Vascular Tree Reconstruction by Minimizing A Physiological Functional Cost

Simulated annealing approach to vascular structure with application to the coronary arteries

Do the complex processes of angiogenesis during organism development ultimately lead to a near optimal coronary vasculature in the organs of adult mammals? We examine this hypothesis using a powerful and universal method, built on physical and physiological principles, for the determination of globally energetically optimal arterial trees. The method is based on simulated annealing, and can be used to examine arteries in hollow organs with arbitrary tissue geometries. We demonstrate that the approach can generate in silico vasculatures which closely match porcine anatomical data for the coronary arteries on all length scales, and that the optimized arterial trees improve systematically as computational time increases. The method presented here is general, and could in principle be used to examine the arteries of other organs. Potential applications include improvement of medical imaging analysis and the design of vascular trees for artificial organs.

Efficient Monte Carlo image analysis for the location of vascular entity

Tubular shaped networks appear not only in medical images like X-ray-, time-of-flight MRI- or CT-angiograms but also in microscopic images of neuronal networks. We present EMILOVE (Efficient Monte-carlo Image-analysis for the Location Of Vascular Entity), a novel modeling algorithm for tubular networks in biomedical images. The model is constructed using tablet shaped particles and edges connecting them. The particles encode the intrinsic information of tubular structure, including position, scale and orientation. The edges connecting the particles determine the topology of the networks. For simulated data, EMILOVE was able to accurately extract the tubular network. EMILOVE showed high performance in real data as well; it successfully modeled vascular networks in real cerebral X-ray and time-of-flight MRI angiograms. We also show some promising, preliminary results on microscopic images of neurons.

NO triggers RGS4 degradation to coordinate angiogenesis and cardiomyocyte growth

Myocardial hypertrophy is an adaptation to increased hemodynamic demands. An increase in heart tissue must be matched by a corresponding expansion of the coronary vasculature to maintain and adequate supply of oxygen and nutrients for the heart. The physiological mechanisms that underlie the coordination of angiogenesis and cardiomyocyte growth are unknown. We report that induction of myocardial angiogenesis promotes cardiomyocyte growth and cardiac hypertrophy through a novel NO-dependent mechanism. We used transgenic, conditional overexpression of placental growth factor (PlGF) in murine cardiac tissues to stimulate myocardial angiogenesis and increase endothelial-derived NO release. NO production, in turn, induced myocardial hypertrophy by promoting proteasomal degradation of regulator of G protein signaling type 4 (RGS4), thus relieving the repression of the Gβγ/PI3Kγ/AKT/mTORC1 pathway that stimulates cardiomyocyte growth. This hypertrophic response was prevented by concomitant transgenic expression of RGS4 in cardiomyocytes. NOS inhibitor L-NAME also significantly attenuated RGS4 degradation, and reduced activation of AKT/mTORC1 signaling and induction of myocardial hypertrophy in PlGF transgenic mice, while conditional cardiac-specific PlGF expression in eNOS knockout mice did not induce myocardial hypertrophy. These findings describe a novel NO/RGS4/Gβγ/PI3Kγ/AKT mechanism that couples cardiac vessel growth with myocyte growth and heart size.

Vessel connectivity using Murray's hypothesis

We describe a new method for vascular image analysis that incorporates a generic physiological principle to estimate vessel connectivity, which is a key issue in reconstructing complete vascular trees from image data. We follow Murray's hypothesis of the minimum work principle to formulate the problem as an optimization problem. This principle reflects a global property of any vascular network, in contrast to various local geometric properties adopted as constraints previously. We demonstrate the effectiveness of our method using a set of microCT mouse coronary images. It is shown that the performance of our method has a statistically significant improvement over the widely adopted minimum spanning tree methods that rely on local geometric constraints.