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Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking

arxiv.org/abs/2609.09577

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Updated 2 h ago · first seen 11 Sept 2026

paper_01M294GMC5DTAEB9C87HEDCJXQ

Published
11 Sept 2026
T1 · 2 h ago
arXiv
2609.09577
T1 · 2 h ago
Category
eess.IV
T1 · 2 h ago

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Claim history · Abstract

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Abstractabstract1

Claim history for Abstract
ValueValid from → toStatusSourceConfidenceExtractor
Myocardial strain from echocardiography is a key biomarker for cardiac function. Recent deep learning methods show strong performance for myocardial motion tracking but often lack physiological constraints, leading to temporal drift across the cardiac cycle. Consequently, tracked points may not return to their relative initial positions at the end of each cardiac cycle, producing inaccurate strain estimates and even divergence in some cases. We propose a deep learning framework that compensates for drift during myocardial tracking. We extend a state-of-the-art echocardiographic tracking method (TAS-Net) with persistent memory tokens that share information across sliding windows over full cardiac cycles. A teacher-student fine-tuning strategy on real echocardiographic data then enforces physiologically consistent cyclic motion while preserving tracking accuracy. Experiments show reduced global and regional strain drift, improved agreement with clinical references, and better test-retest reproducibility, supporting more reliable myocardial strain estimation in clinical practice. → currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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