Visualization, classification and quantification of coronary atherosclerotic plaque using CT soft- and hardware phantom models

Een van de oorzaken van hartfalen is dat de kransslagaders onvoldoende bloed naar de hartspier voeren. Deze problematiek wordt doorgaans met een ‘coronaire angiografie’ in beeld gebracht. Dit is een techniek waarbij bloedvaten en hartkamers met behulp van contrastvloeistof worden afgebeeld met röntgendoorlichting. Uit het onderzoek van Wisnumurti Kristanto blijkt dat in plaats van een angiografie wellicht ook een CT-scan kan worden gebruikt. Dit zou minder belastend zijn voor de patiënt. Met de multi-detector computed tomograaf (MDCT) kan de vaatwijdte en de hoeveelheid kalk in de wand van de kransslagader uitstekend gekwantificeerd worden. MDCT is een krachtig middel voor het onderzoeken van de kransslagaderen, zo stelt Kristanto vast. Wel is er nog ruimte voor verbetering. Met name op het gebied van het visualiseren van vroege stadia van vaatverkalking (plaques) heeft MDCT tekortkomingen die moeten worden opgelost voordat de techniek ook hiervoor in de zorgpraktijk kan worden toegepast. Er is echter aangetoond dat een aantal problemen verminderd en vermeden kan worden met behulp van geavanceerde beeldbewerkingstechnieken. De inzichten die dit onderzoek heeft opgeleverd, dragen bij aan de verbetering van de vroegtijdige, niet-invasieve opsporing van coronaire hartziekten met behulp van MDCT

Quantitative image analysis for the detection of motion artefacts in coronary artery computed tomography

Multi detector-row CT (MDCT), the current preferred method for coronary artery disease assessment, is still affected by motion artefacts. To rule out motion artefacts, qualitative image analysis is usually performed. Our study aimed to develop a quantitative image analysis for motion artefacts detection as an added value to the qualitative analysis. An anthropomorphic moving heart phantom with adjustable heart-rate was scanned on 64-MDCT and dual-source-CT. A new software technique was developed which detected motion artefacts in the coronaries and also in the myocardium, where motion artefacts are more apparent; with direct association to the qualitative analysis. The new quantitative analysis managed to detect motion artefacts in phantom scans and relate them to artefact-induced vessel stenoses. Quantifying these artefacts at corresponding locations in the myocardium, artefact-induced vessel stenosis findings could be avoided. In conclusion, the quantitative analysis together with the qualitative analysis rules out artefact-induced stenosis

Evaluation of a Semi-automatic Right Ventricle Segmentation Method on Short-Axis MR Images

The purpose of this study was to evaluate a semi-automatic right ventricle segmentation method on short-axis cardiac cine MR images which segment all right ventricle contours in a cardiac phase using one seed contour. Twenty-eight consecutive short-axis, four-chamber, and tricuspid valve view cardiac cine MRI examinations of healthy volunteers were used. Two independent observers performed the manual and automatic segmentations of the right ventricles. Analyses were based on the ventricular volume and ejection fraction of the right heart chamber. Reproducibility of the manual and semi-automatic segmentations was assessed using intra- and inter-observer variability. Validity of the semi-automatic segmentations was analyzed with reference to the manual segmentations. The inter- and intra-observer variability of manual segmentations were between 0.8 and 3.2%. The semi-automatic segmentations were highly correlated with the manual segmentations (R2 0.79–0.98), with median difference of 0.9–4.8% and of 3.3% for volume and ejection fraction parameters, respectively. In comparison to the manual segmentation, the semi-automatic segmentation produced contours with median dice metrics of 0.95 and 0.87 and median Hausdorff distance of 5.05 and 7.35 mm for contours at end-diastolic and end-systolic phases, respectively. The inter- and intra-observer variability of the semi-automatic segmentations were lower than observed in the manual segmentations. Both manual and semi-automatic segmentations performed better at the end-diastolic phase than at the end-systolic phase. The investigated semi-automatic segmentation method managed to produce a valid and reproducible alternative to manual right ventricle segmentation

Correction of lumen contrast-enhancement influence on non-calcified coronary atherosclerotic plaque quantification on CT

Lumen contrast-enhancement influences non-calcified atherosclerotic plaque Hounsfield-unit (HU) values in computed tomography (CT). This study aimed to construct and validate an algorithm to correct for this influence. Three coronary vessel phantoms with 1, 2, and 4 mm circular hollow lumina; with normal and plaque-infested walls were scanned simultaneously in oil using a dual-source CT scanner. Scanning was repeated as the lumina were alternately filled with water and four contrast solutions (100-400 HU, at 100 HU intervals). Images were reconstructed at 0.4 mm x-y pixel size. Pixel-by-pixel comparisons of contrast-enhanced and non-contrast-enhanced images confirmed exponential declining patterns in lumen contrast-enhancement influence on wall HU-values from the lumen border (y = Ae(-lambda x) + c). The median difference of the inside and outside 2-pixel radius part of the contrast-enhanced coronary phantom wall to the reference (non-contrast-enhanced images) was 45 and 2 HU, respectively. Based on the lumen contrast-enhancement influence patterns, a generalized correction algorithm was formulated. Application of the generalized correction algorithm to the inside 2-pixel radius part of the wall reduced the median difference to the reference to 4 HU. In conclusion, lumen contrast-enhancement influence on the vessel wall can be defined by an exponential approximation, allowing correction of the CT density of the vessel wall closest to the lumen. With this correction, a more accurate determination of vessel wall composition can be made

Hierarchical classification of HU-criteria for lipid-rich plaques.

<p>Hierarchical classification of HU-criteria for lipid-rich plaques.</p

Hierarchical classification of HU-criteria for fibrous plaques.

<p>Hierarchical classification of HU-criteria for fibrous plaques.</p