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

Abstract

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.

Keywords: Abdominal imaging; Partial volume effects; Radial turbo spin echo; T2 mapping.

Fig. 1

A Illustration of slice profile effects on two-component parameter estimation. Assuming a lesion (blue circle) embedded within background tissue (green), flip angle variations across the excited/refocused slice cause the two tissue types to experience different coherent pathways. B The observed signal from the slice is a cumulative sum of signal from these two components, which depends on the slice profile and the position of the lesion within the slice. When the lesion is at the center of the slice, the signal decay (red curve—position 1) is closer to the decay of the lesion without PV (blue curve). When the lesion is at the edge of the slice, the signal (red curve—position 2) has an intermediate decay between lesion (blue curve) and liver (green curve) due to partial volume

Fig. 2

Effect of slice position on lesion T2 estimation: Data were acquired at 3 T using both the RADTSE-CFA and VFA sequences on a subject with two hemangiomas. A Coronal images showing the acquired slice positions for each lesion, with at least one position centered on the lesion, where PV is minimized. B Axial images for each acquired position; position 2 is centered on the lesion and considered as the position with minimal PV. C Estimated T2 values for the SEPG and proposed SEPG2-SP models for the different slice prescriptions

Fig. 3

Scatter plots of T2 estimates from data acquired at 1.5 T in 27 clinical patients for a total of 68 neoplasms: 33 malignant (29 metastases, 4 HCC) and 35 benign (7 hemangiomas, 28 bile duct hamartomas (BDH)) lesions comparing (A) SEPG (where PV is not considered) to the (B) PV-corrected SPG2-SP model proposed here. SEPG has a significant overlap between hemangiomas and malignancies (AUROC= 0.847), with some overlap between BDH and malignancies (AUROC= 0.995). On the other hand, SEPG2-SP provides complete separation between benign and malignant lesions (AUROC= 1.0). The asterisks indicate example lesions shown in Fig. 4

Fig. 4

T2-weighted images of three lesions showing the slices at the edge and through the middle of these lesions. The T2 values of ROIs corresponding to the edge slices are shown in Fig. 3 (asterisks)