OBJECTIVE: We compared measurements of tumor perfusion from microbubble contrast-enhanced sonography (MCES) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in an animal tumor model. METHODS: Seven mice were implanted with Lewis lung carcinoma cells on their hind limbs and imaged 14 days later with a Philips 5- to 7-MHz sonography system (Philips Medical Systems, Andover, MA) and a Varian 7.0-T MRI system (Varian, Inc, Palo Alto, CA). For sonographic imaging 100 microL of a perfluoropropane microbubble contrast agent (Definity; Bristol-Myers Squibb Medical Imaging, Billerica, MA) was injected and allowed to reach a pseudo steady state, after which a high-mechanical index pulse was delivered to destroy the microbubbles within the field of view, and the replenishment of the microbubbles was imaged for 30 to 60 seconds. The MRI included acquisition of a T(10) map and 35 serial T(1)-weighted images (repetition time, 100 milliseconds; echo time, 3.1 milliseconds; alpha, 30 degrees ) after the injection of 100 microL of 0.2-mmol/kg gadopentetate dimeglumine (Magnevist; Berlex, Wayne, NJ). Region-of-interest and voxel-by-voxel analyses of both data sets were performed; microbubble contrast-enhanced sonography returned estimates of microvessel cross-sectional area, microbubble velocity, and mean blood flow, whereas DCE-MRI returned estimates of a perfusion-permeability index and the extravascular extracellular volume fraction. RESULTS: Comparing similar regions of tumor tissue seen on sonography and MRI, region-of-interest analyses revealed a strong (r(2) = 0.57) and significant relationship (P < .002) between the estimates of perfusion obtained by the two modalities. CONCLUSIONS: Microbubble contrast-enhanced sonography can effectively depict intratumoral heterogeneity in preclinical xenograft models when voxel-by-voxel analysis is performed, and this analysis correlates with similar DCE-MRI measurements.
OBJECTIVE: We compared measurements of tumor perfusion from microbubble contrast-enhanced sonography (MCES) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in an animal tumor model. METHODS: Seven mice were implanted with Lewis lung carcinoma cells on their hind limbs and imaged 14 days later with a Philips 5- to 7-MHz sonography system (Philips Medical Systems, Andover, MA) and a Varian 7.0-T MRI system (Varian, Inc, Palo Alto, CA). For sonographic imaging 100 microL of a perfluoropropane microbubble contrast agent (Definity; Bristol-Myers Squibb Medical Imaging, Billerica, MA) was injected and allowed to reach a pseudo steady state, after which a high-mechanical index pulse was delivered to destroy the microbubbles within the field of view, and the replenishment of the microbubbles was imaged for 30 to 60 seconds. The MRI included acquisition of a T(10) map and 35 serial T(1)-weighted images (repetition time, 100 milliseconds; echo time, 3.1 milliseconds; alpha, 30 degrees ) after the injection of 100 microL of 0.2-mmol/kg gadopentetate dimeglumine (Magnevist; Berlex, Wayne, NJ). Region-of-interest and voxel-by-voxel analyses of both data sets were performed; microbubble contrast-enhanced sonography returned estimates of microvessel cross-sectional area, microbubble velocity, and mean blood flow, whereas DCE-MRI returned estimates of a perfusion-permeability index and the extravascular extracellular volume fraction. RESULTS: Comparing similar regions of tumor tissue seen on sonography and MRI, region-of-interest analyses revealed a strong (r(2) = 0.57) and significant relationship (P < .002) between the estimates of perfusion obtained by the two modalities. CONCLUSIONS: Microbubble contrast-enhanced sonography can effectively depict intratumoral heterogeneity in preclinical xenograft models when voxel-by-voxel analysis is performed, and this analysis correlates with similar DCE-MRI measurements.
Authors: David L Schwartz; James Bankson; Luc Bidaut; Yi He; Ryan Williams; Robert Lemos; Arun Kumar Thitai; Junghwan Oh; Andrei Volgin; Suren Soghomonyan; Hsin-Hsien Yeh; Ryuichi Nishii; Uday Mukhopadhay; Mian Alauddin; Ioseb Mushkudiani; Norihito Kuno; Sunil Krishnan; William Bornman; Stephen Y Lai; Garth Powis; John Hazle; Juri Gelovani Journal: Mol Cancer Res Date: 2011-03-01 Impact factor: 5.852
Authors: Katherine D Watson; Xiaowen Hu; Chun-Yen Lai; Heather A Lindfors; Dana D Hu-Lowe; Theresa A Tuthill; David R Shalinsky; Katherine W Ferrara Journal: Ultrasound Med Biol Date: 2011-04-30 Impact factor: 2.998
Authors: K K Y Cham; J H E Baker; K S Takhar; J A Flexman; M Q Wong; D A Owen; A Yung; P Kozlowski; S A Reinsberg; E M Chu; C-W A Chang; A K Buczkowski; S W Chung; C H Scudamore; A I Minchinton; D T T Yapp; S S W Ng Journal: Br J Cancer Date: 2010-06-08 Impact factor: 7.640
Authors: John M Hudson; Colleen Bailey; Mostafa Atri; Greg Stanisz; Laurent Milot; Ross Williams; Alex Kiss; Peter N Burns; Georg A Bjarnason Journal: Eur Radiol Date: 2018-01-30 Impact factor: 5.315