2019
DOI: 10.1002/mrm.27874
|View full text |Cite
|
Sign up to set email alerts
|

Spatially regularized estimation of the tissue homogeneity model parameters in DCE‐MRI using proximal minimization

Abstract: Purpose The Tofts and the extended Tofts models are the pharmacokinetic models commonly used in dynamic contrast‐enhanced MRI (DCE‐MRI) perfusion analysis, although they do not provide two important biological markers, namely, the plasma flow and the permeability‐surface area product. Estimates of such markers are possible using advanced pharmacokinetic models describing the vascular distribution phase, such as the tissue homogeneity model. However, the disadvantage of the advanced models lies in biased and un… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3

Citation Types

0
3
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 53 publications
0
3
0
Order By: Relevance
“…( 1) contains all the data as vectors. The regularization term 𝜆𝑅(x) adds a priori spatial and/or temporal information, typically using total variation [3,4,5] or its generalized form [6,7]. Some methods use purely deep-learning-based using large image-to-image networks.…”
Section: Introductionmentioning
confidence: 99%
“…( 1) contains all the data as vectors. The regularization term 𝜆𝑅(x) adds a priori spatial and/or temporal information, typically using total variation [3,4,5] or its generalized form [6,7]. Some methods use purely deep-learning-based using large image-to-image networks.…”
Section: Introductionmentioning
confidence: 99%
“…Structured spatial regularisation terms and priors have been used in qMRI by several authors. They have, for instance, been used when estimating relaxation times and proton density (Wang and Cao 2012, Baselice et al 2016, Kumar et al 2012, Raj et al 2014, in diffusion and intra-voxel incoherent motion (IVIM) estimation (While 2017, Orton et al 2014, for B 0 -estimation (Baselice et al 2010), and for dynamic contrast-enhanced MRI (DCE-MRI) (Schmid et al 2006, Kelm et al 2009, Sommer and Schmid 2014, Bartos et al 2019.…”
Section: Introductionmentioning
confidence: 99%
“…Selecting the hyperparameter is difficult, and how it is done varies between studies. Examples of different approaches include visual inspection (Kumar et al 2012), finding good parameters for a representative dataset (Bartos et al 2019, Wang and Cao 2012, Freiman et al 2013, L-curve analysis (Kumar et al 2012), constraints on the size of the residuals (Raj et al 2014), and non-informative priors have been used in the Bayesian framework (Orton et al 2014).…”
Section: Introductionmentioning
confidence: 99%