sensitivity uniformity ratio as a new index to optimize the scanning geometry for fluorescent molecular tomography

Background: molecular fluorescence imaging is widely used as a noninvasive method to study thecellular and molecular mechanisms. in the optical imaging system, the sensitivity is the change ofthe intensity received by the detector for the changed optical characteristics (fluorescence) in eachsample point. sensitivity could be considered as a function of imaging geometry. a favor imagingsystem has a uniform and high‑sensitivity coefficient for each point of the sample. in this study, anew parameter was proposed which the optimal angle between the source and detector could bedetermined based on this parameter. methods: for evaluation of the new method, a two‑dimensionalmesh with a radius of 20 mm and 5133 nodes was built. in each reconstruction, 0.5‑mm fluorescenceheterogeneity with a contrast‑to‑purpose ratio of fluorescence yield of 10 was randomly added atdifferent points of the sample. the source and the detector were simulated in different geometricconditions. the calculations were performed using the nirfast and matlab software. therelationship between mean squared error (mse) and sensitivity uniformity ratio (sur) was evaluatedusing the correlation coefficient. finally, based on the new index, an optimal geometrical strategywas introduced. results: there was a negative correlation coefficient (r = −0.78) with the inverserelationship between the sur and mse indices. the reconstructed images showed that the betterimage quality achieved using the optimal geometry for all scanning depths. for the conventionalgeometry, there is an artifact in the opposite side of the inhomogeneity at the shallow depths, whichhas been eliminated in the reconstructed images achieved using the optimal geometry. conclusion:the sur is a powerful computational tool which could be used to determine the optimal anglesbetween the source and detector for each scanning depth.