Aizaz Mueez, Bierens Juul, Gijbels Marion J J, Schreuder Tobien H C M L, van Orshoven Narender P, Daemen Jan-Willem H C, Mess Werner H, Flohr Thomas, van Oostenbrugge Robert J, Postma Alida A, Kooi M Eline
From the Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.A., J.B., T.F., A.A.P., M.E.K.); CARIM, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands (M.A., J.B., M.J.J.G., W.H.M., R.J.v.O., M.E.K.); Department of Pathology, Maastricht University Medical Center, Maastricht, the Netherlands (M.J.J.G.); Department of Medical Biochemistry, Amsterdam Cardiovascular Sciences: Atherosclerosis & Ischemic Syndrome; Amsterdam Infection and Immunity: Inflammatory Diseases; Amsterdam UMC Location University of Amsterdam, Amsterdam, the Netherlands (M.J.J.G.); Department of Neurology, Zuyderland Medical Center, Heerlen, the Netherlands (T.H.C.M.L.S.); Department of Neurology, Zuyderland Medical Center, Sittard, the Netherlands (N.P.v.O.); Department of Vascular Surgery, Maastricht University Medical Center, Maastricht, the Netherlands (J.-W.H.C.D.); Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands (W.H.M.); Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands (R.J.v.O.); and School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (A.A.P.).
Invest Radiol. 2025 Aug 1;60(8):508-516. doi: 10.1097/RLI.0000000000001153. Epub 2025 Jan 22.
Carotid plaque vulnerability is a strong predictor of recurrent ipsilateral stroke, but differentiation of plaque components using conventional computed tomography (CT) is suboptimal. The aim of our study was to evaluate the ability of dual-energy CT (DECT) to characterize atherosclerotic carotid plaque components based on the effective atomic number and effective electron density using magnetic resonance imaging (MRI) and, where possible, histology as the reference standard.
Patients with recent cerebral ischemia and a ≥2-mm carotid plaque underwent computed tomography angiography and MRI. A subgroup underwent carotid endarterectomy. Trained observers delineated plaque components on histology or MRI, independent of computed tomography angiography. DECT was coregistered with MRI and/or histology. Intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous tissue, and calcifications were delineated on DECT, and ρ eff and Z eff values were determined in the derivation cohort (n = 55). Spatial separation of these components was evaluated in a ρ eff -Z eff -cluster plot. Ranges that optimally differentiate plaque features were determined. For validation, plaque components were quantified in the validation cohort (n = 29) using these ρ eff -Z eff ranges and literature-based Hounsfield unit (HU) ranges and correlated to MRI volumes.
Eighty-four participants (68 ± 8 years; 55 male) were evaluated. In the derivation cohort, plaque components were well separated on the cluster plot, resulting in the following ranges: IPH:ρ eff < 1.15, Z eff < 7.5, LRNC:ρ eff < 1.15, Z eff :7.5-8.75, fibrous tissue:ρ eff < 1.15, Z eff > 8.75, and calcifications: ρ eff > 1.15, Z eff > 0. In the validation cohort, significant correlations were found between ρ eff -Z eff -based and MRI plaque volumes for fibrous tissue ( r = 0.69, P < 0.001), LRNC ( r = 0.94, P < 0.001), IPH ( r = 0.35, P = 0.03), and calcifications ( r = 0.70, P < 0.001). Lower correlations were found between HU-based and MRI plaque volumes for fibrous tissue ( r = 0.40, P = 0.02), LRNC ( r = 0.86, P < 0.001), and calcifications ( r = 0.47, P = 0.005), with no correlation for IPH ( r = 0.02, P = 0.45).
We determined ρ eff -Z eff ranges for plaque assessment. ρ eff -Z eff -based volumes showed strong-to-very strong correlations with MRI for LRNC, fibrous tissue, and calcifications and a weak correlation for IPH. ρ eff -Z eff -based volumes demonstrated superior agreement with MRI for all plaque components compared with HU-based volumes, highlighting the potential of DECT for the identification of patients with vulnerable plaques.
颈动脉斑块易损性是同侧复发性中风的有力预测指标，但使用传统计算机断层扫描（CT）区分斑块成分的效果并不理想。我们研究的目的是评估双能CT（DECT）基于有效原子序数和有效电子密度来表征动脉粥样硬化颈动脉斑块成分的能力，以磁共振成像（MRI）以及在可能的情况下以组织学作为参考标准。
近期有脑缺血且颈动脉斑块≥2mm的患者接受了计算机断层血管造影和MRI检查。一个亚组患者接受了颈动脉内膜切除术。训练有素的观察者在组织学或MRI上勾勒出斑块成分，独立于计算机断层血管造影。DECT与MRI和/或组织学进行配准。在DECT上勾勒出斑块内出血（IPH）、富含脂质的坏死核心（LRNC）、纤维组织和钙化，并在推导队列（n = 55）中确定有效密度（ρeff）和有效原子序数（Zeff）值。在ρeff-Zeff聚类图中评估这些成分的空间分离情况。确定能最佳区分斑块特征的范围。为进行验证，在验证队列（n = 29）中使用这些ρeff-Zeff范围和基于文献的亨氏单位（HU）范围对斑块成分进行量化，并与MRI体积进行相关性分析。
对84名参与者（68±8岁；55名男性）进行了评估。在推导队列中，斑块成分在聚类图上分离良好，得出以下范围：IPH：ρeff < 1.15，Zeff < 7.5；LRNC：ρeff < 1.15，Zeff：7.5 - 8.75；纤维组织：ρeff < 1.1 < 1.15，Zeff > 8.75；钙化：ρeff > 1.15，Zeff > 0。在验证队列中，发现基于ρeff-Zeff的纤维组织、LRNC、IPH和钙化的斑块体积与MRI之间存在显著相关性（纤维组织：r = 0.69，P < 0.001；LRNC：r = 0.94，P < 0.001；IPH：r = 0.35，P = 0.03；钙化：r = 0.70，P < 0.001）。基于HU的纤维组织、LRNC和钙化的斑块体积与MRI之间的相关性较低（纤维组织：r = 0.40，P = 0.02；LRNC：r = 0.86，P < 0.001；钙化：r = 0.47，P = 0.005），IPH无相关性（r = 0.02，P = 0.45）。
我们确定了用于斑块评估的ρeff-Zeff范围。基于ρeff-Zeff的体积与MRI显示，LRNC、纤维组织和钙化之间具有强至非常强的相关性，IPH具有弱相关性。与基于HU的体积相比，基于ρeff-Zeff的体积在所有斑块成分上与MRI的一致性更好，突出了DECT在识别易损斑块患者方面的潜力。
