Estimating vastus lateralis muscle volume from a single ultrasound image

M Moritz Eggelbusch G Guido Weide M Michael Tieland R Renske N. Vergeer L Luuk Vos J Jantine van den Helder S Stephan van der Zwaard R Richard T. Jaspers P Peter J. M. Weijs R Rob C. I. Wüst

Abstract

Abstract The assessment of skeletal muscle volume is valuable for fundamental research and clinical practice, but remains limited in larger cohorts due to its time-consuming nature. Here, we developed a method to accurately estimate vastus lateralis (VL) muscle volume based on a single measurement of anatomical cross-sectional area (ACSA) or tissue thickness. Sixty-nine healthy participants (20–91 years) volunteered. In a subgroup (n = 34) we measured VL volume and ACSAs at 10% intervals along the muscle length to derive a VL muscle shape factor. We subsequently estimated VL volume by multiplying this muscle shape factor with muscle length and a single measure of ACSA at 50% muscle length (ACSAVL50%) or an estimated ACSAVL50% from a single ultrasound scan of tissue thickness in an independent cohort (n = 35). VL muscle shape factor was determined by integrating a fourth-order polynomial of muscle length and ACSA, and was dependent on muscle size. Estimating muscle volume had a high accuracy (R²=0.976, CCC = 0.987), low bias and error (< 8.5%) in both the main cohort and an independent validation group. Estimating muscle volume from stitching 2D images at 50% muscle length or estimating ACSA with a geometric model explained 91–95% of variance in measured volumes, with high accuracy and concordance correlation coefficients. VL muscle volume can be estimated by multiplying a muscle shape factor with muscle length and ACSAVL50% from a single ultrasound image. We present a novel, cost-effective, rapid, yet accurate assessment of VL muscle mass for (large-scale) studies and clinical practice.

Article Details

Volume / Issue Vol. 15, Issue 1
Published August 18, 2025
ISSN 2045-2322
Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (10)

M

Moritz Eggelbusch

G

Guido Weide

M

Michael Tieland

R

Renske N. Vergeer

L

Luuk Vos

J

Jantine van den Helder

S

Stephan van der Zwaard

R

Richard T. Jaspers

P

Peter J. M. Weijs

R

Rob C. I. Wüst