ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024
ENHANCING TRANSCRANIAL FOCUSED ULTRASOUND TREATMENT PLANNING WITH SYNTHETIC CT FROM ULTRA-SHORT ECHO TIME (UTE) MRI: A MULTI-TASK DEEP LEARNING APPROACH.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A proof-of-concept study of multitask learning for cranial synthetic CT generation across heterogeneous MRI field strengths.Medical physics · 2026Article
- A novel method for acoustic modeling of cranial bone based on the porosity index.Scientific reports · 2025Article
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Authors and funding
8 authors.
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Abstract
Utilizing a multi-task deep learning framework, this study generated synthetic CT (sCT) images from a limited dataset of Ultrashort echo time (UTE) MRI for transcranial focused ultrasound (tFUS) planning. A 3D Transformer U-Net was employed to produce sCT images that closely replicated actual CT scans, demonstrated by an average Dice coefficient of 0.868 for morphological accuracy. The acoustic simulation with sCT images showed mean focus absolute pressure differences of 8.85±7.29 % for the anterior cingulate cortex, 11.81±8.63 % for the precuneus, and 7.27±3.64 % for the supplemental motor cortex, with focus position discrepancies within 0.9±0.5 mm. These results underscore the efficacy of UTE-MRI as a non-radiative, cost-effective alternative for tFUS planning, with significant potential for clinical application.
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