Hybrid reflected‐ultrasound computed tomography versus B‐mode‐ultrasound for muscle scoring in spinal muscular atrophy

Abstract Background and Purpose Novel light‐ and sound‐based technologies like multispectral optoacoustic tomography (MSOT) with co‐registered reflected‐ultrasound computed tomography (RUCT) could add additional value to conventional ultrasound (US) for disease phenotyping in pediatric spinal muscular atrophy (SMA). The aim of this study was to investigate the quality of RUCT compared to US for qualitative and quantitative assessment of imaging neuromuscular disorders. Methods Subanalyzing the MSOT SMA study, 288 RUCT and 276 US images from 10 SMA patients (mean age 9.0 ± 3.7) and 10 gender‐ and age‐matched healthy volunteers (HV; mean age 8.7 ± 4.3) were analyzed for quantitative (grayscale levels [GSLs]) and qualitative (echogenicity, distribution pattern, Heckmatt scale, and muscle texture) muscle changes. RUCT and US measures were further correlated with clinical standard motor outcomes. Results Quantitative agreement using GSLs revealed significantly higher GSLs in muscles of SMA patients compared to healthy muscles in both techniques (US mean GSL [SD] SMA vs. HV: 110.70 [27.8] vs. 68.85 [19.2], p < .0001; RUCT mean GSL [SD] SMA vs. HV: 91.81 [21.8] vs. 59.86 [8.2], p < .0001) with good correlation with motor outcome tests, respectively. Qualitative agreement between methods for muscle composition was excellent for differentiation of pathological versus healthy muscles, echogenicity, and distribution pattern, moderate for Heckmatt scale, and poor for muscle texture. Conclusions The data suggest that RUCT may allow the assessment of basic qualitative and quantitative measures for muscular diseases with comparable results to conventional US..

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:33

Enthalten in:

Journal of Neuroimaging - 33(2023), 3, Seite 393-403

Beteiligte Personen:

Danko, Vera [VerfasserIn]
Jüngert, Jörg [VerfasserIn]
Schuessler, Stephanie [VerfasserIn]
Buehler, Adrian [VerfasserIn]
Klett, Daniel [VerfasserIn]
Federle, Anna [VerfasserIn]
Roos, Andreas [VerfasserIn]
Lochmüller, Hanns [VerfasserIn]
Neurath, Markus F. [VerfasserIn]
Woelfle, Joachim [VerfasserIn]
Trollmann, Regina [VerfasserIn]
Waldner, Maximilian J. [VerfasserIn]
Knieling, Ferdinand [VerfasserIn]
Regensburger, Adrian P. [VerfasserIn]
Wagner, Alexandra L. [VerfasserIn]

Anmerkungen:

© 2023 American Society of Neuroimaging.

Umfang:

11

doi:

10.1111/jon.13081

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

WLY016235274