Padhani AR, Miles KA (2010) Multiparametric imaging of tumor response to therapy. Radiology 256(2):348–364

Article  PubMed  Google Scholar 

O’Connor JP et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14(3):169–186

Article  PubMed  Google Scholar 

Galbraith SM et al (2002) Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumours: comparison of quantitative and semi-quantitative analysis. NMR Biomed Int J Devot Dev Appl Magn Reson In Vivo 15(2):132–142

Google Scholar 

Bliesener Y, Acharya J, Nayak KS (2019) Efficient DCE-MRI parameter and uncertainty estimation using a neural network. IEEE Trans Med Imaging 39(5):1712–1723

Article  PubMed  PubMed Central  Google Scholar 

Hornik K (1993) Some new results on neural network approximation. Neural Netw 6(8):1069–1072

Article  Google Scholar 

Cohen O, Zhu B, Rosen MS (2018) MR fingerprinting deep reconstruction network (DRONE). Magn Reson Med 80(3):885–894

Article  PubMed  PubMed Central  Google Scholar 

Cohen O et al (2022) CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction. Magn Reson Med 89:233–249

Article  PubMed  PubMed Central  Google Scholar 

Cohen O, Otazo R (2023) Global deep learning optimization of CEST MR fingerprinting (CEST-MRF) acquisition schedule. NMR Biomed 36:e4954

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ongie G, Jalal A, Metzler CA, Baraniuk RG, Dimakis AG, Willett R (2020) Deep learning techniques for inverse problems in imaging. IEEE J Select Areas Inf Theory 1(1):39–56

Article  Google Scholar 

Genzel M, Macdonald J, März M (2022) Solving inverse problems with deep neural networks–robustness included? IEEE Trans Pattern Anal Mach Intell 45(1):1119–1134

Article  PubMed  Google Scholar 

Rastogi A, Dutta A, Yalavarthy PK (2023) VTDCE-Net: A time invariant deep neural network for direct estimation of pharmacokinetic parameters from undersampled DCE MRI data. Med Phys 50(3):1560–1572

Article  PubMed  Google Scholar 

Zou J, Balter JM, Cao Y (2020) Estimation of pharmacokinetic parameters from DCE-MRI by extracting long and short time-dependent features using an LSTM network. Med Phys 47(8):3447–3457

Article  PubMed  Google Scholar 

Bae J, Li C, Masurkar A, Ge Y, Kim SG (2023) Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function. Neuroimage 278:120284

Article  CAS  PubMed  Google Scholar 

Park J-S (1994) Optimal Latin-hypercube designs for computer experiments. J Stat Plann Inference 39(1):95–111

Article  Google Scholar 

Parker GJ et al (2006) Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med 56(5):993–1000

Article  PubMed  Google Scholar 

Kingma DP,Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

Zur RM, Jiang Y, Pesce LL, Drukker K (2009) Noise injection for training artificial neural networks: A comparison with weight decay and early stopping. Med Phys 36(10):4810–4818

Article  PubMed  PubMed Central  Google Scholar 

Metzner S, Wübbeler G, Flassbeck S, Gatefait C, Kolbitsch C, Elster C (2021) Bayesian uncertainty quantification for magnetic resonance fingerprinting. Phys Med Biol 66(7):075006

Article  Google Scholar 

Sourbron SP, Buckley DL (2013) Classic models for dynamic contrast-enhanced MRI. NMR Biomed 26(8):1004–1027

Article  PubMed  Google Scholar 

Feng L et al (2014) Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med 72(3):707–717

Article  PubMed  Google Scholar 

Feng L, Axel L, Chandarana H, Block KT, Sodickson DK, Otazo R (2016) XD-GRASP: golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn Reson Med 75(2):775–788

Article  PubMed  Google Scholar 

Sacolick LI, Wiesinger F, Hancu I, Vogel MW (2010) B1 mapping by Bloch-Siegert shift. Magn Reson Med 63(5):1315–1322

Article  PubMed  PubMed Central  Google Scholar 

Ahearn TS, Staff RT, Redpath TW, Semple SIK (2005) The use of the Levenberg–Marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data. Phys Med Biol 50(9):N85

Article  CAS  PubMed  Google Scholar 

Well D et al (2007) “Age-related structural and metabolic changes in the pelvic reproductive end organs. Seminars in nuclear medicine. Elsevier, Amsterdam, pp 173–184

Google Scholar 

Angelopoulos K et al (2021) Computed tomography contrast enhancement pattern of the uterus in premenopausal women in relation to menstrual cycle and hormonal contraception. Acta Radiol 62(9):1257–1262

Article  PubMed  Google Scholar 

Langer JE, Oliver ER, Lev-Toaff AS, Coleman BG (2012) Imaging of the female pelvis through the life cycle. Radiographics 32(6):1575–1597

Article  PubMed  Google Scholar 

Ramalho J, Semelka R, Ramalho M, Nunes R, AlObaidy M, Castillo M (2016) Gadolinium-based contrast agent accumulation and toxicity: an update. Am J Neuroradiol 37(7):1192–1198

Article  CAS  PubMed  PubMed Central  Google Scholar 

De Bazelaire CM, Duhamel GD, Rofsky NM, Alsop DC (2004) MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology 230(3):652–659

Article  PubMed  Google Scholar 

Schabel MC, Parker DL (2008) Uncertainty and bias in contrast concentration measurements using spoiled gradient echo pulse sequences. Phys Med Biol 53(9):2345

Article  PubMed  PubMed Central  Google Scholar 

Roberts C, Little R, Watson Y, Zhao S, Buckley DL, Parker GJ (2011) The effect of blood inflow and B1-field inhomogeneity on measurement of the arterial input function in axial 3D spoiled gradient echo dynamic contrast-enhanced MRI. Magn Reson Med 65(1):108–119

Article  PubMed  Google Scholar 

Ottens T et al (2022) Deep learning DCE-MRI parameter estimation: application in pancreatic cancer. Med Image Anal 80:102512

Article  PubMed  Google Scholar 

Donaldson SB et al (2010) A comparison of tracer kinetic models for T1-weighted dynamic contrast-enhanced MRI: APPLICATION in carcinoma of the cervix. Magn Reson Med 63(3):691–700

Article  PubMed  Google Scholar 

Sourbron SP, Buckley DL (2011) On the scope and interpretation of the Tofts models for DCE-MRI. Magn Reson Med 66(3):735–745

Article  PubMed  Google Scholar 

Zhang Q et al (2020) Deep learning-based MR fingerprinting ASL ReconStruction (DeepMARS). Magn Reson Med 84(2):1024–1034

Article  PubMed  Google Scholar 

Perlman O et al (2021) Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning. Nat Biomed Eng. https://doi.org/10.1038/s41551-021-00809-7

Article  PubMed  PubMed Central  Google Scholar 

Parker GJ, Buckley DL (2005) Tracer kinetic modelling for T1-weighted DCE-MRI. Dynamic contrast-enhanced magnetic resonance imaging in oncology. Springer, Berlin, pp 81–92

Chapter  Google Scholar 

Kallehauge J et al (2013) Voxelwise comparison of perfusion parameters estimated using dynamic contrast enhanced (DCE) computed tomography and DCE-magnetic resonance imaging in locally advanced cervical cancer. Acta Oncol 52(7):1360–1368

Article  PubMed