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Xiao G, Zhang X, Tang H, Huang W, Chen Y, Zhuang C, Chen B, Yang L, Chen Y, Yan G, Wu R. Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets. Front Neurosci 2024; 17:1323131. [PMID: 38249588 PMCID: PMC10796656 DOI: 10.3389/fnins.2023.1323131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/13/2023] [Indexed: 01/23/2024] Open
Abstract
A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quantitative analysis, and this prolonged the scan time. For that, we aim to develop a general deep-learning framework to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets so as to reduce the number of experimentally acquired CEST images and achieve scan time reduction. The main innovation works are outlined as follows: (1) a general sequence-to-sequence (seq2seq) framework is proposed to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets; (2) we create a training set from wide-ranging simulated Z-spectra instead of experimentally acquired CEST data, overcoming the limitation of the time and labor consumption in manual annotation; (3) a new seq2seq network that is capable of utilizing information from both short-range and long-range is developed to improve reconstruction ability. One of our intentions is to establish a simple and efficient framework, i.e., traditional seq2seq can solve the reconstruction task and obtain satisfactory results. In addition, we propose a new seq2seq network that includes the short- and long-range ability to boost dense CEST Z-spectra reconstruction. The experimental results demonstrate that the considered seq2seq models can accurately reconstruct dense CEST images from experimentally acquired images at 11 frequency offsets so as to reduce the scan time by at least 2/3, and our new seq2seq network contributes to competitive advantage.
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Affiliation(s)
- Gang Xiao
- School of Mathematics and Statistics, Hanshan Normal University, Chaozhou, China
| | - Xiaolei Zhang
- Department of Radiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Hanjing Tang
- College of Engineering, Shantou University, Shantou, China
| | - Weipeng Huang
- Medical Imaging Center, Jieyang People's Hospital, Jieyang, China
| | - Yaowen Chen
- College of Engineering, Shantou University, Shantou, China
| | - Caiyu Zhuang
- Department of Radiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Beibei Chen
- Department of Radiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Lin Yang
- Department of Radiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yue Chen
- Department of Radiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Gen Yan
- Department of Radiology, Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Renhua Wu
- Department of Radiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, China
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