Scholarlink

Fetal dynamic MRI acquisitions allow insights into fetal motor behaviour, serving as a surrogate for fetal cognitive development. Systematic assessment of these highly temporally resolved 2D images might hence allow novel insights into both normal development and contain crucial information in pathology. Here, we present an automatic deep learning-based assessment for masking of the uterine and fetal structures and fetal and maternal motion quantification tool, applied to 61 low field 0.55T fetal cine MRI datasets acquired between 24 and 40 weeks gestational age at two sites using two different MR contrasts. The lack of cine labels was overcome by imitating cine acquisitions from high-resolution labelled static 3D anatomical balanced steady-state free precession datasets. Results illustrate high segmentation accuracy for the larger uterine structures (mean dice coefficient of 0.86 for the fetal body, 0.88 for the fetal head, 0.61 for the placenta) as well as the ability to detect fetal motion.

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

Automatic Disentanglement of Motion in Fetal Low Field MRI Scans

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Michael Kitzberger,
  • Sara Neves Silva,
  • Jordina Aviles Verdera,
  • Diego Fajardo Rojas,
  • Alena Uus,
  • Susanne Schulz-Heise,
  • Sandy Schmidt,
  • Michael Schneider,
  • Lisa Story,
  • Michael Uder,
  • Mary Rutherford,
  • Jana Hutter

摘要

Fetal dynamic MRI acquisitions allow insights into fetal motor behaviour, serving as a surrogate for fetal cognitive development. Systematic assessment of these highly temporally resolved 2D images might hence allow novel insights into both normal development and contain crucial information in pathology. Here, we present an automatic deep learning-based assessment for masking of the uterine and fetal structures and fetal and maternal motion quantification tool, applied to 61 low field 0.55T fetal cine MRI datasets acquired between 24 and 40 weeks gestational age at two sites using two different MR contrasts. The lack of cine labels was overcome by imitating cine acquisitions from high-resolution labelled static 3D anatomical balanced steady-state free precession datasets. Results illustrate high segmentation accuracy for the larger uterine structures (mean dice coefficient of 0.86 for the fetal body, 0.88 for the fetal head, 0.61 for the placenta) as well as the ability to detect fetal motion.