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This research aims to perform labeling using probabilistic atlases that model spatial vessel and label likelihoods. A cerebral vessel tree is aligned to a probabilistic atlas and subsequently each vessel is labeled by computing the maximum label likelihood per segment from label-specific atlases.
The proposed method was validated on 25 segmented cerebral vessel trees. Labeling accuracies were close to 100% for large vessels, but dropped to 50-60% for small vessels that were only present in less than 50% of the set.
With this work we showed that using solely spatial information of the vessel labels, vessel segments from stable vessels (>50% presence) were reliably classified. This spatial information will form the basis for a future labeling strategy with a very loose topological model.
Fluorescence lifetime imaging to differentiate bound from unbound ICG-cRGD both in vitro and in vivo
In this analysis hemoglobin content between tumor tissue and healthy tissue of the same breast is compared on all four monitoring time points. Furthermore, the predictive power of the tumor-healthy tissue difference of HbO2 for non-responder prediction is assessed.
The difference in HbO2 content between tumor and healthy tissue was statistically significantly higher in responding tumors than in non-responding tumors at baseline (10.88 vs -0.57 μM, P=0.014) and after one cycle of chemotherapy (6.45 vs -1.31 μM, P=0.048). Before surgery this difference had diminished. In the data of this study, classification on the HbO2 difference between tumor and healthy tissue was able to predict tumor (non-)response at baseline and after 1 cycle with an area-under-curve of 0.95 and 0.88, respectively.
While this result suggests that tumor response can be predicted before chemotherapy onset, one should be very careful with interpreting these results. A larger patient population is needed to confirm this finding.
Model driven quantification of left ventricular function from sparse single-beat 3D echocardiography
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