Advanced Physiologic Imaging: Perfusion – Theory and Applications

[1]  J. Boxerman,et al.  Moving Toward a Consensus DSC-MRI Protocol: Validation of a Low–Flip Angle Single-Dose Option as a Reference Standard for Brain Tumors , 2019, American Journal of Neuroradiology.

[2]  M. Gilbert,et al.  Interreader Variability of Dynamic Contrast-enhanced MRI of Recurrent Glioblastoma: The Multicenter ACRIN 6677/RTOG 0625 Study. , 2019, Radiology.

[3]  J. Boxerman,et al.  Optimization of Acquisition and Analysis Methods for Clinical Dynamic Susceptibility Contrast MRI Using a Population-Based Digital Reference Object , 2018, American Journal of Neuroradiology.

[4]  D. Westen,et al.  Arterial spin labeling MR imaging for differentiation between high- and low-grade glioma-a meta-analysis. , 2018 .

[5]  A. Traboulsee,et al.  Gadolinium Deposition in Deep Brain Structures: Relationship with Dose and Ionization of Linear Gadolinium-Based Contrast Agents , 2018, American Journal of Neuroradiology.

[6]  S. Choi,et al.  Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients , 2018, Neuro-oncology.

[7]  Sung Tae Kim,et al.  Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation , 2018, Neuro-oncology.

[8]  C. Bettegowda,et al.  Current state of immunotherapy for glioblastoma , 2018, Nature Reviews Clinical Oncology.

[9]  M. Zilbovicius,et al.  Cerebral blood flow changes after radiation therapy identifies pseudoprogression in diffuse intrinsic pontine gliomas , 2018, Neuro-oncology.

[10]  Chia-Feng Lu,et al.  Machine Learning–Based Radiomics for Molecular Subtyping of Gliomas , 2018, Clinical Cancer Research.

[11]  B. Rosen,et al.  Pharmacodynamics of mutant-IDH1 inhibitors in glioma patients probed by in vivo 3D MRS imaging of 2-hydroxyglutarate , 2018, Nature Communications.

[12]  Till Acker,et al.  DNA methylation-based classification of central nervous system tumours , 2018, Nature.

[13]  H R Jäger,et al.  Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice , 2018, European Radiology.

[14]  O. Andronesi,et al.  Radiomics, Metabolic, and Molecular MRI for Brain Tumors , 2018, Seminars in Neurology.

[15]  Paolo Vitali,et al.  Brain Gliomas: Multicenter Standardized Assessment of Dynamic Contrast-enhanced and Dynamic Susceptibility Contrast MR Images. , 2018, Radiology.

[16]  C. Balañà,et al.  Pseudoprogression as an adverse event of glioblastoma therapy , 2017, Cancer medicine.

[17]  J. Nam,et al.  Added Value of Arterial Spin-Labeling MR Imaging for the Differentiation of Cerebellar Hemangioblastoma from Metastasis , 2017, American Journal of Neuroradiology.

[18]  T. Cloughesy,et al.  Detection of immune responses after immunotherapy in glioblastoma using PET and MRI , 2017, Proceedings of the National Academy of Sciences.

[19]  J. Boxerman,et al.  Pseudoprogression, radionecrosis, inflammation or true tumor progression? challenges associated with glioblastoma response assessment in an evolving therapeutic landscape , 2017, Journal of Neuro-Oncology.

[20]  Sunil Kumar,et al.  Perfusion MR imaging of enhancing brain tumors: Comparison of arterial spin labeling technique with dynamic susceptibility contrast technique , 2017, Neurology India.

[21]  H. Lee,et al.  The Initial Area Under the Curve Derived from Dynamic Contrast-Enhanced MRI Improves Prognosis Prediction in Glioblastoma with Unmethylated MGMT Promoter , 2017, American Journal of Neuroradiology.

[22]  F. Dong,et al.  3D Pseudocontinuous Arterial Spin-Labeling MR Imaging in the Preoperative Evaluation of Gliomas , 2017, American Journal of Neuroradiology.

[23]  Y. Zhang,et al.  Noninvasive Assessment of IDH Mutational Status in World Health Organization Grade II and III Astrocytomas Using DWI and DSC-PWI Combined with Conventional MR Imaging , 2017, American Journal of Neuroradiology.

[24]  Benjamin M Ellingson,et al.  Longitudinal DSC-MRI for Distinguishing Tumor Recurrence From Pseudoprogression in Patients With a High-grade Glioma , 2017, American journal of clinical oncology.

