Aortic vessel wall magnetic resonance imaging at 3.0 Tesla: a reproducibility study of respiratory navigator gated free-breathing 3D black blood magnetic resonance imaging.

The purpose of this study was to evaluate a free-breathing three-dimensional (3D) dual inversion-recovery (DIR) segmented k-space gradient-echo (turbo field echo [TFE]) imaging sequence at 3T for the quantification of aortic vessel wall dimensions. The effect of respiratory motion suppression on image quality was tested. Furthermore, the reproducibility of the aortic vessel wall measurements was investigated. Seven healthy subjects underwent 3D DIR TFE imaging of the aortic vessel wall with and without respiratory navigator. Subsequently, this sequence with respiratory navigator was performed twice in 10 healthy subjects to test its reproducibility. The signal-to-noise (SNR), contrast-to-noise ratio (CNR), vessel wall sharpness, and vessel wall volume (VWV) were assessed. Data were compared using the paired t-test, and the reproducibility of VWV measurements was evaluated using intraclass correlation coefficients (ICCs). SNR, CNR, and vessel wall sharpness were superior in scans performed with respiratory navigator compared to scans performed without. The ICCs concerning intraobserver, interobserver, and interscan reproducibility were excellent (0.99, 0.94, and 0.95, respectively). In conclusion, respiratory motion suppression substantially improves image quality of 3D DIR TFE imaging of the aortic vessel wall at 3T. Furthermore, this optimized technique with respiratory motion suppression enables assessment of aortic vessel wall dimensions with high reproducibility

Automatic tuning of left ventricular segmentation of MR images using genetic algorithms

Cardiac magnetic resonance imaging provides an important tool for the quantitative analysis of left ventricular function. Automatic segmentation of left ventricular endocardium allows the evaluation of its functional performance. Since automatic segmentation algorithms are sensitive to the image characteristics, we designed a self-adaptive optimization system for an automated cardiac left ventricular contour detection algorithm. A Genetic Algorithm (GA) was used as a tuning method to optimize the settings of the automated segmentation of the MR Analytical Software System (MASS) package. The performance of the tuning method was evaluated on 20 clinically obtained short-axis examinations (10 using a TrueFisp and 10 using a Gradient echo pulse sequence) comparing manually and automatically detected contours. The degree of similarity, defined as the percentage of points that are similar between two contours, was used as a quantitative measurement of the performance of the segmentation algorithm. After optimization, the average degree of similarity between automatically detected and manually drawn endocardial contours increased from 58% to 70%. Compared with the inter-observer agreement of 73%, we conclude that GA-based optimization is an effective and efficient method to increase the reliability of our automated contour detection

Optimizing the automatic segmentation of the left ventricle in magnetic resonance images

Automatic segmentation of the left ventricular (LV) myocardial borders in cardiovascular MR (CMR) images allows a significant speed-up of the procedure of quantifying LV function, and improves its reproducibility. The automated boundary delineation is usually based on a set of parameters that define the algorithms. Since the automatic segmentation algorithms are usually sensitive to the image quality and frequently depend heavily on the acquisition protocol, optimizing the parameters of the algorithm for such different protocols may be necessary to obtain optimal results. In other words, using a default set of parameters may be far from optimal for different scanners or protocols. For the MASS-software, for example, this means that a total of 14 parameters need to be optimized. This optimization is a difficult and labor-intensive process. To be able to more consistently and rapidly tune the parameters, an automated optimization system would be extremely desirable. In this paper we propose such an approach, which is based on genetic algorithms (GAs). The GA is an unsupervised iterative tool that generates new sets of parameters and converges toward an optimal set. We implemented and compared two different types of the genetic algorithms: a simple GA (SGA) and a steady state GA (2SGA). The difference between these two algorithms lies in the characteristics of the generated populations: "nonoverlapping populations" and "overlapping populations," respectively "nonoverlapping" population means that the two populations are disjoint, and "overlapping" means that the best parameters found in the previous generation are included in the present population. The performance of both algorithms was evaluated on twenty routinely obtained short-axis examinations (eleven examinations acquired with a steady-state free precession pulse sequence, and nine examinations with a gradient echo pulse sequence). The optimal parameters obtained with the GAs were used for the LV myocardial border delineation. Finally, the automatically outlined contours were compared to the gold standard—manually drawn contours by experts. The result of the comparison was expressed as a degree of similarity after a processing time of less than 72 h to a 59.5% of degree of similarity for SGA and a 66.7% of degree of similarity for 2SGA. In conclusion, genetic algorithms are very suitable to automatically tune the parameters of a border detection algorithm. Based on our data, the 2SGA was more suitable than the SGA method. This approach can be generalized to other optimization problems in medical image processing

Semi-automatic border detection method for left ventricular volume estimation in 4D ultrasound data

We propose a semi-automatic endocardial border detection method for LV volume estimation in 3D time series of cardiac ultrasound data. It is based on pattern matching and dynamic programming techniques and operates on 2D slices of the 4D data requiring minimal user-interaction. We evaluated on data acquired with the Fast Rotating Ultrasound (FRU) transducer: a linear phased array transducer rotated at high speed around its image axis, generating high quality 2D images of the heart. We automatically select a subset of 2D images at typically 10 rotation angles and 16 cardiac phases. From four manually drawn contours a 4D shape model and a 4D edge pattern model is derived. For the selected images, contour shape and edge patterns are estimated using the models. Pattern matching and dynamic programming is applied to detect the contours automatically. The method allows easy corrections in the detected 2D contours, to iteratively achieve more accurate models and improved detections. An evaluation of this method on FRU data against MRI was done for full cycle LV volumes on 10 patients. Good correlations were found against MRI volumes [r=0.94, y=0.72x + 30.3, difference of 9.6 +/- 17.4 ml (Av +/- SD)] and a low interobserver variability for US (r=0.94, y=1.11x - 16.8, difference of 1.4 +/- 14.2 ml). On average only 2.8 corrections per patient were needed (in a total of 160 images). Although the method shows good correlations with MRI without corrections, applying these corrections can make significant improvements