Obtaining Quality Extended Field-of-View Ultrasound Images of Skeletal Muscle to Measure Muscle Fascicle Length
December 14th, 2020
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This study describes how to obtain high quality musculoskeletal images using the extended field-of-view ultrasound (EFOV-US) method for the purpose of making muscle fascicle length measures. We apply this method to muscles with fascicles that extend past the field-of-view of common traditional ultrasound (T-US) probes.
Overview
This study demonstrates the extended field-of-view ultrasound (EFOV-US) method for capturing high-quality musculoskeletal images, enabling accurate muscle fascicle length measurements. This technique is particularly beneficial for muscles with fascicles extending beyond the field of view of traditional ultrasound probes.
Key Study Components
Area of Science
- Musculoskeletal Imaging
- Ultrasound Technology
- Biomechanics
Background
- Traditional ultrasound (T-US) has limitations in capturing longer muscle fascicles.
- EFOV-US allows for direct measurement of curved muscle fascicles.
- This method can be applied to a majority of upper and lower limb muscles.
- A thorough understanding of musculoskeletal anatomy is essential for successful implementation.
Purpose of Study
- To improve in vivo fascicle length data collection for various muscles.
- To enhance the quality of musculoskeletal imaging techniques.
- To provide a practical guide for using EFOV-US effectively.
Methods Used
- Utilization of EFOV-US for imaging muscle fascicles.
- Assessment of image quality during the imaging process.
- Familiarization with the anatomy of targeted muscles.
- Practice in obtaining high-quality ultrasound images.
Main Results
- EFOV-US successfully captures longer muscle fascicles than T-US.
- The method can be applied to over 90% of upper limb and 85% of lower limb muscles.
- Quality images enable accurate measurement of muscle fascicle lengths.
- Increased data availability for muscle fascicle lengths enhances biomechanical research.
Conclusions
- EFOV-US is a valuable tool for musculoskeletal imaging.
- Understanding anatomy and practice are crucial for effective use.
- This method opens new avenues for research in muscle biomechanics.
Frequently Asked Questions
What is the main advantage of EFOV-US?
EFOV-US allows for direct measurement of longer curved muscle fascicles that traditional ultrasound cannot capture.
How does EFOV-US improve data collection?
It increases the number of muscles for which in vivo fascicle length data can be obtained.
What is required for successful implementation of EFOV-US?
A good understanding of musculoskeletal anatomy and practice in obtaining quality images are essential.
Can EFOV-US be applied to all muscles?
It can be applied to over 90% of upper limb and 85% of lower limb muscles with optimal fascicle lengths.
What should researchers familiarize themselves with?
Researchers should become familiar with the EFOV-US system and the anatomy of the muscles of interest.
How can researchers assess the quality of images?
Practice and experience in evaluating images are necessary for accurate assessment.
Executive Industry Relevance
Extended field-of-view ultrasound (EFOV-US) addresses a critical gap in musculoskeletal phenotyping by enabling direct measurement of muscle fascicle lengths that exceed traditional ultrasound field-of-view limitations. This capability expands the pool of skeletal muscles available for in vivo biomechanical characterization in both healthy and disease models, supporting more comprehensive target validation and mechanistic de-risking in neuromuscular and musculoskeletal therapeutic development.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of structural determinants of muscle force generation by quantifying fascicle architecture in vivo.
- Operational Value: Provides a non-invasive, repeatable method to assess target engagement effects on muscle morphology.
Screening & Assay Development
- Scientific Value: Generates quantitative, imaging-based readouts of muscle structural phenotype for compound screening cascades.
- Operational Value: Supports assay standardization through reproducible image acquisition protocols and validated analysis workflows using tools like ImageJ.
Translational & Preclinical Research
- Scientific Value: Facilitates longitudinal tracking of muscle remodeling in disease models, bridging discovery findings to preclinical efficacy assessment.
- Operational Value: Enables cross-species comparability of fascicle length measurements when anatomical landmarks are conserved.
Pipeline & Workflow Integration
EFOV-US fits within the discovery continuum as a phenotypic screening tool that informs target validation and lead optimization decisions through structural biomarker quantification.
- Discovery Biology: Supports hypothesis testing regarding molecular regulators of muscle growth and atrophy by providing direct structural phenotyping.
- Screening: Delivers assay-ready, quantitative outputs (fascicle length in mm) that enable dose-response evaluation of modulators of muscle plasticity.
- Analytics: Requires standardized image analysis (e.g., segmented line tracing in ImageJ) to ensure measurement fidelity across operators and sites.
- Translational Research: Aligns with preclinical continuity by enabling consistent fascicle length assessment from rodent models to human tissue.
- Enterprise Reuse: Represents a portable, adaptable imaging capability applicable across multiple musculoskeletal targets and disease areas.
Operational & Enterprise Impact
- Scientific Value: Reduces mechanistic ambiguity in muscle-targeted interventions by linking molecular changes to structural outcomes.
- Operational Value: Enhances reproducibility through standardized scanning protocols and qualitative image quality checklists.
- Strategic Value: Improves go/no-go decision confidence by providing early structural phenotype data that predicts functional muscle capacity.
- Portfolio Impact: Enables risk-adjusted prioritization of muscle-modulating candidates based on validated morphometric endpoints.
Implementation Considerations
- Requires expertise in musculoskeletal anatomy to identify fascicle planes and anatomical landmarks.
- Dependent on ultrasound systems with extended field-of-view capability and adjustable frequency transducers (5–17 MHz).
- Necessitates cross-team standardization of scanning speed, depth, gain, and focus settings to ensure image consistency.
- Requires adaptation of scanning protocols for different muscle groups (e.g., tibialis anterior vs. biceps brachii) based on fascicle orientation and depth.
- Limited by operator skill in maintaining continuous, smooth transducer motion along the muscle path to avoid image artifacts.
Biopharma FAQs
Why does quantitative fascicle length measurement matter for target validation?
Quantitative fascicle length measurement provides a direct structural readout of muscle force-generating capacity, enabling objective assessment of target modulation effects on muscle architecture in vivo.
How does isolating the fascicle plane as an independent variable improve discovery pipeline efficiency?
Isolating the fascicle plane allows for consistent, reproducible imaging along the muscle path, reducing variability and increasing confidence in longitudinal or comparative studies.
What quantitative dependent variable measurements does EFOV-US enable for phenotypic screening?
EFOV-US enables precise measurement of fascicle length in millimeters using segmented line analysis in ImageJ, providing a continuous, quantitative phenotype for screening campaigns.
Why do replication requirements matter for cross-functional collaboration in muscle phenotyping?
Replication requirements ensure that fascicle length measurements are reliable across operators, sessions, and sites, supporting standardized data sharing between discovery and preclinical teams.
What statistical analysis capabilities are required before implementing EFOV-US in a discovery workflow?
Implementation requires basic descriptive statistics (e.g., mean, SD) and inter-rater reliability assessments to validate measurement consistency and support data-driven decision making.
