DeepEMC-T2 mapping: Deep learning-enabled T2 mapping based on echo modulation curve modeling

Abstract

Purpose: Echo modulation curve (EMC) modeling enables accurate quantification of T₂ relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T₂ mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T₂ mapping, called DeepEMC-T₂ mapping, to efficiently estimate accurate T₂ maps from fewer echoes.

Methods: DeepEMC-T₂ mapping was developed using a modified U-Net to estimate both T₂ and proton density (PD) maps directly from MESE images. The network implements several new features to improve the accuracy of T₂/PD estimation. A total of 67 MESE datasets acquired in axial orientation were used for network training and evaluation. An additional 57 datasets acquired in coronal orientation with different scan parameters were used to evaluate the generalizability of the framework. The performance of DeepEMC-T₂ mapping was evaluated in seven experiments.

Results: Compared to the reference, DeepEMC-T₂ mapping achieved T₂ estimation errors from 1% to 11% and PD estimation errors from 0.4% to 1.5% with ten/seven/five/three echoes, which are more accurate than standard EMC-T₂ mapping. By incorporating datasets acquired with different scan parameters and orientations for joint training, DeepEMC-T₂ exhibits robust generalizability across varying imaging protocols. Increasing the echo spacing and including longer echoes improve the accuracy of parameter estimation. The new features proposed in DeepEMC-T₂ mapping all enabled more accurate T₂ estimation.

Conclusions: DeepEMC-T₂ mapping enables simplified, efficient, and accurate T₂ quantification directly from MESE images without dictionary matching. Accurate T₂ estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times.

Keywords: T2 mapping; deep learning; echo modulation curve; multi‐echo spin‐echo; quantitative MRI.

Figure 1

(a) Standard EMC-T2 framework: A T2 map is generated through pixel-wise T2 dictionary matching and a PD map is generated by back-projecting the first echo image with the estimated T2 map. (b) New DeepEMC-T2 framework: A spatiotemporal U-net is developed and trained on MESE images with variable echoes using a supervised scheme. The reference T2 and PD maps for training are calculated from images with 10 echoes using the standard EMC-T2 mapping approach. The original MESE images with 10 echoes were retrospectively cut to 7, 5, and 3 echoes for evaluation, which can potentially enable increased volumetric coverage and/or reduced SAR.

Figure 2

(a) A representative case comparing T2 maps estimated using EMC-T2 and DeepEMC-T2 from MESE images with varying numbers of echoes. The T2 maps estimated from standard EMC-T2 using all 10 echoes were treated as the reference standard for calculating the error maps. (b) Quantitative comparison of T2 estimation in all test cases with varying numbers of echoes based on averaged pixel-wise errors in different T2 ranges. The error map and quantitative comparison indicate that DeepEMC-T2 mapping enables more accurate T2 map estimation than EMC-T2 mapping, particularly as the number of echoes is reduced. (c) Quantitative comparison of T2 estimation in the selected lesion area averaged over all test cases. It indicates that DeepEMC-T2 mapping allows for more accurate T2 estimation in the lesion region.

Figure 3

(a) A representative case comparing PD map estimated using EMC-T2 and DeepEMC-T2 from MESE images with varying numbers of echoes. The PD maps estimated from standard EMC-T2 using all 10 echoes were treated as the reference for calculating the error maps. (b) Quantitative comparison of PD estimation in all test cases with varying numbers of echoes with different numbers of echoes based on averaged pixel-wise errors. The error map and quantitative comparison indicate that DeepEMC-T2 mapping enables more accurate PD map estimation than EMC-T2 mapping, particularly as the number of echoes is reduced.

Figure 4

(a) A representative example comparing T2 maps estimated from MESE images for echoes 1,5,10 and echoes 1,3,5 with a larger echo spacing, and for 5 echoes (echo 1–5) and 3 echoes (echo 1–3) with the default echo spacing using both EMC-T2 and DeepEMC-T2. (b) Quantitative comparison of T2 maps in test datasets based on averaged pixel-wise errors in different T2 ranges. The results suggest that increasing the echo spacing while reducing the number of echoes has little impact on the accuracy of T2 map estimation in DeepEMC-T2 mapping. This also shows that including the longer echoes while maintaining the same number of echoes can improve the accuracy of the estimated T2 map for DeepEMC-T2 mapping.

