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BACKGROUND: Lower back pain affects 75%-85% of people at some point in their lives. The detection of biochemical changes with sodium (23Na) MRI has potential to enable an earlier and more accurate diagnosis. PURPOSE: To measure 23Na relaxation times and apparent tissue sodium concentration (aTSC) in ex-vivo intervertebral discs (IVDs), and to investigate the relationship between aTSC and histological Thompson grade. STUDY TYPE: Ex-vivo. SPECIMEN: Thirty IVDs from the lumbar spines of 11 human body donors (4 female, 7 male, mean age 86 ± 8 years). FIELD STRENGTH/SEQUENCE: 3 T; density-adapted 3D radial sequence (DA-3D-RAD). ASSESSMENT: IVD 23Na longitudinal (T1), short and long transverse (T2s* and T2l*) relaxation times and the proportion of the short transverse relaxation (ps) were calculated for one IVD per spine sample (11 IVDs). Furthermore, aTSCs were calculated for all IVDs. The degradation of the IVDs was assessed via histological Thompson grading. STATISTICAL TESTS: A Kendall Tau correlation (τ) test was performed between the aTSCs and the Thompson grades. The significance level was set to P < 0.05. RESULTS: Mean 23Na relaxation parameters of a subset of 11 IVDs were T1 = 9.8 ± 1.3 msec, T2s* = 0.7 ± 0.1 msec, T2l* = 7.3 ± 1.1 msec, and ps = 32.7 ± 4.0%. A total of 30 IVDs were examined, of which 3 had Thompson grade 1, 4 had grade 2, 5 had grade 3, 5 had grade 4, and 13 had grade 5. The aTSC decreased with increasing degradation, being 274.6 ± 18.9 mM for Thompson grade 1 and 190.5 ± 29.5 mM for Thompson grade 5. The correlation between whole IVD aTSC and Thompson grade was significant and strongly negative (τ = -0.56). DATA CONCLUSION: This study showed a significant correlation between aTSC and degenerative IVD changes. Consequently, aTSC has potential to be useful as an indicator of degenerative spinal changes. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.
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Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTRasym) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations. Recent noise-reduction algorithms like principal component analysis (PCA), nonlocal mean filtering (NLM), and block matching combined with 3D filtering (BM3D) have shown promise, as there is a burgeoning interest in the utilization of neural networks (NNs), particularly autoencoders, for imaging denoising. This study uses the Bloch-McConnell equations, which allow for the synthetic generation of CEST images and explores NNs efficacy in denoising these images. Using synthetically generated phantoms, autoencoders were created, and their performance was compared with traditional denoising methods using various datasets. The results underscored the superior performance of NNs, notably the ResUNet architectures, in noise identification and abatement compared to analytical approaches across a wide noise gamut. This superiority was particularly pronounced at elevated noise intensities in the in vitro data. Notably, the neural architectures significantly improved the PSNR values, achieving up to 35.0, while some traditional methods struggled, especially in low-noise reduction scenarios. However, the application to the in vivo data presented challenges due to varying noise profiles. This study accentuates the potential of NNs as robust denoising tools, but their translation to clinical settings warrants further investigation.