| bibtype |
V -
Research Report
|
| ARLID |
0391681 |
| utime |
20240103202454.5 |
| mtime |
20130418235959.9 |
| title
(primary) (eng) |
Convolution Model of Time-activity Curves in Blind Source Separation |
| publisher |
| place |
Praha |
| name |
UTIA AV ČR |
| pub_time |
2013 |
|
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| edition |
| name |
Research Report |
| volume_id |
2330 |
|
| keyword |
dynamic medical imaging |
| keyword |
compartment modeling |
| keyword |
Convolution |
| keyword |
blind source separation |
| author
(primary) |
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA13-29225S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0292734 |
|
| abstract
(eng) |
Availability of input and organ functions is a prerequisite for analysis of dynamic image sequences in scintigraphy and positron emission tomography (PET) via kinetic models. In PET, the input function can be directly measured by sampling the arterial blood. This invasive procedure can be substituted by extraction of the input function from the observed images. Standard procedure for the extraction is based on manual selection of a region of interest (ROI) which is user-dependent and inaccurate. The aim of our contribution is to demonstrate a new procedure for simultaneous estimation of the input and organ functions from the observed image sequence. We design a mathematical model that integrates all common assumption of the domain, including convolution of the input function and tissue-specific kernels. The input function as well as the kernel parameters are considered to be unknown. They are estimated from the observed images using the Variational Bayes method. |
| reportyear |
2014 |
| RIV |
BB |
| num_of_auth |
2 |
| mrcbC52 |
4 O 4o 20231122135603.9 |
| permalink |
http://hdl.handle.net/11104/0220796 |
| arlyear |
2013 |
| mrcbTft |
\nSoubory v repozitáři: 0391681.pdf |
| mrcbU10 |
2013 |
| mrcbU10 |
Praha UTIA AV ČR |
|