ArticleQuantitative imaging in medicine and surgery2026
Clinical evaluation of free-breathing cardiac multi-parametric mapping using dictionary-based motion correction.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Free-breathing cardiac multi-parametric mapping is clinically important but requires accurate motion correction (MoCo). The clinical adoption of the dictionary-matching and low-rank (DM + LR) method remains limited due to computational bottlenecks and a lack of clinical validation. This study aimed to develop and validate a modified dictionary-matching and low-rank (mDM + LR) MoCo approach with improved computational efficiency and diagnostic performance in free-breathing cardiac T1/T2 mapping. Methods: This prospective study enrolled 130 patients with cardiac diseases and 23 healthy controls (HCs). All participants underwent cardiac magnetic resonance imaging (MRI) on a 3T scanner (uMR 790) using electrocardiogram-gated balanced Steady-State Free Precession (bSSFP)-based multimapping for joint T1/T2 mapping under free-breathing conditions. Breath-hold multimapping, Modified Look-Locker Inversion recovery (MOLLI), and T2 mapping served as reference standards in a subset of 19 patients. The mDM + LR MoCo method integrated a pre-trained multi-layer perceptron (MLP), trained on 12.5 million extended phase graph (EPG)-simulated samples, to map T1, T2, and RR-interval history to signals, reducing the runtime to ~25 seconds per sample. MoCo accuracy was evaluated against non-MoCo and parametric image registration with total variation-regularization (pTVreg) using quantitative metrics [Dice similarity coefficient (DSC) scores, mean contour distance (MCD) values, and relative dictionary-matching errors (RDMEs)], qualitative map scores assessed by two blinded readers, and T1/T2 quantification accuracy via correlation and Bland-Altman analyses against breath-hold references. Diagnostic performance (i.e., sensitivity, specificity, and accuracy) was assessed using thresholds derived from HC breath-hold data. Statistical analyses included the Shapiro-Wilk test, Results: In the patients, mDM + LR outperformed non-MoCo and pTVreg in quantitative metrics such as DSC scores (78.0%±7.6% Conclusions: mDM + LR significantly improves MoCo, quantification accuracy, and diagnostic performance for free-breathing multi-parametric mapping and thus could be applied in clinical settings.
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