Contrast-enhanced dynamic MRI protocol with improved spatial and time resolution for in vivo microimaging of the mouse with a 1.5-T body scanner and a superconducting surface coil

2005 ◽  
Vol 23 (2) ◽  
pp. 239-243 ◽  
Author(s):  
Jean-Christophe Ginefri ◽  
Marie Poirier-Quinot ◽  
Philippe Robert ◽  
Luc Darrasse
2008 ◽  
Vol 60 (4) ◽  
pp. 917-927 ◽  
Author(s):  
Marie Poirier-Quinot ◽  
Jean-Christophe Ginefri ◽  
Olivier Girard ◽  
Philippe Robert ◽  
Luc Darrasse

2019 ◽  
Vol 1 (1) ◽  
pp. 64-72 ◽  
Author(s):  
Jordana Phillips ◽  
Valerie J Fein-Zachary ◽  
Priscilla J Slanetz

Abstract Contrast-enhanced mammography (CEM) is a promising new imaging modality that uses a dual-energy acquisition to provide both morphologic and vascular assessment of breast lesions. Although no official BI-RADS lexicon exists, interpretation entails using the mammographic BI-RADS lexicon in combination with that for breast MRI. CEM has comparable performance to breast MRI, with sensitivity of 93–100% and specificity of 80–94%. Currently FDA approved for diagnostic imaging, this technology can be helpful in determining disease extent in patients with newly diagnosed breast malignancy, monitoring response to neoadjuvant therapy, identifying mammographically occult malignancies, and diagnostic problem-solving. Studies are ongoing about its role in screening, especially in women with dense breasts or at elevated risk. There are some challenges to successful implementation into practice, but overall, patients tolerate the study well, and exam times are less than the full breast MRI protocol.


2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Bruno Paun ◽  
Daniel García Leon ◽  
Alex Claveria Cabello ◽  
Roso Mares Pages ◽  
Elena de la Calle Vargas ◽  
...  

Abstract Background Skeletal muscle injury characterisation during healing supports trauma prognosis. Given the potential interest of computed tomography (CT) in muscle diseases and lack of in vivo CT methodology to image skeletal muscle wound healing, we tracked skeletal muscle injury recovery using in vivo micro-CT in a rat model to obtain a predictive model. Methods Skeletal muscle injury was performed in 23 rats. Twenty animals were sorted into five groups to image lesion recovery at 2, 4, 7, 10, or 14 days after injury using contrast-enhanced micro-CT. Injury volumes were quantified using a semiautomatic image processing, and these values were used to build a prediction model. The remaining 3 rats were imaged at all monitoring time points as validation. Predictions were compared with Bland-Altman analysis. Results Optimal contrast agent dose was found to be 20 mL/kg injected at 400 μL/min. Injury volumes showed a decreasing tendency from day 0 (32.3 ± 12.0mm3, mean ± standard deviation) to day 2, 4, 7, 10, and 14 after injury (19.6 ± 12.6, 11.0 ± 6.7, 8.2 ± 7.7, 5.7 ± 3.9, and 4.5 ± 4.8 mm3, respectively). Groups with single monitoring time point did not yield significant differences with the validation group lesions. Further exponential model training with single follow-up data (R2 = 0.968) to predict injury recovery in the validation cohort gave a predictions root mean squared error of 6.8 ± 5.4 mm3. Further prediction analysis yielded a bias of 2.327. Conclusion Contrast-enhanced CT allowed in vivo tracking of skeletal muscle injury recovery in rat.


Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 520-530
Author(s):  
Eleftherios Kontopodis ◽  
Kostas Marias ◽  
Georgios C. Manikis ◽  
Katerina Nikiforaki ◽  
Maria Venianaki ◽  
...  

