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<p>Diffusion tensor imaging (DTI) is essential for assessing brain microstructure but requires long acquisition times, limiting clinical use. Recent deep learning (DL) approaches, such as SuperDTI or deepDTI, improve DTI metrics but demand large, high-quality datasets for training. We propose a self-supervised deep learning with fine-tuning (SSDLFT) framework to reduce training data requirements. SSDLFT involves self-supervised pretraining, which denoises data without clean labels, followed by fine-tuning with limited high-quality data. Experiments using Human Connectome Project data show that SSDLFT outperforms traditional methods and other DL approaches in qualitative and quantitative assessments of DWI reconstructions and tensor metrics. SSDLFT’s ability to maintain high performance with fewer training subjects and DWIs presents a significant advancement, enhancing DTI’s practical applications in clinical and research settings.</p>

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Accelerated diffusion tensor imaging with self-supervision and fine-tuning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月14日

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  • Phillip Martin,
  • Diego Martin,
  • Maria Altbach,
  • Ali Bilgin

摘要

Diffusion tensor imaging (DTI) is essential for assessing brain microstructure but requires long acquisition times, limiting clinical use. Recent deep learning (DL) approaches, such as SuperDTI or deepDTI, improve DTI metrics but demand large, high-quality datasets for training. We propose a self-supervised deep learning with fine-tuning (SSDLFT) framework to reduce training data requirements. SSDLFT involves self-supervised pretraining, which denoises data without clean labels, followed by fine-tuning with limited high-quality data. Experiments using Human Connectome Project data show that SSDLFT outperforms traditional methods and other DL approaches in qualitative and quantitative assessments of DWI reconstructions and tensor metrics. SSDLFT’s ability to maintain high performance with fewer training subjects and DWIs presents a significant advancement, enhancing DTI’s practical applications in clinical and research settings.