A Bayesian approach for relaxation times estimation in MRI

Baselice, Fabio; Ferraioli, Giampaolo; Pascazio, Vito

doi:10.1016/j.mri.2015.10.020

Cited by 15 publications

(11 citation statements)

References 31 publications

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“…Since tissue relaxation times vary in disease, QMRI enables the diagnosis of different pathologies, including multiple sclerosis (MS), Alzheimer, Parkinson, epilepsy and cancer [2][3][4][5][6][7] . In addition, the knowledge of tissue relaxation times allows generation of many clinical MR imaging contrasts (such as FLAIR and STIR) off-line, and saves a significant amount of scanning time.…”

Section: Introductionmentioning

confidence: 99%

“…Quantitative Magnetic Resonance Imaging (QMRI) is widely used to measure tissue's intrinsic spin parameters such as the spin-lattice magnetic relaxation time (T1) and the spin-spin magnetic relaxation time (T2) 1 . Since tissue relaxation times vary in disease, QMRI enables the diagnosis of different pathologies, including multiple sclerosis (MS), Alzheimer, Parkinson, epilepsy and cancer [2][3][4][5][6][7] . In addition, the knowledge of tissue relaxation times allows generation of many clinical MR imaging contrasts (such as FLAIR and STIR) off-line, and saves a significant amount of scanning time.…”

Section: Introductionmentioning

confidence: 99%

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Low rank magnetic resonance fingerprinting

Mazor¹,

Weizman²,

Tal³

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

100

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Methods: We exploit the low rank property of the concatenated temporal imaging contrasts, on top of the fact that the MRF signal is sparsely represented in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition.Results: Experimental results consist of retrospective sampling, that allows comparison to a well defined reference, and prospective sampling that shows the performance of FLOR for a real-data sampling scenario. Both experiments demonstrate improved parameter accuracy compared to other compressed-sensing and low-rank based methods for MRF at 5% and 9% sampling ratios, for the retrospective and prospective experiments, respectively. Conclusions:We have shown through retrospective and prospective experiments that by exploiting the low rank nature of the MRF signal, FLOR recovers the MRF temporal undersampled images and provides more accurate parameter maps compared to previous iterative methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low rank magnetic resonance fingerprinting

Mazor¹,

Weizman²,

Tal³

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

100

View full text Add to dashboard Cite

show abstract

“…We can estimate θ by implementing an LS estimator [ 15 , 16 ]:

where M is the number of images acquired with different T E / T R combinations.…”

Section: Methodsmentioning

confidence: 99%

A Novel Statistical Approach for Brain MR Images Segmentation Based on Relaxation Times

Baselice

Ferraioli

Pascazio

2015

BioMed Research International

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Brain tissue segmentation in Magnetic Resonance Imaging is useful for a wide range of applications. Classical approaches exploit the gray levels image and implement criteria for differentiating regions. Within this paper a novel approach for brain tissue joint segmentation and classification is presented. Starting from the estimation of proton density and relaxation times, we propose a novel method for identifying the optimal decision regions. The approach exploits the statistical distribution of the involved signals in the complex domain. The technique, compared to classical threshold based ones, is able to globally improve the classification rate. The effectiveness of the approach is evaluated on both simulated and real datasets.

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“…Quantitative magnetic resonance imaging (QMRI) is widely used to measure tissue's intrinsic spin parameters such as the spin‐lattice magnetic relaxation time (T1) and the spin‐spin magnetic relaxation time (T2) . Since tissue relaxation times vary in disease, QMRI enables the diagnosis of different pathologies, including multiple sclerosis (MS), Alzheimer, Parkinson, epilepsy, and cancer . In addition, the knowledge of tissue relaxation times allows generation of many clinical MR imaging contrasts (such as FLAIR and STIR) offline, and saves a significant amount of scanning time.…”

Section: Introductionmentioning

confidence: 99%

Low‐rank magnetic resonance fingerprinting

et al. 2018

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Abstract-Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values. In this work, we introduce a new approach for quantitative MRI using MRF, called Low Rank MRF. We exploit the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data demonstrate superior results compared to conventional implementation of compressed sensing for MRF at 15% sampling ratio.

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A Bayesian approach for relaxation times estimation in MRI

Cited by 15 publications

References 31 publications

Low rank magnetic resonance fingerprinting

Low rank magnetic resonance fingerprinting

A Novel Statistical Approach for Brain MR Images Segmentation Based on Relaxation Times

Low‐rank magnetic resonance fingerprinting

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