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Diagnosing abdominal neoplasms using a T2 mapping radial turbo spin-echo technique with partial volume correction
Mahesh B Keerthivasan1,2, Brian Toner1,3, Jean-Philippe Galons1
1Department of Radiology and Imaging Sciences, University of Arizona, Tucson, Arizona, USA.
European Radiology
|August 30, 2025
Summary
A new two-component T2 estimation technique accurately classifies focal liver lesions, even small ones, by overcoming partial volume effects. This method reliably differentiates malignancies from benign lesions like hemangiomas and bile duct hamartomas.
Area of Science:
- Magnetic Resonance Imaging (MRI)
- Medical Imaging Physics
- Hepatobiliary Imaging
Background:
- T2 mapping is crucial for classifying focal liver lesions, distinguishing malignancies from benign entities like hemangiomas and bile duct hamartomas (BDH).
- Partial volume (PV) effects, where liver and lesion share a voxel, introduce errors in T2 estimation, complicating the accurate classification of small lesions.
Purpose of the Study:
- To develop and validate a robust two-component T2 estimation technique (SEPG2-SP) for accurate T2 quantification in the presence of PV.
- To improve the differentiation of focal liver lesions, particularly small ones, affected by PV.
Main Methods:
- Evaluation of the SEPG2-SP model using computer simulations, physical phantom data, and in vivo imaging of 27 subjects with focal liver lesions at 1.5T using a radial turbo spin-echo (RADTSE) technique.
- Comparison of the SEPG2-SP model against a conventional single-component model that does not account for PV.
- Analysis of lesion classification accuracy using the area under the receiver operator characteristic curve (AUROC).
Main Results:
- The SEPG2-SP model demonstrated significantly lower T2 estimation errors (2-9%) on phantom data compared to the single-component model (9-23%).
- In vivo analysis of 68 lesions showed the SEPG2-SP model achieved perfect classification (AUROC = 1), correctly differentiating all malignancies, hemangiomas, and BDH regardless of size.
- The single-component model exhibited overlap between lesion types (AUROC = 0.84), misclassifying hemangiomas as malignancies due to PV effects.
Conclusions:
- The developed two-component T2 model effectively mitigates partial volume effects, leading to improved T2 estimation accuracy for focal liver lesions.
- This technique enables complete and accurate separation of malignant liver lesions from common benign types, even when affected by PV.
- The SEPG2-SP T2 mapping offers a practical and reliable quantitative approach for the clinical characterization of focal liver lesions.
