Morphological image processing for multiscale analysis of superresolution ultrasound images of tissue microvascular networks

摘要

Diabetes is a major disease and known to impair microvascular recruitment due to insulin resistance. Previousquantifications of the changes in microvascular networks at the capillary level were being performed with either full ormanually selected region-of-interests (ROIs) from super-resolution ultrasound (SR-US) images. However, theseapproaches were imprecise, time-consuming, and unsuitable for automated processes. Here we provided a customsoftware solution for automated multiscale analysis of SR-US images of tissue microvascularity patterns. An AcusonSequoia 512 ultrasound (US) scanner equipped with a 15L8-S linear array transducer was used in a nonlinear imagingmode to collect all data. C57BL/6J male mice fed standard chow and studied at age 13-16 wk comprised the lean group(N = 14), and 24-31 wk-old mice who received a high-fat diet provided the obese group (N = 8). After administration ofa microbubble (MB) contrast agent, the proximal hindlimb adductor muscle of each animal was imaged (dynamiccontrast-enhanced US, DCE-US) for 10 min at baseline and again at 1 h and towards the end of a 2 h hyperinsulinemiceuglycemicclamp. Vascular structures were enhanced with a multiscale vessel enhancement filter and binary vesselsegments were delineated using Otsu’s global threshold method. We then computed vessel diameters by employingmorphological image processing methods for quantitative analysis. Our custom software enabled automated multiscaleimage examination by defining a diameter threshold to limit the analysis at the capillary level. Longitudinal changes inAUC, I_(PK), and MVD were significant for lean group (p < 0.02 using Full-ROI and p < 0.01 using 150 μm-ROI) and forobese group (p < 0.02 using Full-ROI, p < 0.03 using 150 μm-ROI). By eliminating large vessels from the ROI (above150 μm in diameter), perfusion parameters were more sensitive to changes exhibited by the smaller vessels, that areknown to be more impacted by disease and treatment.