Lin Chen1,2, Suyi Cao3, Raymond C Koehler3, Peter C M van Zijl1,2, Jiadi Xu1,2. 1. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA. 2. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 3. Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Abstract
PURPOSE: To obtain high-sensitivity CEST maps by exploiting the spatiotemporal correlation between CEST images. METHODS: A postprocessing method accomplished by multilinear singular value decomposition (MLSVD) was used to enhance the CEST SNR by exploiting the correlation between the Z-spectrum for each voxel and the low-rank property of the overall CEST data. The performance of this method was evaluated using CrCEST in ischemic mouse brain at 11.7 tesla. Then, MLSVD CEST was applied to obtain Cr, amide, and amine CEST maps of the ischemic mouse brain to demonstrate its general applications. RESULTS: Complex-valued Gaussian noise was added to CEST k-space data to mimic a low SNR situation. MLSVD CEST analysis was able to suppress the noise, recover the degraded CEST peak, and provide better CrCEST quality compared to the smoothing and singular value decomposition (SVD)-based denoising methods. High-resolution Cr, amide, and amine CEST maps of an ischemic stroke using MLSVD CEST suggest that CrCEST is also a sensitive pH mapping method, and a wide range of pH changes can be detected by combing CrCEST with amine CEST at high magnetic fields. CONCLUSION: MLSVD CEST provides a simple and efficient way to improve the SNR of CEST images.
PURPOSE: To obtain high-sensitivity CEST maps by exploiting the spatiotemporal correlation between CEST images. METHODS: A postprocessing method accomplished by multilinear singular value decomposition (MLSVD) was used to enhance the CEST SNR by exploiting the correlation between the Z-spectrum for each voxel and the low-rank property of the overall CEST data. The performance of this method was evaluated using CrCEST in ischemicmouse brain at 11.7 tesla. Then, MLSVD CEST was applied to obtain Cr, amide, and amine CEST maps of the ischemicmouse brain to demonstrate its general applications. RESULTS: Complex-valued Gaussian noise was added to CEST k-space data to mimic a low SNR situation. MLSVD CEST analysis was able to suppress the noise, recover the degraded CEST peak, and provide better CrCEST quality compared to the smoothing and singular value decomposition (SVD)-based denoising methods. High-resolution Cr, amide, and amine CEST maps of an ischemic stroke using MLSVD CEST suggest that CrCEST is also a sensitive pH mapping method, and a wide range of pH changes can be detected by combing CrCEST with amine CEST at high magnetic fields. CONCLUSION: MLSVD CEST provides a simple and efficient way to improve the SNR of CEST images.
Authors: Jinyuan Zhou; Jean-Francois Payen; David A Wilson; Richard J Traystman; Peter C M van Zijl Journal: Nat Med Date: 2003-07-20 Impact factor: 53.440
Authors: Xiao-Yong Zhang; Jingping Xie; Feng Wang; Eugene C Lin; Junzhong Xu; Daniel F Gochberg; John C Gore; Zhongliang Zu Journal: Magn Reson Med Date: 2017-06-26 Impact factor: 4.668
Authors: Moritz Zaiss; Zhongliang Zu; Junzhong Xu; Patrick Schuenke; Daniel F Gochberg; John C Gore; Mark E Ladd; Peter Bachert Journal: NMR Biomed Date: 2014-12-15 Impact factor: 4.044
Authors: Peter C M van Zijl; Craig K Jones; Jimin Ren; Craig R Malloy; A Dean Sherry Journal: Proc Natl Acad Sci U S A Date: 2007-03-05 Impact factor: 11.205
Authors: Ran Sui; Lin Chen; Yuguo Li; Jianpan Huang; Kannie W Y Chan; Xiang Xu; Peter C M van Zijl; Jiadi Xu Journal: Magn Reson Med Date: 2021-03-27 Impact factor: 3.737