1.
QJM
; 114(6): 426-427, 2021 10 07.
Article
in English
| MEDLINE
| ID: mdl-33647980
2.
QJM
; 114(2): 122-123, 2021 04 27.
Article
in English
| MEDLINE
| ID: mdl-33165617
3.
Methods Inf Med
; 36(4-5): 329-31, 1997 Dec.
Article
in English
| MEDLINE
| ID: mdl-9470391
ABSTRACT
In PET image analysis, conventional deconvolution alone will not give sufficient information for a precise study of a localized brain function. In the deconvolution process, which is a type of inverse problem, it is important to confine the solution space by incorporating a priori knowledge such as the tissue distribution given by MR images as well as smoothness in the blood flow distribution profile. An MR-embedded neural-network model is described to reduce the partial volume effect in the restoration of blood flow profiles from PET images.