Nahla M H Elsaid1, Nadine L Dispenza2, R Todd Constable1,3, Hemant D Tagare1,2, and Gigi Galiana1
1Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States, 2Department of Biomedical Engineering, Yale University, New Haven, CT, United States, 3Neurosurgery, Yale University, New Haven, CT, United States
1Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States, 2Department of Biomedical Engineering, Yale University, New Haven, CT, United States, 3Neurosurgery, Yale University, New Haven, CT, United States
Constrained alternating minimization
for parameter mapping (CAMP) improves the image quality in highly accelerated
parameter mapping by incorporating a linear constraint that relates consecutive
images.
Figure 1. (a) Radial turbo-spin echo T2w
in-vivo brain image quality improvements with CAMP (rightmost column) compared
to CG (middle column) and KWIC (leftmost column) reconstructions shown at an
example echo time of 75 ms with undersampling factors 6.3, and 12.6 (rows 2 and
3) respectively. Panel (b) shows T2-maps generated from KWIC, CG, and CAMP. NRMSE and HFEN 5
values are shown for the undersampled maps. Panel (c) shows an example of the
undersampling pattern used in (a) and (b).
Figure2. (a) Cartesian spin-echo brain
data shows image quality improvements when T2w-images are
reconstructed with CAMP (right column) compared to regularized CG (left column)
at undersampling factors 4 and 8 (middle and bottom rows). (b) Exponential fits of undersampled images
propagate the undersampling artifacts of the image series, but these are
reduced in T2-maps generated by CAMP reconstruction as evidenced by the halving
of NRMSE at R=8. Panel (c) shows an example of the undersampling pattern used
in (a) and (b).