scholarly journals Optimal mass transport kinetic modeling for head and neck DCE‐MRI: Initial analysis

2019 ◽  
Vol 82 (6) ◽  
pp. 2314-2325
Author(s):  
Rena Elkin ◽  
Saad Nadeem ◽  
Eve LoCastro ◽  
Ramesh Paudyal ◽  
Vaios Hatzoglou ◽  
...  

2019 ◽  
Author(s):  
Rena Elkin ◽  
Saad Nadeem ◽  
Eve LoCastro ◽  
Ramesh Paudyal ◽  
Vaios Hatzoglou ◽  
...  

AbstractCurrent state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows. Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and under-went DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux (ΦF) and backward flux (ΦB). OMT-derived ΦF was compared with the volume transfer constant for CA, Ktrans, derived from the Extended Tofts Model (ETM). The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 ± 0.0091 (min-1) whereas the mean advective component was 0.0052±0.0086 (min-1) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67–18.76% and 12.76–30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux (ΦF) is comparable with the ETM-derived Ktrans. This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.







2018 ◽  
Vol 149 (03) ◽  
pp. 691-718 ◽  
Author(s):  
Nguyen Lam

AbstractIn this paper, we will use optimal mass transport combining with suitable transforms to study the sharp constants and optimizers for a class of the Gagliardo–Nirenberg and Caffarelli–Kohn–Nirenberg inequalities. Moreover, we will investigate these inequalities with and without the monomial weights $x_{1}^{A_{1}} \cdots x_{N}^{A_{N}}$ on ℝN.



2021 ◽  
Vol 30 (3) ◽  
pp. 177-186
Author(s):  
Hande Melike Bülbül ◽  
Ogün Bülbül ◽  
Sülen Sarıoğlu ◽  
Özhan Özdoğan ◽  
Ersoy Doğan ◽  
...  


Author(s):  
Julien Rabin ◽  
Gabriel Peyré ◽  
Laurent D. Cohen






2009 ◽  
Vol 36 (6Part4) ◽  
pp. 2455-2455
Author(s):  
M La Fontaine ◽  
M Nyflot ◽  
C Song ◽  
L Gentry ◽  
R Jeraj


Sign in / Sign up

Export Citation Format

Share Document