Abstract #2656

Deep learning based assessment of diagnostic image quality from Free Induction Decay Navigators

Serge Vasylechko¹, Tess E. Wallace², Tobias Kober^3,4,5, Camilo Jaimes⁶, Joanne Rispoli¹, Simon K. Warfield¹, Sila Kurugol¹, and Onur Afacan¹

¹Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States, ²Siemens Medical Solutions, Boston, MA, United States, ³Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, ⁴Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ⁵LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁶Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: Motion Correction, Data Acquisition

Motivation: Motion-induced artifacts in pediatric MRI lead to frequent need for rescan, which increases examination time, costs and patient's discomfort.

Goal(s): To develop and validate a deep learning-based method for automated assessment of diagnostic image quality, overcoming limitations of existing motion measurement techniques.

Approach: FID navigators embedded into MPRAGE sequence can provide valuable motion information without prolonging scan time. We train a deep neural network on these signals to accurately predict the diagnostic quality of the image that is to be reconstructed.

Results: Our method surpasses the existing FIDnavΔ approach, achieving AUC of 0.90, with 30% higher specificity and 21% improved precision.

Impact: Our model streamlines MRI procedures by accurately predicting the need for rescans due to patient motion. It has potential to reduce healthcare costs and patient discomfort, and opens new avenues for early scan termination and enhanced clinical workflow efficiency.

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