Intraductal papillary mucinous neoplasm (IPMN) is best defined by mucin-secreting cell intraductal proliferation found in the main pancreatic duct or its branches, which often can progress into dysplasia and invasive carcinomas. Utilization of imaging modalities are critical to treatment as evaluating malignancy risk is crucial for treatment; however, detection accuracy and lesion conspicuity are reduced due to poor image quality. A deep learning model was developed by the researchers to assess pancreatic IPMNs by utilizing 162 patient results as determined by surgical specimens or biopsies. Their developed technique was found to significantly improve both overall image quality and cystic lesion conspicuity as compared to standard reconstruction models. The model also had a high overall diagnostic accuracy for predicting malignant IPMN, finding the highest individual diagnostic accuracy for mural nodules and main pancreatic duct size, indicative of better assessment. These shorter acquisition times allow for a wide range of clinical benefits as compared to current diagnostic tools for cancer.