Potential clinical impact of multiparametric quantitative MR spectroscopy in neurological disorders: A review and analysis

Kirov, Ivan I.; Tal, Assaf

doi:10.1002/mrm.27912

Cited by 19 publications

(26 citation statements)

References 142 publications

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“…These limitations are important to consider in planning any 1 H MRS study, and especially when using sequences incurring T 2 and T 1 weighting, as presented here. However, as an initial aid in planning a hippocampal study in schizophrenia, mild cognitive impairment or AD, we refer the reader to a recent literature review by Kirov and Tal 59 . It lists, in absolute amounts, the metabolite concentrations of patients and controls for all studies that report statistically significant differences (for estimating the magnitude of the expected difference); and any published metabolite (and water) relaxation times in controls and these diseases (for estimating possible errors).…”

Section: Discussionmentioning

confidence: 99%

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

et al. 2021

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The hippocampus is one of the most challenging brain regions for proton MR spectroscopy (MRS) applications. Moreover, quantification of J‐coupled species such as myo‐inositol (m‐Ins) and glutamate + glutamine (Glx) is affected by the presence of macromolecular background. While long echo time (TE) MRS eliminates the macromolecules, it also decreases the m‐Ins and Glx signal and, as a result, these metabolites are studied mainly with short TE. Here, we investigate the feasibility of reproducibly measuring their concentrations at a long TE of 120 ms, using a semi‐adiabatic localization by adiabatic selective refocusing (sLASER) sequence, as this sequence was recently recommended as a standard for clinical MRS. Gradient offset‐independent adiabatic refocusing pulses were implemented, and an optimal long TE for the detection of m‐Ins and Glx was determined using the T2 relaxation times of macromolecules. Metabolite concentrations and their coefficients of variation (CVs) were obtained for a 3.4‐mL voxel centered on the left hippocampus on 3‐T MR systems at two different sites with three healthy subjects (aged 32.5 ± 10.2 years [mean ± standard deviation]) per site, with each subject scanned over two sessions, and with each session comprising three scans. Concentrations of m‐Ins, choline, creatine, Glx and N‐acetyl‐aspartate were 5.4 ± 1.5, 1.7 ± 0.2, 5.8 ± 0.3, 11.6 ± 1.2 and 5.9 ± 0.4 mM (mean ± standard deviation), respectively. Their respective mean within‐session CVs were 14.5% ± 5.9%, 6.5% ± 5.3%, 6.0% ± 3.4%, 10.6% ± 6.2% and 3.5% ± 1.4%, and their mean within‐subject CVs were 17.8% ± 18.2%, 7.5% ± 6.3%, 7.4% ± 6.4%, 12.4% ± 5.3% and 4.8% ± 3.0%. The between‐subject CVs were 25.0%, 12.3%, 5.3%, 10.7% and 6.4%, respectively. Hippocampal long‐TE sLASER single voxel spectroscopy can provide macromolecule‐independent assessment of all major metabolites including Glx and m‐Ins.

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Section: Discussionmentioning

confidence: 99%

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

et al. 2021

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“…For example, current approaches to early detection of cancer are increasingly relying on the discriminative ability of diagnostic tests using a combination of biomarkers. This approach has recently been leveraged to detect prostate cancer 32 and neurological disorders 33 using multiparametric MRI such as some anatomic misregistration may be present between the MRI and histopathologic data. We believe that this error introduced noise to the correlations and logistic models presented in this study.…”

Section: Resultsmentioning

confidence: 99%

“…For example, current approaches to early detection of cancer are increasingly relying on the discriminative ability of diagnostic tests using a combination of biomarkers. This approach has recently been leveraged to detect prostate cancer 32 and neurological disorders 33 using multiparametric MRI such as T 1 ‐, T 2 ‐ and diffusion‐weighted MRI. These previously successful efforts have motivated the MRI‐biomarker‐based multivariate analysis applied in the current study.…”

