Dataset Information

Fast T₂ mapping using multi-echo spin-echo MRI: A linear order approach.

ABSTRACT:

Purpose

Multi-echo spin-echo sequence is commonly used for T₂ mapping. The estimated values using conventional exponential fit, however, are hampered by stimulated and indirect echoes leading to overestimation of T₂ . Here, we present fast analysis of multi-echo spin-echo (FAMESE) as a novel approach to decrease the complexity of the search space, which leads to accelerated measurement of T₂ .

Methods

We developed FAMESE based on mathematical analysis of the Bloch equations in which the search space dimension decreased to only one. Then, we tested it in both phantom and human brain. Bland-Altman plot was used to assess the agreement between the estimated T₂ values from FAMESE and the ones estimated from single-echo spin-echo sequence. The reliability of FAMESE was assessed by intraclass correlation coefficients. In addition, we investigated the noise stability of the method in synthetic and experimental data.

Results

In both phantom and healthy participants, FAMESE provided accelerated and SNR-resistant T₂ maps. The FAMESE had a very good agreement with the single-echo spin echo for the whole range of T₂ values. The intraclass correlation coefficient values for FAMESE were excellent (ie, 0.9998 and 0.9860 < intraclass correlation coefficient < 0.9942 for the phantom and humans, respectively).

Conclusion

Our developed method FAMESE could be considered as a candidate for rapid T₂ mapping with a clinically feasible scan time.

SUBMITTER: Fatemi Y

PROVIDER: S-EPMC7402028 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Fast T<sub>2</sub> mapping using multi-echo spin-echo MRI: A linear order approach.

Fatemi Yaghoub Y Danyali Habibollah H Helfroush Mohammad Sadegh MS Amiri Houshang H

Magnetic resonance in medicine 20200519 5

<h4>Purpose</h4>Multi-echo spin-echo sequence is commonly used for T<sub>2</sub> mapping. The estimated values using conventional exponential fit, however, are hampered by stimulated and indirect echoes leading to overestimation of T<sub>2</sub> . Here, we present fast analysis of multi-echo spin-echo (FAMESE) as a novel approach to decrease the complexity of the search space, which leads to accelerated measurement of T<sub>2</sub> .<h4>Methods</h4>We developed FAMESE based on mathematical analy ...[more]

PMID: 32430979

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Project description:Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness. © RSNA, 2018 Online supplemental material is available for this article.

Dataset Information

Fast T₂ mapping using multi-echo spin-echo MRI: A linear order approach.

Purpose

Methods

Results

Conclusion

Publications

Fast T<sub>2</sub> mapping using multi-echo spin-echo MRI: A linear order approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Fast T2 mapping using multi-echo spin-echo MRI: A linear order approach.

Purpose

Methods

Results

Conclusion

Publications

Fast T<sub>2</sub> mapping using multi-echo spin-echo MRI: A linear order approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Fast T₂ mapping using multi-echo spin-echo MRI: A linear order approach.