Pulmonary blood volume measured by contrast enhanced ultrasound: a comparison with transpulmonary thermodilution.

BACKGROUND: Blood volume quantification is essential for haemodynamic evaluation guiding fluid management in anaesthesia and intensive care practice. Ultrasound contrast agent (UCA)-dilution measured by contrast enhanced ultrasound (CEUS) can provide the UCA mean transit time (MTT) between the right and left heart, enabling the assessment of the intrathoracic blood volume (ITBV(UCA)). The purpose of the present study was to investigate the agreement between UCA-dilution using CEUS and transpulmonary thermodilution (TPTD) in vitro and in vivo. METHODS: In an in vitro setup, with variable flows and volumes, we injected a double indicator, ice-cold saline with SonoVue(®), and performed volume measurements using transesophageal echo and thermodilution by PiCCO(®). In a pilot study, we assigned 17 patients undergoing elective cardiac surgery for pulmonary blood volume (PBV) measurement using TPTD by PiCCO(®) and ITBV by UCA-dilution. Correlation coefficients and Bland-Altman analysis were performed for all volume measurements. RESULTS: In vitro, 73 experimental MTT's were obtained using PiCCO(®) and UCA-dilution. The volumes by PiCCO(®) and UCA-dilution correlated with true volumes; r(s)=0.96 (95% CI, 0.93-0.97; P<0.0001) and r(s)=0.97 (95% CI, 0.95-0.98; P<0.0001), respectively. The bias of PBV by PiCCO(®) and ITBV(UCA) were -380 ml and -42 ml, respectively. In 16 patients, 86 measurements were performed. The correlation between PBV by PiCCO(®) and ITBV(UCA) was r(s)=0.69 (95% CI 0.55-0.79; P<0.0001). Bland-Altman analysis revealed a bias of -323 ml. CONCLUSIONS: ITBV assessment with CEUS seems a promising technique for blood volume measurement, which is minimally-invasive and bedside applicable. CLINICAL TRIAL REGISTRATION: ISRCTN90330260.

Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation.

Transcatheter aortic valve implantation is a minimal-invasive intervention for implanting prosthetic valves in patients with aortic stenosis. Accurate automated sizing for planning and patient selection is expected to reduce adverse effects such as paravalvular leakage and stroke. Segmentation of the aortic root in CTA is pivotal to enable automated sizing and planning. We present a fully automated segmentation algorithm to extract the aortic root from CTA volumes consisting of a number of steps: first, the volume of interest is automatically detected, and the centerline through the ascending aorta and aortic root centerline are determined. Subsequently, high intensities due to calcifications are masked. Next, the aortic root is represented in cylindrical coordinates. Finally, the aortic root is segmented using 3D normalized cuts. The method was validated against manual delineations by calculating Dice coefficients and average distance error in 20 patients. The method successfully segmented the aortic root in all 20 cases. The mean Dice coefficient was 0.95 ± 0.03, and the mean radial absolute error was 0.74 ± 0.39 mm, where the interobserver Dice coefficient was 0.95 ± 0.03 and the mean error was 0.68 ± 0.34 mm. The proposed algorithm showed accurate results compared to manual segmentations.

A novel 3D multi-scale lineness filter for vessel detection.

The branching pattern and geometry of coronary microvessels are of high interest to understand and model the blood flow distribution and the processes of contrast invasion, ischemic changes and repair in the heart in detail. Analysis is performed on high resolution, 3D volumes of the arterial microvasculature of entire goat hearts, which are acquired with an imaging cryomicrotome. Multi-scale vessel detection is an important step required for a detailed quantitative analysis of the coronary microvasculature. Based on visual inspection, the derived lineness filter shows promising results on real data and digital phantoms, on the way towards accurate computerized reconstructions of entire coronary trees. The novel lineness filter exploits the local first and second order multi-scale derivatives in order to give an intensity-independent response to line centers and to suppress unwanted responses to steep edges.

Parametric optimization of a model-based segmentation algorithm for cardiac MR image analysis: a grid-computing approach.

In this work we present a Grid-based optimization approach performed on a set of parameters that affects both the geometric and grey-level appearance properties of a three-dimensional model-based algorithm for cardiac MRI segmentation. The search for optimal values was assessed by a Monte Carlo procedure using computational Grid technology. A series of segmentation runs were conducted on an evaluation database comprising 30 studies at two phases of the cardiac cycle (60 datasets), using three shape models constructed by different methods. For each of these model-patient combinations, six parameters were optimized in two steps: those which affect the grey-level properties of the algorithm first and those relating to the geometrical properties, secondly. Two post-processing tasks (one for each stage) collected and processed (in total) more than 70000 retrieved result files. Qualitative and quantitative validation of the fitting results indicates that the segmentation performance was greatly improved with the tuning. Based on the experienced benefits with the use of our middleware, and foreseeing the advent of large-scale tests and applications in cardiovascular imaging, we strongly believe that the use of Grid computing technology in medical image analysis constitutes a real necessity.

