Adaptively Optimized Combination (AOC) of Phased-Array MR Spectroscopy Data in the Presence of Correlated Noise: Compared with Noise-Decorrelated or Whitened Methods

Multi-channel magnetic resonance spectroscopy graphical user interface (McMRSGUI)

This work introduces an open-sourced graphical user interface (GUI) software enabling the combination of multi-channel magnetic resonance spectroscopy data with different literature-based methods for the improvement of the quality and reliability of combined spectra. The multi-channel magnetic resonance spectroscopy graphical user interface (McMRSGUI) is a MATLAB-based spectroscopy processing GUI equipped to load multi-channel MRS data, pre-process, combine, and export combined data for evaluation with open-source quantification software (jMRUI). A literature-based, decision-tree process was incorporated into the combination type selection to serve as a guide to minimize spectral distortion in selecting between weighting methods. Multi-channel, simulated spectra were combined with the different combination techniques and evaluated for spectral distortion to validate the code. The incorporation of the combination methods into a single processing software enables multi-channel magnetic resonance spectroscopy (MRS) data to be combined and compared for improved spectral quality with little user knowledge of combination techniques. Through the spectral peak distortion simulation of the combination methods, combined signal-to-noise ratio (SNR) values from the literature were verified. The spectral peak distortion simulation provides a secondary tool for researchers to estimate the spectral SNR levels when spectral distortion could occur and use this knowledge to further guide the selection of their combination technique. The McMRSGUI provides a software toolkit for evaluating multi-channel MRS data and their combination. Simulations evaluating spectral distortion at different noise levels were performed for each combination method to validate the GUI and demonstrate a method for researchers to assess the combined SNR levels at which they could be introducing spectral distortion.

Phased-array combination of 2D MRS for lipid composition quantification in patients with breast cancer

Lipid composition in breast cancer, a central marker of disease progression, can be non-invasively quantified using 2D MRS method of double quantum filtered correlation spectroscopy (DQF-COSY). The low signal to noise ratio (SNR), arising from signal retention of only 25% and depleted lipids within tumour, demands improvement approaches beyond signal averaging for clinically viable applications. We therefore adapted and examined combination algorithms, designed for 1D MRS, for 2D MRS with both internal and external references. Lipid composition spectra were acquired from 17 breast tumour specimens, 15 healthy female volunteers and 25 patients with breast cancer on a clinical 3 T MRI scanner. Whitened singular value decomposition (WSVD) with internal reference yielded maximal SNR with an improvement of 53.3% (40.3-106.9%) in specimens, 84.4 ± 40.6% in volunteers, 96.9 ± 54.2% in peritumoural adipose tissue and 52.4% (25.1-108.0%) in tumours in vivo. Non-uniformity, as variance of improvement across peaks, was low at 21.1% (13.7-28.1%) in specimens, 5.5% (4.2-7.2%) in volunteers, 6.1% (5.0-9.0%) in peritumoural tissue, and 20.7% (17.4-31.7%) in tumours in vivo. The bias (slope) in improvement ranged from - 1.08 to 0.21%/ppm along the diagonal directions. WSVD is therefore the optimal algorithm for lipid composition spectra with highest SNR uniformly across peaks, reducing acquisition time by up to 70% in patients, enabling clinical applications.

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.

Optimizing acquisition and fitting conditions for 1 H MR spectroscopy investigations in global brain pathology

PURPOSE

To optimize acquisition and fitting conditions for nonfocal disease in terms of voxel size and use of individual coil element data. Increasing the voxel size yields a higher signal-to-noise ratio, but leads to larger linewidths and more artifacts. Several ways to improve the spectral quality for large voxels are exploited and the optimal use of individual coil signals investigated.

METHODS

Ten human subjects were measured at 3 T using a 64-channel receive head coil with a semi-LASER localization sequence under optimized and deliberately mis-set field homogeneity. Eight different voxel sizes (8 to 99 cm³ ) were probed. Spectra were fitted either as weighted sums of the individual coil elements or simultaneously without summation. Eighteen metabolites were included in the fit model that also included the lineshapes from all coil elements as reflected in water reference data. Fitting errors for creatine, myo-Inositol and glutamate are reported as representative parameters to judge optimal acquisition and evaluation conditions.

RESULTS

Minimal Cramér-Rao lower bounds and thus optimal acquisition conditions were found for a voxel size of ~ 70 cm³ for the representative upfield metabolites. Spectral quality in terms of lineshape and artifact appearance was determined to differ substantially between coil elements. Simultaneous fitting of spectra from individual coil elements instead of traditional fitting of a weighted sum spectrum reduced Cramer-Rao lower bounds by up to 17% for large voxel sizes.

CONCLUSION

The optimal voxel size for best precision in determined metabolite content is surprisingly large. Such an acquisition condition is most relevant for detection of low-concentration metabolites, like NAD⁺ or phenylalanine, but also for longitudinal studies where very small alterations in metabolite content are targeted. In addition, simultaneous fitting of single channel spectra enforcing lineshape and coil sensitivity information proved to be superior to traditional signal combination with subsequent fitting.

Optimal Phased-Array Signal Combination For Polyunsaturated Fatty Acids Measurement In Breast Cancer Using Multiple Quantum Coherence MR Spectroscopy At 3T

Polyunsaturated fatty acid (PUFA), a key marker in breast cancer, is non-invasively quantifiable using multiple quantum coherence (MQC) magnetic resonance spectroscopy (MRS) at the expense of losing half of the signal. Signal combination for phased array coils provides potential pathways to enhance the signal to noise ratio (SNR), with current algorithms developed for conventional brain MRS. Since PUFA spectra and the biochemical environment in the breast deviate significantly from those in the brain, we set out to identify the optimal algorithm for PUFA in breast cancer. Combination algorithms were compared using PUFA spectra from 17 human breast tumour specimens, 15 healthy female volunteers, and 5 patients with breast cancer on a clinical 3 T MRI scanner. Adaptively Optimised Combination (AOC) yielded the maximum SNR improvement in specimens (median, 39.5%; interquartile range: 35.5-53.2%, p < 0.05), volunteers (82.4 ± 37.4%, p < 0.001), and patients (median, 61%; range: 34-105%, p < 0.05), while independent from voxel volume (rho = 0.125, p = 0.632), PUFA content (rho = 0.256, p = 0.320) or water/fat ratio (rho = 0.353, p = 0.165). Using AOC, acquisition in patients is 1.5 times faster compared to non-noise decorrelated algorithms. Therefore, AOC is the most suitable current algorithm to improve SNR or accelerate the acquisition of PUFA MRS from breast in a clinical setting.