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A Proof-of-Concept Study of Multitask Learning for Cranial Synthetic CT Generation Across Heterogeneous MRI Field Strengths

arXiv 2605.00923 ↗First posted 30 Apr 2026eess.IV cs.CVcited by 0

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Abstract (arXiv)

Accurate synthesis of computed tomography (CT) images from magnetic resonance imaging (MRI) is clinically valuable for cranial applications such as attenuation correction, radiotherapy planning, and image-guided interventions. However, heterogeneity across MRI field strengths and acquisition protocols limits the generalizability of existing methods. In this study, we formulate cranial CT synthesis as a modular, structurally coupled problem and propose a deep learning framework to improve robustness across heterogeneous MRI conditions. The model is designed to adapt to variations in field strength and imaging protocols while preserving anatomical consistency. Experiments on multi-site datasets demonstrate improved performance and generalization compared with conventional approaches. The proposed method enables reliable CT synthesis across heterogeneous MRI settings, supporting broader clinical translation.

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References · 12

  1. 2017Attention Is All You Needcited by 60,361
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  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  4. 2021Pre-Trained Models: Past, Present and Futurecited by 319
  5. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
  6. 2024Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentationcited by 133
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  8. 2023Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Modelcited by 29
  9. 2022Improving Across-Dataset Brain Tissue Segmentation Using Transformercited by 7
  10. 2023Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesiscited by 2
  11. 2024MedSegMamba: 3D CNN-Mamba Hybrid Architecture for Brain Segmentationcited by 2
  12. 2023Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network frameworkcited by 1

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