[25]  Sang Joon Kim,et al.  Differentiation of Recurrent Glioblastoma from Delayed Radiation Necrosis by Using Voxel-based Multiparametric Analysis of MR Imaging Data. , 2017, Radiology.

[26]  Kai Xu,et al.  Three-dimensional arterial spin labeling imaging and dynamic susceptibility contrast perfusion-weighted imaging value in diagnosing glioma grade prior to surgery , 2017, Experimental and therapeutic medicine.

[27]  Jong-Hee Chang,et al.  Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient , 2017, European Radiology.

[28]  Haibo Xu,et al.  The diagnostic performance of perfusion MRI for differentiating glioma recurrence from pseudoprogression , 2017, Medicine.

[29]  J. Boxerman,et al.  Effects of MRI Protocol Parameters, Preload Injection Dose, Fractionation Strategies, and Leakage Correction Algorithms on the Fidelity of Dynamic-Susceptibility Contrast MRI Estimates of Relative Cerebral Blood Volume in Gliomas , 2017, American Journal of Neuroradiology.

[30]  J. Boxerman,et al.  Toxoplasmosis versus lymphoma: Cerebral lesion characterization using DSC-MRI revisited , 2017, Clinical Neurology and Neurosurgery.

[31]  D. Galanaud,et al.  Arterial Spin Labeling to Predict Brain Tumor Grading: Limits of Cutoff Cerebral Blood Flow Values. , 2017, Radiology.

[32]  Rakesh K. Jain,et al.  New Directions in Anti-Angiogenic Therapy for Glioblastoma , 2017, Neurotherapeutics.

[33]  Y. Ra,et al.  Advanced MRI for Pediatric Brain Tumors with Emphasis on Clinical Benefits , 2017, Korean journal of radiology.

[34]  R. Thornhill,et al.  Correlation of Tumor Immunohistochemistry with Dynamic Contrast-Enhanced and DSC-MRI Parameters in Patients with Gliomas , 2016, American Journal of Neuroradiology.

[35]  J. Pfeuffer,et al.  Improving the Grading Accuracy of Astrocytic Neoplasms Noninvasively by Combining Timing Information with Cerebral Blood Flow: A Multi-TI Arterial Spin-Labeling MR Imaging Study , 2016, American Journal of Neuroradiology.

[36]  Dafna Ben Bashat,et al.  Optimization of DCE-MRI protocol for the assessment of patients with brain tumors. , 2016, Magnetic resonance imaging.

[37]  Ashley M Stokes,et al.  Assessment of a simplified spin and gradient echo (sSAGE) approach for human brain tumor perfusion imaging. , 2016, Magnetic resonance imaging.

[38]  E. Maher,et al.  Prospective Longitudinal Analysis of 2-Hydroxyglutarate Magnetic Resonance Spectroscopy Identifies Broad Clinical Utility for the Management of Patients With IDH-Mutant Glioma. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[39]  Peng Cao,et al.  Utility of dynamic contrast-enhanced magnetic resonance imaging for differentiating glioblastoma, primary central nervous system lymphoma and brain metastatic tumor. , 2016, European journal of radiology.

[40]  Martin Sill,et al.  Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features. , 2016, Radiology.

[41]  Wenzhen Zhu,et al.  Intravoxel incoherent motion diffusion‐weighted imaging analysis of diffusion and microperfusion in grading gliomas and comparison with arterial spin labeling for evaluation of tumor perfusion , 2016, Journal of magnetic resonance imaging : JMRI.

[42]  Leland S. Hu,et al.  Radiogenomics to characterize regional genetic heterogeneity in glioblastoma , 2016, Neuro-oncology.

[43]  Hyeong-Seok Lim,et al.  Dynamic contrast‐enhanced MRI for oncology drug development , 2016, Journal of magnetic resonance imaging : JMRI.

[44]  M. Götz,et al.  Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models. , 2016, Radiology.

[45]  P. Varlet,et al.  Arterial Spin Labeling to Predict Brain Tumor Grading in Children: Correlations between Histopathologic Vascular Density and Perfusion MR Imaging. , 2016, Radiology.

[46]  Buhai Wang,et al.  Differentiation between recurrent gliomas and radiation necrosis using arterial spin labeling perfusion imaging. , 2016, Experimental and therapeutic medicine.

[47]  K. Peck,et al.  A prospective trial of dynamic contrast-enhanced MRI perfusion and fluorine-18 FDG PET-CT in differentiating brain tumor progression from radiation injury after cranial irradiation. , 2016, Neuro-oncology.

