The images were acquired in the T2-weighted (T2W), including the cervical vertebral body, intervertebral disc, spinal canal, and spinal cord. The whole MRI dataset contained 600 images in total, with 16 categories: cervical bodies: C2, C3, C4, C5, C6, C7, and T1; cervical intervertebral disc: C2/3, C3/4, C4/5, C5/6, C6/7, and C7/T1; spinal canal; spinal cord; and background. Since each MRI contained a complete set of 16 categories, the total dataset had a total of 600 of each category.
3.0t scanners
The 3.0T scanners are high-field magnetic resonance imaging (MRI) systems designed for clinical and research applications. These scanners utilize a 3.0 Tesla (T) superconducting magnet to generate a strong, stable magnetic field, which enables the acquisition of high-resolution images. The core function of the 3.0T scanners is to provide advanced imaging capabilities for a variety of medical and scientific purposes.
Lab products found in correlation
4 protocols using 3.0t scanners
Retrospective Cervical MRI Analysis
The images were acquired in the T2-weighted (T2W), including the cervical vertebral body, intervertebral disc, spinal canal, and spinal cord. The whole MRI dataset contained 600 images in total, with 16 categories: cervical bodies: C2, C3, C4, C5, C6, C7, and T1; cervical intervertebral disc: C2/3, C3/4, C4/5, C5/6, C6/7, and C7/T1; spinal canal; spinal cord; and background. Since each MRI contained a complete set of 16 categories, the total dataset had a total of 600 of each category.
Cervical MRI Radiomics Prediction Model
The data were randomly divided into a training set (1,830 study samples of 305 patients) and a test set (780 study samples of 130 patients) according to the ratio of 7:3. The radiomics analysis, feature selection, and model development were implemented in the training set, and then the related radiomics models were validated in the test set.
High-Resolution 3D T1-Weighted MRI Protocol
Standardized Preprocessing of Structural MRI Data
Before preprocessing, the magnetic resonance imaging (MRI) images of raw DICOM format were reviewed and converted into the Neuroimaging Informatics Technology Initiative (NII) format using MRICRON software. All NII images were preprocessed and analyzed using the CAT12 toolbox (Computational Anatomy Toolbox;
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