S Sanchez1, A Raghuram1, R Fakih1, L Wendt2, G Bathla3, M Hickerson1, S Ortega-Gutierrez1,3,4, E Leira1, E A Samaniego5,3,4. 1. From the Departments of Neurology (S.S., A.R., R.F., M.H., S.O.-G., E.L., E.A.S.). 2. Institute for Clinical and Translational Science (L.W.), University of Iowa, Iowa City, Iowa. 3. Radiology (G.B., S.O.-G., E.A.S.). 4. Neurosurgery (S.O.-G., E.A.S.), University of Iowa Hospitals and Clinics, Iowa City, Iowa. 5. From the Departments of Neurology (S.S., A.R., R.F., M.H., S.O.-G., E.L., E.A.S.) edgarsama@gmail.com.
Abstract
BACKGROUND AND PURPOSE: High-resolution MR imaging allows the identification of culprit symptomatic plaques after the administration of gadolinium. Current high-resolution MR imaging methods are limited by 2D multiplanar views and manual sampling of ROIs. We analyzed a new 3D method to objectively quantify gadolinium plaque enhancement. MATERIALS AND METHODS: Patients with stroke due to intracranial atherosclerotic disease underwent 7T high-resolution MR imaging. 3D segmentations of the plaque and its parent vessel were generated. Signal intensity probes were automatically extended from the lumen into the plaque and the vessel wall to generate 3D enhancement color maps. Plaque gadolinium (Gd) uptake was quantified from 3D color maps as gadolinium uptake = (µPlaque T1 + Gd -µPlaque T1/SDPlaque T1). Additional metrics of enhancement such as enhancement ratio, variance, and plaque-versus-parent vessel enhancement were also calculated. Conventional 2D measures of enhancement were collected for comparison. RESULTS: Thirty-six culprit and 44 nonculprit plaques from 36 patients were analyzed. Culprit plaques had higher gadolinium uptake than nonculprit plaques (P < .001). Gadolinium uptake was the most accurate metric for identifying culprit plaques (OR, 3.9; 95% CI 2.1-8.3). Gadolinium uptake was more sensitive (86% versus 70%) and specific (71% versus 68%) in identifying culprit plaques than conventional 2D measurements. A multivariate model, including gadolinium uptake and plaque burden, identified culprit plaques with an 83% sensitivity and 86% specificity. CONCLUSIONS: The new 3D color map method of plaque-enhancement analysis is more accurate for identifying culprit plaques than conventional 2D methods. This new method generates a new set of metrics that could potentially be used to assess disease progression.
BACKGROUND AND PURPOSE: High-resolution MR imaging allows the identification of culprit symptomatic plaques after the administration of gadolinium. Current high-resolution MR imaging methods are limited by 2D multiplanar views and manual sampling of ROIs. We analyzed a new 3D method to objectively quantify gadolinium plaque enhancement. MATERIALS AND METHODS: Patients with stroke due to intracranial atherosclerotic disease underwent 7T high-resolution MR imaging. 3D segmentations of the plaque and its parent vessel were generated. Signal intensity probes were automatically extended from the lumen into the plaque and the vessel wall to generate 3D enhancement color maps. Plaque gadolinium (Gd) uptake was quantified from 3D color maps as gadolinium uptake = (µPlaque T1 + Gd -µPlaque T1/SDPlaque T1). Additional metrics of enhancement such as enhancement ratio, variance, and plaque-versus-parent vessel enhancement were also calculated. Conventional 2D measures of enhancement were collected for comparison. RESULTS: Thirty-six culprit and 44 nonculprit plaques from 36 patients were analyzed. Culprit plaques had higher gadolinium uptake than nonculprit plaques (P < .001). Gadolinium uptake was the most accurate metric for identifying culprit plaques (OR, 3.9; 95% CI 2.1-8.3). Gadolinium uptake was more sensitive (86% versus 70%) and specific (71% versus 68%) in identifying culprit plaques than conventional 2D measurements. A multivariate model, including gadolinium uptake and plaque burden, identified culprit plaques with an 83% sensitivity and 86% specificity. CONCLUSIONS: The new 3D color map method of plaque-enhancement analysis is more accurate for identifying culprit plaques than conventional 2D methods. This new method generates a new set of metrics that could potentially be used to assess disease progression.
Authors: Anita A Harteveld; Nerissa P Denswil; Wim Van Hecke; Hugo J Kuijf; Aryan Vink; Wim G M Spliet; Mat J Daemen; Peter R Luijten; Jaco J M Zwanenburg; Jeroen Hendrikse; Anja G van der Kolk Journal: Atherosclerosis Date: 2018-04-24 Impact factor: 5.162
Authors: Edgar A Samaniego; Amir Shaban; Santiago Ortega-Gutierrez; Jorge A Roa; David M Hasan; Colin Derdeyn; Biyue Dai; Harold Adams; Enrique Leira Journal: Stroke Vasc Neurol Date: 2019-07-30
Authors: Jiayu Xiao; Shlee S Song; Konrad H Schlick; Shuang Xia; Tao Jiang; Tong Han; Robert J Jackson; Marcio A Diniz; Oana M Dumitrascu; Marcel M Maya; Patrick D Lyden; Debiao Li; Qi Yang; Zhaoyang Fan Journal: Neuroradiol J Date: 2021-06-23
Authors: Ashrita Raghuram; Alberto Varon; Jorge A Roa; Daizo Ishii; Yongjun Lu; Madhavan L Raghavan; Chaorong Wu; Vincent A Magnotta; David M Hasan; Timothy R Koscik; Edgar A Samaniego Journal: Sci Rep Date: 2021-09-15 Impact factor: 4.379