AbstractThis study aims to examine a time-extended dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) protocol and report a comparative study with three different pharmacokinetic (PK) models, for accurate determination of subtle blood–brain barrier (BBB) disruption in patients with multiple sclerosis (MS). This time-extended DCE-MRI perfusion protocol, called Snaps, was applied on 24 active demyelinating lesions of 12 MS patients. Statistical analysis was performed for both protocols through three different PK models. The Snaps protocol achieved triple the window time of perfusion observation by extending the magnetic resonance acquisition time by less than 2 min on average for all patients. In addition, the statistical analysis in terms of adj-R2 goodness of fit demonstrated that the Snaps protocol outperformed the conventional DCE-MRI protocol by detecting 49% more pixels on average. The exclusive pixels identified from the Snaps protocol lie in the low ktrans range, potentially reflecting areas with subtle BBB disruption. Finally, the extended Tofts model was found to have the highest fitting accuracy for both analyzed protocols. The previously proposed time-extended DCE protocol, called Snaps, provides additional temporal perfusion information at the expense of a minimal extension of the conventional DCE acquisition time.


Author(s):  
L. A. R. Righesso ◽  
M. Terekhov ◽  
H. Götz ◽  
M. Ackermann ◽  
T. Emrich ◽  
...  

Abstract Objectives Micro-computed tomography (μ-CT) and histology, the current gold standard methods for assessing the formation of new bone and blood vessels, are invasive and/or destructive. With that in mind, a more conservative tool, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), was tested for its accuracy and reproducibility in monitoring neovascularization during bone regeneration. Additionally, the suitability of blood perfusion as a surrogate of the efficacy of osteoplastic materials was evaluated. Materials and methods Sixteen rabbits were used and equally divided into four groups, according to the time of euthanasia (2, 3, 4, and 6 weeks after surgery). The animals were submitted to two 8-mm craniotomies that were filled with blood or autogenous bone. Neovascularization was assessed in vivo through DCE-MRI, and bone regeneration, ex vivo, through μ-CT and histology. Results The defects could be consistently identified, and their blood perfusion measured through DCE-MRI, there being statistically significant differences within the blood clot group between 3 and 6 weeks (p = 0.029), and between the former and autogenous bone at six weeks (p = 0.017). Nonetheless, no significant correlations between DCE-MRI findings on neovascularization and μ-CT (r =−0.101, 95% CI [−0.445; 0.268]) or histology (r = 0.305, 95% CI [−0.133; 0.644]) findings on bone regeneration were observed. Conclusions These results support the hypothesis that DCE-MRI can be used to monitor neovascularization but contradict the premise that it could predict bone regeneration as well.


2001 ◽  
Vol 13 (4) ◽  
pp. 607-614 ◽  
Author(s):  
Henkjan J. Huisman ◽  
Marc R. Engelbrecht ◽  
Jelle O. Barentsz

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoqin Qian ◽  
Wenping Wang ◽  
Wentao Kong ◽  
Yu Chen

A novel anticancer drug delivery system with contrast-enhanced ultrasound-imaging performance was synthesized by a typical hard-templating method using monodispersed silica nanoparticles as the templates, which was based on unique molecularly organic/inorganic hybrid hollow periodic mesoporous organosilicas (HPMOs). The highly dispersed HPMOs show the uniform spherical morphology, large hollow interior, and well-defined mesoporous structures, which are very beneficial for ultrasound-based theranostics. The obtained HPMOs exhibit excellent performances in contrast-enhanced ultrasonography bothin vitroandin vivoand can be used for the real-time determination of the progress of lesion tissues during the chemotherapeutic process. Importantly, hydrophobic paclitaxel- (PTX-) loaded HPMOs combined with ultrasound irradiation show fast ultrasound responsiveness for controlled drug release and higherin vitroandin vivotumor inhibition rates compared with free PTX and PTX-loaded HPMOs, which is due to the enhanced ultrasound-triggered drug release and ultrasound-induced cavitation effect. Therefore, the achieved novel HPMOs-based nanoparticle systems will find broad application potentials in clinically ultrasound-based imaging and auxiliary tumor chemotherapy.


2016 ◽  
Vol 41 (10) ◽  
pp. 1973-1979 ◽  
Author(s):  
Zhu Wang ◽  
Wei Wang ◽  
Guang-Jian Liu ◽  
Zheng Yang ◽  
Li-Da Chen ◽  
...  

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