Estimation of intravoxel incoherent motion parameters using low b-values

Chen Ye; Daoyun Xu; Yongbin Qin; Lihui Wang; Rongpin Wang; Wuchao Li; Zixiang Kuai; Yuemin Zhu

doi:10.1371/journal.pone.0211911

Estimation of intravoxel incoherent motion parameters using low b-values

PLoS One. 2019 Feb 6;14(2):e0211911. doi: 10.1371/journal.pone.0211911. eCollection 2019.

Authors

Chen Ye¹, Daoyun Xu¹, Yongbin Qin¹, Lihui Wang¹, Rongpin Wang², Wuchao Li², Zixiang Kuai³, Yuemin Zhu⁴

Affiliations

¹ Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China.
² Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China.
³ Harbin Medical University Cancer Hospital, Harbin, China.
⁴ Univ Lyon, INSA Lyon, CNRS, INSERM, CREATIS UMR 5220, U1206, Lyon, France.

Abstract

Intravoxel incoherent motion (IVIM) imaging is a magnetic resonance imaging (MRI) technique widely used in clinical applications for various organs. However, IVIM imaging at low b-values is a persistent problem. This paper aims to investigate in a systematic and detailed manner how the number of low b-values influences the estimation of IVIM parameters. To this end, diffusion-weighted (DW) data with different low b-values were simulated to get insight into the distributions of subsequent IVIM parameters. Then, in vivo DW data with different numbers of low b-values and different number of excitations (NEX) were acquired. Finally, least-squares (LSQ) and Bayesian shrinkage prior (BSP) fitting methods were implemented to estimate IVIM parameters. The influence of the number of low b-values on IVIM parameters was analyzed in terms of relative error (RE) and structural similarity (SSIM). The results showed that the influence of the number of low b-values on IVIM parameters is variable. LSQ is more dependent on the number of low b-values than BSP, but the latter is more sensitive to noise.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Humans
Image Processing, Computer-Assisted / methods*
Magnetic Resonance Imaging*
Models, Theoretical*
Motion
Prospective Studies

Grants and funding

This work was funded in part by the National Nature Science Foundations of China (Grant No. 61762019, 61661010, 81701654), the Funds for Talents of Guizhou University (No. 2013-33), the Nature Science Foundation of Guizhou province (Qiankehe J No.20152044, [2016]1096), and the Program PHC-Cai Yuanpei 2018 (N° 41400TC).