Section: Discussionmentioning

confidence: 99%

Multivariate use of MRI biomarkers to classify histologically confirmed necrosis in symptomatic total hip arthroplasty

Sherafati

Bauer

Potter

et al. 2020

Journal Orthopaedic Research

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The failure of total hip arthroplasty (THA) is commonly associated with the necrosis of the periprosthetic tissue. To date, there is no established method to noninvasively quantify the progression of such necrosis. Magnetic resonance imaging (MRI) of soft tissues near implants has undergone a recent renaissance due to the development of multispectral metal‐artifact reduction techniques. Advanced analysis of multispectral MRI has been shown capable of detecting small magnetism effects of metallic debris in periprosthetic tissue. The purpose of this study is to demonstrate the diagnostic utility of these MRI‐based tissue‐magnetism signatures. Together with morphological MRI metrics, such as synovial volume and thickness, these measurements are utilized as biomarkers to noninvasively detect soft‐tissue necrosis in symptomatic THA patients (N=78). All subjects underwent an advanced MRI scan before revision surgery and tissue biopsies utilized for necrosis grading. Statistical analyses demonstrated a weak, but significant positive correlation (P = .04) between MRI magnetism signatures and necrosis scores, while indicating no meaningful association between the latter and serum cobalt and chromium ion levels. Receiver‐operating characteristic (ROC) analyses were then performed based on uni‐ and multivariate logistic regression models utilizing the measured MRI biomarkers as predictors of severe necrosis. The area under the curve of the ROC plots for MRI biomarkers as combined predictors were found to be 0.70 and 0.84 for cross‐validation and precision‐recall tests, respectively.

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“…Development of such a technique can be valuable in situations in which data sampling is highly limited, such as in spectroscopic magnetic resonance fingerprinting . The potential clinical importance of multiparametric MRS has been reviewed recently …”

Section: Discussionmentioning

confidence: 99%

Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy

Lee

Kim

2020

Magnetic Resonance in Med

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Purpose:To explore the applicability of convolutional neural networks (CNNs) in the reconstruction of spectra from truncated FIDs (tFIDs) in 1 H-MRS, which can be valuable in situations in which data sampling is highly limited, such as spectroscopic magnetic resonance fingerprinting. Methods: Rat brain FIDs were simulated at 9.4 T based on in vivo data (N = 11) and randomly truncated by retaining 8,16,32, 64, 128, 256, 512, and 1024 (null truncation) points (denoted as tFID 8 , tFID 16 , … tFID 1024 ). Using a U-net, 3 CNNs were individually trained (N = 40 000) in time domain only (FID to FID [ FID CNN FID ]), in frequency domain only (spectrum to spectrum [ spec CNN spec ]), and across the domains (FID to spectrum [ FID CNN spec ]) to map the truncated data to their fully sampled versions. The CNNs were tested on the simulated data (N = 5000), and the CNN with the best performance was further tested on the in vivo data, for which the CNNpredicted fully sampled data were analyzed using the LCModel and the results were compared with those from the original, fully sampled data. Results: The best result on the simulated data was obtained with spec CNN spec , which effectively recovered the spectral details even for those input spectra that appear as a hump due to substantial FID truncation (spectra from tFID 16 and tFID 32 ). Overall, its performance was significantly degraded on the in vivo data. Nonetheless, using spec CNN spec , several coupled spins in addition to the major singlets can be quantified from tFID 128 with the error no larger than 10%. Conclusion: Upon the availability of more realistically simulated training data, CNNs can also be used in the reconstruction of spectra from truncated FIDs. K E Y W O R D Sconvolutional neural network, deep learning, free induction decay, proton magnetic resonance spectroscopy, truncation

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Potential clinical impact of multiparametric quantitative MR spectroscopy in neurological disorders: A review and analysis

Cited by 19 publications

References 142 publications

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

Hippocampal single‐voxel MR spectroscopy with a long echo time at 3 T using semi‐LASER sequence

Multivariate use of MRI biomarkers to classify histologically confirmed necrosis in symptomatic total hip arthroplasty

Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy

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