[48]  G. Reifenberger,et al.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.

[49]  S. Heiland,et al.  MR Perfusion-derived Hemodynamic Parametric Response Mapping of Bevacizumab Efficacy in Recurrent Glioblastoma. , 2016, Radiology.

[50]  J. Boxerman,et al.  Physiologic MRI for assessment of response to therapy and prognosis in glioblastoma. , 2016, Neuro-oncology.

[51]  Luke Macyszyn,et al.  Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. , 2016, Neuro-oncology.

[52]  Susan M. Chang,et al.  Magnetic resonance analysis of malignant transformation in recurrent glioma , 2016, Neuro-oncology.

[53]  Steven J. M. Jones,et al.  Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma , 2016, Cell.

[54]  G. Liberman,et al.  Differentiation between treatment-related changes and progressive disease in patients with high grade brain tumors using support vector machine classification based on DCE MRI , 2016, Journal of Neuro-Oncology.

[55]  S. Heiland,et al.  IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma , 2015, Scientific Reports.

[56]  Naoya Hashimoto,et al.  Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. , 2015, The Lancet. Oncology.

[57]  R. Stupp,et al.  LB-05PHASE III TRIAL EXPLORING THE COMBINATION OF BEVACIZUMAB AND LOMUSTINE IN PATIENTS WITH FIRST RECURRENCE OF A GLIOBLASTOMA: THE EORTC 26101 TRIAL , 2015 .

[58]  Simona Marzi,et al.  Early biomarkers from dynamic contrast-enhanced magnetic resonance imaging to predict the response to antiangiogenic therapy in high-grade gliomas , 2015, Neuroradiology.

[59]  E. Achten,et al.  A neuroradiologist’s guide to arterial spin labeling MRI in clinical practice , 2015, Neuroradiology.

[60]  T. Mayer Can We Predict Bevacizumab Responders in Patients With Glioblastoma? , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[61]  Marion Smits,et al.  Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. , 2015, Neuro-oncology.

[62]  Kyung K Peck,et al.  Dynamic Contrast‐Enhanced Perfusion MRI and Diffusion‐Weighted Imaging in Grading of Gliomas , 2015, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[63]  Sang Joon Kim,et al.  Uninterpretable Dynamic Susceptibility Contrast-Enhanced Perfusion MR Images in Patients with Post-Treatment Glioblastomas: Cross-Validation of Alternative Imaging Options , 2015, PloS one.

[64]  Kyung K. Peck,et al.  Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from recurrent glioblastoma , 2015, Journal of Neuro-Oncology.

[65]  A Gregory Sorensen,et al.  Dynamic susceptibility contrast MRI measures of relative cerebral blood volume as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 multicenter trial. , 2015, Neuro-oncology.

[66]  S. Heiland,et al.  Relative cerebral blood volume is a potential predictive imaging biomarker of bevacizumab efficacy in recurrent glioblastoma. , 2015, Neuro-oncology.

[67]  M. V. van Osch,et al.  Quantitative Functional Arterial Spin Labeling (fASL) MRI – Sensitivity and Reproducibility of Regional CBF Changes Using Pseudo-Continuous ASL Product Sequences , 2015, PloS one.

[68]  David Bonekamp,et al.  Association of overall survival in patients with newly diagnosed glioblastoma with contrast‐enhanced perfusion MRI: Comparison of intraindividually matched T1‐ and T2*‐based bolus techniques , 2015, Journal of magnetic resonance imaging : JMRI.

[69]  Steven J. M. Jones,et al.  Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. , 2015, The New England journal of medicine.

[70]  M Wintermark,et al.  ASFNR Recommendations for Clinical Performance of MR Dynamic Susceptibility Contrast Perfusion Imaging of the Brain , 2015, American Journal of Neuroradiology.

[71]  Danny J. J. Wang,et al.  Astrocytic tumour grading: a comparative study of three-dimensional pseudocontinuous arterial spin labelling, dynamic susceptibility contrast-enhanced perfusion-weighted imaging, and diffusion-weighted imaging , 2015, European Radiology.

[72]  A. Omuro,et al.  Diffusion and Perfusion MRI to Differentiate Treatment-Related Changes Including Pseudoprogression from Recurrent Tumors in High-Grade Gliomas with Histopathologic Evidence , 2015, American Journal of Neuroradiology.

