Automatic calculation of Mercuri grades from CT and MR muscle images.
Brain Dev 2019;
41:870-877. [PMID:
31307822 DOI:
10.1016/j.braindev.2019.06.008]
[Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/25/2019] [Accepted: 06/28/2019] [Indexed: 12/29/2022]
Abstract
BACKGROUND
Mercuri grading of muscle images is a useful method to evaluate the progression of muscular dystrophies. However, because Mercuri grading is skill-based, few competent experts are available. We therefore developed an automated method for Mercuri grade calculations.
METHODS
We used computed tomography (CT) and magnetic resonance (MR) images of the thigh and lower leg muscles taken from a Japanese limb-girdle muscular dystrophy patient database. We calculated muscle impairment ratios based on the CT images, and then converted the ratios to revised Mercuri grades. This method was also applied to T1-weighted MR images. Additionally, radiation absorption doses in muscle and chest CT images from a separate patient group were also analyzed.
RESULTS
We observed a close correlation between our automatically calculated Mercuri grades and skill-based visually determined Mercuri grades in both CT and MR images. The radiation absorption, measured by total dose length product, was lower in muscle CT (121.8 mGy-cm) than in chest CT (524.1 mGy-cm).
CONCLUSIONS
We developed a new automatic Mercuri grading method using values obtained from CT images. This method was also applied to calculate the Mercuri grade of T1-weighted MR images. In addition, the radiation doses from muscle CT were observed to be lower than those from chest CT.
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