Faisal Najeeb1, Jichang Zhang2, Xinpei Wang2, Chengbo Wang2, Hammad Omer1, Penny Gowland3, Sue Francis3, and Paul Glover3
1MIPRG,Comsats University, Islamabad, Pakistan, 2SPMIC, The University of Nottingham Ningbo China, Ningbo, China, 3SPMIC, The University of Nottingham, Nottingham, United Kingdom
1MIPRG,Comsats University, Islamabad, Pakistan, 2SPMIC, The University of Nottingham Ningbo China, Ningbo, China, 3SPMIC, The University of Nottingham, Nottingham, United Kingdom
We
have developed a new reconstruction framework which provides higher time
efficiency and better image quality for DCE-MRI. Improved temporal resolution
and dynamic contrast was also achieved simultaneously by proposed method.
Figure 1: Data
Sorting/Temporal subdivision methods for golden angle radial sampled pattern:
(a) GRASP based method subdivides the acquired spokes into multiple frames
according to the temporal order while there are no repeated spokes among
frames; (b) proposed method subdivides the acquired spokes with a temporal
windowing function. In the example above, frames 4 in the proposed method
contains the similar pattern as frame 2 in general method while the number of
frames has been increased 2 times.
Figure
3: Three contrast phases of DCE-MRI liver images reconstructed by Racer-GRASP
and the Proposed Method. Our method provides better reconstruction
quality about tissue details (straight-line arrows) with less artefacts.
Meanwhile, L+S decomposition is able to present the dynamic MRI naturally while
better dynamic contrast was observed in the proposed method. The dynamic
contrast in arterial region is better than RACER-GRASP (dashed-line arrows). The
signal intensity of the arterial region signed by red circle was selected as
the standard for evaluating dynamic contrast.
