Higher Resolution with Improved Image Quality without Increased Scan Time: Is it possible with MRI Deep Learning Reconstruction?
Hung Do1, Carly Lockard2, Dawn Berkeley1, Brian Tymkiw1, Nathan Dulude3, Scott Tashman2, Garry Gold4, Erin Kelly1, and Charles Ho2
1Canon Medical Systems USA, Inc., Tustin, CA, United States, 2Steadman Philippon Research Institute, Vail, CO, United States, 3The Steadman Clinic, Vail, CO, United States, 4Stanford University, Stanford, CA, United States
DLR is shown to enable increased resolution and improved
image quality simultaneously without increased scan time. Specifically, high
resolution DLR images were rated statistically higher than routine images from
two experienced specialists.
Figure 1: Five reconstructions (DLR, NL2, GA43, GA53, and REF) from each sequence. The labels of the 5 reconstructions were removed and
their order is randomized before sharing with 2 MSK specialists for blinded
review via a cloud-based webPACS. ROIs and feature profile placements are at
identical locations in all 5 images. Mean and the standard deviation of signal
intensities within an ROI were used for SNR and CNR calculations while signal profile was
used for calculating the full-width-at-half-maximum (FWHM) of the small
features.
Figure 3: SNR,
CNR, and FWHM measured from DLR, NL2, GA43, GA53, and REF reconstructed images.
DLR’s SNR and CNR were statistically higher than those of NL2, GA43, and GA53 (p
< 0.001) and not statistically different from that of REF (p > 0.049).
DLR’s FWHM is statistically higher than that of REF (p < 0.006) and not statistically
different from that of NL2, GA43, and GA53 (p > 0.17).