[73]  Jennie W. Taylor,et al.  Lessons from anti-vascular endothelial growth factor and anti-vascular endothelial growth factor receptor trials in patients with glioblastoma. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[74]  G. Fuller,et al.  Dynamic Contrast-Enhanced Perfusion Processing for Neuroradiologists: Model-Dependent Analysis May Not Be Necessary for Determining Recurrent High-Grade Glioma versus Treatment Effect , 2015, American Journal of Neuroradiology.

[75]  T. Cloughesy,et al.  Arterial Spin-Labeling Perfusion MRI Stratifies Progression-Free Survival and Correlates with Epidermal Growth Factor Receptor Status in Glioblastoma , 2015, American Journal of Neuroradiology.

[76]  Sumei Wang,et al.  Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging , 2015, Cancer Imaging.

[77]  S. Choi,et al.  Glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy: differentiation of true progression from pseudoprogression with quantitative dynamic contrast-enhanced MR imaging. , 2015, Radiology.

[78]  Sang Joon Kim,et al.  Pseudoprogression in Patients with Glioblastoma: Assessment by Using Volume-weighted Voxel-based Multiparametric Clustering of MR Imaging Data in an Independent Test Set. , 2015, Radiology.

[79]  G. Zaharchuk,et al.  Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. , 2015, Magnetic resonance in medicine.

[80]  M. Fujiki,et al.  Comparison of Multiple Parameters Obtained on 3T Pulsed Arterial Spin-Labeling, Diffusion Tensor Imaging, and MRS and the Ki-67 Labeling Index in Evaluating Glioma Grading , 2014, American Journal of Neuroradiology.

[81]  S. Ng,et al.  Differentiation of Brain Abscesses from Glioblastomas and Metastatic Brain Tumors: Comparisons of Diagnostic Performance of Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging before and after Mathematic Contrast Leakage Correction , 2014, PloS one.

[82]  H. Cebeci,et al.  Assesment of perfusion in glial tumors with arterial spin labeling; comparison with dynamic susceptibility contrast method. , 2014, European journal of radiology.

[83]  F. Calamante,et al.  Perfusion Magnetic Resonance Imaging: A Comprehensive Update on Principles and Techniques , 2014, Korean journal of radiology.

[84]  S. Heiland,et al.  Evaluation of Microvascular Permeability with Dynamic Contrast-Enhanced MRI for the Differentiation of Primary CNS Lymphoma and Glioblastoma: Radiologic-Pathologic Correlation , 2014, American Journal of Neuroradiology.

[85]  Dafna Ben Bashat,et al.  Differentiation between vasogenic-edema versus tumor-infiltrative area in patients with glioblastoma during bevacizumab therapy: a longitudinal MRI study. , 2014, European journal of radiology.

[86]  S. Kim,et al.  Differentiation of Tumor Progression from Pseudoprogression in Patients with Posttreatment Glioblastoma Using Multiparametric Histogram Analysis , 2014, American Journal of Neuroradiology.

[87]  B. Bender,et al.  Prognostic Value of Blood Flow Measurements Using Arterial Spin Labeling in Gliomas , 2014, PloS one.

[88]  Seong Ho Park,et al.  Glioma: Application of Histogram Analysis of Pharmacokinetic Parameters from T1-Weighted Dynamic Contrast-Enhanced MR Imaging to Tumor Grading , 2014, American Journal of Neuroradiology.

[89]  W. Mueller,et al.  Dynamic-susceptibility contrast agent MRI measures of relative cerebral blood volume predict response to bevacizumab in recurrent high-grade glioma. , 2014, Neuro-oncology.

[90]  Namkug Kim,et al.  Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. , 2014, Radiology.

[91]  Alexander Radbruch,et al.  Progression types after antiangiogenic therapy are related to outcome in recurrent glioblastoma , 2014, Neurology.

[92]  Alexander Radbruch,et al.  Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging. , 2014, Radiology.

[93]  Ahmedin Jemal,et al.  Childhood and adolescent cancer statistics, 2014 , 2014, CA: a cancer journal for clinicians.

[94]  C. Marosi,et al.  Arterial Spin-Labeling Assessment of Normalized Vascular Intratumoral Signal Intensity as a Predictor of Histologic Grade of Astrocytic Neoplasms , 2014, American Journal of Neuroradiology.

[95]  Lutz Tellmann,et al.  Relationship of regional cerebral blood flow and kinetic behaviour of O-(2-18F-fluoroethyl)-L-tyrosine uptake in cerebral gliomas , 2014, Nuclear medicine communications.

[96]  K. Aldape,et al.  A randomized trial of bevacizumab for newly diagnosed glioblastoma. , 2014, The New England journal of medicine.

