Purpose: Pancreatic ductal adenocarcinoma is forecast to become the second most significant cause of cancer mortality as the number of patients with cancer in the main duct of the pancreas grows, and measurement of the pancreatic duct diameter from medical images has been identified as relevant for its early diagnosis.
Approach: We propose an automated pancreatic duct centerline tracing method from computed tomography (CT) images that is based on deep reinforcement learning, which employs an artificial agent to interact with the environment and calculates rewards by combining the distances from the target and the centerline. A deep neural network is implemented to forecast step-wise values for each potential action.
The outstanding capabilities of modern Positron Emission Tomography (PET) to highlight small tumor lesions and provide pathological function assessment are at peril from image quality degradation caused by respiratory and cardiac motion. However, the advent of the long axial field-of-view (LAFOV) scanners with increased sensitivity, alongside the precise time-of-flight (TOF) of modern PET systems, enables the acquisition of ultrafast time resolution images, which can be used for estimating and correcting the cyclic motion. 0.
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October 2012
We tested the claim that inter-individual CBF variability in Alzheimer's disease (AD) is substantially reduced after correction for arterial carbon dioxide tension (PaCO(2)). Specifically, we tested whether the variability of CBF in brain of patients with AD differed significantly from brain of age-matched healthy control subjects (HC). To eliminate the CO(2)-induced variability, we developed a novel and generally applicable approach to the correction of CBF for changes of PaCO(2) and applied the method to positron emission tomographic (PET) measures of CBF in AD and HC groups of subjects.
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