Periodically Rotated Spiral Sampling (PRSS) and Multi-offset Transformer Reconstruction (MoTR) for CEST MRI

Motivation: CEST MRI is often limited in its application due to its time-consuming nature. Also, multiple saturation frequency offsets are required to accurately measure CEST effects. Goal(s): Accelerate CEST imaging by undersampling k-space of each frequency offset below Nyquist rate. Approach: Periodically rotated spiral sampling (PRSS) is proposed to make adjacent offsets capture different k-space subregions. Besides, a multi-offset transformer reconstruction (MoTR) network is further presented to fuse complementary information from multiple offsets. Results: Compared to fully-sampled images, reconstructed images using our method can achieve an average SSIM/MAE of 0.9899/0.0032, resulting a coefficient R value of 0.93/0.95 in terms of CEST signals at -3.5ppm/+3.5ppm. Impact: Our method can realize 4× acceleration of CEST imaging without sacrificing down-stream CEST analysis performance. Besides, our method has the potential to work on MRI scanner for CEST acceleration, which can further expand the application of CEST MRI.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/4462
Primary Topic
Atomic and Subatomic Physics Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Periodically Rotated Spiral Sampling (PRSS) and Multi-offset Transformer Reconstruction (MoTR) for CEST MRI

Huabing Liu, Jianpan Huang, Kevin Ka Shing Chan, Abdul‐mojeed Olabisi Ilyas et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Atomic and Subatomic Physics Research
article

Periodically Rotated Spiral Sampling (PRSS) and Multi-offset Transformer Reconstruction (MoTR) for CEST MRI

Huabing Liu, Jianpan Huang, Kevin Ka Shing Chan, Abdul‐mojeed Olabisi Ilyas, Yan Liu, Dinggang Shen
article en

Abstract

Motivation: CEST MRI is often limited in its application due to its time-consuming nature. Also, multiple saturation frequency offsets are required to accurately measure CEST effects. Goal(s): Accelerate CEST imaging by undersampling k-space of each frequency offset below Nyquist rate. Approach: Periodically rotated spiral sampling (PRSS) is proposed to make adjacent offsets capture different k-space subregions. Besides, a multi-offset transformer reconstruction (MoTR) network is further presented to fuse complementary information from multiple offsets. Results: Compared to fully-sampled images, reconstructed images using our method can achieve an average SSIM/MAE of 0.9899/0.0032, resulting a coefficient R value of 0.93/0.95 in terms of CEST signals at -3.5ppm/+3.5ppm. Impact: Our method can realize 4× acceleration of CEST imaging without sacrificing down-stream CEST analysis performance. Besides, our method has the potential to work on MRI scanner for CEST acceleration, which can further expand the application of CEST MRI.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 81%
Atomic and Subatomic Physics Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.