8/22/2023 0 Comments Simplify 3d change model position![]() Results show that slice thickness and detection sensitivity relationships from simulations and real data were well-matched with 99% correlation and 2% root-mean-square (RMS) error. We validated the model using real data from two independent studies: fluorescent microspheres in a pig heart and fluorescently labeled stem cells in a mouse model. ![]() This approach allows cryo-imaging operators to use larger slice thickness to expedite the scan time without significant loss of cell count. The model also suggests a correction method to compensate for missed cells in the case that image data were acquired with overly large slice thickness. The model suggests an optimal slice thickness value that provides near-ideal sensitivity while minimizing scan time. Key factors include: section thickness ( X), fluorescent cell intensity ( I fluo), effective tissue attenuation coefficient ( μ T), and a detection threshold ( T). In this study, we developed a model for detection of fluorescent cells or microspheres to aid optimal slice thickness determination. If slices are too thick, then cells can be missed. ![]() However, if slices are too thin, there will be data overload and excessive scan times. Sequential slice-by-slice fluorescent imaging enables detection of fluorescent cells or microspheres for corresponding quantification of their distribution in tissue. ![]() Cryo-imaging has been effectively used to study the biodistribution of fluorescent cells or microspheres in animal models. ![]()
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