Contribution of Sinerem® used as blood-pool contrast agent: Detection of cerebral blood volume changes during apnea in the rabbit

1996 ◽  
Vol 36 (3) ◽  
pp. 415-419 ◽  
Author(s):  
Isabelle Berry ◽  
Soraya Benderbous ◽  
Jean-Philippe Ranjeva ◽  
Dominique Gracia-Meavilla ◽  
Claude Manelfe ◽  
...  
2012 ◽  
Vol 70 (3) ◽  
pp. 705-710 ◽  
Author(s):  
Thomas Christen ◽  
Wendy Ni ◽  
Deqiang Qiu ◽  
Heiko Schmiedeskamp ◽  
Roland Bammer ◽  
...  

2010 ◽  
Vol 31 (1) ◽  
pp. 82-89 ◽  
Author(s):  
Peter Dechent ◽  
Gunther Schütze ◽  
Gunther Helms ◽  
Klaus Dietmar Merboldt ◽  
Jens Frahm

One of the characteristics of the blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) response to functional challenges of the brain is the poststimulation undershoot, which has been suggested to originate from a delayed recovery of either cerebral blood volume (CBV) or cerebral metabolic rate of oxygen to baseline. Using bolus-tracking MRI in humans, we recently showed that relative CBV rapidly normalizes after the end of stimulation. As this observation contradicts at least part of the blood-pool contrast agent studies performed in animals, we reinvestigated the CBV contribution by dynamic T1-weighted three-dimensional MRI (8 seconds temporal resolution) and Vasovist at 3 T (12 subjects). Initially, we determined the time constants of individual BOLD responses. After injection of Vasovist, CBV-related T1-weighted signal changes revealed a signal increase during visual stimulation (1.7%±0.4%), but no change relative to baseline in the poststimulation phase (0.2%±0.3%). This finding renders the specific nature of the contrast agent unlikely to be responsible for the discrepancy between human and animal studies. With the assumption of normalized cerebral blood flow after stimulus cessation, a normalized CBV lends support to the idea that the BOLD MRI undershoot reflects a prolonged elevation of oxidative metabolism.


Author(s):  
Lalit N. Goswami ◽  
Shatadru Chakravarty ◽  
Quan-Yu Cai ◽  
Erik M. Shapiro ◽  
M. Frederick Hawthorne ◽  
...  

Radiology ◽  
2006 ◽  
Vol 241 (3) ◽  
pp. 861-872 ◽  
Author(s):  
Konstantin Nikolaou ◽  
Harald Kramer ◽  
Christina Grosse ◽  
Dirk Clevert ◽  
Olaf Dietrich ◽  
...  

NeuroImage ◽  
2019 ◽  
Vol 185 ◽  
pp. 154-163 ◽  
Author(s):  
Eulanca Y. Liu ◽  
Frank Haist ◽  
David J. Dubowitz ◽  
Richard B. Buxton

2017 ◽  
Vol 24 (6) ◽  
pp. 1348-1357 ◽  
Author(s):  
Andrej Babič ◽  
Vassily Vorobiev ◽  
Céline Xayaphoummine ◽  
Gaëlle Lapicorey ◽  
Anne-Sophie Chauvin ◽  
...  

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrew A. Badachhape ◽  
Laxman Devkota ◽  
Igor V. Stupin ◽  
Poonam Sarkar ◽  
Mayank Srivastava ◽  
...  

AbstractNon-invasive methods for estimating placental fractional blood volume (FBV) are of great interest for characterization of vascular perfusion in placentae during pregnancy to identify placental insufficiency that may be indicative of local ischemia or fetal growth restriction (FGR). Nanoparticle contrast-enhanced magnetic resonance imaging (CE-MRI) may enable direct placental FBV estimation and may provide a reliable, 3D alternative to assess maternal-side placental perfusion. In this pre-clinical study, we investigated if placental FBV at 14, 16, and 18 days of gestation could be estimated through contrast-enhanced MRI using a long circulating blood-pool liposomal gadolinium contrast agent that does not penetrate the placental barrier. Placental FBV estimates of 0.47 ± 0.06 (E14.5), 0.50 ± 0.04 (E16.5), and 0.52 ± 0.04 (E18.5) were found through fitting pre-contrast and post-contrast T1 values in placental tissue using a variable flip angle method. MRI-derived placental FBV was validated against nanoparticle contrast-enhanced computed tomography (CE-CT) derived placental FBV, where signal is directly proportional to the concentration of iodine contrast agent. The results demonstrate successful estimation of the placental FBV, with values statistically indistinguishable from the CT derived values.


Placenta ◽  
2017 ◽  
Vol 57 ◽  
pp. 60-70 ◽  
Author(s):  
Ketan B. Ghaghada ◽  
Zbigniew A. Starosolski ◽  
Saakshi Bhayana ◽  
Igor Stupin ◽  
Chandreshkumar V. Patel ◽  
...  

2011 ◽  
Vol 24 (10) ◽  
pp. 1313-1325 ◽  
Author(s):  
Jun Hua ◽  
Qin Qin ◽  
James J. Pekar ◽  
Peter C. M. van Zijl

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