Abstract #3772

Assessing the Impact of Upstream Reconstruction Models on Downstream Image Analysis: A Workflow-Centric Evaluation

Ben Viggiano¹, Aashna Desai², Elka Rubin³, Andrew Schmidt³, Robert Boutin³, Kathryn J Stevens³, Garry E Gold³, Christopher Ré⁴, Akshay S Chaudhari^1,3, and Arjun D Desai^3,5

¹Biomedical Data Science, Stanford University, Stanford, CA, United States, ²Department of Neuroscience, University of California Berkeley, Berkeley, CA, United States, ³Department of Radiology, Stanford University, Stanford, CA, United States, ⁴Department of Computer Science, Stanford University, Stanford, CA, United States, ⁵Department of Electrical Engineering, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, Segmentation, ClassificationDeep learning (DL) techniques have shown promise for both reconstruction and image analysis stages of MRI workflows. However, traditional benchmarking methods evaluate each stage separately. As a result, the impact of reconstruction on downstream image analysis tasks and biomarker quantification remains unknown. In this study, we explore how changing aspects of upstream reconstruction affects the downstream analysis. We find that insights from evaluating reconstruction models as a component of a broader end-to-end workflow do not correlate with conventional, task-specific image quality metrics. We use these findings to motivate the discussion of evaluating DL methods at the workflow level.

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