Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering
✍ Scribed by Shi, Lin; Wang, Defeng; Liu, Wen; Fang, Kui; Wang, Yi-Xiang J.; Huang, Wenhua; King, Ann D.; Heng, Pheng Ann; Ahuja, Anil T.
- Book ID
- 127308204
- Publisher
- John Wiley and Sons
- Year
- 2013
- Tongue
- English
- Weight
- 808 KB
- Volume
- 39
- Category
- Article
- ISSN
- 1053-1807
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
## Abstract ## Purpose To characterize misregistration artifact in arterial input function (AIF) pixels in dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) using a two‐dimensional non‐echo‐planar imaging (EPI)‐based gradient‐recalled echo (GRE) sequence. ## Materials and Methods Dy
## Abstract For clinical dynamic contrast‐enhanced (DCE) MRI studies, it is often not possible to obtain reliable arterial input function (AIF) in each measurement. Thus, it is important to find a representative AIF for pharmacokinetic modeling of DCE‐MRI data when individual AIF (Ind‐AIF) measurem
## Abstract Dynamic susceptibility contrast‐MRI requires an arterial input function (AIF) to obtain cerebral blood flow, cerebral blood volume, and mean transit time. The current AIF selection criteria discriminate venous, capillary, and arterial profiles based on shape and timing characteristics o
To prevent systematic errors in quantitative brain perfusion studies using dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI), a reliable determination of the arterial input function (AIF) is essential. We propose a novel algorithm for correcting distortions of the AIF cau