Abstract #2905
A Time efficient IVIM analysis method using fuzzy clustering algorithm
Kaining Shi 1 , He Wang 2 , Guang Cao 3 , Ying Qi 4 , and Xiaoming Wang 4
1
Imaging Systems Clinical Science, Philips
Healthcare (China), Beijing, China,
2
Philips
Research (China), Shanghai, China,
3
Imaging
Systems Clinical Science, Philips Healthcare (China),
Hongkong, China,
4
Radiology
Department, Shengjing Hospital of China Medical
University, Shenyang, Liaoning, China
The nonlinear bi-exponential curve-fitting in the
Intravoxel Incoherent Motion (IVIM) model is sensitive
to noise and time-consuming. In this work, fussy
clustering technique is used to improve the reliability
of curve-fitting and reduce the total calculation
time.16 b-values DWI data of 2 PRES patients and 2
volunteers was processed by the fussy clustering
analysis method. The new algorithm achieved brain
segmentation successfully and generated similar
parameters as the pixel-by-pixel approach, with 1.3-3.3%
time cost and 11.4~79.0% curve-fit residual.
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