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Abstract #3987

HDNLS: Hybrid Deep Learning and Non-linear Least Squares-based Method for Fast Multi-Component T1$$$\rho$$$ Mapping in the Knee Joint

Dilbag Singh1,2, Ravinder R. Regatte1,2, and Marcelo V. W. Zibetti2,3
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New york, NY, United States, New York, NY, United States, 3Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New york, NY, United States

Synopsis

Keywords: Analysis/Processing, Quantitative Imaging, Nonlinear Least Squares, Mono-exponential, Deep Learning, Multi-component T1$$$\rho$$$ maps

Motivation: Non-linear least squares (NLS)-based T1$$$\rho$$$ fitting converges slowly and sensitive to initial guesses. Deep learning-based methods offer efficient alternatives, but suffer from overfitting and generalization problems.

Goal(s): To overcome the limitations of NLS and DL, we introduce HDNLS, which uses DL-based T1ρ fitting as an initial guess for NLS.

Approach: The optimal number of NLS iterations is determined by analyzing the trade-off between precision and fitting time. Finally, four HDNLS variants are suggested: Ultrafast-HDNLS, Superfast-HDNLS, HDNLS, and Relaxed-HDNLS.

Results: HDNLS exhibits similar behavior to NLS and Regularized NLS (RNLS) with a minimum of 13-times increase in fitting speed.

Impact: Our results indicate that HDNLS is faster and exhibits similar behavior to NLS for whole knee joint T1$$$\rho$$$ mapping than NLS. Thus, HDNLS is an alternative to replace NLS and RNLS for T1$$$\rho$$$ mapping when computational time is an issue.

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Keywords