[97]  K. Hoang-Xuan,et al.  Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. , 2014, The New England journal of medicine.

[98]  U. S. Torres,et al.  The Role of Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging in Differentiating between Infectious and Neoplastic Focal Brain Lesions: Results from a Cohort of 100 Consecutive Patients , 2013, PloS one.

[99]  D Balvay,et al.  Perfusion and vascular permeability: basic concepts and measurement in DCE-CT and DCE-MRI. , 2013, Diagnostic and interventional imaging.

[100]  Namkug Kim,et al.  Recurrent glioblastoma: optimum area under the curve method derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging. , 2013, Radiology.

[101]  X. Zhang,et al.  In vivo blood T1 measurements at 1.5 T, 3 T, and 7 T , 2013, Magnetic resonance in medicine.

[102]  B. Rosen,et al.  Detection of oncogenic IDH1 mutations using magnetic resonance spectroscopy of 2-hydroxyglutarate. , 2013, The Journal of clinical investigation.

[103]  S. Ng,et al.  Differentiation of Primary Central Nervous System Lymphomas and Glioblastomas: Comparisons of Diagnostic Performance of Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging without and with Contrast-Leakage Correction , 2013, American Journal of Neuroradiology.

[104]  Jinna Kim,et al.  Glioma Grading Capability: Comparisons among Parameters from Dynamic Contrast-Enhanced MRI and ADC Value on DWI , 2013, Korean journal of radiology.

[105]  À. Rovira,et al.  Dynamic Contrast-Enhanced MR: Importance of Reaching the Washout Phase , 2013, American Journal of Neuroradiology.

[106]  Geon-Ho Jahng,et al.  Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging , 2013, Acta radiologica.

[107]  David Gutman,et al.  Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. , 2013, Radiology.

[108]  Y. Xiong,et al.  R-2-hydroxyglutarate as the key effector of IDH mutations promoting oncogenesis. , 2013, Cancer cell.

[109]  Satoshi O. Suzuki,et al.  Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography , 2013, Neuroradiology.

[110]  Max Wintermark,et al.  Perfusion MRI: the five most frequently asked clinical questions. , 2013, AJR. American journal of roentgenology.

[111]  Theodoros N. Arvanitis,et al.  Functional imaging in adult and paediatric brain tumours , 2012, Nature Reviews Clinical Oncology.

[112]  G. Jayson,et al.  Do Imaging Biomarkers Relate to Outcome in Patients Treated with VEGF Inhibitors? , 2012, Clinical Cancer Research.

[113]  Namkug Kim,et al.  Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. , 2012, Radiology.

[114]  Bensheng Qiu,et al.  Correlation of volume transfer coefficient Ktrans with histopathologic grades of gliomas , 2012, Journal of magnetic resonance imaging : JMRI.

[115]  Satoshi O. Suzuki,et al.  Arterial spin labeling of hemangioblastoma: differentiation from metastatic brain tumors based on quantitative blood flow measurement , 2012, Neuroradiology.

[116]  G. Zaharchuk,et al.  Combined spin‐ and gradient‐echo perfusion‐weighted imaging , 2012, Magnetic resonance in medicine.

[117]  J. Peltier,et al.  Perfusion MRI as a Neurosurgical Tool for Improved Targeting in Stereotactic Tumor Biopsies , 2012, Stereotactic and Functional Neurosurgery.

[118]  J. Boxerman,et al.  The Role of Preload and Leakage Correction in Gadolinium-Based Cerebral Blood Volume Estimation Determined by Comparison with MION as a Criterion Standard , 2012, American Journal of Neuroradiology.

[119]  D. Geng,et al.  Quantitative analysis of neovascular permeability in glioma by dynamic contrast-enhanced MR imaging , 2012, Journal of Clinical Neuroscience.

[120]  R. Verhaak,et al.  Transformation by the R Enantiomer of 2-Hydroxyglutarate Linked to EglN Activation , 2012, Nature.

[121]  J. Debbins,et al.  Correlations between Perfusion MR Imaging Cerebral Blood Volume, Microvessel Quantification, and Clinical Outcome Using Stereotactic Analysis in Recurrent High-Grade Glioma , 2012, American Journal of Neuroradiology.

[122]  J P B O'Connor,et al.  Dynamic contrast-enhanced imaging techniques: CT and MRI. , 2011, The British journal of radiology.

[123]  Uwe Himmelreich,et al.  MR perfusion and diffusion imaging in the follow-up of recurrent glioblastoma treated with dendritic cell immunotherapy: a pilot study , 2011, Neuroradiology.

