Preoperative Assessment of Blood Vessels and Intratumoral Microbleeds in Brain Tumors Based on a 3D Contrast-Enhanced T1 -Weighted Flow-Sensitive Black-Blood Sequence.

BACKGROUND: Three-dimensional (3D) contrast-enhanced T1 -weighted flow-sensitive black-blood (CE-T1 WI FSBB) is a newly developed black blood sequence by adding motion probing gradient pulses to gradient echo (GRE) sequences, which has important value for the preoperative assessment of tumor brain blood supply vessels and intratumoral microbleeds. PURPOSE: To compare 3D CE-T1 WI FSBB and 3D contrast-enhanced fast spin echo (FSE) sequence for T1 WI for preoperative assessment of blood vessels and microbleeds in brain tumors and to investigate the correlation between visible vessels and microbleeds. STUDY TYPE: Prospective. SUBJECTS: One hundred and seventy-five patients with brain tumors, 65 were male, 110 were female. Including histologically confirmed 73 meningiomas, 23 schwannomas, 20 gliomas, 7 hemangioblastomas, 5 metastases, 2 lymphomas, 2 hemangiopericytomas, 2 germ cell tumors, 1 craniopharyngioma, and 1 cholesteatoma. FIELD STRENGTH/SEQUENCE: A 3-T, CE-T1 WI FSBB, GRE; 3-T, CE-T1 WI, FSE. ASSESSMENT: Three neuroradiologists counted the number of intratumoral vessels on CE-T1 WI and CE-T1 WI FSBB images separately, and they counted the number of intratumoral microbleeds on CE-T1 WI FSBB images. Brain tumors were classified into grade I, grade II, and grade IV according to the World Health Organization (WHO) grading. Differences in the ability of CE-T1 WI FSBB and CE-T1 WI to display intratumoral vessels were compared. The mean counts of three observers were used to study the correlation between vessels and microbleeds. STATISTICAL TESTS: Two-way random intraclass correlation coeficient (ICC) was used for inter-reader agreement regarding intratumoral vessel and microbleed counts, and the linear regression analysis (with F-test) was used to study the correlation between intratumoral vessels and microbleeds based on CE-T1 WI FSBB (α = 0.05). RESULTS: Inter-reader agreements for intratumoral vessel count on CE-T1 WI (ICC = 0.93) and CE-T1 WI FSBB (ICC = 0.92), and the agreement for intratumoral microbleed count on CE-T1 WI FSBB (ICC = 0.99) were excellent. There were statistically significant differences in intratumoral vessel counts between CE-T1 WI and CE-T1 WI FSBB using Mann-Whitney U -test: image readers could identify more intratumoral vessels on CE-T1 WI FSBB images, particularly for meningiomas, schwannomas, gliomas, and WHO grade I tumors. The number of intratumoral vessels had a significant positive effect on the number of intratumoral microbleeds (microbleeds = 5.024 + 1.665 × vessels; F = 11.51). DATA CONCLUSION: More intratumoral vessels could potentially be identified using a 3D CE-T1 WI FSBB sequence compared to a CE-T1 WI sequence, and the number of intratumoral vessels showed a positive linear relationship with the number of intratumoral microbleeds, which might suggest that brain tumors with rich blood supply were more prone to intratumoral microbleeds. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Differentiation of Cerebral Dissecting Aneurysm from Hemorrhagic Saccular Aneurysm by Machine-Learning Based on Vessel Wall MRI: A Multicenter Study.

The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation of surgical procedure. We aimed to build a radiomic model based on high-resolution vessel wall magnetic resonance imaging (VW-MRI) and a machine-learning algorithm. In total, 851 radiomic features from 146 cases were analyzed retrospectively, and the ElasticNet algorithm was used to establish the radiomic model in a training set of 77 cases. A clinico-radiological model using clinical features and MRI features was also built. Then an integrated model was built by combining the radiomic model and clinico-radiological model. The area under the ROC curve (AUC) was used to quantify the performance of models. The models were evaluated using leave-one-out cross-validation in a training set, and further validated in an external test set of 69 cases. The diagnostic performance of experienced radiologists was also assessed for comparison. Eight features were used to establish the radiomic model, and the radiomic model performs better (AUC = 0.831) than the clinico-radiological model (AUC = 0.717), integrated model (AUC = 0.813), and even experienced radiologists (AUC = 0.801). Therefore, a radiomic model based on VW-MRI can reliably be used to distinguish DA and hemorrhagic SA, and, thus, be widely applied in clinical practice.

[Design and implementation of a VB-based virtual operation system for the console in an x-ray machine].

A new experimental software of an X-ray machine's console virtual operation system based on VB is introduced. This software can simulate the manual operation on the X-ray machine's console, and has the characteristics of easy operation, multi-function and good interactivity.