TY - JOUR
T1 - ESPRESO
T2 - An algorithm to estimate the slice profile of a single magnetic resonance image
AU - Han, Shuo
AU - Remedios, Samuel W.
AU - Schär, Michael
AU - Carass, Aaron
AU - Prince, Jerry L.
N1 - Funding Information:
The authors would like to thank our colleagues from the Johns Hopkins University. The work was supported by a 2019 Johns Hopkins Discovery Award , an NMSS Grant RG-1907–34570 , National Science Foundation Graduate Research Fellowship under Grant No. DGE-1746891 , and CDMRP Grant W81XWH2010912 .
Funding Information:
The authors would like to thank our colleagues from the Johns Hopkins University. The work was supported by a 2019 Johns Hopkins Discovery Award, an NMSS Grant RG-1907–34570, National Science Foundation Graduate Research Fellowship under Grant No. DGE-1746891, and CDMRP Grant W81XWH2010912. The work uses the OASIS3 dataset: Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P50 AG00561, P30 NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1 TR000448, R01 EB009352.
Publisher Copyright:
© 2023
PY - 2023/5
Y1 - 2023/5
N2 - To reduce scan time, magnetic resonance (MR) images are often acquired using 2D multi-slice protocols with thick slices that may also have gaps between them. The resulting image volumes have lower resolution in the through-plane direction than in the in-plane direction, and the through-plane resolution is in part characterized by the protocol's slice profile which acts as a through-plane point spread function (PSF). Although super-resolution (SR) has been shown to improve the visualization and down-stream processing of 2D multi-slice MR acquisitions, previous algorithms are usually unaware of the true slice profile, which may lead to sub-optimal SR performance. In this work, we present an algorithm to estimate the slice profile of a 2D multi-slice acquisition given only its own image volume without any external training data. We assume that an anatomical image is isotropic in the sense that, after accounting for a correctly estimated slice profile, the image patches along different orientations have the same probability distribution. Our proposed algorithm uses a modified generative adversarial network (GAN) where the generator network estimates the slice profile to reduce the resolution of the in-plane direction, and the discriminator network determines whether a direction is generated or real low resolution. The proposed algorithm, ESPRESO, which stands for “estimating the slice profile for resolution enhancement of a single image only”, was tested with a state-of-the-art internally supervised SR algorithm. Specifically, ESPRESO is used to create training data for this SR algorithm, and results show improvements when ESPRESO is used over commonly-used PSFs.
AB - To reduce scan time, magnetic resonance (MR) images are often acquired using 2D multi-slice protocols with thick slices that may also have gaps between them. The resulting image volumes have lower resolution in the through-plane direction than in the in-plane direction, and the through-plane resolution is in part characterized by the protocol's slice profile which acts as a through-plane point spread function (PSF). Although super-resolution (SR) has been shown to improve the visualization and down-stream processing of 2D multi-slice MR acquisitions, previous algorithms are usually unaware of the true slice profile, which may lead to sub-optimal SR performance. In this work, we present an algorithm to estimate the slice profile of a 2D multi-slice acquisition given only its own image volume without any external training data. We assume that an anatomical image is isotropic in the sense that, after accounting for a correctly estimated slice profile, the image patches along different orientations have the same probability distribution. Our proposed algorithm uses a modified generative adversarial network (GAN) where the generator network estimates the slice profile to reduce the resolution of the in-plane direction, and the discriminator network determines whether a direction is generated or real low resolution. The proposed algorithm, ESPRESO, which stands for “estimating the slice profile for resolution enhancement of a single image only”, was tested with a state-of-the-art internally supervised SR algorithm. Specifically, ESPRESO is used to create training data for this SR algorithm, and results show improvements when ESPRESO is used over commonly-used PSFs.
KW - GAN
KW - MRI
KW - Slice profile
KW - Super-resolution
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U2 - 10.1016/j.mri.2023.01.012
DO - 10.1016/j.mri.2023.01.012
M3 - Article
C2 - 36702167
AN - SCOPUS:85147274110
SN - 0730-725X
VL - 98
SP - 155
EP - 163
JO - Magnetic Resonance Imaging
JF - Magnetic Resonance Imaging
ER -