Access the full text.
Sign up today, get DeepDyve free for 14 days.
H. Cheng, Jun Chen, P. Babyn, W. Farhat (2005)
Dynamic Gd‐DTPA enhanced MRI as a surrogate marker of angiogenesis in tissue‐engineered bladder constructs: A feasibility study in rabbitsJournal of Magnetic Resonance Imaging, 21
C. Kuhl, Peter Mielcareck, S. Klaschik, C. Leutner, E. Wardelmann, J. Gieseke, H. Schild (1999)
Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?Radiology, 211 1
R. Meng, Silvia Chang, Silvia Chang, Edward Jones, S. Goldenberg, S. Goldenberg, P. Kozlowski (2010)
Comparison between population average and experimentally measured arterial input function in predicting biopsy results in prostate cancer.Academic radiology, 17 4
R. Port, M. Knopp, G. Brix (2001)
Dynamic contrast‐enhanced MRI using Gd‐DTPA: Interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumorsMagnetic Resonance in Medicine, 45
R. Alonzi, A. Padhani, C. Allen (2007)
Dynamic contrast enhanced MRI in prostate cancer.European journal of radiology, 63 3
L. Kershaw, H. Cheng (2010)
Temporal resolution and SNR requirements for accurate DCE‐MRI data analysis using the AATH modelMagnetic Resonance in Medicine, 64
P. Tofts, A. Kermode (1991)
Measurement of the blood‐brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental conceptsMagnetic Resonance in Medicine, 17
Xin Li, Yu Cai, Brendan Moloney, Yiyi Chen, Wei Huang, M. Woods, F. Coakley, W. Rooney, M. Garzotto, C. Springer (2016)
Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling.Journal of magnetic resonance, 269
Geoff Parker, C. Roberts, A. MacDonald, G. Buonaccorsi, S. Cheung, D. Buckley, A. Jackson, Y. Watson, K. Davies, G. Jayson (2006)
Experimentally‐derived functional form for a population‐averaged high‐temporal‐resolution arterial input function for dynamic contrast‐enhanced MRIMagnetic Resonance in Medicine, 56
J. Kratochvíla, R. Jiřík, M. Bartoš, M. Standara, Z. Starčuk, T. Taxt (2016)
Distributed capillary adiabatic tissue homogeneity model in parametric multi‐channel blind AIF estimation using DCE‐MRIMagnetic Resonance in Medicine, 75
D. McGrath, D. Bradley, J. Tessier, T. Lacey, Chris Taylor, G. Parker (2009)
Comparison of model‐based arterial input functions for dynamic contrast‐enhanced MRI in tumor bearing ratsMagnetic Resonance in Medicine, 61
M. Mlynash, I. Eyngorn, R. Bammer, M. Moseley, D. Tong (2005)
Automated method for generating the arterial input function on perfusion-weighted MR imaging: validation in patients with stroke.AJNR. American journal of neuroradiology, 26 6
Young Kim, Kelly Rebro, K. Schmainda (2002)
Water exchange and inflow affect the accuracy of T1‐GRE blood volume measurements: Implications for the evaluation of tumor angiogenesisMagnetic Resonance in Medicine, 47
J. Fluckiger, M. Schabel, E. DiBella (2009)
Model‐based blind estimation of kinetic parameters in dynamic contrast enhanced (DCE)‐MRIMagnetic Resonance in Medicine, 62
M. Spanakis, E. Kontopodis, Sophie Cauter, V. Sakkalis, K. Marias (2016)
Assessment of DCE–MRI parameters for brain tumors through implementation of physiologically–based pharmacokinetic model approaches for Gd-DOTAJournal of Pharmacokinetics and Pharmacodynamics, 43
P. Tofts, G. Brix, D. Buckley, J. Evelhoch, E. Henderson, M. Knopp, H. Larsson, Ting-Yim Lee, N. Mayr, G. Parker, R. Port, June Taylor, R. Weisskoff (1999)
Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbolsJournal of Magnetic Resonance Imaging, 10
