数据表1：结合定量MRI脂肪分数与纹理分析评估脑性瘫痪患儿痉挛肌肉（DOCX）

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy.DOCX
• DOI：10.3389/fneur.2021.633808.s001
• 发布日期：2021-03-22
• 文件数：1 个
• 文件总体积：9.83 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Combination_of_Quantitative_MRI_Fat_Fraction_and_Texture_Analysis_to_Evaluate_Spastic_Muscles_of_Children_With_Cerebral_Palsy_DOCX/14257067

命令行下载

curl -L -o "Data_Sheet_1_Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy.DOCX" "https://ndownloader.figshare.com/files/26920619"

文件清单

• Data_Sheet_1_Combination of Quantitative MRI Fat Fraction and Texture Analysis to Evaluate Spastic Muscles of Children With Cerebral Palsy.DOCX（9.83 MB）

文件总体积 9.83 MB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Background: Cerebral palsy (CP) is the most common cause of physical disability in childhood. Muscle pathologies occur due to spasticity and contractures; therefore, diagnostic imaging to detect pathologies is often required. Imaging has been used to assess torsion or estimate muscle volume, but additional methods for characterizing muscle composition have not thoroughly been investigated. MRI fat fraction (FF) measurement can quantify muscle fat and is often a part of standard imaging in neuromuscular dystrophies. To date, FF has been used to quantify muscle fat and assess function in CP. In this study, we aimed to utilize a radiomics and FF analysis along with the combination of both methods to differentiate affected muscles from healthy ones.
Materials and Methods: A total of 9 patients (age range 8–15 years) with CP and 12 healthy controls (age range 9–16 years) were prospectively enrolled (2018–2020) after ethics committee approval. Multi-echo Dixon acquisition of the calf muscles was used for FF calculation. The images of the second echo (TE = 2.87 ms) were used for feature extraction from the soleus, gastrocnemius medialis, and gastrocnemius lateralis muscles. The least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. RM, FF model (FFM), and combined model (CM) were built for each calf muscle. The receiver operating characteristic (ROC) curve and their respective area under the curve (AUC) values were used to evaluate model performance.
Results: In total, the affected legs of 9 CP patients and the dominant legs of 12 healthy controls were analyzed. The performance of RM for soleus, gastrocnemius medialis, and gastrocnemius lateralis (AUC 0.92, 0.92, 0.82, respectively) was better than the FFM (AUC 0.88, 0.85, 0.69, respectively). The combination of both models always had a better performance than RM or FFM (AUC 0.95, 0.93, 0.83). FF was higher in the patient group (FFS 9.1%, FFGM 8.5%, and FFGL 10.2%) than control group (FFS 3.3%, FFGM 4.1%, FFGL 6.6%).
Conclusion: The combination of MRI quantitative fat fraction analysis and texture analysis of muscles is a promising tool to evaluate muscle pathologies due

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fneur.2021.633808.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Combination_of_Quantitative_MRI_Fat_Fraction_and_Texture_Analysis_to_Evaluate_Spastic_Muscles_of_Children_With_Cerebral_Palsy_DOCX/14257067）。