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タイトル: | Spatio-temporal reconstruction of substance dynamics using compressed sensing in multi-spectral magnetic resonance spectroscopic imaging |
著者: | Yamamoto, Utako Imai, Hirohiko ![]() ![]() Sano, Kei Ohzeki, Masayuki Matsuda, Tetsuya Tanaka, Toshiyuki ![]() ![]() ![]() |
著者名の別形: | 山本, 詩子 今井, 宏彦 佐野, 圭 松田, 哲也 田中, 利幸 |
キーワード: | Magnetic resonance spectroscopic imaging Spatio-temporal reconstruction Substance dynamics Compressed sensing 𝓁1regularization Alternating direction method of multipliers |
発行日: | 1-Dec-2023 |
出版者: | Elsevier BV |
誌名: | Expert Systems with Applications |
巻: | 232 |
論文番号: | 120744 |
抄録: | The objective of our study is to observe dynamics of multiple substances in vivo with high temporal resolution from multi-spectral magnetic resonance spectroscopic imaging (MRSI) data. The multi-spectral MRSI can effectively separate spectral peaks of multiple substances and is useful to measure spatial distributions of substances. However it is difficult to measure time-varying substance distributions directly by ordinary full sampling because the measurement requires a significantly long time. In this study, we propose a novel method to reconstruct the spatio-temporal distributions of substances from randomly undersampled multi-spectral MRSI data on the basis of compressed sensing (CS) and the partially separable function model with base spectra of substances. In our method, we have employed spatio-temporal sparsity and temporal smoothness of the substance distributions as prior knowledge to perform CS. By directly reconstructing the spatio-temporal distributions of the substances themselves without reconstructing the spectra, this method significantly reduces the amount of MRSI data required per single time frame. We have formulated a regularized minimization problem for reconstruction and solved it by the alternating direction method of multipliers (ADMM). The effectiveness of our method has been evaluated using phantom data sets of glass tubes filled with glucose or lactate solution in increasing amounts over time and animal data sets of a tumor-bearing mouse to observe the metabolic dynamics involved in the Warburg effect in vivo. The reconstructed results are consistent with the expected behaviors, showing that our method can reconstruct the spatio-temporal distribution of substances with a temporal resolution of four seconds which is extremely short time scale compared with that of full sampling. Since this method utilizes only prior knowledge naturally assumed for the spatio-temporal distributions of substances and is independent of the number of the spectral and spatial dimensions or the acquisition sequence of MRSI, it is expected to contribute to revealing the underlying substance dynamics in MRSI data already acquired or to be acquired in the future. |
著作権等: | © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license The full-text file will be made open to the public on 1 December 2025 in accordance with publisher's 'Terms and Conditions for Self-Archiving'. This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
URI: | http://hdl.handle.net/2433/285214 |
DOI(出版社版): | 10.1016/j.eswa.2023.120744 |
出現コレクション: | 学術雑誌掲載論文等 |

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