Ultrasound imaging settings can optimize spatial frequency analysis and pathology discrimination in supraspinatus tendon

Ultrasound (US) provides non-invasive visualization of tissue morphology for musculoskeletal disorders. Spatial Frequency Analysis (SFA) of US images quantitatively characterizes tissue morphology, and has shown the ability to distinguish healthy from pathological tendons. However, the impact of US machine settings on SFA for tendon pathology remains underexplored. Methods: Five participants with unilateral supraspinatus tendon partial tears were imaged bilaterally to examine how variations in US settings (frequency, dynamic range, gain) influence SFA parameters. Tendons were scanned at 9 and 12 MHz with all possible dynamic range and gain combinations, resulting in 1680 images processed via SFA. A linear mixed-effects model analyzed the impact of US settings and tendon conditions on four SFA parameters. Results: The SFA peak spatial frequency radius (PSFR) significantly differed between healthy and pathological tendons, regardless of machine settings (Coef. = 0.55, SE = 0.04, p < 0.001). The 12 MHz frequency notably enhanced PSFR’s discriminatory power, showing higher values in healthy tendons (mean PSFR = 1.34 mm⁻¹) and lower in partial tears (mean PSFR = 1.09 mm⁻¹) compared to 9 MHz (healthy = 1.22 mm⁻¹, partial tear = 1.04 mm⁻¹). P6, Q6, and Amax parameters were affected by machine settings. P6 and Q6 differentiated tendon conditions but required consistent dynamic range settings. Conclusion: Optimal US settings enhance SFA's ability to discriminate between healthy and pathological supraspinatus tendons, with PSFR as the most robust parameter. 12 MHz significantly improves PSFR’s discriminatory capability. Consistency in US parameters is crucial for reliable SFA results, particularly for P6 and Q6.

超声波成像（US）为肌骨骼疾病提供了非侵入性的组织形态可视化。空间频率分析（SFA）对超声波图像进行定量描述，并已显示出区分健康与病理性肌腱的能力。然而，超声波机器设置对SFA在肌腱病理学上的影响尚未得到充分研究。研究方法：选取五位单侧肩袖肌腱部分撕裂的参与者进行双侧成像，以考察超声波设置（频率、动态范围、增益）的变化如何影响SFA参数。肌腱在9 MHz和12 MHz的频率下，使用所有可能的动态范围和增益组合进行扫描，共生成1680张图像，通过SFA进行处理。线性混合效应模型分析了超声波设置和肌腱条件对四个SFA参数的影响。研究结果：与健康肌腱相比，病理性肌腱的SFA峰值空间频率半径（PSFR）在无论机器设置如何的情况下均存在显著差异（系数 = 0.55，标准误 = 0.04，p < 0.001）。12 MHz频率显著增强了PSFR的鉴别能力，在健康肌腱中显示出更高的值（平均PSFR = 1.34 mm⁻¹），而在部分撕裂中显示出较低的值（平均PSFR = 1.09 mm⁻¹），与9 MHz相比（健康肌腱 = 1.22 mm⁻¹，部分撕裂 = 1.04 mm⁻¹）。P6、Q6和Amax参数受到机器设置的影响。P6和Q6能够区分肌腱条件，但需要一致的动态范围设置。结论：最佳的超声波设置可以增强SFA在区分健康与病理性肩袖肌腱方面的能力，其中PSFR是最稳定的参数。12 MHz显著提高了PSFR的鉴别能力。保持超声波参数的一致性对于可靠的SFA结果至关重要，尤其是对于P6和Q6。