This paper presents a dynamical appearance model based on sparse representation and dictionary learning for tracking both endocardial and epicardial contours of the left ventricle in echocardiographic sequences. Instead of learning offline spatiotemporal priors from databases, we exploit the inherent spatiotemporal coherence of individual data to constraint cardiac contour estimation. The contour tracker is initialized with a manual tracing of the first frame. It employs multiscale sparse representation of local image appearance and learns online multiscale appearance dictionaries in a boosting framework as the image sequence is segmented frame-by-frame sequentially. The weights of multiscale appearance dictionaries are optimized automatically. Our region-based level set segmentation integrates a spectrum of complementary multilevel information including intensity, multiscale local appearance, and dynamical shape prediction. The approach is validated on twenty-six 4D canine echocardiographic images acquired from both healthy and post-infarct canines. The segmentation results agree well with expert manual tracings. The ejection fraction estimates also show good agreement with manual results. Advantages of our approach are demonstrated by comparisons with a conventional pure intensity model, a registration-based contour tracker, and a state-of-the-art database-dependent offline dynamical shape model. We also demonstrate the feasibility of clinical application by applying the method to four 4D human data sets.

Despite advanced cardiomyopathy, SVR determines left ventricular volume reduction and improved systolic function. Baseline absent-to-moderate mitral regurgitation and a more spherical left ventricular geometry predict a less favourable clinical and functional outcome, suggesting a possible rationale for wider indications for combined correction of 2+ mitral regurgitation and undersizing of the mitral annulus, particularly in patients with sphericity index > or =0.75.

Despite a decline in the last three decades, postinfarction ventricular free wall rupture still complicates more than 3% of acute ST-elevation myocardial infarctions and remains a surgical challenge. TachoSil (Nycomed, Zurich, Switzerland) is an equine collagen patch coated with human fibrinogen and human thrombin, which has recently been used for haemostasis in cardiovascular surgery, but its potential usefulness in free wall rupture has not been reported. Initial clinical experience with an on-pump sutureless technique without cardioplegia, using wide TachoSil patching to achieve free wall rupture repair, has been described.

Cryoballoon ablation is safe and feasible under RT 3D TEE guidance. This imaging tool permits perfect visualization of all PV ostia and neighbouring LA structures. Most importantly, it proved very efficient in guiding the operator to achieve complete occlusion and successful isolation in all veins.

Cardiovascular progenitor cells (CPCs) expressing the ISL1-LIM–homeodomain transcription factor contribute developmentally to cardiomyocytes in all 4 chambers of the heart. Here, we show that ISL1-CPCs can be applied to myocardial regeneration following injury. We used a rapid 3D methylcellulose approach to form murine and human ISL1-CPC spheroids that engrafted after myocardial infarction in murine hearts, where they differentiated into cardiomyocytes and endothelial cells, integrating into the myocardium and forming new blood vessels. ISL1-CPC spheroid–treated mice exhibited reduced infarct area and increased blood vessel formation compared with control animals. Moreover, left ventricular (LV) contractile function was significantly better in mice transplanted with ISL1-CPCs 4 weeks after injury than that in control animals. These results provide proof-of-concept of a cardiac repair strategy employing ISL1-CPCs that, based on our previous lineage-tracing studies, are committed to forming heart tissue, in combination with a robust methylcellulose spheroid–based delivery approach.

Combination of undersized mitral annuloplasty and coronary revascularization presents low operative mortality and determines left ventricular unloading in patients with intermediate-degree ischemic mitral regurgitation. Global and regional wall motion are powerful predictors of late outcome. Stiffer mitral annular repair promotes functional recovery and predicts higher probability and earlier timing of reverse remodeling.

A case of isolated ventricular non-compaction associated to three-vessel disease and a mitral regurgitation is described. The patient underwent triple coronary artery bypass graft and restrictive mitral annuloplasty. The postoperative course was unsuccessful despite the very depressed left ventricular (LV) function. At two years follow-up, no major adverse cardiac event has occurred and the LV function was slightly improved.

Dictionary learning has been shown to be effective in exploiting spatiotemporal coherence for echocardiographic segmentation. To overcome the limitations of previous methods, we present a stochastic online dictionary learning approach for segmenting left ventricular borders from 4D echocardiography. It is based on stochastic approximations and processes a mini-batch of samples at a time, which results in lower memory consumption and lower computational cost than classical batch algorithms. In contrast to the previous methods, where dictionaries and their weights are optimized only on the most recently segmented frame, our stochastic online learning procedure optimizes the dictionaries and the corresponding weights by aggregating all the past information while adapting them to the dynamically changing data. The rate of updating the past information is controlled and varied according to the appearance scale to seek a balance between old and new information. Results on 26 4D echocardiographic images show the proposed method is more accurate, more robust, and faster than the previous batch algorithm.