Effects of proximity and noise level of phased array coil elements on overall signal-to-noise in parallel MR spectroscopy

An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis

MR spectroscopy (MRS) is a noninvasive imaging method enabling chemical and molecular profiling of tissues in a localized, multiplexed, and nonionizing manner. As metabolic reprogramming is a hallmark of cancer, MRS provides valuable metabolic and molecular information for cancer diagnosis, prognosis, treatment monitoring, and patient management. This review provides an update on the use of MRS for clinical cancer management. The first section includes an overview of the principles of MRS, current methods, and conventional metabolites of interest. The remainder of the review is focused on three key areas: advances in instrumentation, specifically ultrahigh-field-strength MRI scanners and hybrid systems; emerging methods for acquisition, including deuterium imaging, hyperpolarized carbon 13 MRI and MRS, chemical exchange saturation transfer, diffusion-weighted MRS, MR fingerprinting, and fast acquisition; and analysis aided by artificial intelligence. The review concludes with future recommendations to facilitate routine use of MRS in cancer management. Keywords: MR Spectroscopy, Spectroscopic Imaging, Molecular Imaging in Oncology, Metabolic Reprogramming, Clinical Cancer Management © RSNA, 2024.

Reproducibility of 7-T brain spectroscopy using an ultrashort echo time STimulated Echo Acquisition Mode sequence and automated voxel repositioning

Establishing the reproducibility of brain MRS is important for clinical studies so that researchers can evaluate changes in metabolites due to treatment or the course of a disease and better understand the brain in healthy and disordered states. Prior 7-T MRS reproducibility studies using the stimulated echo acquisition mode (STEAM) sequence have focused on the anterior cingulate cortex or posterior cingulate cortex and precuneus. The purpose of this study was to evaluate the reproducibility of metabolite measurements in the dorsolateral prefrontal cortex (DLPFC) using an ultrashort echo time (TE) STEAM sequence and automated voxel repositioning. Spectra were acquired during two scan sessions from nine subjects using the AutoAlign method for voxel repositioning. Reproducibility was evaluated with coefficients of variation (CVs) and percentage differences. The mean intrasubject CVs were less than 6% for the major metabolites glutamate, N-acetylaspartate, total creatine, total choline, and myo-inositol. The mean CVs were less than 20% for the smaller signals of GABA, glutamine, glutathione, and taurine. These results indicate that 7-T MRS using a STEAM sequence with ultrashort TE and automated voxel repositioning provides excellent reproducibility of metabolites in the DLPFC.

Spectral Wavelet-feature Analysis and Classification Assisted Denoising for enhancing magnetic resonance spectroscopy

Magnetic resonance spectroscopy (MRS) is capable of revealing important biochemical and metabolic information of tissues noninvasively. However, the low concentrations of metabolites often lead to poor signal-to-noise ratio (SNR) and a long acquisition time. Therefore, the applications of MRS in detection and quantitative measurements of metabolites in vivo remain limited. Reducing or even eliminating noise can improve SNR sufficiently to obtain high quality spectra in addition to increasing the number of signal averaging (NSA) or the field strength, both of which are limited in clinical applications. We present a Spectral Wavelet-feature ANalysis and Classification Assisted Denoising (SWANCAD) approach to differentiate signal and noise peaks in magnetic resonance spectra based on their respective wavelet features, followed by removing the identified noise components to improve SNR. The performance of this new denoising approach was evaluated by measuring and comparing SNRs and quantified metabolite levels of low NSA spectra (e.g. NSA = 8) before and after denoising using the SWANCAD approach or by conventional spectral fitting and denoising methods, such as LCModel and wavelet threshold methods, as well as the high NSA spectra (e.g. NSA = 192) recorded in the same sampling volumes. The results demonstrated that SWANCAD offers a more effective way to detect the signals and improve SNR by removing noise from the noisy spectra collected with low NSA or in the subminute scan time (e.g. NSA = 8 or 16 s). The potential applications of SWANCAD include using low NSA to accelerate MRS acquisition while maintaining adequate spectroscopic information for detection and quantification of the metabolites of interest when a limited time is available for an MRS examination in the clinical setting.

Optimized truncation to integrate multi-channel MRS data using rank-R singular value decomposition

Multi-channel phased receive arrays have been widely adopted for magnetic resonance imaging (MRI) and spectroscopy (MRS). An important step in the use of receive arrays for MRS is the combination of spectra collected from individual coil channels. The goal of this work was to implement an improved strategy termed OpTIMUS (i.e., optimized truncation to integrate multi-channel MRS data using rank-R singular value decomposition) for combining data from individual channels. OpTIMUS relies on spectral windowing coupled with a rank-R decomposition to calculate the optimal coil channel weights. MRS data acquired from a brain spectroscopy phantom and 11 healthy volunteers were first processed using a whitening transformation to remove correlated noise. Whitened spectra were then iteratively windowed or truncated, followed by a rank-R singular value decomposition (SVD) to empirically determine the coil channel weights. Spectra combined using the vendor-supplied method, signal/noise² weighting, previously reported whitened SVD (rank-1), and OpTIMUS were evaluated using the signal-to-noise ratio (SNR). Significant increases in SNR ranging from 6% to 33% (P ≤ 0.05) were observed for brain MRS data combined with OpTIMUS compared with the three other combination algorithms. The assumption that a rank-1 SVD maximizes SNR was tested empirically, and a higher rank-R decomposition, combined with spectral windowing prior to SVD, resulted in increased SNR.

Selection and application of coils in temporomandibular joint MRI

Objective:

To compare and evaluate the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) values between a 15-channel phased array head coil and 6-channel dS Flex M surface coil in the MRI of temporomandibular joint.

Methods:

300 patients were randomly assigned to two groups: 150 patients were examined by using a 15-channel phased array head coil and the other 150 patients were scanned by using a 6-channel dS Flex M surface coil. All of the data were set in the same 6 regions of interest including the temporal lobe, condyle neck, lateral pterygoid muscle, parotid gland, the adipose area and an area of the background noise）. SNR and CNR values were measured respectively.

Results:

The numerical variation law of SNR and CNR values measured in regionsof interest of each group was similar, although different coils were used. There were statistically significant differences of SNR values in all of the oblique sagittal (OSag) proton density-weighted imaging, the part of OSag T₂ weighted image (T₂WI) except for SNR₄ and SNR₅. and oblique coronal (OCor) T₂WI sequence except for SNR₂. On the contrary, SNR₄ and SNR₅ values in the OCor T₂WI and SNR₅ values in OSag T₂WI sequences by using the surface coil were higher than those by using the head coil. There were no statistically significant intergroup differences of CNR values in OSag proton density-weighted imaging sequence except CNR₁ and in OSag T₂WI sequence except CNR₅. But, statistically significant differences of all the values in the OCor T₂WI sequence except for CNR₁ were observed.

Conclusion:

Both the phased array head coil and dS Flex M surface coil can be used for temporomandibular joint MRI.