• Je něco špatně v tomto záznamu ?

Semi-parametric arterial input functions for quantitative dynamic contrast enhanced magnetic resonance imaging in mice

T. Taxt, RK. Reed, T. Pavlin, CB. Rygh, E. Andersen, R. Jiřík,

. 2018 ; 46 (-) : 10-20. [pub] 20171021

Jazyk angličtina Země Nizozemsko

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc18033632

OBJECTIVE: An extension of single- and multi-channel blind deconvolution is presented to improve the estimation of the arterial input function (AIF) in quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). METHODS: The Lucy-Richardson expectation-maximization algorithm is used to obtain estimates of the AIF and the tissue residue function (TRF). In the first part of the algorithm, nonparametric estimates of the AIF and TRF are obtained. In the second part, the decaying part of the AIF is approximated by three decaying exponential functions with the same delay, giving an almost noise free semi-parametric AIF. Simultaneously, the TRF is approximated using the adiabatic approximation of the Johnson-Wilson (aaJW) pharmacokinetic model. RESULTS: In simulations and tests on real data, use of this AIF gave perfusion values close to those obtained with the corresponding previously published nonparametric AIF, and are more noise robust. CONCLUSION: When used subsequently in voxelwise perfusion analysis, these semi-parametric AIFs should give more correct perfusion analysis maps less affected by recording noise than the corresponding nonparametric AIFs, and AIFs obtained from arteries. SIGNIFICANCE: This paper presents a method to increase the noise robustness in the estimation of the perfusion parameter values in DCE-MRI.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc18033632
003      
CZ-PrNML
005      
20181016120230.0
007      
ta
008      
181008s2018 ne f 000 0|eng||
009      
AR
024    7_
$a 10.1016/j.mri.2017.10.004 $2 doi
035    __
$a (PubMed)29066294
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a ne
100    1_
$a Taxt, Torfinn $u Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway; Dept. of Radiology, Haukeland University Hospital, Jonas Lies vei 83, Bergen N-5020, Norway.
245    10
$a Semi-parametric arterial input functions for quantitative dynamic contrast enhanced magnetic resonance imaging in mice / $c T. Taxt, RK. Reed, T. Pavlin, CB. Rygh, E. Andersen, R. Jiřík,
520    9_
$a OBJECTIVE: An extension of single- and multi-channel blind deconvolution is presented to improve the estimation of the arterial input function (AIF) in quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). METHODS: The Lucy-Richardson expectation-maximization algorithm is used to obtain estimates of the AIF and the tissue residue function (TRF). In the first part of the algorithm, nonparametric estimates of the AIF and TRF are obtained. In the second part, the decaying part of the AIF is approximated by three decaying exponential functions with the same delay, giving an almost noise free semi-parametric AIF. Simultaneously, the TRF is approximated using the adiabatic approximation of the Johnson-Wilson (aaJW) pharmacokinetic model. RESULTS: In simulations and tests on real data, use of this AIF gave perfusion values close to those obtained with the corresponding previously published nonparametric AIF, and are more noise robust. CONCLUSION: When used subsequently in voxelwise perfusion analysis, these semi-parametric AIFs should give more correct perfusion analysis maps less affected by recording noise than the corresponding nonparametric AIFs, and AIFs obtained from arteries. SIGNIFICANCE: This paper presents a method to increase the noise robustness in the estimation of the perfusion parameter values in DCE-MRI.
650    _2
$a algoritmy $7 D000465
650    _2
$a zvířata $7 D000818
650    _2
$a arterie $x patologie $7 D001158
650    _2
$a počítačová simulace $7 D003198
650    _2
$a kontrastní látky $x chemie $x farmakokinetika $7 D003287
650    _2
$a ženské pohlaví $7 D005260
650    12
$a vylepšení obrazu $7 D007089
650    12
$a počítačové zpracování obrazu $7 D007091
650    12
$a magnetická rezonanční tomografie $7 D008279
650    _2
$a myši $7 D051379
650    _2
$a myši inbrední C57BL $7 D008810
650    _2
$a perfuze $7 D010477
650    _2
$a reprodukovatelnost výsledků $7 D015203
655    _2
$a časopisecké články $7 D016428
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Reed, Rolf K $u Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway; Centre for Cancer Biomarkers (CCBIO), University of Bergen, Jonas Lies vei 87, Bergen N-5021, Norway.
700    1_
$a Pavlin, Tina $u Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway; Dept. of Radiology, Haukeland University Hospital, Jonas Lies vei 83, Bergen N-5020, Norway.
700    1_
$a Rygh, Cecilie Brekke $u Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway.
700    1_
$a Andersen, Erling $u Dept. of Clinical Engineering, Haukeland University Hospital, Jonas Lies vei 83, Bergen N-5020, Norway.
700    1_
$a Jiřík, Radovan $u Czech Academy of Sciences, Inst. of Scientific Instruments, Královopolská 147, Brno 61264, Czech Republic. Electronic address: jirik@isibrno.cz.
773    0_
$w MED00003171 $t Magnetic resonance imaging $x 1873-5894 $g Roč. 46, č. - (2018), s. 10-20
856    41
$u https://pubmed.ncbi.nlm.nih.gov/29066294 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y a $z 0
990    __
$a 20181008 $b ABA008
991    __
$a 20181016120727 $b ABA008
999    __
$a ok $b bmc $g 1339533 $s 1030626
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2018 $b 46 $c - $d 10-20 $e 20171021 $i 1873-5894 $m Magnetic resonance imaging $n Magn Reson Imaging $x MED00003171
LZP    __
$a Pubmed-20181008

Najít záznam

Citační ukazatele

Možnosti archivace