@article {1971,
	title = {Joint Maximum Likelihood estimation of motion and T1 parameters from magnetic resonance images in a super-resolution framework: a simulation study},
	journal = {Fundamenta Informaticae},
	volume = {172},
	year = {2020},
	pages = {105{\textendash}128},
	abstract = {Magnetic resonance imaging (MRI) based T1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T1, which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson{\textquoteright}s disease. In conventional T1 MR relaxometry, a quantitative T1 map is obtained from a series of T1-weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low resolution (LR) T1-weighted images is acquired and from which a high resolution (HR) T1 map is directly estimated. In this paper, the SRR T1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T1-weighted images is modeled and the motion parameters are estimated simultaneously with the T1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T1 maps compared to a previously reported SRR based T1 mapping approach.},
	doi = {10.3233/FI-2020-1896},
	author = {Quinten Beirinckx and Gabriel Ramos-Llord{\'e}n and Ben Jeurissen and Dirk H J Poot and Paul M Parizel and Marleen Verhoye and Jan Sijbers and Arnold Jan den Dekker}
}
@conference {1827,
	title = {Accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	pages = {5664},
	address = {Paris, France},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1828,
	title = {An educational presentation on accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation},
	year = {2018},
	address = {Antwerp, Belgium},
	abstract = {MRI relaxometry holds the promise of providing biomarkers for monitoring, staging and follow up of diseases. Imperative to meet minimum standards for objective, reproducible, and reliable biomarkers is the need for accurate, precise, quantitative parameters maps, such as T1~or T2. While unrealistic physical modelling is often argued as the main cause of lack of accuracy, little effort has been made on discussing the impact that inadequate parameter estimation methods have on the accuracy and precision of MRI relaxometry techniques. This educational poster attempts to introduce young MR students/researchers into the basics of modern statistical parameter estimation theory, and its application for accurate and precise relaxometry. 
},
	author = {Gabriel Ramos-Llord{\'e}n and Quinten Beirinckx and Arnold Jan den Dekker and Jan Sijbers}
}
@mastersthesis {1832,
	title = {Improved MRI Relaxometry through Statistical Signal Processing},
	volume = { Doctor of Science},
	year = {2018},
	month = {02/2018},
	school = {University of Antwerp},
	type = {PhD thesis},
	address = {Antwerp},
	abstract = {Magnetic Resonance Imaging (MRI) relaxometry is a quantitative MRI modality that deals with the estimation of the spin-lattice, T1, and the spin-spin, T2, relaxation times. Both relaxation times are fundamental parameters that describe the spin dynamics within a tissue during the relaxation process of the Nuclear Magnetic Resonance (NMR) phenomenon. During the last decades, spatial T1 and T2 maps have been analyzed to study and monitor the states of a multitude of human diseases. Those studies have shown that MRI relaxometry holds the promise of generating robust, objective image-based biomarkers for central nervous system pathologies, cardiovascular diseases and beyond. Unfortunately, quantitative biomarkers derived from MRI relaxometry are not yet sufficiently specific, sensitive, and robust to be routinely used in clinical practice. On top of that, high-resolution relaxation maps demand a clinically unfeasible long scanning time.

This PhD thesis tries to reduce the obstacles that preclude MRI relaxometry from being a fast, accurate, and precise quantitative MRI modality for clinical use by improving the way MR relaxometry data are acquired, processed and analyzed. In particular, by adopting a statistical signal processing approach, three contributions that address common problems of the field are given. Firstly, we present a unified relaxometry-based processing framework where the T1 map estimation and motion correction are accounted for in a synergistic manner, being both the T1 map and the motion parameters simultaneously estimated with a Maximum-Likelihood estimator. It is demonstrated that substantially more accurate T1 maps are obtained with our proposed integrated approach in comparison to the yet typical but suboptimal two-step approach: T1 model fitting after image registration.  Secondly, we developed a fast, robust, T1 estimator for Variable Flip Angle (VFA) T1 mapping that can provide statistically optimal T1 estimates with unprecedentedly short computation time, thereby enabling optimal, real-time, VFA T1 mapping.  Finally, we were able to reduce the long overall scanning time of MRI relaxometry studies using our novel k-space reconstruction technique that permits the reconstruction of individual MR images with less number of samples than it is commonly required.},
	author = {Gabriel Ramos-Llord{\'e}n}
}
@article {1842,
