Pancreatic Ductal Adenocarcinoma (PDAC) is the third leading cause of cancer-related death in the United States, and is characterized with dismal odds due to advanced disease at late stage diagnosis. The 5-year survival rate ranges from 7% for patients with non-resectable disease (~80% of patients), to ~23% for patients with resectable tumors (< 20%of patients). PDAC, like most solid tumors, is highly hypoxic, resulting from both poor oxygen delivery due to leaky and chaotic vasculature and increased oxygen consumption due to high metabolic activity. This makes drug delivery to the tumor microenvironment more difficult and often ineffective. In the past 40 years, only marginal improvements in complete response (CR), progression free survival (PFS), overall survival (OS), and quality of life (QoL) have occurred despite the development of a number of promising chemotherapy, or chemoradiation regimens. New and improved therapeutic options are urgently needed to improve the treatment of this disease. Mild hyperthermia (HT) in the range of 39-42C has shown to improve blood flow to tumors, enhancing the delivery of therapeutics to the hypoxic tumor microenvironment. Numerous randomized clinical trials have shown to provide CR, PFS, OR and QoL benefits but implementation in clinical practice has been lagging due to the inability to provide safe, quantifiable and controlled therapeutic thermal dose through accurate temperature monitoring. NTO has designed and built the VectronTTx device which can effectively deliver heat non-invasively in deep tissue without causing skin burns or excessive fat heating (the most common adverse effect encountered with current technologies) without the need for a cooling mechanism such as a water bolus that is a frequent cause for interrupted treatments due to patient discomfort. The heating elements of the device are integrated into an applicator with magnetic resonance (MR) transmit and receive channels to allow for real-time MR thermometry imaging (MRTI) measurements. In this Phase I proposal, we will develop novel MRTI methodologies that significantly improve the state-of-the-art by accurately measuring temperature over HT treatment time scales (30-60 minutes) in an abdominal environment compensating for interfering processes including field drift and motion-induced field variations. We will also develop a treatment- modeling architecture that combines virtual models, patient-specific features, and an artificial intelligence (AI)- based architecture to create extrapolated patient-specific treatment plans. Upon successful completion of Phase I and validation of the developed real-time MRTI monitoring techniques, we will develop methods in Phase II for accurate measurements of temperature in fat by incorporating interleaved or hybrid fat-based techniques into the field-corrected MRTI sequences. In phase II, we will also develop and implement the infrastructure to perform personalized treatment planning. This treatment planning will be based on conventional image transformations, conventional EM-solving, and AI learning of thermal sink and tissue properties.