Abstract:Purpose:
We hypothesize that MRI texture‐based tumor outcome prediction models could be optimized via numerical simulations of image acquisitions. These simulations require knowledge of T1 and T2 relaxation times as inputs. The goal of this study is to evaluate the feasibility of using machine learning techniques to infer T1 and T2 tumor maps with accurate texture preservation for simulation inputs from clinical sequences.
Methods:
Clinical T1‐weighted (T1w) and T2‐weighted fat‐saturated (T2FS) scans, and meas… Show more
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