Effect of offset-frequency step size and interpolation methods on chemical exchange saturation transfer MRI computation in human brain

Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging

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.

B0 Correction for 3T Amide Proton Transfer (APT) MRI Using a Simplified Two-Pool Lorentzian Model of Symmetric Water and Asymmetric Solutes

Amide proton transfer (APT)-weighted MRI is a promising molecular imaging technique that has been employed in clinic for detection and grading of brain tumors. MTRasym, the quantification method of APT, is easily influenced by B0 inhomogeneity and causes artifacts. Current model-free interpolation methods have enabled moderate B0 correction for middle offsets, but have performed poorly at limbic offsets. To address this shortcoming, we proposed a practical B0 correction approach that is suitable under time-limited sparse acquisition scenarios and for B1 ≥ 1 μT under 3T. In this study, this approach employed a simplified Lorentzian model containing only two pools of symmetric water and asymmetric solutes, to describe the Z-spectral shape with wide and ‘invisible’ CEST peaks. The B0 correction was then performed on the basis of the fitted two-pool Lorentzian lines, instead of using conventional model-free interpolation. The approach was firstly evaluated on densely sampled Z-spectra data by using the spline interpolation of all acquired 16 offsets as the gold standard. When only six offsets were available for B0 correction, our method outperformed conventional methods. In particular, the errors at limbic offsets were significantly reduced (n = 8, p < 0.01). Secondly, our method was assessed on the six-offset APT data of nine brain tumor patients. Our MTRasym (3.5 ppm), using the two-pool model, displayed a similar contrast to the vendor-provided B0-orrected MTRasym (3.5 ppm). While the vendor failed in correcting B0 at 4.3 and 2.7 ppm for a large portion of voxels, our method enabled well differentiation of B0 artifacts from tumors. In conclusion, the proposed approach could alleviate analysis errors caused by B0 inhomogeneity, which is useful for facilitating the comprehensive metabolic analysis of brain tumors.

Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors

Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.

Glutamate Chemical Exchange Saturation Transfer Imaging and Functional Alterations of Hippocampus in Rat Depression Model: A Pilot Study

BACKGROUND

Adjusting abnormal glutamate neurotransmission is a crucial mechanism in the treatment of depression. However, few non-invasive techniques could effectively detect changes in glutamate neurotransmitters, and no consensus exists on whether glutamate could affect resting-state function changes in depression.

PURPOSE

To study the changes in glutamate chemical exchange saturation transfer (GluCEST) value in the hippocampus of rat model exposed to chronic unpredictable mild stress (CUMS), and to explore the effect of this change on the activity of hippocampal glutamatergic neurons.

STUDY TYPE

Prospective animal study.

ANIMAL MODEL

Twenty male Sprague-Dawley rats (200-300 g).

FIELD STRENGTH/SEQUENCE

7.0 T scanner. Fat rapid acquisition relaxation enhancement sequence for GluCEST, and echo planner imaging sequence for resting-state functional magnetic resonance imaging (rs_fMRI).

ASSESSMENT

Rats were divided into two groups: CUMS group (N = 10) and control group (CTRL, N = 10). The magnetization transfer ratio asymmetry analysis was used to quantify the GluCEST data, and evaluate the rs_fMRI data through the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) analysis.

STATISTICAL TESTS

A t-test was used to compare the difference in GluCEST or rs_fMRI between CUMS and CTRL groups. Spearman's correlation was applied to explore the correlation between GluCEST values and abnormal fMRI values in hippocampus. Statistical significance was set at P < 0.05.

RESULTS

The GluCEST value in the left hippocampus has changed significantly (3.3 ± 0.3 [CUMS] vs. 3.9 ± 0.4 [CTRL], P < 0.05). In addition, the GluCEST value was significantly positively correlated with the ALFF values (r = 0.5, P < 0. 05, df = 7) and negatively correlated with the ReHo values (r = -0.6, P < 0.05, df = 7).

DATA CONCLUSION

GluCEST technique has the feasibility of mapping glutamate changes in rat depression. Glutamate neurotransmitters are important factors affecting the abnormal function of neural activity.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.