[124]  Dinesh Rakheja,et al.  2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated glioma patients , 2011, Nature Medicine.

[125]  T. Mikkelsen,et al.  Differentiating treatment-induced necrosis from recurrent/progressive brain tumor using nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion. , 2011, Neuro-oncology.

[126]  Jeroen Hendrikse,et al.  Intra- and Multicenter Reproducibility of Pulsed, Continuous and Pseudo-Continuous Arterial Spin Labeling Methods for Measuring Cerebral Perfusion , 2011, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[127]  Timothy D Johnson,et al.  Prospective Analysis of Parametric Response Map–Derived MRI Biomarkers: Identification of Early and Distinct Glioma Response Patterns Not Predicted by Standard Radiographic Assessment , 2011, Clinical Cancer Research.

[128]  Yufen Chen,et al.  Test–retest reliability of arterial spin labeling with common labeling strategies , 2011, Journal of magnetic resonance imaging : JMRI.

[129]  E. Melhem,et al.  Differentiation between Glioblastomas, Solitary Brain Metastases, and Primary Cerebral Lymphomas Using Diffusion Tensor and Dynamic Susceptibility Contrast-Enhanced MR Imaging , 2011, American Journal of Neuroradiology.

[130]  D. Kong,et al.  Diagnostic Dilemma of Pseudoprogression in the Treatment of Newly Diagnosed Glioblastomas: The Role of Assessing Relative Cerebral Blood Flow Volume and Oxygen-6-Methylguanine-DNA Methyltransferase Promoter Methylation Status , 2011, American Journal of Neuroradiology.

[131]  G. Reifenberger,et al.  Patients with IDH 1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH 1-mutated glioblastomas , and IDH 1 mutation status accounts for the unfavorable prognostic effect of higher age : implications for classification of gliomas , 2010 .

[132]  J. Reijneveld,et al.  UvA-DARE ( Digital Academic Repository ) Angiogenesis inhibition in high grade glioma Verhoeff , 2009 .

[133]  M. Perrin,et al.  A Comparative Study of Perfusion Measurement in Brain Tumours at 3 Tesla MR: Arterial Spin Labeling versus Dynamic Susceptibility Contrast-Enhanced MRI , 2010, European Neurology.

[134]  Timothy D Johnson,et al.  Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[135]  Susan M. Chang,et al.  Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[136]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[137]  Kim Mouridsen,et al.  The QUASAR reproducibility study, Part II: Results from a multi-center Arterial Spin Labeling test–retest study , 2010, NeuroImage.

[138]  M. Berger,et al.  Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. , 2009, Radiology.

[139]  Hai-Ling Margaret Cheng,et al.  Improved correlation to quantitative DCE‐MRI pharmacokinetic parameters using a modified initial area under the uptake curve (mIAUC) approach , 2009, Journal of magnetic resonance imaging : JMRI.

[140]  P. Wen,et al.  A "vascular normalization index" as potential mechanistic biomarker to predict survival after a single dose of cediranib in recurrent glioblastoma patients. , 2009, Cancer research.

[141]  Michael J Paldino,et al.  Fundamentals of quantitative dynamic contrast-enhanced MR imaging. , 2009, Magnetic resonance imaging clinics of North America.

[142]  Joseph A Maldjian,et al.  Arterial spin-labeled MR perfusion imaging: clinical applications. , 2009, Magnetic resonance imaging clinics of North America.

[143]  R. Jain,et al.  VEGF inhibitors in the treatment of cerebral edema in patients with brain cancer , 2009, Nature Reviews Clinical Oncology.

[144]  J E Heiserman,et al.  Relative Cerebral Blood Volume Values to Differentiate High-Grade Glioma Recurrence from Posttreatment Radiation Effect: Direct Correlation between Image-Guided Tissue Histopathology and Localized Dynamic Susceptibility-Weighted Contrast-Enhanced Perfusion MR Imaging Measurements , 2009, American Journal of Neuroradiology.

[145]  Olav Jansen,et al.  Intraoperative dynamic susceptibility contrast weighted magnetic resonance imaging (iDSC-MRI) — Technical considerations and feasibility , 2009, NeuroImage.

[146]  P. Wen,et al.  Effect of adding temozolomide to radiation therapy on the incidence of pseudo-progression , 2009, Journal of Neuro-Oncology.

[147]  K. Schmainda,et al.  Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. , 2008, Radiology.

