Anatomical feature-prioritized loss for enhanced MR to CT translation.

Objective: Accurate reconstruction of localized anatomical details is essential in medical image synthesis, particularly when addressing specific clinical requirements such as the identification or measurement of fine structures. Traditional methods for image translation and synthesis are generally optimized for global image reconstruction but often fall short in providing the finesse required for detailed local analysis. This study represents a step toward addressing this challenge by introducing a novel anatomical feature-prioritized (AFP) loss function into the synthesis process. Approach. The AFP loss integrates features from pre-trained task-specific models, such as anatomical segmentation networks, into the image synthesis pipeline to enforce attention to critical structures. This loss function is evaluated across multiple architectures, including GAN-based and CNN-based models, and applied in two cross-modality contexts: (1) lung MR to CT translation with an emphasis on bronchial structure preservation, using a private thoracic dataset; and (2) pelvis MR to CT synthesis, targeting organ and muscle reconstruction, using the public SynthRAD2023 dataset. Feature embeddings from domain-specific segmentation networks are extracted to guide synthesis toward anatomically meaningful outputs. Results. The AFP loss demonstrated consistent improvements in downstream segmentation accuracy across both domains. For lung airway reconstruction, the Dice coefficient increased from 0.534 with standard L1 loss to 0.584 using AFP loss. In pelvic imaging, bone reconstruction Dice scores improved from 0.738 using L1 loss to 0.780 with AFP loss. These results confirm that the AFP loss improves the reconstruction of anatomical structures while maintaining comparable intensity-based metrics, indicating that global image quality is not compromised. Significance. The proposed AFP loss provides a modular and generalizable approach for embedding anatomical task-awareness into medical image synthesis. By aligning image translation objectives with clinically relevant features, it offers a pathway toward more precise and useful synthetic images for downstream tasks, supporting broader integration of image synthesis in clinical workflows.