Dynamic contrast‐enhanced MRI: Study of inter‐software accuracy and reproducibility using simulated and clinical data

To test the reproducibility and accuracy of pharmacokinetic parameter measurements on five analysis software packages (SPs) for dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI), using simulated and clinical data.

[1]  C. Kremser,et al.  Dynamic T1 mapping predicts outcome of chemoradiation therapy in primary rectal carcinoma: Sequence implementation and data analysis , 2007 .

[2]  N. Rofsky,et al.  MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. , 2004, Radiology.

[3]  Philippe Lambin,et al.  Dynamic contrast-enhanced magnetic resonance imaging of radiation therapy-induced microcirculation changes in rectal cancer. , 2005, International journal of radiation oncology, biology, physics.

[4]  A. Beckett,et al.  AKUFO AND IBARAPA. , 1965, Lancet.

[5]  Steven P Sourbron,et al.  On the scope and interpretation of the Tofts models for DCE‐MRI , 2011, Magnetic resonance in medicine.

[6]  Viktor Novikov,et al.  The Choice of Region of Interest Measures in Contrast-Enhanced Magnetic Resonance Image Characterization of Experimental Breast Tumors , 2005, Investigative radiology.

[7]  C. Yi,et al.  Semiautomatic Determination of Arterial Input Functions for Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Non-Small Cell Lung Cancer Patients , 2015, Investigative radiology.

[8]  M. Bergamino,et al.  A review of technical aspects of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in human brain tumors. , 2014, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[9]  N. Hylton Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  Myeong-Jin Kim,et al.  Perfusion MRI for the prediction of treatment response after preoperative chemoradiotherapy in locally advanced rectal cancer , 2012, European Radiology.

[11]  D. Balvayb,et al.  Perfusion and vascular permeability : Basic concepts and measurement in DCE-CT and DCE-MRI , 2013 .

[12]  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.

[13]  G. Parker,et al.  DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents , 2007, British Journal of Cancer.

[14]  Myeong-Jin Kim,et al.  Perfusion Parameters of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Patients with Rectal Cancer: Correlation with Microvascular Density and Vascular Endothelial Growth Factor Expression , 2013, Korean journal of radiology.

[15]  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.

[16]  Ron Kikinis,et al.  Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. , 2014, Translational oncology.

[17]  Matthew S Davenport,et al.  Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions. , 2013, Radiology.

[18]  Evis Sala,et al.  Dynamic contrast-enhanced MRI as a predictor of tumour response to radiotherapy. , 2007, The Lancet. Oncology.

[19]  A. Jackson,et al.  Comparative study into the robustness of compartmental modeling and model‐free analysis in DCE‐MRI studies , 2006, Journal of magnetic resonance imaging : JMRI.

[20]  M. Port,et al.  Comparative Relaxivities and Efficacies of Gadolinium-based Commercial Contrast Agents , 2012 .

[21]  Myung Ah Lee,et al.  Correlation of dynamic contrast‐enhanced MRI perfusion parameters with angiogenesis and biologic aggressiveness of rectal cancer: Preliminary results , 2015, Journal of magnetic resonance imaging : JMRI.

[22]  M. Reiser,et al.  Dynamic Contrast-Enhanced Magnetic Resonance Imaging Measurements in Renal Cell Carcinoma: Effect of Region of Interest Size and Positioning on Interobserver and Intraobserver Variability , 2015, Investigative radiology.

[23]  M. Weiser,et al.  Dynamic contrast enhanced-MRI for the detection of pathological complete response to neoadjuvant chemotherapy for locally advanced rectal cancer , 2012, European Radiology.

[24]  Lothar R. Schad,et al.  UMMPerfusion: an Open Source Software Tool Towards Quantitative MRI Perfusion Analysis in Clinical Routine , 2013, Journal of Digital Imaging.

[25]  Jeong Min Lee,et al.  Dynamic contrast‐enhanced MRI to evaluate the therapeutic response to neoadjuvant chemoradiation therapy in locally advanced rectal cancer , 2014, Journal of magnetic resonance imaging : JMRI.

[26]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[27]  Osman Ratib,et al.  OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images , 2004, Journal of Digital Imaging.

[28]  J. MacFall,et al.  Comparison of three physiologically‐based pharmacokinetic models for the prediction of contrast agent distribution measured by dynamic MR imaging , 2008, Journal of magnetic resonance imaging : JMRI.

[29]  Kenya Murase,et al.  Efficient method for calculating kinetic parameters using T1‐weighted dynamic contrast‐enhanced magnetic resonance imaging , 2004, Magnetic resonance in medicine.

[30]  E. Merkle,et al.  Impact of precontrast T10 relaxation times on dynamic contrast‐enhanced MRI pharmacokinetic parameters: T10 mapping versus a fixed T10 reference value , 2014, Journal of magnetic resonance imaging : JMRI.

[31]  P S Tofts,et al.  Quantitative Analysis of Dynamic Gd‐DTPA Enhancement in Breast Tumors Using a Permeability Model , 1995, Magnetic resonance in medicine.

[32]  Erik Butterworth,et al.  JSim, an open-source modeling system for data analysis , 2013, F1000Research.