Abstract #3989

Deep Learning-Enhanced Pharmacokinetic Parameter Estimation for Low-Dose Multitasking DCE-MRI

Chaowei Wu^1,2, Lingceng Ma³, Lixia Wang¹, Srinivas Gaddam⁴, Hsu-Lei Lee¹, Nan Wang^1,5, Yibin Xie¹, Anthony G. Christodoulou^2,3, and Debiao Li^1,2

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ⁴Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ⁵Radiology Department, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Analysis/Processing, Perfusion, DCE, Parametric Fitting

Motivation: Non-linear least-squares (NLLS) fitting to quantify DCE-MRI is sensitive to noise, non-convex, and slow, leading to unreliable parameter estimation especially for low-dose/low-SNR protocols.

Goal(s): To enhance the reliability and efficiency of pharmacokinetic parameter fitting in DCE-MRI using AI.

Approach: We trained a deep neural network using extensive simulations across 20%- to full-dose scenarios and evaluated its performance in in-vivo multitasking DCE-MRI data.

Results: AI-based fitting provides pharmacokinetic parameters that (1) align with literature values, (2) improve tumor vs. healthy pancreas differentiation, (3) offer greater homogeneity in healthy pancreas regions, and (4) reduce processing time.

Impact: Deep learning significantly improves the precision, homogeneity, and speed of pharmacokinetic fitting in DCE-MRI, making it an attractive alternative to NLLS. This advancement supports more efficient, accurate, and clinically feasible quantitative imaging for various biomedical applications.

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