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Open AccessArticle

Clinical Evaluation of Duchenne Muscular Dystrophy Severity Using Ultrasound Small-Window Entropy Imaging

1
School of Microelectronics, Tianjin University, Tianjin 300072, China
2
Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu 30059, Taiwan
3
Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei 100229, Taiwan
4
Department of Pediatrics, National Taiwan University Hospital, and College of Medicine, National Taiwan University, Taipei 100233, Taiwan
5
Department of Pediatric Neurology, National Taiwan University Children’s Hospital, Taipei 100226, Taiwan
6
Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
7
Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan
8
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan
*
Authors to whom correspondence should be addressed.
Entropy 2020, 22(7), 715; https://doi.org/10.3390/e22070715
Received: 12 March 2020 / Revised: 16 June 2020 / Accepted: 25 June 2020 / Published: 28 June 2020
(This article belongs to the Special Issue Entropy in Image Analysis II)
Information entropy of ultrasound imaging recently receives much attention in the diagnosis of Duchenne muscular dystrophy (DMD). DMD is the most common muscular disorder; patients lose their ambulation in the later stages of the disease. Ultrasound imaging enables routine examinations and the follow-up of patients with DMD. Conventionally, the probability distribution of the received backscattered echo signals can be described using statistical models for ultrasound parametric imaging to characterize muscle tissue. Small-window entropy imaging is an efficient nonmodel-based approach to analyzing the backscattered statistical properties. This study explored the feasibility of using ultrasound small-window entropy imaging in evaluating the severity of DMD. A total of 85 participants were recruited. For each patient, ultrasound scans of the gastrocnemius were performed to acquire raw image data for B-mode and small-window entropy imaging, which were compared with clinical diagnoses of DMD by using the receiver operating characteristic curve. The results indicated that entropy imaging can visualize changes in the information uncertainty of ultrasound backscattered signals. The median with interquartile range (IQR) of the entropy value was 4.99 (IQR: 4.98–5.00) for the control group, 5.04 (IQR: 5.01–5.05) for stage 1 patients, 5.07 (IQR: 5.06–5.07) for stage 2 patients, and 5.07 (IQR: 5.06–5.07) for stage 3 patients. The diagnostic accuracies were 89.41%, 87.06%, and 72.94% for ≥stage 1, ≥stage 2, and ≥stage 3, respectively. Comparisons with previous studies revealed that the small-window entropy imaging technique exhibits higher diagnostic performance than conventional methods. Its further development is recommended for potential use in clinical evaluations and the follow-up of patients with DMD. View Full-Text
Keywords: Duchenne muscular dystrophy; entropy; ultrasound; backscattered signals Duchenne muscular dystrophy; entropy; ultrasound; backscattered signals
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Yan, D.; Li, Q.; Lin, C.-W.; Shieh, J.-Y.; Weng, W.-C.; Tsui, P.-H. Clinical Evaluation of Duchenne Muscular Dystrophy Severity Using Ultrasound Small-Window Entropy Imaging. Entropy 2020, 22, 715.

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