Influence of image contrasts and reconstruction methods on the classification of multiple sclerosis-like lesions in simulated sodium magnetic resonance imaging

Ruck L, Mennecke A, Wilferth T, Lachner S, Müller M, Egger N, Dörfler A, Uder M, Nagel AM (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Abstract

Purpose To evaluate the classifiability of small multiple sclerosis (MS)-like lesions in simulated sodium (Na-23) MRI for different Na-23 MRI contrasts and reconstruction methods. Methods Na-23 MRI and Na-23 inversion recovery (IR) MRI of a phantom and simulated brain with and without lesions of different volumes (V = 1.3-38.2 nominal voxels) were simulated 100 times by adding Gaussian noise matching the SNR of real 3T measurements. Each simulation was reconstructed with four different reconstruction methods (Gridding without and with Hamming filter, Compressed sensing (CS) reconstruction without and with anatomical H-1 prior information). Based on the mean signals within the lesion volumes of simulations with and without lesions, receiver operating characteristics (ROC) were determined and the area under the curve (AUC) was calculated to assess the classifiability for each lesion volume. Results Lesions show higher classifiability in Na-23 MRI than in Na-23 IR MRI. For typical parameters and SNR of a 3T scan, the voxel normed minimal classifiable lesion volume (AUC > 0.9) is 2.8 voxels for Na-23 MRI and 19 voxels for Na-23 IR MRI, respectively. In terms of classifiability, Gridding with Hamming filter and CS without anatomical H-1 prior outperform CS reconstruction with anatomical H-1 prior. Conclusion Reliability of lesion classifiability strongly depends on the lesion volume and the Na-23 MRI contrast. Additional incorporation of H-1 prior information in the CS reconstruction was not beneficial for the classification of small MS-like lesions in Na-23 MRI.

Authors with CRIS profile

Laurent Ruck Professur für metabolische und funktionelle MR-Bildgebung Angelika Mennecke Department of Neuroradiology Tobias Wilferth Institute of Radiology Sebastian Lachner Institute of Radiology Max Müller Institute of Radiology Nico Egger Professur für metabolische und funktionelle MR-Bildgebung Arnd Dörfler Department of Neuroradiology Michael Uder Lehrstuhl für Diagnostische Radiologie Armin Michael Nagel Professur für metabolische und funktionelle MR-Bildgebung

How to cite

APA:

Ruck, L., Mennecke, A., Wilferth, T., Lachner, S., Müller, M., Egger, N.,... Nagel, A.M. (2022). Influence of image contrasts and reconstruction methods on the classification of multiple sclerosis-like lesions in simulated sodium magnetic resonance imaging. Magnetic Resonance in Medicine. https://dx.doi.org/10.1002/mrm.29476

MLA:

Ruck, Laurent, et al. "Influence of image contrasts and reconstruction methods on the classification of multiple sclerosis-like lesions in simulated sodium magnetic resonance imaging." Magnetic Resonance in Medicine (2022).

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