Figure 5

(a) A representative example comparing PD maps estimated from MESE images for echoes 1,5,10 and echoes 1,3,5 with a larger echo spacing, and for 5 echoes (echo 1–5) and 3 echoes (echo 1–3) with the default echo spacing using both EMC-T2 and DeepEMC-T2. (b) Quantitative comparison of PD maps estimated in 15 test datasets based on averaged pixel-wise errors. The results demonstrated that increasing the echo space while reducing the number of echoes has little impact on the accuracy of PD map estimation in DeepEMC-T2 mapping. This also suggests that including the longer echoes while maintaining the same number of echoes can improve the accuracy of the estimated PD map for DeepEMC-T2 mapping.

Figure 6

(a) A representative case from the axial-TE15ms dataset comparing T2 maps reconstructed using the axial-TE15ms model, the coronal-TE12ms model, and the joint DeepEMC-T2 Mapping network model with varying numbers of echoes. (b) Quantitative comparison of T2 estimation error across all the test cases of the axial-TE15ms dataset reconstructed using the axial-TE15ms model, the coronal-TE12ms model, and the joint DeepEMC-T2 Mapping network model with varying numbers of echoes. The result indicates that the model trained solely on coronal-TE12ms datasets lacks generalizability to axial-TE15ms datasets in estimating T2 maps. However, the joint model demonstrates robust generalization on axial-TE15ms datasets without reducing accuracy in estimating T2 maps.

Figure 7

(a) A representative case from the axial-TE15ms dataset comparing PD maps reconstructed using the axial-TE15ms model, the coronal-TE12ms model, and the joint DeepEMC-T2 Mapping network model with varying numbers of echoes. (b) Quantitative comparison of PD estimation error across all the test cases of the axial-TE15ms dataset reconstructed using the axial-TE15ms model, the coronal-TE12ms model, and the joint DeepEMC-T2 Mapping network model with varying numbers of echoes. The result indicates that the model trained solely on coronal-TE12ms datasets lacks generalizability to axial-TE15ms datasets in estimating PD maps. However, the joint model demonstrates robust generalization on axial-TE15ms datasets without reducing accuracy in estimating PD maps.

Figure 8

(a) A representative case from the coronal-TE12ms dataset comparing T2 maps reconstructed using the axial-TE15ms model, the coronal-TE12ms model, and the joint DeepEMC-T2 Mapping network model with varying numbers of echoes. (b) Quantitative comparison of T2 estimation error across all the test cases of the coronal-TE12ms dataset reconstructed using the axial-TE15ms model, the coronal-TE12ms model, and the joint DeepEMC-T2 Mapping network model with varying numbers of echoes. The result indicates that the model trained solely on axial-TE15ms datasets lacks generalizability to coronal-TE12ms datasets in estimating T2 maps. However, the joint model demonstrates robust generalization on coronal-TE12ms datasets without reducing accuracy in estimating T2 maps.

Figure 9

(a) A representative case from the coronal-TE12ms dataset comparing PD maps reconstructed using the axial-TE15ms model, the coronal-TE12ms model, and the joint DeepEMC-T2 Mapping network model with varying numbers of echoes. (b) Quantitative comparison of PD estimation error across all the test cases of the coronal-TE12ms dataset reconstructed using the axial-TE15ms model, the coronal-TE12ms model, and the joint DeepEMC-T2 Mapping network model with varying numbers of echoes. The result indicates that the model trained solely on axial-TE15ms datasets lacks generalizability to coronal-TE12ms datasets in estimating PD maps. However, the joint model demonstrates robust generalization on coronal-TE12ms datasets without reducing accuracy in estimating PD maps.