Figure 1:

Illustration of the MANTIS framework, which features two loss components. The first loss term (loss 1) ensures that the reconstructed parameter maps from the CNN mapping produce synthetic undersampled k-space data ($\tilde{d}$) matching the acquired k-space measurements (d) in the k-space domain. The second loss term (loss 2) ensures that the undersampled multiecho images produce parameter maps $\left({\tilde{\text{I}}}_0,{\tilde{\text{T}}}_2\right)$ that are same as the reference parameter maps (i₀, t₂) genrated from reference multiecho images. The MANTIS framework considers both the data-driven deep learning component and signal model from the basic MR physics. The notation in this figure follows the main text description.