Lin Jia1, Xia Wu2,3, Qian Wan3,4,5, Liwen Wan3,4,5, Wenxiao Jia1, Na Zhang3,4,5. 1. Department of Radiology, The First Affiliated Hospital of Xin Jiang Medical University, Urumqi, China. 2. School of Information Engineering, Wuhan University of Technology, Wuhan, China. 3. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. 4. CAS Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. 5. Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Abstract
OBJECTIVE: To evaluate the effect of artery input function (AIF) derived from different arteries for pharmacokinetic modeling on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in the grading of gliomas. METHODS: 49 patients with pathologically confirmed gliomas were recruited and underwent DCE-MRI. A modified Tofts model with different AIFs derived from anterior cerebral artery (ACA), ipsilateral and contralateral middle cerebral artery (MCA) and posterior cerebral artery (PCA) was used to estimate quantitative parameters such as Ktrans (volume transfer constant) and Ve (fractional extracellular-extravascular space volume) for distinguishing the low grade glioma from high grade glioma. The Ktrans and Ve were compared between different arteries using Two Related Samples Tests (TRST) (i.e. Wilcoxon Signed Ranks Test). In addition, these parameters were compared between the low and high grades as well as between the grade II and III using the Mann-Whitney U-test. A p-value of less than 0.05 was regarded as statistically significant. RESULTS: All the patients completed the DCE-MRI successfully. Sharp wash-in and wash-out phases were observed in all AIFs derived from the different arteries. The quantitative parameters (Ktrans and Ve) calculated from PCA were significant higher than those from ACA and MCA for low and high grades, respectively (p < 0.05). Despite the differences of quantitative parameters derived from ACA, MCA and PCA, the Ktrans and Ve from any AIFs could distinguish between low and high grade, however, only Ktrans from any AIFs could distinguish grades II and III. There was no significant correlation between parameters and the distance from the artery, which the AIF was extracted, to the tumor. CONCLUSION: Both quantitative parameters Ktrans and Ve calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas, however, only Ktrans can distinguish grades II and III. ADVANCES IN KNOWLEDGE: We sought to assess the effect of AIF on DCE-MRI for determining grades of gliomas. Both quantitative parameters Ktrans and Ve calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas.
OBJECTIVE: To evaluate the effect of artery input function (AIF) derived from different arteries for pharmacokinetic modeling on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in the grading of gliomas. METHODS: 49 patients with pathologically confirmed gliomas were recruited and underwent DCE-MRI. A modified Tofts model with different AIFs derived from anterior cerebral artery (ACA), ipsilateral and contralateral middle cerebral artery (MCA) and posterior cerebral artery (PCA) was used to estimate quantitative parameters such as Ktrans (volume transfer constant) and Ve (fractional extracellular-extravascular space volume) for distinguishing the low grade glioma from high grade glioma. The Ktrans and Ve were compared between different arteries using Two Related Samples Tests (TRST) (i.e. Wilcoxon Signed Ranks Test). In addition, these parameters were compared between the low and high grades as well as between the grade II and III using the Mann-Whitney U-test. A p-value of less than 0.05 was regarded as statistically significant. RESULTS: All the patients completed the DCE-MRI successfully. Sharp wash-in and wash-out phases were observed in all AIFs derived from the different arteries. The quantitative parameters (Ktrans and Ve) calculated from PCA were significant higher than those from ACA and MCA for low and high grades, respectively (p < 0.05). Despite the differences of quantitative parameters derived from ACA, MCA and PCA, the Ktrans and Ve from any AIFs could distinguish between low and high grade, however, only Ktrans from any AIFs could distinguish grades II and III. There was no significant correlation between parameters and the distance from the artery, which the AIF was extracted, to the tumor. CONCLUSION: Both quantitative parameters Ktrans and Ve calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas, however, only Ktrans can distinguish grades II and III. ADVANCES IN KNOWLEDGE: We sought to assess the effect of AIF on DCE-MRI for determining grades of gliomas. Both quantitative parameters Ktrans and Ve calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas.
Authors: Vera C Keil; Burkhard Mädler; Jürgen Gieseke; Rolf Fimmers; Elke Hattingen; Hans H Schild; Dariusch R Hadizadeh Journal: Magn Reson Imaging Date: 2017-04-21 Impact factor: 2.546
Authors: Manil D Chouhan; Alan Bainbridge; David Atkinson; Shonit Punwani; Rajeshwar P Mookerjee; Mark F Lythgoe; Stuart A Taylor Journal: Phys Med Biol Date: 2016-12-21 Impact factor: 3.609