Scholarlink

<p>Transcranial magnetic resonance-guided focused ultrasound surgery (MRgFUS) is increasingly recognized as a promising therapeutic option for patients with conditions including pain, tremor, and epilepsy. However, challenges such as slow magnetic resonance imaging acquisition and physical restrictions imposed by stereotactic frames limit the realization of this technology’s full potential. Machine learning technologies have recently seen rapid growth in their clinical applicability, reflected by a corresponding increase in regulatory approvals for clinical artificial intelligence. Advancements in machine learning for transcranial MRgFUS have the potential to overhaul the entire operative workflow, from increasing scanner speed and image granularity to more intelligent patient identification, enabling entirely new techniques in which MRgFUS can be leveraged. In this narrative review, we explore potential applications of deep learning in transcranial MRgFUS, synthesizing peer-reviewed literature on prior successful applications of artificial intelligence in neurosurgery and MRgFUS in other clinical domains.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Navigating transcranial mr guided focused ultrasound complexities with machine learning: Overcoming obstacles and expanding therapeutic horizons

  • 期刊论文

  • 发表日期：

    2026年3月28日

查看全文
  • Anmol Warman,
  • Austin Carmichael,
  • Jeremy R. Ellis,
  • Luke M. Tomasovic,
  • Roshan Warman,
  • Tej D. Azad

摘要

Transcranial magnetic resonance-guided focused ultrasound surgery (MRgFUS) is increasingly recognized as a promising therapeutic option for patients with conditions including pain, tremor, and epilepsy. However, challenges such as slow magnetic resonance imaging acquisition and physical restrictions imposed by stereotactic frames limit the realization of this technology’s full potential. Machine learning technologies have recently seen rapid growth in their clinical applicability, reflected by a corresponding increase in regulatory approvals for clinical artificial intelligence. Advancements in machine learning for transcranial MRgFUS have the potential to overhaul the entire operative workflow, from increasing scanner speed and image granularity to more intelligent patient identification, enabling entirely new techniques in which MRgFUS can be leveraged. In this narrative review, we explore potential applications of deep learning in transcranial MRgFUS, synthesizing peer-reviewed literature on prior successful applications of artificial intelligence in neurosurgery and MRgFUS in other clinical domains.