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.

Accurate object localization in gray level images using the center of gravity measure: accuracy versus precision.

A widely used subpixel precision estimate of an object center is the weighted center of gravity (COG). We derive three maximum-likelihood estimators for the variance of the two-dimensional (2-D) COG as a function of the noise in the image. We assume that the noise is additive, Gaussian distributed and independent between neighboring pixels. Repeated experiments using 2500 generated 2-D bell-shaped markers superimposed with an increasing amount of Gaussian noise were performed, to compare the three approximations. The error of the most exact approximative variance estimate with respect to true variance was always less than 5% of the latter. This deviation decreases with increasing signal-to-noise ratio. Our second approximation to the variance estimate performed better than the third approximation, which was originally presented by Oron et al. by up to a factor approximately 10. The difference in performance between these two approximations increased with an increasing misplacement of the window in which the COG was calculated with respect to the real COG.

Automated calibration in vascular X-ray images using the accurate localization of catheter marker bands.

RATIONALE AND OBJECTIVES: To develop a new automated calibration method for vessel measurements in vascular x-ray images. METHODS: Radiopaque marker bands mounted equidistantly on a small catheter were acquired in vitro at five image intensifier (II) sizes in x-ray projection images. The positions of the marker centers were detected by using a Hough transform and were computed at subpixel precision by using either a novel, iterative center-of-gravity approach (CGA) or a symmetry filter. Curve-fitting procedures were used to reject false-positive marker detections and to calculate intermarker distances. The calibration factor was calculated from the true marker distance and the average of the measured distances in pixels. Results were compared statistically with a grid calibration method, which was taken as the gold standard. A simulation study was performed to assess the influence of image noise on the CGA method. RESULTS: The iterative CGA method was convergent and faster than the symmetry-based technique. For four II sizes (17, 20, 25, and 31 cm), the results from the CGA method were not significantly different from the results obtained with grid calibration. For the II size of 38 cm, a significant difference (0.3% of the grid calibration factor) was found; however, this was caused by the quantification error in the image data and was not clinically relevant. In general, the performance of the CGA method improved with increasing signal-to-noise ratio. CONCLUSIONS: A practical new calibration method for small catheter sizes was developed and validated for quantitative vascular arteriography.

Vessel diameter measurements in gadolinium contrast-enhanced three-dimensional MRA of peripheral arteries.

In this study, the possibilities for quantification of vessel diameters of peripheral arteries in gadolinium contrast-enhanced magnetic resonance angiography (Gd CE MRA) were evaluated. Absolute vessel diameter measurements were assessed objectively and semi-automatically in maximum intensity projections (MIPs) of contrast-enhanced T1-weighted 3D spoiled gradient-echo datasets, studied with digital subtraction techniques. In vivo, the complete peripheral arterial bed of six patients was studied, from the aorto-iliac bifurcation down to the distal run-off. By measuring the signal intensity (SI) over the lumen of a vessel in the MIP, an SI-plot was obtained. Next, the vessel boundaries were determined using a threshold algorithm; from these boundary points individual diameter values could be obtained along the trajectory of the vessel. In an in vitro study, an optimal threshold value of 30% of the range of SI-values between the background and the maximal SI in the vessel was obtained for accurate diameter measurement in Gd CE MRA (i.e., full-width 30%-maximum). Furthermore, the relationship between the accuracy of these measurements and the scan resolution was investigated. Accuracy was found to be acceptable (i.e., less than 10% over/underestimation) for vessel sizes covering at least 3 pixels. In six patients, diameters were measured in MIPs of the total datasets (i.e., D(T)) as well as in selective MIPs of the clipped datasets (i.e., D(S)) (n = 209). D(T) and D(S) were statistically significantly correlated (p < 0.01) with a Pearson correlation coefficient rP = 0.98. Measurements in the total MIPs yielded statistically significant (p < 0.01) smaller diameter values compared with measurements in selective MIPs, with a mean difference of 0.15 mm. Diameter values from the selective MIPs of the aorto-iliac arteries were also compared with diameter values measured at corresponding anatomic positions in X-ray angiograms of these patients (i.e., D(x)) (n = 70). D(X) and D(S) were statistically significantly correlated (p < 0.01) with a Pearson correlation coefficient rP = 0.92. Diameters measured in the selective MIPs were smaller than those measured in the X-ray angiograms (mean difference 0.49 mm) and this difference was statistically significant (p < 0.01). In conclusion, diameter values can be evaluated accurately in MIPs of vessels with at least 3 pixels in diameter, using the full-width 30%-maximum criterion.