Biceps Femoris Long-Head Architecture Assessed Using Different Sonographic Techniques

Area of Science:

• Biomechanics and Musculoskeletal Ultrasound
• Human Anatomy and Physiology
• Sports Medicine and Injury Prevention

Background:

• Accurate quantification of biceps femoris long head (BFlh) architecture is essential for understanding muscle function and injury risk.
• Previous sonographic techniques may not fully capture the complex fascicle trajectories, potentially leading to measurement errors.
• Evaluating different ultrasound methods is necessary to determine the most reliable approach for BFlh architecture assessment.

Purpose of the Study:

• To compare the repeatability and measurement agreement of four distinct sonographic techniques for quantifying BFlh architecture.
• To identify the most accurate ultrasound method for assessing BFlh fascicle length and angle.

Main Methods:

• Four sonographic techniques were evaluated: static-image with linear extrapolation, linear-EFOV with straight analysis, linear-EFOV with segmented analysis, and nonlinear-EFOV with segmented analysis.
• Twenty healthy individuals were assessed twice within a 1-hour interval using B-mode ultrasound with a linear probe.
• Measurements were standardized to specific percentages of femur and BFlh length.

Main Results:

• All four techniques demonstrated acceptable repeatability for BFlh fascicle length and angle.
• The nonlinear-EFOV technique exhibited the highest repeatability for both fascicle length (ICC3,k = 0.95) and angle (ICC3,k = 0.97).
• Static-image techniques overestimated fascicle length and underestimated fascicle angle compared to EFOV methods, with significant rank order variations.

Conclusions:

• While all techniques showed good repeatability, static-image and linear-EFOV methods introduced absolute errors due to not following complex fascicle paths.
• The nonlinear-EFOV technique provides superior accuracy and repeatability for assessing BFlh architecture.
• Differences in measurement accuracy between techniques can lead to varied estimations of muscle function and injury risk.