Alternating direction method of multipliers for displacement estimation in ultrasound strain elastography
Notice bibliographique
Résumé
Abstract Background Ultrasound strain imaging, which delineates mechanical properties to detect tissue abnormalities, involves estimating the time delay between two radio‐frequency (RF) frames collected before and after tissue deformation. The existing regularized optimization‐based time‐delay estimation (TDE) techniques suffer from at least one of the following drawbacks: (1) The regularizer is not aligned with the tissue deformation physics due to taking only the first‐order displacement derivative into account; (2) The ‐norm of the displacement derivatives, which oversmooths the estimated time‐delay, is utilized as the regularizer; (3) The modulus function defined mathematically should be approximated by a smooth function to facilitate the optimization of ‐norm. Purpose Our purpose is to develop a novel TDE technique that resolves the aforementioned shortcomings of the existing algorithms. Methods Herein, we propose employing the alternating direction method of multipliers (ADMM) for optimizing a novel cost function consisting of ‐norm data fidelity term and ‐norm first‐ and second‐order spatial continuity terms. ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high‐contrast strain images with smooth backgrounds and sharp boundaries. We name our technique A DMM for tota L varia T ion R eg U lar I zation in ultrasound ST rain imaging (ALTRUIST). ALTRUIST's efficacy is quantified using absolute error (AE), Structural SIMilarity (SSIM), signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), and strain ratio (SR) with respect to GLUE, OVERWIND, and ‐SOUL, three recently published energy‐based techniques, and UMEN‐Net, a state‐of‐the‐art deep learning‐based algorithm. Analysis of variance (ANOVA)‐led multiple comparison tests and paired ‐tests at overall significance level were conducted to assess the statistical significance of our findings. The Bonferroni correction was taken into account in all statistical tests. Two simulated layer phantoms, three simulated resolution phantoms, one hard‐inclusion simulated phantom, one multi‐inclusion simulated phantom, one experimental breast phantom, and three in vivo liver cancer datasets have been used for validation experiments. We have published the ALTRUIST code at http://code.sonography.ai . Results ALTRUIST substantially outperforms the four state‐of‐the‐art benchmarks in all validation experiments, both qualitatively and quantitatively. ALTRUIST yields up to , , and SNR improvements and , , and CNR improvements over ‐SOUL, its closest competitor, for simulated, phantom, and in vivo liver cancer datasets, respectively, where the asterisk (*) indicates statistical significance. In addition, ANOVA‐led multiple comparison tests and paired ‐tests indicate that ALTRUIST generally achieves statistically significant improvements over GLUE, UMEN‐Net, OVERWIND, and ‐SOUL in terms of AE, SSIM map, SNR, and CNR. Conclusions A novel ultrasonic displacement tracking algorithm named ALTRUIST has been developed. The principal novelty of ALTRUIST is incorporating ADMM for optimizing an ‐norm regularization‐based cost function. ALTRUIST exhibits promising performance in simulation, phantom, and in vivo experiments.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».