Recent advances in optical imaging systems and systemically administered fluorescent probes have significantly improved the ways by which we can visualize proteomics in vivo. A key component in the design of fluorescent probes is a favorable biodistribution, i.e., localization only in the targeted diseased tissue, in order to achieve high contrast and good detection characteristics. In practice, however, there is always some level of background fluorescence present that could result in distorted or obscured visualization and quantification of measured signals. In this study we observe the effects of background fluorescence in tomographic imaging. We demonstrate that increasing levels of background fluorescence result in artifacts when using a linear perturbation algorithm, along with a significant loss of image fidelity and quantification accuracy. To correct for effects of background fluorescence, we have applied cubic polynomial fits to bulk raw measurements obtained from spatially homogeneous and heterogeneous phantoms. We show that subtraction of the average fluorescence response from the raw data before reconstruction can improve image quality and quantification accuracy as shown in relatively homogeneous or heterogeneous phantoms. Subtraction methods thus appear to be a promising route for adaptively correcting nonspecific background fluorochrome distribution.
The purpose of this study is to identify the difference in nodule characteristics manifested on computed topography (CT) and X-ray images and to evaluate the ability of radiographic features to differentiate between benign and malignant nodules, when compared to the features extracted from CT. We collected 79 consecutive computed radiographic (CR) chest images with one or more CT-documented lung nodules. Upon viewing the CT slices, corresponding nodules were localized on CR images by an experienced chest radiologist. Of the 79 CT nodules (19 benign, 60 malignant), 61 (14 benign, 47 malignant) were considered to be definitely visible on the CR, and the rest were considered to be invisible or did not qualify for distinct feature assessment. Eleven nodule features each were visually extracted from CT and CR images. These features were used to characterize the nodule in terms of size, shape, lobulation, spiculation, density, etc. Correlation between the CT and CR features was calculated for the 61 definitely CR-visible nodules. Receiver operating characteristics (ROC) analysis was performed to evaluate the ability of these features in the task of differentiating between benign and malignant nodules. Results showed that CR and CT images agreed well in characterizing nodules in terms of shape, lobulation, spiculation and density features. We found that 40-50%of the cases had same CR and CT ratings and 41-51% of cases were rated by a difference of one between their ratings on CT and CR for shape (3-point scale), lobulation (4-point scale) and speculation (4-point scale) features. Ninety-two percent of the cases had same CT and CR ratings on the density feature. Size yielded a correlation coefficient of 0.84. In the task of differentiating between benign and malignant lung nodules, ROC analysis of individual features yielded an A z value ranging from 0.52 to 0.77 for the 14 CT features and from 0.52 to 0.75 for the CR features. In addition, we examined the characteristics of the 18 nodules that were excluded from feature analysis. On average, these 18 nodules were smaller in size (15.2 mm measured from CT) than the 61 CR-visible nodules (23.5 mm). We found that CR features agreed reasonably well with CT features and their ability to differentiate between benign and malignant nodules were similar to that of the CT features.
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