口面部扩散加权成像衍生参数与动态对比增强磁共振成像衍生参数之间的相关性。

IF 0.9 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Acta radiologica open Pub Date : 2024-03-28 eCollection Date: 2024-03-01 DOI:10.1177/20584601241244777
Toru Chikui, Masahiro Ohga, Yukiko Kami, Osamu Togao, Shintaro Kawano, Tamotsu Kiyoshima, Kazunori Yoshiura
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引用次数: 0

摘要

背景:扩散加权成像(DWI)和动态对比增强磁共振成像(DCE-MRI)被广泛应用于口面部区域。此外,定量分析已被证明非常有用。目的:评估 DWI 和 DCE-MRI 所获参数之间的相关性,并在良性和恶性病变之间进行比较:材料和方法:分析了 50 例口腔病变。通过 DWI 估算表观扩散系数(ADC)、真实扩散系数(D)、假扩散系数(D*)和灌注分数(f)。对于 DCE-MRI,进行了 TK 模型分析以估算生理参数,例如流入细胞外-血管外空间(EES)的前向体积转移常数(Ktrans)以及 EES 和血浆成分的分数体积(ve 和 vp):ADC 和 D 与 ve 呈中度正相关(ρ = 0.640 和 0.645)。Ktrans 与 f 的相关性稍弱(ρ = 0.296),而 vp 与 f 或 D* 没有相关性;因此,IVIM 灌注相关参数与 TK 模型灌注相关参数并不直接相关。D和ve对良性病变和恶性肿瘤的诊断率都很高,曲线下面积(AUC)分别为0.830和0.782:结论:D 和 ve 都是可靠的参数,有助于鉴别诊断。此外,真实扩散系数(D)受 EES 体积分数的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Correlation between diffusion-weighted image-derived parameters and dynamic contrast-enhanced magnetic resonance imaging-derived parameters in the orofacial region.

Background: Diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are widely used in the orofacial region. Furthermore, quantitative analyses have proven useful. However, a few reports have described the correlation between DWI-derived parameters and DCE-MRI-derived parameters, and the results have been controversial.

Purpose: To evaluate the correlation among parameters obtained by DWI and DCE-MRI and to compare them between benign and malignant lesions.

Material and methods: Fifty orofacial lesions were analysed. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*) and perfusion fraction (f) were estimated by DWI. For DCE-MRI, TK model analysis was performed to estimate physiological parameters, for example, the influx forward volume transfer constant into the extracellular-extravascular space (EES) (Ktrans) and fractional volumes of EES and plasma components (ve and vp).

Results: Both ADC and D showed a moderate positive correlation with ve (ρ = 0.640 and 0.645, respectively). Ktrans showed a marginally weak correlation with f (ρ = 0.296), while vp was not correlated with f or D*; therefore, IVIM perfusion-related parameters and TK model perfusion-related parameters were not straightforward. Both D and ve yielded high diagnostic power between benign lesions and malignant tumours with areas under the curve (AUCs) of 0.830 and 0.782, respectively.

Conclusion: Both D and ve were reliable parameters that were useful for the differential diagnosis. In addition, the true diffusion coefficient (D) was affected by the fractional volume of EES.

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