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April 26, 2026Medical Physics0 citations

A proof‐of‐concept study of multitask learning for cranial synthetic CT generation across heterogeneous MRI field strengths

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ZXZhuoyao XinKennedy Krieger InstituteYZYiren ZhangCase Western Reserve UniversityCWChristopher WuColumbia University Irving Medical Center

Key Points

  • This research aims to assess the efficacy of multitask learning in generating synthetic cranial CT images using varying MRI field strengths.
  • Conducted a proof-of-concept study utilizing multitask learning for modality transformation.
  • Evaluated the methodology's performance across different MRI field strengths.
  • Task-structured training improved accuracy and generalizability of synthetic CT generation.
  • The proposed methodology showed consistent results across heterogeneous MRI conditions.

Abstract

These findings support that task-structured training for modality transformation markedly improves both accuracy and generalizability of cranial CT synthesis across heterogeneous MRI conditions. The observed consistency across field strengths validates the robustness of the proposed methodology.

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Cite This Study

Xin et al. (2026) studied this question.

synapsesocial.com/papers/69edacbd4a46254e215b473ehttps://doi.org/10.1002/mp.70429
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