Burhaneddin Yaman^{1,2}, Seyed Amir Hossein Hosseini^{1,2}, and Mehmet Akcakaya^{1,2}

^{1}Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, ^{2}Center for Magnetic Resonance Research, Minneapolis, MN, United States

While self-supervised learning enables training of deep learning reconstruction without fully-sampled data, it still requires a database. Moreover, performance of pretrained models may degrade when applied to out-of-distribution data. We propose a zero-shot subject-specific self-supervised learning via data undersampling (ZS-SSDU) method, where acquired data from a single scan is split into at least three disjoint sets, which are respectively used only in physics-guided neural network, to define training loss, and to establish an early stopping strategy to avoid overfitting. Results on knee and brain MRI show that ZS-SSDU achieves improved artifact-free reconstruction, while tackling generalization issues of trained database models.

$$\arg\min_{\bf x}\|\mathbf{y}_{\Omega}-\mathbf{E}_{\Omega}\mathbf{x}\|^2_2+\cal{R}(\mathbf{x}), (1)$$

where

$$ \min_{\bf\theta} \frac1N\sum_{i=1}^{N}\mathcal{L}({\bf y}_{\textrm{ref}}^i,\:{\bf E}_\textrm{full}^if({\bf y}_{\Omega}^i,{\bf E}_{\Omega}^i;{\bf \theta})),(2)$$

where

In absence of fully-sampled data, SSDU enables training by splitting acquired data locations

$$\min_{\bf\theta}\frac1N\sum_{i=1}^{N}\mathcal{L}\Big({\bf y}_{\Lambda}^i,\: {\bf E}_{\Lambda}^i \big(f({\bf y}_{\Theta}^i,{\bf E}_{\Theta}^i;{\bf \theta})\big)\Big). (3)$$

In the proposed, database-free ZS-SSDU (

$$\Omega=\Theta\sqcup\Lambda\sqcup\Gamma, (4)$$

where

$$\min_{\bf \theta}\frac{1}{K}\sum_{k=1}^{K} \mathcal{L}\Big({\bf y}_{\Lambda_k},\: {\bf E}_{\Lambda_k} \big(f({\bf y}_{\Theta_k},{\bf E}_{\Theta_k};{\bf\theta})\big)\Big), (5)$$

along with a k-space self-validation loss, which tests the generalization performance of the trained network on the validation partition

$$\mathcal{L}\Big({\bf y}_{\Gamma},\: {\bf E}_{\Gamma}\big(f({\bf y}_{\Omega\backslash\Gamma},{\bf E}_{\Omega\backslash\Gamma};{\bf\theta}^{(l)})\big)\Big).$$

All PG-DL approaches were trained using Adam optimizer, learning rate=5×10

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DOI: https://doi.org/10.58530/2022/4810