1K-5 Information Theoretic Ultrasound Imaging Differentiates Dystrophin-Deficient and Normal Skeletal Muscle in Humans

The dystrophinopathies are a group of X-linked genetic diseases that result from dystrophin deficiency. Duchenne's muscular dystrophy (DMD) is the most severe dystrophinopathy, with an incidence of 1:3500 male births. Current techniques, such as strength testing, for monitoring progress of disease and therapy in DMD patients, are imprecise and physically demanding. However, ultrasound is well-suited to detect changes in structure and organization in muscle tissue in a manner that makes low demands on the patient. Therefore, we investigated the use of ultrasound to quantitatively phenotype patients with DMD. Beam-formed RF data were acquired from the skeletal muscles of nine DMD and five normal subjects using a clinical imaging system (HDI5000 w/ 7 MHz probe applied above left biceps muscle). From these data, images were reconstructed using B-mode (log of analytic signal magnitude) and information-theoretic receivers (Hf-receiver). Hf images obtained from dystrophic muscle contained extensive "mottled" regions (i.e. areas with heterogeneous image contrast) that were not readily apparent from the B-mode images. The two dimensional autocorrelation of DMD Hf images have broader peaks than those of normal subjects, which is indicative of larger scatterer sizes, consistent with pathological changes of fibers, edema, and fatty infiltration. Comparison of the relative peak widths (full width measured at 60% maximum) of the autocorrelation of the DMD and normal H f images shows a quantitative difference between the two groups (p < 0.005, student two-tailed unpaired t-test). Consequently, these imaging techniques may prove useful for longitudinal monitoring of disease progression and therapy

[1]  Hyuing Zhang,et al.  Characterization of digital waveforms using thermodynamic analogs: detection of contrast-targeted tissue in vivo , 2006, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[2]  S. Wickline,et al.  Characterization of digital waveforms using thermodynamic analogs: applications to detection of materials defects , 2005, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[3]  J. Arbeit,et al.  Ultrasonic molecular imaging of primordial angiogenic vessels in rabbit and mouse models with /spl alpha//sub v//spl beta//sub 3/ -integrin targeted nanoparticles using information-theoretic signal detection: results at high frequency and in the clinical diagnostic frequency range , 2005, IEEE Ultrasonics Symposium, 2005..

[4]  S. Wickline,et al.  Characterization of digital waveforms using thermodynamic analogs: detection of contrast targeted tissue in mda 435 tumors implanted in athymic mice , 2005, IEEE Ultrasonics Symposium, 2005..

[5]  E. K. Lacy,et al.  In vivo ultrasonic detection of angiogenesis with site-targeted nanoparticle contrast agents using measure-theoretic signal receivers , 2004, IEEE Ultrasonics Symposium, 2004.

[6]  J. Fock,et al.  Muscle ultrasound in children: normal values and application to neuromuscular disorders. , 2004, Ultrasound in medicine & biology.

[7]  J. H. van der Hoeven,et al.  Muscle ultrasound analysis: normal values and differentiation between myopathies and neuropathies. , 2003, Ultrasound in medicine & biology.

[8]  C. Reimers,et al.  Calf enlargement in neuromuscular diseases: a quantitative ultrasound study in 350 patients and review of the literature , 1996, Journal of the Neurological Sciences.

[9]  K. Shung,et al.  A study of the relationship between mechanical and ultrasonic properties of dystrophic and normal skeletal muscle. , 1995, Ultrasound in medicine & biology.

[10]  Michael S. Hughes,et al.  Analysis of digitized waveforms using Shannon entropy. II. High‐speed algorithms based on Green’s functions , 1994 .

[11]  Michael S. Hughes,et al.  Analysis of digitized waveforms using Shannon entropy , 1993 .

[12]  G. Bentley,et al.  A randomized controlled trial of early surgery in duchenne muscular dystrophy , 1992, Neuromuscular Disorders.

[13]  Michael S. Hughes,et al.  Analysis of ultrasonic waveforms using Shannon entropy , 1992, IEEE 1992 Ultrasonics Symposium Proceedings.

[14]  Michael S. Hughes,et al.  A comparison of Shannon entropy versus signal energy for acoustic detection of artificially induced defects in Plexiglas , 1992 .

[15]  S. Leeman,et al.  Quantitative Sonography of Muscle , 1989 .

[16]  I. Suramo,et al.  High‐frequency ultrasonography of skeletal muscle in children with neuromuscular disease. , 1988, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[17]  V. Dubowitz,et al.  Assessment of quadriceps femoris muscle atrophy and hypertrophy in neuromuscular disease in children , 1988, Journal of clinical ultrasound : JCU.

[18]  V. Dubowitz,et al.  Real‐time ultrasound imaging of muscles , 1988, Muscle & nerve.

[19]  M. Fink,et al.  Optimal Precision in Ultrasound Attenuation Estimation and Application to the Detection of Duchenne Muscular Dystrophy Carriers , 1987 .

[20]  V. Dubowitz,et al.  DETECTING THE DUCHENNE CARRIER BY ULTRASOUND AND COMPUTERISED TOMOGRAPHY , 1983, The Lancet.

[21]  S Leeman,et al.  Ultrasound imaging in the diagnosis of muscle disease. , 1982, The Journal of pediatrics.

[22]  Sidney Leeman,et al.  DETECTION OF PATHOLOGICAL CHANGE IN DYSTROPHIC MUSCLE WITH B-SCAN ULTRASOUND IMAGING , 1980, The Lancet.