Related Experiment Video
Updated: Feb 18, 2026
Diffusion Imaging in the Rat Cervical Spinal Cord
Published on: April 7, 2015
Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images
Mahdi Alizadeh1, Chris J Conklin2, Devon M Middleton2
1Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States.
Insights
This study developed an automated pipeline to remove ghost artifacts in pediatric spinal cord diffusion tensor imaging (DTI). The method achieved 84% accuracy, improving image quality for better diagnostics.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Neuroscience
Background:
- Ghost artifacts significantly degrade spinal cord diffusion tensor imaging (DTI) quality.
- Accurate artifact removal is essential for reliable spinal cord DTI analysis, especially in pediatric populations.
Purpose of the Study:
- To design, implement, and validate a multi-stage post-processing pipeline for automatic ghost artifact removal in pediatric spinal cord DTI.
- To improve the diagnostic utility of DTI by enhancing image clarity and reducing motion-related artifacts.
Main Methods:
- A cohort of 12 pediatric subjects (healthy and with spinal cord injury) was studied.
- Region of interests (ROIs) of ghost/true cords were segmented using mathematical morphological processing.
- Texture features were extracted and selected based on mutual information, then classified using an adaptive neuro-fuzzy interface system.
Main Results:
- The pipeline successfully separated ghost artifacts from true cord structures.
- The classifier achieved 91% sensitivity, 79% specificity, and 84% accuracy in distinguishing true cords from ghost artifacts.
Conclusions:
- The proposed method shows promise for automatic ghost artifact detection in spinal cord DTI.
- This automated approach is a crucial step towards developing robust post-processing pipelines for DTI analysis.
Purpose:
Ghost artifacts are a major contributor to degradation of spinal cord diffusion tensor images. A multi-stage post-processing pipeline was designed, implemented and validated to automatically remove ghost artifacts arising from reduced field of view diffusion tensor imaging (DTI) of the pediatric spinal cord.
Method:
A total of 12 pediatric subjects including 7 healthy subjects (mean age=11.34years) with no evidence of spinal cord injury or pathology and 5 patients (mean age=10.96years) with cervical spinal cord injury were studied. Ghost/true cords, labeled as region of interests (ROIs), in non-diffusion weighted b0 images were segmented automatically using mathematical morphological processing. Initially, 21 texture features were extracted from each segmented ROI including 5 first-order features based on the histogram of the image (mean, variance, skewness, kurtosis and entropy) and 16s-order feature vector elements, incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence matrices in directions of 0°, 45°, 90° and 135°. Next, ten features with a high value of mutual information (MI) relative to the pre-defined target class and within the features were selected as final features which were input to a trained classifier (adaptive neuro-fuzzy interface system) to separate the true cord from the ghost cord.
Results:
The implemented pipeline was successfully able to separate the ghost artifacts from true cord structures. The results obtained from the classifier showed a sensitivity of 91%, specificity of 79%, and accuracy of 84% in separating the true cord from ghost artifacts.
Conclusion:
The results show that the proposed method is promising for the automatic detection of ghost cords present in DTI images of the spinal cord. This step is crucial towards development of accurate, automatic DTI spinal cord post processing pipelines.
