Improving the reproducibility of MR-derived left ventricular volume and function measurements with a semi-automatic threshold-based segmentation algorithm

Abstract

To validate a novel semi-automatic segmentation algorithm for MR-derived volume and function measurements by comparing it with the standard method of manual contour tracing. The new algorithms excludes papillary muscles and trabeculae from the blood pool, while the manual approach includes these objects in the blood pool. An epicardial contour served as input for both methods. Multiphase 2D steady-state free precession short axis images were acquired in 12 subjects with normal heart function and in a dynamic anthropomorphic heart phantom on a 1.5 T MR system. In the heart phantom, manually and semi-automatically measured cardiac parameters were compared to the true end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF). In the subjects, the semi-automatic method was compared to manual contouring in terms of difference in measured EDV, ESV, EF and myocardial volume (MV). For all measures, intra- and inter-observer agreement was determined. In the heart phantom, EDV and ESV were underestimated for both the semi-automatic. As the papillary muscles were excluded from the blood pool with the semi-automatic method, EDV and ESV were approximately 20 ml lower in the patients, whereas EF was approximately 16 % higher. Intra- and inter-observer agreement was overall improved with the semi-automatic method compared to the manual method. Correlation between manual and semi-automatic measurements was high (EDV: R = 0.99, ESV: R = 0.96; EF: R = 0.80, MV: R = 0.99). The semi-automatic method could exclude endoluminal muscular structures from the blood volume with significantly improved intra- and inter-observer variabilities in cardiac function measurements compared to the conventional, manual method, which includes endoluminal structures in the blood volume.

Clinical feasibility of a myocardial signal intensity threshold-based semi-automated cardiac magnetic resonance segmentation method

“…Epicardial contours were traced by automated contour detection and manually adjusted if necessary. A thresholding algorithm (MassK, Medis) was used to discriminate between LV chamber blood and myocardium without the use of enodocardial contours [7]. This algorithm estimates the signal intensity (SI) of blood and myocardium and calculates a blood percentage (BP) value for any given pixel using the following equation:…”

“…The goal of our study was to evaluate the accuracy and efficiency of a commercially available, threshold-based, semi-automated method [7] for LV functional measurement in comparison to conventional manual contour-based segmentation, with aortic flow measurements as a reference given the lack of a non-MRI gold standard.…”

A jobbkamra-funkció echokardiográfiás és szív MR-paramétereinek összehasonlítása COPD-ben

“…Így célul tűztük ki a jobb kamrai üregmére-tek és funkciók vizsgálatát echokardiográfi ával és szív MR-rel, valamint a kapott eredmények összehasonlítá-sát COPD-ben. Az utóbbi években elterjedt módszernek köszönhető-en kétféle elemzési technika használható a vizsgálati eredmények értékelésére (19), így célunk volt annak megítélése is, hogy a hagyományos, manuális kontúro-zással kapott volumen-és funkcióeredményeket meny- …”

Cardiovascular imaging 2013 in the International Journal of Cardiovascular Imaging

“…Coronary microemboli were found to have an acute impact on LV function. The reproducibility of CMR derived LV volume and function measurements were improved using a semi-automatic threshold-based segmentation algorithm [36]. In a confirmatory study of highly trained asymptomatic male endurance athletes, myocardial mass was increased in participates having ECG abnormalities independent of late gadolinium enhancement (LGE) [37].…”