A data‐driven T2 relaxation analysis approach for myelin water imaging: Spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME‐ECOS). Issue 2 (7th September 2021)
- Record Type:
- Journal Article
- Title:
- A data‐driven T2 relaxation analysis approach for myelin water imaging: Spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME‐ECOS). Issue 2 (7th September 2021)
- Main Title:
- A data‐driven T2 relaxation analysis approach for myelin water imaging: Spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME‐ECOS)
- Authors:
- Liu, Hanwen
Joseph, Tigris S.
Xiang, Qing‐San
Tam, Roger
Kozlowski, Piotr
Li, David K. B.
MacKay, Alex L.
Kramer, John L. K.
Laule, Cornelia - Abstract:
- Abstract : Purpose: The decomposition of multi‐exponential decay data into a T2 spectrum poses substantial challenges for conventional fitting algorithms, including non‐negative least squares (NNLS). Based on a combination of the resolution limit constraint and machine learning neural network algorithm, a data‐driven and highly tailorable analysis method named spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME‐ECOS) was proposed. Theory and Methods: The theory of SAME‐ECOS was derived. Then, a paradigm was presented to demonstrate the SAME‐ECOS workflow, consisting of a series of calculation, simulation, and model training operations. The performance of the trained SAME‐ECOS model was evaluated using simulations and six in vivo brain datasets. The code is available at https://github.com/hanwencat/SAME‐ECOS . Results: Using NNLS as the baseline, SAME‐ECOS achieved over 15% higher overall cosine similarity scores in producing the T2 spectrum, and more than 10% lower mean absolute error in calculating the myelin water fraction (MWF), as well as demonstrated better robustness to noise in the simulation tests. Applying to in vivo data, MWF from SAME‐ECOS and NNLS was highly correlated among all study participants. However, a distinct separation of the myelin water peak and the intra/extra‐cellular water peak was only observed in the mean T2 spectra determined using SAME‐ECOS. In terms of data processing speed, SAME‐ECOS isAbstract : Purpose: The decomposition of multi‐exponential decay data into a T2 spectrum poses substantial challenges for conventional fitting algorithms, including non‐negative least squares (NNLS). Based on a combination of the resolution limit constraint and machine learning neural network algorithm, a data‐driven and highly tailorable analysis method named spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME‐ECOS) was proposed. Theory and Methods: The theory of SAME‐ECOS was derived. Then, a paradigm was presented to demonstrate the SAME‐ECOS workflow, consisting of a series of calculation, simulation, and model training operations. The performance of the trained SAME‐ECOS model was evaluated using simulations and six in vivo brain datasets. The code is available at https://github.com/hanwencat/SAME‐ECOS . Results: Using NNLS as the baseline, SAME‐ECOS achieved over 15% higher overall cosine similarity scores in producing the T2 spectrum, and more than 10% lower mean absolute error in calculating the myelin water fraction (MWF), as well as demonstrated better robustness to noise in the simulation tests. Applying to in vivo data, MWF from SAME‐ECOS and NNLS was highly correlated among all study participants. However, a distinct separation of the myelin water peak and the intra/extra‐cellular water peak was only observed in the mean T2 spectra determined using SAME‐ECOS. In terms of data processing speed, SAME‐ECOS is approximately 30 times faster than NNLS, achieving a whole‐brain analysis in 3 min. Conclusion: Compared with NNLS, the SAME‐ECOS method yields much more reliable T2 spectra in a dramatically shorter time, increasing the feasibility of multi‐component T2 decay analysis in clinical settings. … (more)
- Is Part Of:
- Magnetic resonance in medicine. Volume 87:Issue 2(2022)
- Journal:
- Magnetic resonance in medicine
- Issue:
- Volume 87:Issue 2(2022)
- Issue Display:
- Volume 87, Issue 2 (2022)
- Year:
- 2022
- Volume:
- 87
- Issue:
- 2
- Issue Sort Value:
- 2022-0087-0002-0000
- Page Start:
- 915
- Page End:
- 931
- Publication Date:
- 2021-09-07
- Subjects:
- data‐driven -- machine learning -- myelin water imaging -- non‐negative least squares -- resolution limit -- SAME‐ECOS
Nuclear magnetic resonance -- Periodicals
Electron paramagnetic resonance -- Periodicals
616.07548 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1522-2594 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/mrm.29000 ↗
- Languages:
- English
- ISSNs:
- 0740-3194
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5337.798000
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British Library HMNTS - ELD Digital store - Ingest File:
- 26462.xml