Background parenchymal enhancement (BPE) observed in breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been identified as an important biomarker associated with risk for developing breast cancer. In this study, we present a fully automated framework for quantification of BPE. We initially segmented fibroglandular tissue (FGT) of the breasts using an improved version of an existing method. Subsequently, we computed BPEabs (volume of the enhancing tissue), BPErf (BPEabs divided by FGT volume) and BPErb (BPEabs divided by breast volume), using different relative enhancement threshold values between 1% and 100%. To evaluate and compare the previous and improved FGT segmentation methods, we used 20 breast DCE-MRI scans and we computed Dice similarity coefficient (DSC) values with respect to manual segmentations. For evaluation of the BPE quantification, we used a dataset of 95 breast DCE-MRI scans. Two radiologists, in individual reading sessions, visually analyzed the dataset and categorized each breast into minimal, mild, moderate and marked BPE. To measure the correlation between automated BPE values to the radiologists' assessments, we converted these values into ordinal categories and we used Spearman's rho as a measure of correlation. According to our results, the new segmentation method obtained an average DSC of 0.81 0.09, which was significantly higher (p<0.001) compared to the previous method (0.76 0.10). The highest correlation values between automated BPE categories and radiologists' assessments were obtained with the BPErf measurement (r=0.55, r=0.49, p<0.001 for both), while the correlation between the scores given by the two radiologists was 0.82 (p<0.001). The presented framework can be used to systematically investigate the correlation between BPE and risk in large screening cohorts.
One of the remaining challenges in functional connectivity (FC) studies is investigation of the temporal variability of FC networks. Recent studies focusing on the dynamic FC mostly use functional magnetic resonance imaging as an imaging tool to investigate the temporal variability of FC. We attempted to quantify this variability via analyzing the functional near-infrared spectroscopy (fNIRS) signals, which were recorded from the prefrontal cortex (PFC) of 12 healthy subjects during a Stroop test. Mutual information was used as a metric to determine functional connectivity between PFC regions. Two-dimensional correlation based similarity measure was used as a method to analyze within-subject and intersubject consistency of FC maps and how they change in time. We found that within-subject consistency (0.61±0.09) is higher than intersubject consistency (0.28±0.13). Within-subject consistency was not found to be task-specific. Results also revealed that there is a gradual change in FC patterns during a Stroop session for congruent and neutral conditions, where there is no such trend in the presence of an interference effect. In conclusion, we have demonstrated the between-subject, within-subject, and temporal variability of FC and the feasibility of using fNIRS for studying dynamic FC.