Machine Learning Applications in Magnetic Resonance Imaging beyond Image Acquisition and Interpretation
Machine Learning Applications in Magnetic Resonance Imaging beyond Image Acquisition and Interpretation
(Non-FAU Project)
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Start date: September 1, 2017
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Abstract
Research project in cooperation with Siemens Healthineers, Erlangen
Magnetic Resonance Imaging (MRI) is an important but complex imaging modality in current radiology. Artificial intelligence (AI) can play an important role for acclerating MR sequence acquisition as well as supporting image interpretation and diagnosis. However, there are also opportunities besides image acquisition and interpretation for which AI can play a vital role to optimze the clinical workflow and decrease costs.
Automated Protocoling
One critical workflow step for an MRI exam is protocoling, i.e., selecting an adequate imaging protocol under consideration of the ordered procedure, clinical indication, and medical history. Due to the complexity of MRI exams and the heterogeneity of MR protocols, this is a nontrivial task. The aim of this project is to analyze and quantify challenges complicating a robust approach for automated protocoling, and propose solutions to these challenges.
Automated Billing
Moreover, reporting and documentation is a crucial step in the radiology workflow. We have therefore automated the selection of billing codes from modality log data for an MRI exam. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding administrative task, enhance the MRI workflow, and prevent coding errors.