Multiparametric mapping in the brain from conventional contrast-weighted images using deep learning - PubMed (original) (raw)

. 2022 Jan;87(1):488-495.

doi: 10.1002/mrm.28962. Epub 2021 Aug 10.

Affiliations

Multiparametric mapping in the brain from conventional contrast-weighted images using deep learning

Shihan Qiu et al. Magn Reson Med. 2022 Jan.

Abstract

Purpose: To develop a deep-learning-based method to quantify multiple parameters in the brain from conventional contrast-weighted images.

Methods: Eighteen subjects were imaged using an MR Multitasking sequence to generate reference T1 and T2 maps in the brain. Conventional contrast-weighted images consisting of T1 MPRAGE, T1 GRE, and T2 FLAIR were acquired as input images. A U-Net-based neural network was trained to estimate T1 and T2 maps simultaneously from the contrast-weighted images. Six-fold cross-validation was performed to compare the network outputs with the MR Multitasking references.

Results: The deep-learning T1 /T2 maps were comparable with the references, and brain tissue structures and image contrasts were well preserved. A peak signal-to-noise ratio >32 dB and a structural similarity index >0.97 were achieved for both parameter maps. Calculated on brain parenchyma (excluding CSF), the mean absolute errors (and mean percentage errors) for T1 and T2 maps were 52.7 ms (5.1%) and 5.4 ms (7.1%), respectively. ROI measurements on four tissue compartments (cortical gray matter, white matter, putamen, and thalamus) showed that T1 and T2 values provided by the network outputs were in agreement with the MR Multitasking reference maps. The mean differences were smaller than ± 1%, and limits of agreement were within ± 5% for T1 and within ± 10% for T2 after taking the mean differences into account.

Conclusion: A deep-learning-based technique was developed to estimate T1 and T2 maps from conventional contrast-weighted images in the brain, enabling simultaneous qualitative and quantitative MRI without modifying clinical protocols.

Keywords: brain; deep learning; magnetic resonance imaging (MRI); multiparametric mapping; quantitative imaging.

© 2021 International Society for Magnetic Resonance in Medicine.

PubMed Disclaimer

Figures

FIGURE 1

FIGURE 1

Network design. A 2D U-Net-based architecture with four down-sampling steps and four up-sampling steps was used. 2×2 average pooling layers were used in the down-sampling track. Bilinear interpolation-based up-sampling layers followed by 2×2 convolution were used in the up-sampling track. At each resolution scale, 3 convolutional groups were sequentially applied, each consisting of 3×3 convolution followed by batch normalization and rectified linear unit (ReLU). Long skip connections were added to recover fine details in the up-sampling track. Slices of the three conventional weighted images (i.e., T1 MPRAGE, T1 GRE and T2 FLAIR) were concatenated as the 3-channel input. Slices of T1 and T2 maps formed the 2-channel target. A combination of L1 loss and SSIM loss was used as the loss function.

FIGURE 2

FIGURE 2

Representative cases of the deep learning (DL) T1/T2 maps and the MR Multitasking (MT) references from 6 different MS patients (A-F). The black arrows show hyperintense lesions. The yellow arrows indicate some small white matter hyperintensities affected by the blurring.

FIGURE 3

FIGURE 3

Bland-Altman plots for T1/T2 measurements on the four types of tissues. Each color represents one tissue compartment.

References

    1. Lescher S, Jurcoane A, Veit A, Bahr O, Deichmann R, Hattingen E. Quantitative T1 and T2 mapping in recurrent glioblastomas under bevacizumab: Earlier detection of tumor progression compared to conventional MRI. Neuroradiology. 2015;57:11–20. -PubMed
    1. Blystad I, Haåkansson I, Tisell A, et al. Quantitative MRI for analysis of active multiple sclerosis lesions without gadolinium-based contrast agent. AJNR Am J Neuroradiol. 2016;37:94–100. -PMC -PubMed
    1. Gracien RM, Reitz SC, Hof SM, et al. Changes and variability of proton density and T1 relaxation times in early multiple sclerosis: MRI markers of neuronal damage in the cerebral cortex. Eur Radiol. 2016;26(8):2578–2586. -PubMed
    1. Reitz SC, Hof SM, Fleischer V, et al. Multi-parametric quantitative MRI of normal appearing white matter in multiple sclerosis, and the effect of disease activity on T2. Brain Imaging Behav. 2017;11(3):744–753. -PubMed
    1. Baudrexel S, Nuürnberger L, Ruüb U, et al. Quantitative mapping of T1 and T2* discloses nigral and brainstem pathology in early Parkinson's disease. Neuroimage. 2010;51:512–520. -PubMed

Publication types

MeSH terms

LinkOut - more resources