The Department of Genetic Medicine at Weill Cornell leads a dynamic and innovative translational research program, advancing diverse fields such as Genetic Therapy and Personalized Medicine.
Our translational research program aims to leverage our expertise in genetic therapies and personalized medicine to develop clinical solutions that target the molecular causes of human diseases.
The Department of Genetic Medicine advances treatments and diagnostics through diverse clinical trials, including drug testing and research to better understand diseases.
The Department of Genetic Medicine at Weill Cornell leads a dynamic and innovative translational research program, advancing diverse fields such as Genetic Therapy and Personalized Medicine.
Our translational research program aims to leverage our expertise in genetic therapies and personalized medicine to develop clinical solutions that target the molecular causes of human diseases.
The Department of Genetic Medicine advances treatments and diagnostics through diverse clinical trials, including drug testing and research to better understand diseases.
An automated iterative algorithm for water and fat decomposition in three-point Dixon magnetic resonance imaging.
Publication Type
Academic Article
Authors
Chen Q, Schneider E, Aghazadeh B, Weinhous M, Humm J, Ballon D
Journal
Med Phys
Volume
26
Issue
11
Pagination
2341-7
Date Published
11/01/1999
ISSN
0094-2405
Keywords
Adipose Tissue, Image Enhancement, Magnetic Resonance Imaging, Water
Abstract
An iterative, outlier exclusion, second-order surface fitting algorithm has been developed to solve the well-known phase wraparound problem associated with in vivo applications of the three-point Dixon magnetic resonance imaging method. The technique was optimized for speed by reducing the problem to a pair of planar fits. The spatial misalignment between water and fat components due to the chemical shift was handled on a subpixel level by invoking the shift theorem of Fourier transformation. From the chemical shift corrected water and fat images, high quality recombined MR images were generated. The algorithm was validated in both phantom and patient studies. In vivo breast images and pelvic images are provided as a demonstration of the method.