Dynamic signal variation among different time frames in arterial region in the selected ROI in different reconstruction schemes at AF = 8 and 16 respectively for Dataset-1.

Dynamic signal variation among different time frames in arterial region in the selected ROI in different reconstruction schemes at AF = 8 and 16 respectively for Dataset-1.

<p>The proposed method provides better dynamic contrast than the other two techniques due to additional sparsity constraint in L+S reconstruction.</p>

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Bibliographic Details
Main Author: Faisal Najeeb (20542714) (author)
Other Authors: Kashif Amjad (7665488) (author), Irfan Ullah (847820) (author), Hammad Omer (17412783) (author)
Published: 2025
Subjects:
Cell Biology
Physiology
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Physical Sciences not elsewhere classified
Information Systems not elsewhere classified
rank plus sparse
nonlinear conjugate gradient
high computational burden
div >< p
arterial signal intensity
repeated nufft gridding
radial sampling pattern
multiple sparsity constraints
convergence analysis shows
improved temporal resolution
e ., grasp
reconstructing dynamic contrast
dce mr images
coil mri data
reconstruction time due
highly accelerated free
grasp reconstruction technique
mri reconstruction techniques
angle radial data
radial data
dynamic contrast
reconstruction time
sparsity transform
radial dce
grog gridding
convergence error
mri reconstruction
temporal incoherence
accelerated golden
reconstruction results
grasp include
grasp ).
parallel mri
mri datasets
mri ).
vivo dce
variable acceleration
selected region
results show
proposed method
paper introduces
optimization problem
optimal solution
novel approach
monotone fista
method combines
mean values
major limitations
contemporary method
compressed sensing
cartesian grid
address limitations
acceleration capability
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