[148]  Jon D Wilson,et al.  Primary central nervous system lymphoma. , 2008, Archives of pathology & laboratory medicine.

[149]  T. Hirai,et al.  Prognostic Value of Perfusion MR Imaging of High-Grade Astrocytomas: Long-Term Follow-Up Study , 2008, American Journal of Neuroradiology.

[150]  R. Kraft,et al.  Arterial Spin-Labeling in Routine Clinical Practice, Part 1: Technique and Artifacts , 2008, American Journal of Neuroradiology.

[151]  A. Brandes,et al.  MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[152]  Douglas C. Miller,et al.  Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. , 2008, Radiology.

[153]  S. Y. Kim,et al.  Diagnostic accuracy and interobserver variability of pulsed arterial spin labeling for glioma grading , 2008, Acta radiologica.

[154]  T Sasaki,et al.  Perfusion Imaging of Brain Tumors Using Arterial Spin-Labeling: Correlation with Histopathologic Vascular Density , 2008, American Journal of Neuroradiology.

[155]  A. Waldman,et al.  Low-grade gliomas: do changes in rCBV measurements at longitudinal perfusion-weighted MR imaging predict malignant transformation? , 2008, Radiology.

[156]  P. Barker,et al.  Can Proton MR Spectroscopic and Perfusion Imaging Differentiate Between Neoplastic and Nonneoplastic Brain Lesions in Adults? , 2008, American Journal of Neuroradiology.

[157]  P. Baraldi,et al.  Multimodal MRI in the characterization of glial neoplasms: the combined role of single-voxel MR spectroscopy, diffusion imaging and echo-planar perfusion imaging , 2007, Neuroradiology.

[158]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[159]  Geoff J M Parker,et al.  Imaging Tumor Vascular Heterogeneity and Angiogenesis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging , 2007, Clinical Cancer Research.

[160]  A. Jackson,et al.  Experimentally‐derived functional form for a population‐averaged high‐temporal‐resolution arterial input function for dynamic contrast‐enhanced MRI , 2006, Magnetic resonance in medicine.

[161]  E. Larsson,et al.  Dynamic susceptibility contrast-enhanced perfusion magnetic resonance (MR) imaging combined with contrast-enhanced MR imaging in the follow-up of immunogene-treated glioblastoma multiforme , 2006, Acta radiologica.

[162]  Bahattin Hakyemez,et al.  Evaluation of different cerebral mass lesions by perfusion‐weighted MR imaging , 2006, Journal of magnetic resonance imaging : JMRI.

[163]  X Golay,et al.  Non-invasive Measurement of Perfusion: a Critical Review of Arterial Spin Labelling Techniques , 2022 .

[164]  M P Lichy,et al.  Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors , 2006, Neurology.

[165]  R M Weisskoff,et al.  Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. , 2006, AJNR. American journal of neuroradiology.

[166]  Søren Christensen,et al.  Automatic selection of arterial input function using cluster analysis , 2006, Magnetic resonance in medicine.

[167]  Glyn Johnson,et al.  Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging--prediction of patient clinical response. , 2006, Radiology.

[168]  T. Poussaint,et al.  Advanced neuroimaging of pediatric brain tumors: MR diffusion, MR perfusion, and MR spectroscopy. , 2006, Neuroimaging clinics of North America.

[169]  Geoff J M Parker,et al.  Is volume transfer coefficient (K(trans)) related to histologic grade in human gliomas? , 2005, AJNR. American journal of neuroradiology.

[170]  J. Detre,et al.  Grading of CNS neoplasms using continuous arterial spin labeled perfusion MR imaging at 3 Tesla , 2005, Journal of magnetic resonance imaging : JMRI.

[171]  Peter Wust,et al.  Quantitative measurement of leakage volume and permeability in gliomas, meningiomas and brain metastases with dynamic contrast-enhanced MRI. , 2005, Magnetic resonance imaging.

[172]  M. Knopp,et al.  The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations , 2005, British Journal of Cancer.

[173]  James H Thrall,et al.  Imaging angiogenesis: applications and potential for drug development. , 2005, Journal of the National Cancer Institute.

[174]  Nancy J Fischbein,et al.  Differentiation of low-grade oligodendrogliomas from low-grade astrocytomas by using quantitative blood-volume measurements derived from dynamic susceptibility contrast-enhanced MR imaging. , 2005, AJNR. American journal of neuroradiology.

[175]  A. Maia,et al.  Stereotactic biopsy guidance in adults with supratentorial nonenhancing gliomas: role of perfusion-weighted magnetic resonance imaging. , 2004, Journal of neurosurgery.

