Improved quantification in CEST-MRI by joint spatial total generalized variation

Huemer M, Stilianu C, Maier O, Fabian M, Schmidt M, Dörfler A, Bredies K, Zaiß M, Stollberger R (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Book Volume: 92

Pages Range: 1683-1697

Journal Issue: 4

DOI: 10.1002/mrm.30129

Abstract

Purpose: In this work, the use of joint Total Generalized Variation (TGV) regularization to improve Multipool-Lorentzian fitting of chemical exchange saturation transfer (CEST) Spectra in terms of stability and parameter signal-to-noise ratio (SNR) was investigated. Theory and Methods: The joint TGV term was integrated into the nonlinear parameter fitting problem. To increase convergence and weight the gradients, preconditioning using a voxel-wise singular value decomposition was applied to the problem, which was then solved using the iteratively regularized Gauss–Newton method combined with a Primal-Dual splitting algorithm. The TGV method was evaluated on simulated numerical phantoms, 3T phantom data and 7T in vivo data with respect to systematic errors and robustness. Three reference methods were also implemented: The standard nonlinear fitting, a method using a nonlocal-means filter for denoising and the pyramid scheme, which uses downsampled images to acquire accurate start values. Results: The proposed regularized fitting method showed significantly improved robustness (compared to the reference methods). In testing, over a range of SNR values the TGV fit outperformed the other methods and showed accurate results even for large amounts of added noise. Parameter values found were closer or comparable to the ground truth. For in vivo datasets, the added regularization increased the parameter map SNR and prevented instabilities. Conclusion: The proposed fitting method using TGV regularization leads to improved results over a range of different data-sets and noise levels. Furthermore, it can be applied to all Z-spectrum data, with different amounts of pools, where the improved SNR and stability can increase diagnostic confidence.

Authors with CRIS profile

Involved external institutions

Technische Universität Graz Austria (AT) Karl-Franzens-Universität Graz Austria (AT)

How to cite

APA:

Huemer, M., Stilianu, C., Maier, O., Fabian, M., Schmidt, M., Dörfler, A.,... Stollberger, R. (2024). Improved quantification in CEST-MRI by joint spatial total generalized variation. Magnetic Resonance in Medicine, 92(4), 1683-1697. https://doi.org/10.1002/mrm.30129

MLA:

Huemer, Markus, et al. "Improved quantification in CEST-MRI by joint spatial total generalized variation." Magnetic Resonance in Medicine 92.4 (2024): 1683-1697.

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