A. Petrillo, R. Fusco, M. Petrillo, V. Granata, M. Sansone, A. Avallone, P. Delrio, B. Pecori, F. Tatangelo, G. Ciliberto (2015)
Standardized Index of Shape (SIS): a quantitative DCE-MRI parameter to discriminate responders by non-responders after neoadjuvant therapy in LARCEuropean Radiology, 25
D. Mustafi, Xiaobing Fan, U. Dougherty, M. Bissonnette, G. Karczmar, A. Oto, J. Hart, E. Markiewicz, M. Zamora (2010)
High‐resolution magnetic resonance colonography and dynamic contrast‐enhanced magnetic resonance imaging in a murine model of colitisMagnetic Resonance in Medicine, 63
Cheng Yang, W. Stadler, G. Karczmar, M. Milosevic, I. Yeung, M. Haider (2010)
Comparison of quantitative parameters in cervix cancer measured by dynamic contrast–enhanced MRI and CTMagnetic Resonance in Medicine, 63
P. Farace, F. Merigo, S. Fiorini, E. Nicolato, S. Tambalo, Alessandro Daducci, A. Degrassi, A. Sbarbati, D. Rubello, P. Marzola (2011)
DCE-MRI using small-molecular and albumin-binding contrast agents in experimental carcinomas with different stromal content.European journal of radiology, 78 1
Wei Huang, Yiyi Chen, Andrey Fedorov, Xia Li, G. Jajamovich, D. Malyarenko, M. Aryal, P. LaViolette, Matthew Oborski, F. O’Sullivan, R. Abramson, K. Jafari-Khouzani, Aneela Afzal, A. Tudorica, Brendan Moloney, Sandeep Gupta, C. Besa, Jayashree Kalpathy-Cramer, J. Mountz, C. Laymon, M. Muzi, Paul Kinahan, K. Schmainda, Yue Cao, T. Chenevert, B. Taouli, T. Yankeelov, F. Fennessy, Xin Li (2016)
The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis ChallengeTomography, 2
K. Pinker, W. Bogner, P. Baltzer, S. Trattnig, S. Gruber, O. Abeyakoon, M. Bernathova, O. Zaric, P. Dubsky, Z. Bago-Horvath, Michael Weber, D. Leithner, T. Helbich (2014)
Clinical application of bilateral high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging of the breast at 7 TEuropean Radiology, 24
Cheng Yang, G. Karczmar, M. Medved, A. Oto, M. Zamora, W. Stadler (2009)
Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced–MRI analysisMagnetic Resonance in Medicine, 61
J. Ewing, Stephen Brown, Mei Lu, Swayamprava Panda, G. Ding, R. Knight, Yue Cao, Q. Jiang, T. Nagaraja, Jamie Churchman, J. Fenstermacher (2006)
Model Selection in Magnetic Resonance Imaging Measurements of Vascular Permeability: Gadomer in a 9L Model of Rat Cerebral TumorJournal of Cerebral Blood Flow & Metabolism, 26
N. Simpson, Zhanquan He, J. Evelhoch (1999)
Deuterium NMR tissue perfusion measurements using the tracer uptake approach: I. optimization of methodsMagnetic Resonance in Medicine, 42
MC Schabel, JU Fluckiger, EV DiBella (2010)
A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: I. SimulationsPhys Med Biol, 55
N. Just, D. Koh, J. d’Arcy, D. Collins, M. Leach (2011)
Assessment of the effect of haematocrit‐dependent arterial input functions on the accuracy of pharmacokinetic parameters in dynamic contrast‐enhanced MRINMR in Biomedicine, 24
D. Workie, B. Dardzinski (2005)
Quantifying dynamic contrast‐enhanced MRI of the knee in children with juvenile rheumatoid arthritis using an arterial input function (AIF) extracted from popliteal artery enhancement, and the effect of the choice of the AIF on the kinetic parametersMagnetic Resonance in Medicine, 54
P. Myles, J. Cui (2007)
Using the Bland-Altman method to measure agreement with repeated measures.British journal of anaesthesia, 99 3
M. Bouhrara, D. Reiter, H. Celik, J. Bonny, Vanessa Lukas, K. Fishbein, R. Spencer (2015)
