Table 11.

Shortcomings and Mitigation Strategies of DL-based MRI Reconstruction Models.

Shortcoming	Description	Mitigation Strategies
Data Dependency [205,206]	DL models require large labeled training datasets, which may be challenging to obtain, limiting model generalization.	Data augmentation, transfer learning, and domain adaptation techniques can address data scarcity and improve generalization.
Limited Generalization [205,207,208]	Models trained on specific datasets may not perform well on data from different scanners or protocols due to variations in imaging characteristics.	Domain adaptation, model ensemble techniques, and domain-specific regularization methods can enhance generalization across different imaging settings.
Black Box Nature and Limited Explainability [209,210]	DL models lack transparency, interpretability, and the ability to provide detailed explanations for their predictions or reconstruction outputs.	Explainable AI techniques, such as attention mechanisms, interpretability methods, and integration with clinical knowledge or rule-based models, can enhance interpretability and provide explainable outputs.
Computational Resource Requirements [211,212,213]	Training and deploying DL models for MRI reconstruction can be computationally demanding, limiting accessibility in clinical settings.	Model compression techniques, efficient network architectures, and hardware acceleration can help alleviate computational resource requirements.
Susceptibility to Adversarial Attacks [214,215]	DL models can be vulnerable to adversarial attacks, raising concerns about their robustness and reliability.	Adversarial training, input preprocessing (e.g., denoising, smoothing), and defensive mechanisms (e.g., detection, certification) can enhance model robustness against adversarial attacks.
Handling Artifacts and Novel Cases [119,216]	DL models may struggle with complex artifacts that differ significantly from the training data distribution.	Augmenting training data with diverse artifacts, using data-specific loss functions, and incorporating domain knowledge can improve model performance on artifacts.
Hyperparameter Tuning [217,218]	The performance of DL models is sensitive to hyperparameter settings, requiring careful tuning.	Automated hyperparameter tuning techniques (e.g., grid search, Bayesian optimization) and model-specific optimization strategies including metaheuristics can enhance model performance through effective hyperparameter tuning.