Parameter-efficient adaptation of foundational models for automated myocardial strain analysis.

PURPOSE: Automated myocardial strain analysis requires accurate segmentation and motion tracking, but deep learning methods trained on one dataset often fail when applied to images from different scanners or institutions. We investigate how general-purpose foundational models can be efficiently adapted for cardiac strain estimation, establishing the minimal data requirements for robust cross-domain deployment. METHODS: We propose CardiacSAM, a lightweight adaptation of the Segment Anything Model (SAM) fine-tuned with Low-Rank Adaptation (LoRA), for cardiac structure segmentation, paired with uniGradICON for myocardial motion estimation. We compute global strain in cardiac coordinates for both short-axis (SAX) and long-axis (LAX) views. RESULTS: Validated on four public and one private dataset comprising over 900 subjects, CardiacSAM achieved mean left ventricle (LV) Dice scores of 0.90 ± 0.02 using only 10 training studies per domain for SAX (50 for LAX). Motion estimation achieved median average endpoint error (AEPE) of 3.08 mm on 15 volunteers without known cardiac disease with expert landmark annotations. Strain measurements discriminated significantly between reference and disease groups across all datasets. Comparing SAX and LAX strain in 360 subjects showed moderate correlation for radial strain (r=0.64) but weak correlation for longitudinal strain (r=0.17), suggesting SAX and LAX capture complementary information about cardiac function. CONCLUSION: Parameter-efficient adaptation of foundational models enables automated cardiac strain assessment with high data efficiency and robust cross-dataset generalization.