Objective T2 mapping allows for the classification of focal liver lesions, differentiating malignancies from the most common benign liver lesions, hemangiomas, and bile duct hamartomas (BDH). Partial volume (PV) due to the presence of liver and lesion within the same voxel confounds the classification of small lesions. Our objective is to develop a robust two-component T2 estimation technique (SEPG2-SP) to enable accurate T2 estimation in the presence of PV. Materials and methods T2 estimation accuracy was evaluated using computer simulations, physical phantom data, and in vivo in 27 subjects with focal liver lesions (16 males, 62.4 ± 14.3 years old; 11 females, 66.8 ± 5.8 years old) imaged at 1.5 T with a radial turbo spin-echo (RADTSE) technique. The SEPG2-SP model was compared to a single-component model, which does not account for PV. The area under the receiver operator characteristic curve (AUROC) was used to analyze lesion classification. Results Phantom data showed that the SEPG2-SP model had a T2 estimation error of 2–9% while the single component model had a larger error of 9–23%. Analysis of in vivo data from 68 focal liver lesions (33 malignancies, 7 hemangiomas, and 28 BDH) showed that the SEPG2-SP model classified all lesions correctly (AUROC = 1), regardless of their size. On the other hand, with the single-component model, there was overlap between malignancies and benign lesions driven by misclassification of hemangiomas as malignancies (AUROC = 0.84). Conclusions The two-component T2 model improved the characterization of focal liver lesions affected by PV, yielding complete separation of malignancies from the most common benign liver lesions. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Partial volume effects result in T2 estimation errors that confound the classification of small focal liver lesions. <Emphasis Type="BoldItalic">Findings</Emphasis> The proposed two-component T2 estimation technique improves T2 estimation accuracy and allows accurate characterization of focal liver lesions in the presence of partial volume. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> The T2 mapping technique described here offers a practical and reliable approach for quantitatively classifying focal liver lesions. It enables differentiation between the most common benign liver lesions and malignancies, even in small tumors impacted by partial volume effects. Graphical Abstract

Diagnosing abdominal neoplasms using a T2 mapping radial turbo spin-echo technique with partial volume correction

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2025年8月30日

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Mahesh B. Keerthivasan,
Brian Toner,
Jean-Philippe Galons,
Kevin Johnson,
Ali Bilgin,
Diego R. Martin,
Maria I. Altbach

摘要

Objective

T2 mapping allows for the classification of focal liver lesions, differentiating malignancies from the most common benign liver lesions, hemangiomas, and bile duct hamartomas (BDH). Partial volume (PV) due to the presence of liver and lesion within the same voxel confounds the classification of small lesions. Our objective is to develop a robust two-component T2 estimation technique (SEPG2-SP) to enable accurate T2 estimation in the presence of PV.

Materials and methods

T2 estimation accuracy was evaluated using computer simulations, physical phantom data, and in vivo in 27 subjects with focal liver lesions (16 males, 62.4 ± 14.3 years old; 11 females, 66.8 ± 5.8 years old) imaged at 1.5 T with a radial turbo spin-echo (RADTSE) technique. The SEPG2-SP model was compared to a single-component model, which does not account for PV. The area under the receiver operator characteristic curve (AUROC) was used to analyze lesion classification.

Results

Phantom data showed that the SEPG2-SP model had a T2 estimation error of 2–9% while the single component model had a larger error of 9–23%. Analysis of in vivo data from 68 focal liver lesions (33 malignancies, 7 hemangiomas, and 28 BDH) showed that the SEPG2-SP model classified all lesions correctly (AUROC = 1), regardless of their size. On the other hand, with the single-component model, there was overlap between malignancies and benign lesions driven by misclassification of hemangiomas as malignancies (AUROC = 0.84).

Conclusions

The two-component T2 model improved the characterization of focal liver lesions affected by PV, yielding complete separation of malignancies from the most common benign liver lesions.

Key Points

Question Partial volume effects result in T2 estimation errors that confound the classification of small focal liver lesions.

Findings The proposed two-component T2 estimation technique improves T2 estimation accuracy and allows accurate characterization of focal liver lesions in the presence of partial volume.

Clinical relevance The T2 mapping technique described here offers a practical and reliable approach for quantitatively classifying focal liver lesions. It enables differentiation between the most common benign liver lesions and malignancies, even in small tumors impacted by partial volume effects.

Graphical Abstract