Incorporation of rician noise in the analysis of biexponential transverse relaxation in cartilage using a multiple gradient echo sequence at 3 and 7 teslaMagnetic Resonance in Medicine, 73
P. Tofts (1997)
Modeling tracer kinetics in dynamic Gd‐DTPA MR imagingJournal of Magnetic Resonance Imaging, 7
S. Saito, Yuki Moriyama, Shuichiro Kobayashi, Ryota Ogihara, Daichi Koto, Akihiro Kitamura, T. Matsushita, Motoko Nishiura, K. Murase (2012)
Assessment of liver function in thioacetamide‐induced rat acute liver injury using an empirical mathematical model and dynamic contrast‐enhanced MRI with Gd‐EOB‐DTPAJournal of Magnetic Resonance Imaging, 36
F. Calamante, D. Gadian, A. Connelly (2000)
Delay and dispersion effects in dynamic susceptibility contrast MRI: Simulations using singular value decompositionMagnetic Resonance in Medicine, 44
M. Gollub, D. Gultekin, O. Akin, Richard Do, J. Fuqua, M. Gonen, D. Kuk, M. Weiser, L. Saltz, D. Schrag, K. Goodman, P. Paty, J. Guillem, G. Nash, L. Temple, J. Shia, Lawrence Schwartz (2012)
Dynamic contrast enhanced-MRI for the detection of pathological complete response to neoadjuvant chemotherapy for locally advanced rectal cancerEuropean Radiology, 22
D. Woolf, N. Taylor, A. Makris, N. Tunariu, D. Collins, Sonia Li, M. Ah-See, M. Beresford, A. Padhani (2016)
Arterial input functions in dynamic contrast-enhanced magnetic resonance imaging: which model performs best when assessing breast cancer response?The British journal of radiology, 89 1063
M. Cutajar, I. Mendichovszky, P. Tofts, I. Gordon (2010)
The importance of AIF ROI selection in DCE-MRI renography: reproducibility and variability of renal perfusion and filtration.European journal of radiology, 74 3
T. Yankeelov, J. Luci, M. Lepage, Rui Li, L. Debusk, P. Lin, Ronald Price, John Gore (2005)
Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model.Magnetic resonance imaging, 23 4
Stephanie Barnes, J. Whisenant, M. Loveless, T. Yankeelov (2012)
Practical Dynamic Contrast Enhanced MRI in Small Animal Models of Cancer: Data Acquisition, Data Analysis, and InterpretationPharmaceutics, 4
R. Abramson, Xia Li, Tamarya Hoyt, P. Su, L. Arlinghaus, Kevin Wilson, V. Abramson, A. Chakravarthy, T. Yankeelov (2013)
Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results.Magnetic resonance imaging, 31 9
E. Vos, G. Litjens, T. Kobus, T. Hambrock, C. Kaa, J. Barentsz, H. Huisman, T. Scheenen (2013)
Assessment of prostate cancer aggressiveness using dynamic contrast-enhanced magnetic resonance imaging at 3 T.European urology, 64 3
S. Jansen, Xiaobing Fan, M. Medved, H. Abe, A. Shimauchi, Cheng Yang, M. Zamora, S. Foxley, O. Olopade, G. Karczmar, G. Newstead (2010)
Characterizing early contrast uptake of ductal carcinoma in situ with high temporal resolution dynamic contrast-enhanced MRI of the breast: a pilot studyPhysics in Medicine & Biology, 55
Stefan Hindel, Anika Söhner, Marc Maaß, W. Sauerwein, D. Möllmann, H. Baba, M. Kramer, L. Lüdemann (2017)
Validation of Blood Volume Fraction Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Porcine Skeletal MusclePLoS ONE, 12
R. Alonzi, N. Taylor, J. Stirling, J. d’Arcy, D. Collins, M. Saunders, Peter Hoskin, A. Padhani (2010)
Reproducibility and correlation between quantitative and semiquantitative dynamic and intrinsic susceptibility‐weighted MRI parameters in the benign and malignant human prostateJournal of Magnetic Resonance Imaging, 32
