KPC (KrasG12D:Trp53R172H:Pdx1-Cre) and CKS (KrasG12D:Smad4L/L:Ptf1a-Cre) mice are genetically engineered mouse (GEM) models that capture features of human pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasms (IPMN), respectively. We compared these autochthonous tumors using quantitative imaging metrics from diffusion-weighted MRI (DW-MRI) and dynamic contrast enhanced (DCE)-MRI in reference to quantitative histological metrics including cell density, fibrosis, and microvasculature density. Our results revealed distinct DW-MRI metrics between the KPC vs. CKS model (mimicking human PDAC vs. IPMN lesion): the apparent diffusion coefficient (ADC) of CKS tumors is significantly higher than that of KPC, with little overlap (mean ± SD 2.24±0.2 vs. 1.66±0.2, p<10−10) despite intratumor and intertumor variability. Kurtosis index (KI) is also distinctively separated in the two models. DW imaging metrics are consistent with growth pattern, cell density, and the cystic nature of the CKS tumors. Coregistration of ex vivo ADC maps with H&E-stained sections allowed for regional comparison and showed a correlation between local cell density and ADC value. In conclusion, studies in GEM models demonstrate the potential utility of diffusion-weighted MRI metrics for distinguishing pancreatic cancer from benign pancreatic cysts such as IPMN.

Application of quantitative dynamic contrast-enhanced (DCE) MRI in mouse models of abdominal cancer is challenging due to the effects of RF inhomogeneity, image corruption from rapid respiratory motion and the need for high spatial and temporal resolutions. Here we demonstrate a DCE protocol optimized for such applications. The method consists of three acquisitions: (1) actual flip-angle B1 mapping, (2) variable flip-angle T1 mapping and (3) acquisition of the DCE series using a motion-robust radial strategy with k-space weighted image contrast (KWIC) reconstruction. All three acquisitions employ spoiled radial imaging with stack-of-stars sampling (SoS) and golden-angle increments between the views. This scheme is shown to minimize artifacts due to respiratory motion while simultaneously facilitating view-sharing image reconstruction for the dynamic series. The method is demonstrated in a genetically engineered mouse model of pancreatic ductal adenocarcinoma and yielded mean perfusion parameters of Ktrans = 0.23 ± 0.14 min−1 and ve = 0.31 ± 0.17 (n = 22) over a wide range of tumor sizes. The SoS-sampled DCE method is shown to produce artifact-free images with good SNR leading to robust estimation of DCE parameters.

Intraductal Papillary Mucinous Neoplasms (IPMN) are recognized as important precursors to invasive pancreatic ductal adenocarcinoma (PDAC). While IPMN requires surveillance without treatment, a clinical marker is lacking which can identify those undergoing malignant transformation. In two genetic engineered mouse models (KPC and CKS), which resemble human PDAC and IPMN, respectively, we tested the hypothesis that differences in cellular architecture and stromal features between PDAC and IPMN present themselves in DW-MRI and /or DCE-MRI metrics. Our data revealed an almost complete separation of ADC values between CKS (benign) vs. KPC (malignant) tumors and identified histopathological features corroborating the imaging metrics.

Dynamic contrast enhanced MRI data in the abdomens of small animal models is often corrupted due to the effects respiratory and peristaltic motion. Here a DCE protocol that employs stack of stars sampling throughout was implemented and was shown to be robust with respect to motion artifacts. The sampling scheme also facilitates image reconstruction methods that employ view sharing, notably KWIC, yielding images with high temporal and spatial resolution. The protocol was demonstrated in an orthotopic murine model of pancreatic cancer. The resulting data were analyzed using the reference tissue method and provided high quality Ktrans and ve parameter maps.