[176]  Glyn Johnson,et al.  Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. , 2004, AJNR. American journal of neuroradiology.

[177]  Michael H Lev,et al.  Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. , 2004, AJNR. American journal of neuroradiology.

[178]  Glyn Johnson,et al.  Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. , 2003, AJNR. American journal of neuroradiology.

[179]  C. Zimmer,et al.  Quantification of blood flow in brain tumors: comparison of arterial spin labeling and dynamic susceptibility-weighted contrast-enhanced MR imaging. , 2003, Radiology.

[180]  D. McDonald,et al.  Significance of blood vessel leakiness in cancer. , 2002, Cancer research.

[181]  Glyn Johnson,et al.  Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. , 2002, Radiology.

[182]  K. Murase,et al.  Determination of arterial input function using fuzzy clustering for quantification of cerebral blood flow with dynamic susceptibility contrast‐enhanced MR imaging , 2001, Journal of magnetic resonance imaging : JMRI.

[183]  A P Pathak,et al.  Utility of simultaneously acquired gradient‐echo and spin‐echo cerebral blood volume and morphology maps in brain tumor patients , 2000, Magnetic resonance in medicine.

[184]  W P Dillon,et al.  Quantitative measurement of microvascular permeability in human brain tumors achieved using dynamic contrast-enhanced MR imaging: correlation with histologic grade. , 2000, AJNR. American journal of neuroradiology.

[185]  J L Evelhoch,et al.  Key factors in the acquisition of contrast kinetic data for oncology , 1999, Journal of magnetic resonance imaging : JMRI.

[186]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[187]  M. Viergever,et al.  Measurement of cerebral perfusion with dual‐echo multi‐slice quantitative dynamic susceptibility contrast MRI , 1999 .

[188]  J. Detre,et al.  Reduced Transit-Time Sensitivity in Noninvasive Magnetic Resonance Imaging of Human Cerebral Blood Flow , 1996, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[189]  B R Rosen,et al.  Mr contrast due to intravascular magnetic susceptibility perturbations , 1995, Magnetic resonance in medicine.

[190]  B. Rosen,et al.  Pitfalls in MR measurement of tissue blood flow with intravascular tracers: Which mean transit time? , 1993, Magnetic resonance in medicine.

[191]  Donald S. Williams,et al.  Magnetic resonance imaging of perfusion using spin inversion of arterial water , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[192]  Mark S. Cohen,et al.  Contrast agents and cerebral hemodynamics , 1991, Magnetic resonance in medicine.

[193]  P. Tofts,et al.  Measurement of the blood‐brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts , 1991, Magnetic resonance in medicine.

[194]  K. Zierler,et al.  On the theory of the indicator-dilution method for measurement of blood flow and volume. , 1954, Journal of applied physiology.

[195]  Roland Bammer,et al.  Comprar MR and CT Perfusion and Pharmacokinetic Imaging: Clinical Applications and Theoretical Principles | Roland Bammer | 9781451147155 | Lippincott Williams & Wilkins , 2016 .

[196]  S. Heiland,et al.  Pseudoprogression in patients with glioblastoma: clinical relevance despite low incidence. , 2015, Neuro-oncology.

[197]  Dafna Ben Bashat,et al.  Classification of tumor area using combined DCE and DSC MRI in patients with glioblastoma , 2014, Journal of Neuro-Oncology.

[198]  S. Heiland,et al.  Evaluation of dynamic contrast-enhanced MRI derived microvascular permeability in recurrent glioblastoma treated with bevacizumab , 2014, Journal of Neuro-Oncology.

[199]  T. Cloughesy,et al.  Imaging biomarkers for antiangiogenic therapy in malignant gliomas. , 2013, CNS oncology.

[200]  Timothy A. Chan,et al.  MRI perfusion in determining pseudoprogression in patients with glioblastoma. , 2013, Clinical imaging.

[201]  M. Essig,et al.  Perfusion MRI: the five most frequently asked technical questions. , 2013, AJR. American journal of roentgenology.

[202]  F. Zanella,et al.  Metabolism and regional cerebral blood volume in autoimmune inflammatory demyelinating lesions mimicking malignant gliomas , 2010, Journal of Neurology.

[203]  R. Cox,et al.  Acute Effects of Bevacizumab on Glioblastoma Vascularity Assessed with DCE-MRI and Relation to Patient Survival , 2009 .

[204]  D. Louis WHO classification of tumours of the central nervous system , 2007 .