UltraTimTrack:基于卡尔曼滤波器的算法,用于跟踪超声波图像序列中的肌肉筋束

bioRxiv Pub Date : 2024-08-09 DOI:10.1101/2024.08.07.607010
Tim J. van der Zee, Paolo Tecchio, Daniel Hahn, B. Raiteri
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We applied the hybrid algorithm to ultrasound image sequences collected from the human medial gastrocnemius of healthy individuals (N=8, 4 women), who performed cyclical submaximal plantar flexion contractions or remained at rest during passive ankle joint rotations at given frequencies and amplitudes whilst seated in a dynamometer chair. We quantified the algorithm’s tracking accuracy, noise, and drift as the respective mean, cycle-to-cycle, and accumulated between-contraction variability in fascicle length and fascicle angle. We expected UltraTimTrack’s estimates to be less noisy and to drift less across experimental conditions and image acquisition settings, compared with estimates from its parent algorithms. Results The proposed algorithm had low-noise estimates like UltraTrack and was drift-free like TimTrack across the broad range of conditions we tested. Estimated fascicle length and fascicle angle deviations accumulated to 2.1 ± 1.3 mm (mean ± s.d.) and 0.8 ± 0.7 deg, respectively, over 120 cyclical contractions. Average cycle-to-cycle variability was 1.4 ± 0.4 mm and 0.6 ± 0.3 deg, respectively. In comparison, UltraTrack had similar cycle-to-cycle variability (1.1 ± 0.3 mm, 0.5 ± 0.1 deg) but greater cumulative deviation (67.0 ± 59.3 mm, 9.3 ± 8.6 deg), whereas TimTrack had similar cumulative deviation (1.9 ± 2.2 mm, 0.9 ± 1.0 deg) but greater variability (3.5 ± 1.0 mm, 1.4 ± 0.5 deg). UltraTimTrack was significantly less affected by experimental conditions and image acquisition settings than its parent algorithms. 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引用次数: 0

摘要

背景亮度模式(B-mode)超声波是对骨骼肌运动过程中结构变化进行无创成像的重要工具,但自动估算筋膜长度等结构特征仍是一项重大挑战。现有的筋膜跟踪算法要么需要耗时的漂移校正,要么会产生需要后处理的噪声估计值。因此,我们的目标是开发一种算法,在各种实验条件和图像采集设置下都能无漂移、无噪声地跟踪束带。方法 我们应用卡尔曼滤波器,将现有和公开的 UltraTrack 和 TimTrack 算法中的筋膜长度和筋膜角度估计值结合成一种混合算法,称为 UltraTimTrack。我们将该混合算法应用于从健康人(8 人,4 名女性)的人体内侧腓肠肌采集的超声波图像序列,这些健康人坐在测力计椅子上,以给定的频率和振幅进行周期性次最大跖屈收缩或在踝关节被动旋转时保持静止。我们将算法的跟踪准确性、噪声和漂移量化为筋膜长度和筋膜角度各自的平均值、周期到周期以及收缩间累积变异性。我们希望 UltraTimTrack 的估计值在不同实验条件和图像采集设置下的噪声和漂移都小于同类算法的估计值。结果 拟议的算法与 UltraTrack 一样具有低噪声估计值,与 TimTrack 一样在我们测试的各种条件下均无漂移。在 120 个周期的收缩过程中,估计的筋膜长度和筋膜角度偏差分别累计为 2.1 ± 1.3 毫米(平均值 ± s.d.)和 0.8 ± 0.7 度。周期与周期之间的平均偏差分别为 1.4 ± 0.4 毫米和 0.6 ± 0.3 度。相比之下,UltraTrack 的周期间变异性相似(1.1 ± 0.3 毫米,0.5 ± 0.1 度),但累积偏差更大(67.0 ± 59.3 毫米,9.3 ± 8.6 度),而 TimTrack 的累积偏差相似(1.9 ± 2.2 毫米,0.9 ± 1.0 度),但变异性更大(3.5 ± 1.0 毫米,1.4 ± 0.5 度)。UltraTimTrack 受实验条件和图像采集设置的影响明显小于其上级算法。与最近提出的混合算法(筋膜长度:4.5 毫米,筋膜角度:0.8 度)和机器学习算法(DL_Track)(筋膜长度:8.2 毫米,筋膜角度:4.8 度)相比,UltraTimTrack 在以前发表的人体胫骨前肌图像序列上也表现良好,与人工追踪(筋膜长度:2.7 毫米,筋膜角度:0.7 度)相比,产生的均方根偏差较小。结论 我们开发了一种基于卡尔曼滤波的方法,以改善 B 型超声图像序列中的筋膜跟踪。所提出的算法可提供低噪声、无漂移的肌肉结构变化估计值,从而为肌肉功能解释提供更好的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences
Background Brightness-mode (B-mode) ultrasound is a valuable tool to non-invasively image skeletal muscle architectural changes during movement, but automatically estimating architectural features such as fascicle length remains a major challenge. Existing fascicle tracking algorithms either require time-consuming drift corrections or yield noisy estimates that require post-processing. We therefore aimed to develop an algorithm that tracks fascicles without drift and noise across a range of experimental conditions and image acquisition settings. Methods We applied a Kalman filter to combine fascicle length and fascicle angle estimates from existing and openly available UltraTrack and TimTrack algorithms into a hybrid algorithm called UltraTimTrack. We applied the hybrid algorithm to ultrasound image sequences collected from the human medial gastrocnemius of healthy individuals (N=8, 4 women), who performed cyclical submaximal plantar flexion contractions or remained at rest during passive ankle joint rotations at given frequencies and amplitudes whilst seated in a dynamometer chair. We quantified the algorithm’s tracking accuracy, noise, and drift as the respective mean, cycle-to-cycle, and accumulated between-contraction variability in fascicle length and fascicle angle. We expected UltraTimTrack’s estimates to be less noisy and to drift less across experimental conditions and image acquisition settings, compared with estimates from its parent algorithms. Results The proposed algorithm had low-noise estimates like UltraTrack and was drift-free like TimTrack across the broad range of conditions we tested. Estimated fascicle length and fascicle angle deviations accumulated to 2.1 ± 1.3 mm (mean ± s.d.) and 0.8 ± 0.7 deg, respectively, over 120 cyclical contractions. Average cycle-to-cycle variability was 1.4 ± 0.4 mm and 0.6 ± 0.3 deg, respectively. In comparison, UltraTrack had similar cycle-to-cycle variability (1.1 ± 0.3 mm, 0.5 ± 0.1 deg) but greater cumulative deviation (67.0 ± 59.3 mm, 9.3 ± 8.6 deg), whereas TimTrack had similar cumulative deviation (1.9 ± 2.2 mm, 0.9 ± 1.0 deg) but greater variability (3.5 ± 1.0 mm, 1.4 ± 0.5 deg). UltraTimTrack was significantly less affected by experimental conditions and image acquisition settings than its parent algorithms. It also performed well on a previously published image sequence from the human tibialis anterior, yielding a smaller root-mean-square deviation from manual tracking (fascicle length: 2.7 mm, fascicle angle: 0.7 deg) than a recently proposed hybrid algorithm (fascicle length: 4.5 mm, fascicle angle: 0.8 deg) and a machine-learning (DL_Track) algorithm (fascicle length: 8.2 mm, fascicle angle: 4.8 deg). Conclusion We developed a Kalman-filter-based method to improve fascicle tracking from B-mode ultrasound image sequences. The proposed algorithm provides low-noise, drift-free estimates of muscle architectural changes that may better inform muscle function interpretations.
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