Gunnar Brix, W. Semmler, R. Port, L. Schad, G. Layer, W. Lorenz (1991)
Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging.Journal of computer assisted tomography, 15 4
B Daniel, Y Yen, G Glover, D Ikeda, R. Birdwell, A. Sawyer-Glover, J. Black, S Plevritis, S Jeffrey, R. Herfkens (1998)
Breast disease: dynamic spiral MR imaging.Radiology, 209 2
M. Azahaf, Marc Haberley, N. Betrouni, O. Ernst, H. Behal, A. Duhamel, A. Ouzzane, P. Puech (2016)
Impact of arterial input function selection on the accuracy of dynamic contrast‐enhanced MRI quantitative analysis for the diagnosis of clinically significant prostate cancerJournal of Magnetic Resonance Imaging, 43
Yu‐Jen Chen, W. Chu, Y. Pu, S. Chueh, C. Shun, W. Tseng (2012)
Washout gradient in dynamic contrast‐enhanced MRI is associated with tumor aggressiveness of prostate cancerJournal of Magnetic Resonance Imaging, 36
Shiyang Wang, Xiaobing Fan, M. Medved, F. Pineda, Ambereen Yousuf, A. Oto, G. Karczmar (2016)
Arterial input functions (AIFs) measured directly from arteries with low and standard doses of contrast agent, and AIFs derived from reference tissues.Magnetic resonance imaging, 34 2
M. Su, Jo-Chi Jao, O. Nalcioglu (1994)
Measurement of vascular volume fraction and blood‐tissue permeability constants with a pharmacokinetic model: Studies in rat muscle tumors with dynamic Gd‐DTPA enhanced MRIMagnetic Resonance in Medicine, 32
M. Schabel, E. DiBella, R. Jensen, K. Salzman (2010)
A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: II. In vivo resultsPhysics in Medicine & Biology, 55
Due to large inter- and intra-patient variabilities of arterial input functions (AIFs), accurately modeling and using patient-specific AIF are very important for quantitative analysis of dynamic contrast enhanced MRI. Computer simulations were performed to evaluate and compare nine population AIF models with the Parker AIF used as ‘gold standard’. The Parker AIF was calculated with a temporal resolution of 1.5 s, and then the other nine AIF models were used to fit the Parker AIF. A total of 100 randomly generated volume transfer constants (Ktrans) and distribution volumes (ve) were used to calculate the contrast agent concentration curves based on the Parker AIF and the extended Tofts model with blood plasma volume (vp) = 0.0, 0.01, 0.05 and 0.10. Subsequently, nine AIF models were used to fit these curves to extract physiological parameters (Ktrans, ve and vp). The agreements between generated and extracted Ktrans and ve values were evaluated using Bland–Altman analysis. The effects of the second pass of the Parker AIF model with and without adding Rician noise on extracted physiological parameters were evaluated by 1000 simulations using one of the nine mathematical AIF models closest to the Parker model with the smallest number of parameters. The results demonstrated that a six-parameter linear function plus bi-exponential function AIF model was almost equivalent to the Parker AIF and that the corresponding generated and extracted Ktrans and ve were in excellent agreements. The effects of the second pass of contrast agent circulation were small on extracted physiological parameters using the extended Tofts model, unless noise was added with signal to noise ratio less than 10 dB.
Australasian Physical & Engineering Sciences in Medicine – Springer Journals
Published: Mar 23, 2018
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.