Enhancing pancreatic adenocarcinoma delineation in diffusion derived intravoxel incoherent motion f-maps through automatic vessel and duct segmentation.

Diffusion-based intravoxel incoherent motion imaging has recently gained interest as a method to detect and characterize pancreatic lesions, especially as it could provide a radiation- and contrast agent-free alternative to existing diagnostic methods. However, tumor delineation on intravoxel incoherent motion-derived parameter maps is impeded by poor lesion-to-pancreatic duct contrast in the f-maps and poor lesion-to-vessel contrast in the D-maps. The distribution of the diffusion and perfusion parameters within vessels, ducts, and tumors were extracted from a group of 42 patients with pancreatic adenocarcinoma. Clearly separable combinations of f and D were observed, and receiver operating characteristic analysis was used to determine the optimal cutoff values for an automated segmentation of vessels and ducts to improve lesion detection and delineation on the individual intravoxel incoherent motion-derived maps. Receiver operating characteristic analysis identified f = 0.28 as the cutoff for vessels (Area under the curve (AUC) = 0.901) versus tumor/duct and D = 1.85 μm(2) /ms for separating duct from tumor tissue (AUC = 0.988). These values were incorporated in an automatic segmentation algorithm and then applied to 42 patients. This yielded clearly improved tumor delineation compared to individual intravoxel incoherent motion-derived maps. Furthermore, previous findings that indicated that the f value in pancreatic cancer is strongly reduced compared to healthy pancreatic tissue were reconfirmed.