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Quantitative MRI of cerebral perfusion at high spatial resolution.


Contrast enhanced MRI affords high resolution maps of different hemodynamic parameters; however, the quantification of perfusion using such techniques has been difficult. On the other hand, Arterial Spin Labeling (ASL) methods can accurately map cerebral perfusion but at low spatial resolution. The objective of the present study is to develop a new method that combines contrast enhanced with ASL images to generate quantitative maps of the cerebral perfusion at high spatial resolution. The results of this study may have a significant impact in the clinical practice, improving neuroradiological diagnostics and surgical planning. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
RAMOS, JHONATA EMERICK; KIM, HAE YONG; TANCREDI, FELIPE BRUNETTO. Using Convolutional Neural Network to Automate ACR MRI Low-Contrast Detectability Test. IEEE ACCESS, v. 10, p. 10-pg., . (15/27022-0)
RAMOS, JHONATA E.; KIM, HAE YONG; TANCREDI, F. B.; LI, W; LI, Q; WANG, L. Automation of the ACR MRI Low-Contrast Resolution Test Using Machine Learning. 2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018), v. N/A, p. 6-pg., . (15/27022-0)

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