Keerthivasan Mahesh B, Toner Brian, Galons Jean-Philippe, Johnson Kevin, Bilgin Ali, Martin Diego R, Altbach Maria I
Department of Radiology and Imaging Sciences, University of Arizona, Tucson, Arizona, USA.
MR R&D Collaborations, Siemens Medical Solutions USA, New York, New York, USA.
Eur Radiol. 2025 Aug 30. doi: 10.1007/s00330-025-11931-4.
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.
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.
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).
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.
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.
T2 映射有助于对肝脏局灶性病变进行分类，区分恶性肿瘤与最常见的良性肝脏病变、肝血管瘤和胆管错构瘤（BDH）。由于同一体素内存在肝脏和病变而导致的部分容积（PV）会混淆小病变的分类。我们的目标是开发一种强大的双组分 T2 估计技术（SEPG2-SP），以便在存在 PV 的情况下实现准确的 T2 估计。
使用计算机模拟、物理体模数据以及对 27 名患有肝脏局灶性病变的受试者（16 名男性，62.4±14.3 岁；11 名女性，66.8±5.8 岁）进行的体内研究来评估 T2 估计的准确性，这些受试者在 1.5T 下采用径向涡轮自旋回波（RADTSE）技术进行成像。将 SEPG2-SP 模型与不考虑 PV 的单组分模型进行比较。使用受试者操作特征曲线下面积（AUROC）来分析病变分类。
体模数据显示，SEPG2-SP 模型的 T2 估计误差为 2 - 9%，而单组分模型的误差更大，为 9 - 23%。对 68 个肝脏局灶性病变（33 个恶性肿瘤、7 个肝血管瘤和 28 个 BDH）的体内数据分析表明，SEPG2-SP 模型能够正确分类所有病变（AUROC = 1），无论其大小如何。另一方面，使用单组分模型时，由于肝血管瘤被误分类为恶性肿瘤，导致恶性肿瘤和良性病变之间存在重叠（AUROC = 0.84）。
双组分 T2 模型改善了受 PV 影响的肝脏局灶性病变的特征描述，实现了恶性肿瘤与最常见良性肝脏病变的完全分离。
问题 部分容积效应导致 T2 估计误差，混淆了小肝脏局灶性病变的分类。发现 所提出的双组分 T2 估计技术提高了 T2 估计准确性，并能在存在部分容积的情况下准确描述肝脏局灶性病变。临床意义 本文所述的 T2 映射技术为定量分类肝脏局灶性病变提供了一种实用且可靠的方法。即使在受部分容积效应影响的小肿瘤中，它也能够区分最常见的良性肝脏病变和恶性肿瘤。
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