	title = {NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {37},
	year = {2018},
	pages = {2414 - 2427},
	doi = {10.1109/TMI.2018.2833288},
	author = {Gabriel Ramos-Llord{\'e}n and Gonzalo Vegas-S{\'a}nchez-Ferrero and Marcus Bj{\"o}rk and Floris Vanhevel and Paul M Parizel and Raul San Jos{\'e} Est{\'e}par and Arnold Jan den Dekker and Jan Sijbers}
}
@article {1722,
	title = {Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {1041 - 1053},
	doi = {10.1109/TMI.2016.2645122},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1818,
	title = {Statistically optimal separation of multi-component MR signals with a Majorize-Minimize approach: application to MWF estimation},
	year = {2017},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Piet Bladt and A. Cuyt and Jan Sijbers}
}
@article {1706,
	title = {A unified Maximum Likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {433 - 446},
	doi = {10.1109/TMI.2016.2611653},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Ben Jeurissen and Floris Vanhevel and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {1653,
	title = {Multi-voxel algorithm for quantitative bi-exponential MRI T1 estimation},
	booktitle = {SPIE Medical Imaging},
	volume = {9784},
	year = {2016},
	pages = {978402},
	address = {San Diego, California, United States of America},
	abstract = {In this work, we propose a joint multi-voxel bi-exponential estimator (JMBE) for quantitative bi-exponential T1 estimation in magnetic resonance imaging, to account for partial volume effects and to yield more accurate results compared to single-voxel bi-exponential estimators (SBEs). Using a numerical brain phantom with voxels containing two tissues, the minimal signal-to-noise ratio (SNR) needed to estimate both T1 values with sufficient accuracy was investigated. Compared to the SBE, and for clinically achievable single-voxel SNRs, the JMBE yields accurate T1 estimates if four or more neighboring voxels are used in the joint estimation framework, in which case it is also efficient.},
	doi = {http://dx.doi.org/10.1117/12.2216831},
	author = {Piet Bladt and Gwendolyn Van Steenkiste and Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1647,
	title = {NOVIFAST: A fast non-linear least squares method for accurate and precise estimation of T1 from SPGR signals},
	year = {2016},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Marcus Bj{\"o}rk and Marleen Verhoye and Jan Sijbers}
}
@conference {1561,
	title = {Partial discreteness: a new type of prior knowledge for MRI reconstruction},
	volume = {23},
	year = {2015},
	pages = {3417},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@inproceedings {7351081,
	title = {Partially discrete magnetic resonance tomography},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {1653-1657},
	keywords = {Bayes methods, Bayesian segmentation, Bayesian segmentation regularization, biomedical MRI, Breast, breast implant MR images, computerised tomography, Discrete tomography, image reconstruction, image representation, image segmentation, Implants, medical image processing, MR angiography images, MR image reconstruction, MRI, partially discrete magnetic resonance tomography, reconstruction, Tomography, TV},
	doi = {10.1109/ICIP.2015.7351081},
	author = {Gabriel Ramos-Llord{\'e}n and Segers, Hilde and Willem Jan Palenstijn and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	year = {2015},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@conference {1537,
	title = {Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping},
	volume = {23},
	year = {2015},
	pages = {447},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@inproceedings {7351386,
	title = {Simultaneous motion correction and T1 estimation in quantitative T1 mapping: An ML restoration approach},
	booktitle = {2015 IEEE International Conference on Image Processing (ICIP)},
	year = {2015},
	month = {Sept},
	pages = {3160-3164},
	keywords = {alignment, Approximation methods, biomedical MRI, image restoration, interpolation effect, Magnetic Resonance Imaging, maximum likelihood approach, maximum likelihood estimation, medical image processing, ML approach, ML restoration approach, motion estimation, motion model parameters, quantitative T1 mapping, registration, relaxometry, Rician channels, Signal to noise ratio, simultaneous motion correction, Standards, T1 estimation, T1 mapping, T1-weighted images, tissue spin-lattice relaxation time, voxel-wise estimation},
	doi = {10.1109/ICIP.2015.7351386},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Gwendolyn Van Steenkiste and Johan Van Audekerke and Marleen Verhoye and Jan Sijbers}
}
@booklet {1538,
	title = {Joint motion correction and estimation for T1 mapping: proof of concept},
	howpublished = {Medical Imaging Summer School 2014, Favignana, Italy},
	year = {2014},
	author = {Gabriel Ramos-Llord{\'e}n and Arnold Jan den Dekker and Jan Sijbers}
}
@conference {1475,
	title = {Misalignment correction for T1 maps using a maximum likelihood estimator approach},
	year = {2013},
	doi = {10.3389},
	author = {Gabriel Ramos-Llord{\'e}n and Jan Sijbers}
}
