods. The first results obtained in healthy adults and in patients with coronary artery disease are presented.

3D MRA coronary axis determination using a minimum cost path approach

A method is introduced to automatically find the coronary axis based on two or more user-defined points, even in the presence of a severe stenosis. The coronary axis is determined by finding a minimum cost path (MCP) in a feature image in which the tubular-like structures are enhanced. The results of the proposed method were compared with manually drawn central axes to estimate the accuracy. In 32 3D TFE-EPI acquisitions of patients and volunteers, 14 right coronary arteries (RCAs), 15 left anterior descending arteries (LADs), and eight left circumflex arteries (LCXs) were manually tracked twice by two operators to determine a reference axis and to assess the inter- and intra-user variability. On average, the maximum distance to the reference axis, based on only two user-defined points, is less than 1.5 mm; the average distance is around 0.65 mm, which is less than the average in-plane resolution. The results of the method are comparable to those of the manual operators.

Selective three-dimensional visualization of the coronary arterial lumen using arterial spin tagging

Conventional coronary magnetic resonance angiography (MRA) techniques display the coronary blood-pool along with the surrounding structures, including the myocardium, the ventricular and atrial blood-pool, and the great vessels. This representation of the coronary lumen is not directly analogous to the information provided by x-ray coronary angiography, in which the coronary lumen displayed by iodinated contrast agent is seen. Analogous "luminographic" data may be obtained using MR arterial spin tagging (projection coronary MRA) techniques. Such an approach was implemented using a 2D selective "pencil" excitation for aortic spin tagging in concert with a 3D interleaved segmented spiral imaging sequence with free-breathing, and real-time navigator technology. This technique allows for selective 3D visualization of the coronary lumen blood-pool, while signal from the surrounding structures is suppressed.

Separation of arteries and veins using flow-induced phase effects in contrast-enhanced MRA of the lower extremities

In 3-D contrast-enhanced magnetic resonance (MR) angiography of the lower extremities the goal is most often to enhance arterial structures while keeping veins and surrounding tissue unenhanced. Imaging during steady-state concentration of a blood pool agent or during poor timing of an extra-cellular contrast medium may result in simultaneous venous enhancement, making interpretation of the angiogram difficult. The aim of this study was to develop a post-processing method to separate the arteries from the veins in standard contrast-enhanced MR angiograms. The method was based on the different accumulation of flow-induced phase in the arteries and veins of the lower extremities. The method was tested in both phantom experiments and volunteers undergoing 3-D contrast-enhanced MR angiography using both an extra-cellular contrast medium and a blood pool agent. In the phantom studies, opposite directional flow was successfully separated at mean flow velocities as low as 9 cm/s. In the volunteer studies, the larger veins were successfully extinguished while the larger arteries were left unaffected. In smaller vessels with low flow velocities the separation was less successful. This was most apparent in vessels not oriented superior-inferior. The method developed here is promising for separating arteries from veins in contrast-enhanced MR angiography although the results could be further improved by either a different pulse sequence design or combining this method with other segmentation methods.

Coronary artery magnetic resonance imaging: a patient-tailored approach

Coronary artery magnetic resonance imaging strategies have tended to focus on the use of a single method performed during either breath-holding or free-breathing for all patients. However, significant variations exist among patients in terms of breath-holding ability and respiratory regularity that make the use of a single technique alone not universally successful. Therefore, it is prudent to make available a number of magnetic resonance imaging methods such that an appropriate respiratory motion reduction strategy can be tailored to suit the patient's respiratory pattern and characteristics. A tailored approach that can draw on different image acquisition techniques for coronary artery imaging is presented. A decision tree is proposed to triage patients into imaging regimes with the greatest probability of success, according to the patient's ability to breath-hold or exhibit steady respiration. Methods include volume free-breathing acquisitions using navigator echoes for respiratory monitoring in the 8- to 10-min scan time range, two-dimensional spiral navigators (2- to 3-min scan time), breath-held multislice and vessel-tracking spirals (16- to 20-second scan time), and real-time imaging approaches incorporating adaptive signal averaging. The development of multiple acquisition strategies substantially improves the opportunities to generate high-quality, diagnostic images of the coronary arteries.