Muscle ultrasound: Present state and future opportunities

Abstract

Muscle ultrasound is a valuable addition to the neuromuscular toolkit in both the clinic and research settings, with proven value and reliability. However, it is currently not fulfilling its full potential in the diagnostic care of patients with neuromuscular disease. This review highlights the possibilities and pitfalls of muscle ultrasound as a diagnostic tool and biomarker, and discusses challenges to its widespread implementation. We expect that limitations in visual image interpretation, posed by user inexperience, could be overcome with simpler scoring systems and the help of deep-learning algorithms. In addition, more information should be collected on the relation between specific neuromuscular disorders, disease stages, and expected ultrasound abnormalities, as this will enhance specificity of the technique and enable the use of muscle ultrasound as a biomarker. Quantified muscle ultrasound gives the most sensitive results but is hampered by the need for device-specific reference values. Efforts in creating dedicated muscle ultrasound systems and artificial intelligence to help with image interpretation are expected to improve usability. Finally, the standard inclusion of muscle and nerve ultrasound in neuromuscular teaching curricula and guidelines will facilitate further implementation in practice. Our hope is that this review will help in unleashing muscle ultrasound's full potential.

Keywords: biomarker; diagnostic screening; implementation; muscle ultrasound; neuromuscular ultrasound.

Conflict of interest statement

Dr. Wijntjes has no conflict of interest to disclose. Dr. van Alfen provides muscle ultrasound consultancy for Dynacure; payment goes to her employer.

© 2020 The Authors. Muscle & Nerve published by Wiley Periodicals LLC.

Figures

FIGURE 1

Properties of sound reflections (A). Transverse and longitudinal muscle ultrasound image of the gastrocnemius muscle of a healthy person (B). Ultrasound reflections in healthy (left) and diseased (right) muscle (C) and its histologic correlation (D). Figure 1D represents a microscopic image of a muscle biopsy, which is of a smaller scale than what is seen with ultrasound

FIGURE 2

Different pathological changes on muscle ultrasound in different neuromuscular disorders. Normal “starry night” appearance of a healthy biceps brachii muscle (A). Ground glass appearance of the tibialis anterior muscle in a patient with facioscapulohumeral dystrophy (FSH) (B). Attenuation of the ultrasound beam in the rectus femoris muscle of an FSHD patient (B,C). Focal areas of increased echogenicity (arrow) in the flexor carpi radialis muscle of a dermatomyositis patient (D). See‐through appearance of the deltoid muscle of a patient with suspected myositis (E). Inflammation of the pretibial skin and subcutaneous layer (arrow) of a dermatomyositis patient (F). Patchy‐appearing biceps brachii muscle with overall high echogenicity of a patient with long‐standing myositis (G). Calcifications (arrowheads) with typical posterior acoustic shadowing in the rectus femoris muscle of a dermatomyositis patient (H). Thickening of fascia (arrow) in the rectus femoris muscle of a patient with eosinophilic fasciitis (I). Affected deep finger flexor muscles (arrow) in a patient with inclusion body myositis (J). Unaffected and affected forearm muscles in a young girl with left arm peripheral nerve trauma (K). Moth‐eaten pattern (arrowheads) in the interosseous dorsalis muscle of a patient with long‐standing neurogenic disease (L)

FIGURE 3

Heckmatt grading scale from 1‐4 (rectus femoris muscle). Grade 1: Appearance of normal muscle structure and echogenicity. Grade 2: Increased muscle grayscale level with still a distinct bone echo. Grade 3: Marked increased grayscale level of the muscle with diminished bone echo. Grade 4: Very strongly increased grayscale level with a total loss of bone echo

FIGURE 4

Grayscale histograms function in ImageJ (0 = black and 255 = white) for a region of interest (ROI) in a healthy (A) and diseased muscle (B)

FIGURE 5

Devices A and B produce different grayscale images of the same region (A). Effect of the angle of insonation on echogenicity (B). Influence of age increase on grayscale levels (C). Influence of obesity (reflected by the BMI) on grayscale levels. Subcutaneous fat layer thickness is indicated by the double arrow (D). Image quality of muscle ultrasound images in the early 1980s (original ventral leg image from the paper of Heckmatt is shown, 6 with permission) compared to current image quality of muscle ultrasound (E). Heckmatt grade 4 rectus femoris muscle of a patient with facioscapulohumeral dystrophy (FSHD) with a relatively (falsely) low Z‐score due to scattering effect (F). Gastrocnemius muscle of a patient with FSHD. The left image (asterisk) shows decreased echogenicity while the MRI shows complete fatty replacement of the same muscle (asterisk). A completely fatty degenerated muscle will have very few tissue transitions left to reflect the ultrasound beam, it will result in an, again, hypoechoic image that will look the same as the subcutaneous fat layer in terms of grayscale levels. On visual inspection however, the ultrasound images clearly shows an abnormal muscle texture with loss of the typical muscle architectural features (G)