| project |
| project_id |
LO1212 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0303710 |
|
| project |
| project_id |
MSM100751802 |
| agency |
AV ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0359895 |
|
| project |
| project_id |
EF16_013/0001775 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0362565 |
|
| project |
| project_id |
ED0017/01/01 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0266369 |
|
| project |
| project_id |
GA16-13830S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0338628 |
|
| abstract
(eng) |
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 plasmaflow 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 theadvanced models lies in biased and uncertain estimates, especially when the estimates are computed voxelwise. The goal of this work is to improve the reliability of the estimates by including information from neighboring voxels. Information from the neighboring voxels is incorporated in the estimation process through spatial regularization in the form of total variation. The spatial regularization is applied on five maps of perfusion parameters estimated using the tissue homogeneity model. Since the total variation is not differentiable, two proximal techniques of convex optimization are used to solve the problem numerically. The proposed algorithm helps to reduce noise in the estimated perfusionparameter maps together with improving accuracy of the estimates. These conclusions are proved using a numerical phantom. In addition, experiments on real data\nshow improved spatial consistency and readability of perfusion maps without considerable lowering of the quality of fit. The reliability of the DCE‐MRI perfusion analysis using the tissue homogeneity model can be improved by employing spatial regularization. The proposed utilization of modern optimization techniques implies only slightly higher computational costs compared to the standard approach without spatial regularization. |
| result_subspec |
WOS |
| RIV |
FS |
| FORD0 |
20000 |
| FORD1 |
20600 |
| FORD2 |
20601 |
| reportyear |
2020 |
| num_of_auth |
6 |
| mrcbC47 |
UPT-D 20000 20600 20601 |
| mrcbC52 |
4 A hod sml 4ah 4as 20231122144204.8 |
| inst_support |
RVO:67985556 |
| inst_support |
RVO:68081731 |
| permalink |
http://hdl.handle.net/11104/0298678 |
| cooperation |
| ARLID |
cav_un_auth*0314450 |
| name |
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií |
|
| cooperation |
| ARLID |
cav_un_auth*0346020 |
| name |
Luxembourg Institute of Health |
| country |
LU |
|
| mrcbC64 |
1 Magnetic Resonance and Cryogenics UPT-D 30224 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING |
| mrcbC64 |
1 Department of Image Processing UTIA-B 30224 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING |
| confidential |
S |
| contract |
| name |
COPYRIGHT TRANSFER AGREEMENT |
| date |
20190604 |
|
| mrcbC83 |
RIV/68081731:_____/19:00507670!RIV20-MSM-68081731 192327327 doplnění projektů UPT-D |
| mrcbC86 |
3+4 Article Radiology Nuclear Medicine Medical Imaging |
| mrcbC91 |
C |
| mrcbT16-e |
RADIOLOGYNUCLEARMEDICINEMEDICALIMAGING |
| mrcbT16-j |
1.002 |
| mrcbT16-s |
1.751 |
| mrcbT16-B |
69.736 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q1 |
| arlyear |
2019 |
| mrcbTft |
\nSoubory v repozitáři: bartos-507670.pdf, bartos-0507670 -LicenseCopy.pdf |
| mrcbU14 |
85071714198 SCOPUS |
| mrcbU24 |
31317577 PUBMED |
| mrcbU34 |
000483797700024 WOS |
| mrcbU63 |
cav_un_epca*0257207 Magnetic Resonance in Medicine 0740-3194 1522-2594 Roč. 82 č. 6 2019 2257 2272 Wiley |