Improved deep learning-based IVIM parameter estimation via the use of more "realistic" simulated brain data.

Journal: Medical physics
PMID: 39704604

Abstract

BACKGROUND: Due to the low signal-to-noise ratio (SNR) and the limited number of b-values, precise parameter estimation of intravoxel incoherent motion (IVIM) imaging remains an open issue to date, especially for brain imaging where the relatively small difference between D and D easily leads to outliers and obvious graininess in estimated results.

Authors

  • Lu Wang
    Department of Laboratory, Akesu Center of Disease Control and Prevention, Akesu, China.
  • Jiechao Wang
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, People's Republic of China.
  • Qinqin Yang
    Institute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, China.
  • Congbo Cai
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
  • Zhen Xing
    Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Taijiang District, Fuzhou 350005, People's Republic of China.
  • Zhong Chen
    Institute of HIV/AIDS The First Hospital of Changsha, Changsha, China.
  • Dairong Cao
    Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China. Electronic address: dairongcao@163.com.
  • Shuhui Cai
    Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.

Keywords

External Resources

Popular Topics
Recent Journals