Related Experiment Video
Updated: May 3, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
Published on: December 18, 2016
Addressing Multi-Center Variability in Radiomic Analysis: A Comparative Study of Image Acquisition Methods Across Two
Claudia Tocilă-Mătășel1,2, Sorin Marian Dudea1, Gheorghe Iana2
1Department of Radiology, Faculty of Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.
Abstract:
Background: Radiomics has become a valuable tool in medical imaging, but its clinical use is limited by data variability and a lack of reproducibility between centers. This study aims to assess the differences between two scanners and provide guidance on image acquisition methods to reduce variations between images obtained from different centers. Methods: This study utilized medical images obtained in two different imaging centers, with two different 3T MRI scanners. For each scanner, 3D T2 FLAIR sequences were acquired in two forms: the raw and the clinical practice images typically used in diagnostic workflows. The differences between images were analyzed regarding resolution, SNR, CNR, and radiomic features. To facilitate comparison, bias field correction was applied, and the data were standardized to the same scale using Z-score normalization. Descriptive and inferential statistical methods were used to analyze the data. Results: The results show that there are significant differences between centers. Filtering and zero-padding significantly influence the resolution, SNR, CNR values, and radiomics features. Applying Z-score normalization has resolved variations in features sensitive to scale differences, but features reflecting dispersion and extreme values remain significantly different between scanners. Some feature differences may be resolved by analyzing the raw images in both centers. Conclusions: Variations arise due to different acquisition parameters and the differing quality and sensitivity of the equipment. In multi-center studies, acquiring raw images and then applying standardized post-processing methods across all images can enhance the robustness of results. This approach minimizes technical differences, and preserves the integrity of the information, reflecting a more accurate representation of reality and contributing to more reliable and reproducible findings.
More Related Videos
07:13Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
Published on: October 27, 2023
02:09Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
Related Concept Videos
Imaging Studies II: Positron Emission Tomography and Scintigraphy
Fundamental Principles of PET
Imaging Studies for Cardiovascular System IV: CMRI
Imaging Studies III: Computed Tomography