TY - JOUR
T1 - Low-field magnetic resonance image enhancement via stochastic image quality transfer
AU - Lin, Hongxiang
AU - Figini, Matteo
AU - D'Arco, Felice
AU - Ogbole, Godwin
AU - Tanno, Ryutaro
AU - Blumberg, Stefano B.
AU - Ronan, Lisa
AU - Brown, Biobele J.
AU - Carmichael, David W.
AU - Lagunju, Ikeoluwa
AU - Cross, Judith Helen
AU - Fernandez-Reyes, Delmiro
AU - Alexander, Daniel C.
N1 - Funding Information:
This work was supported by EPSRC, United Kingdom grants ( EP/R014019/1 , EP/R006032/1 and EP/M020533/1 ), the NIHR UCLH Biomedical Research Centre, United Kingdom , and the NIHR Biomedical Research Centre at Great Ormond Street Hospital, United Kingdom . 3T T1w and T2w images were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657 ) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, United States ; and by the McDonnell Center for Systems Neuroscience at Washington University, United States . High-field FLAIR images were used in part from the Leipzig Study for Mind-Body-Emotion Interactions (LEMON) data set provided by the Mind-Body-Emotion group at the Max Planck Institute for Human Cognitive and Brain Sciences. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/7
Y1 - 2023/7
N2 - Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly have lower resolution and poorer contrast than images from high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field. Our approach uses (i) a stochastic low-field image simulator as the forward model to capture uncertainty and variation in the contrast of low-field images corresponding to a particular high-field image, and (ii) an anisotropic U-Net variant specifically designed for the IQT inverse problem. We evaluate the proposed algorithm both in simulation and using multi-contrast (T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical low-field MRI data from an LMIC hospital. We show the efficacy of IQT in improving contrast and resolution of low-field MR images. We demonstrate that IQT-enhanced images have potential for enhancing visualisation of anatomical structures and pathological lesions of clinical relevance from the perspective of radiologists. IQT is proved to have capability of boosting the diagnostic value of low-field MRI, especially in low-resource settings.
AB - Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly have lower resolution and poorer contrast than images from high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field. Our approach uses (i) a stochastic low-field image simulator as the forward model to capture uncertainty and variation in the contrast of low-field images corresponding to a particular high-field image, and (ii) an anisotropic U-Net variant specifically designed for the IQT inverse problem. We evaluate the proposed algorithm both in simulation and using multi-contrast (T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical low-field MRI data from an LMIC hospital. We show the efficacy of IQT in improving contrast and resolution of low-field MR images. We demonstrate that IQT-enhanced images have potential for enhancing visualisation of anatomical structures and pathological lesions of clinical relevance from the perspective of radiologists. IQT is proved to have capability of boosting the diagnostic value of low-field MRI, especially in low-resource settings.
KW - Deep neural networks
KW - Image quality transfer
KW - Low-field MRI
KW - Stochastic simulator
UR - http://www.scopus.com/inward/record.url?scp=85153683220&partnerID=8YFLogxK
U2 - 10.1016/j.media.2023.102807
DO - 10.1016/j.media.2023.102807
M3 - Article
AN - SCOPUS:85153683220
SN - 1361-8415
VL - 87
JO - Medical Image Analysis
JF - Medical Image Analysis
M1 - 102807
ER -