Quantitative ultrasound assessment of duchenne muscular dystrophy using edge detection analysis

Sisir Koppaka, Irina Shklyar, Seward B. Rutkove, Basil T. Darras, Brian W. Anthony, Craig M. Zaidman, Jim S. Wu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Objectives-The purpose of this study was to investigate the ability of quantitative ultrasound (US) using edge detection analysis to assess patients with Duchenne muscular dystrophy (DMD). Methods-After Institutional Review Board approval, US examinations with fixed technical parameters were performed unilaterally in 6 muscles (biceps, deltoid, wrist flexors, quadriceps, medial gastrocnemius, and tibialis anterior) in 19 boys with DMD and 21 age-matched control participants. The muscles of interest were outlined by a tracing tool, and the upper third of the muscle was used for analysis. Edge detection values for each muscle were quantified by the Canny edge detection algorithm and then normalized to the number of edge pixels in the muscle region. The edge detection values were extracted at multiple sensitivity thresholds (0.01-0.99) to determine the optimal threshold for distinguishing DMD from normal. Area under the receiver operating curve values were generated for each muscle and averaged across the 6 muscles. Results-The average age in the DMD group was 8.8 years (range, 3.0-14.3 years), and the average age in the control group was 8.7 years (range, 3.4-13.5 years). For edge detection, a Canny threshold of 0.05 provided the best discrimination between DMD and normal (area under the curve, 0.96; 95% confidence interval, 0.84-1.00). According to a Mann-Whitney test, edge detection values were significantly different between DMD and controls (P < .0001). Conclusions-Quantitative US imaging using edge detection can distinguish patients with DMD from healthy controls at low Canny thresholds, at which discrimination of small structures is best. Edge detection by itself or in combination with other tests can potentially serve as a useful biomarker of disease progression and effectiveness of therapy in muscle disorders.

Original languageEnglish
Pages (from-to)1889-1897
Number of pages9
JournalJournal of Ultrasound in Medicine
Volume35
Issue number9
DOIs
StatePublished - Sep 1 2016

Keywords

  • Duchenne muscular dystrophy
  • Edge detection
  • Muscle
  • Musculoskeletal ultrasound
  • Quantitative ultrasound

Fingerprint

Dive into the research topics of 'Quantitative ultrasound assessment of duchenne muscular dystrophy using edge detection analysis'. Together they form a unique fingerprint.

Cite this