Diffusion‐weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion‐weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on “Best Practices & Tools for Diffusion MR Spectroscopy” held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources

Relapsed childhood acute myeloid leukemia: prognostic factors and outcomes: experience from a single oncology center

Introduction: Over recent decades, significant progress in the treatment of childhood acute myeloid leukemia (AML) has been made. However, the relapsed disease remains a challenge. The aim of this study was to analyze therapy results in pediatric patients treated for relapsed AML in a single oncology center, with a particular focus on prognostic factors. Materials and methods: Data from patients younger than 19 years with AML diagnosed between January 1994 and December 2020 treated in the Department of Pediatric Hematology and Oncology in Bydgoszcz, Poland was analyzed, with detailed analysis of patients with relapsed disease. Results: A total of 77 children were diagnosed with AML in the analyzed period and 21 had a relapsed disease (27.3%). Bone marrow relapse was the most common. The risk factors of relapse included white blood cells >100 G/L at initial diagnosis and classification to the high risk group. Late relapse was related to poorer outcomes. The 5-year probability of overall survival for the entire group was 28.6%, and this was significantly higher in patients who achieved second remission compared to those who did not (44.9% vs. 0.0%, p <0.001). The main reason for death was progression of disease, which occurred in 10 patients. Conclusions: Outcomes in relapsed AML in children are still dismal. Lack of second remission suggests the need for experimental therapy

Changing risk factors in childhood acute lymphoblastic leukemia: experience from Kujawsko-Pomorski region 1976–2018

Introduction: Acute lymphoblastic leukemia (ALL) is the most common malignancy in children. Risk factors in childhood ALL have changed during recent decades, mostly due to treatment personalization. The aim of this study was to analyze therapy results and prognostic factors in childhood ALL in the Kujawsko-Pomorski region of Poland between 1976 and 2018. Material and methods: Data from 495 patients (0–18 years old) diagnosed with ALL from the Kujawsko-Pomorski region between 1976 and 2018 was analyzed. Prognostic factors were analyzed separately in specific therapeutic groups, which were defined by several therapy protocols. Results: Prognostic factors have changed over the course of consecutive therapeutic periods. Between 1976 and 1988 (the first and second therapeutic protocols), central nervous system involvement was the most important risk factor. During the third therapeutic period, an unsatisfactory treatment response on days 8 and 14 was related to a poor outcome. In 1995–2002, the risk factors were hepatomegaly, splenomegaly, lymph nodes involvement, and unsatisfactory therapy response on days 15 and 33. Between 2002 and 2011, immunophenotype other than ‘common’ and hemoglobin level at diagnosis were the risk factors, and a lack of BCR-ABL aberration was related to better therapy results. During the final analyzed period (2011–2018), failure to achieve remission on day 33 was a risk factor, and patients classified as non-high risk group and those aged <6 years had better outcomes. Conclusions: The changing profile of risk factors in ALL has reflected progress in ALL therapy, with the gradual elimination of factors related to poor outcomes, mostly due to modifications in treatment and the development of diagnostic methods as well as therapy monitoring

Results and interpretation of a fitting challenge for MR spectroscopy set up by the MRS study group of ISMRM.

PURPOSE Fitting of MRS data plays an important role in the quantification of metabolite concentrations. Many different spectral fitting packages are used by the MRS community. A fitting challenge was set up to allow comparison of fitting methods on the basis of performance and robustness. METHODS Synthetic data were generated for 28 datasets. Short-echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near-normal brain concentrations. Macromolecular contributions were also included. Modulations of signal-to-noise ratio (SNR); lineshape type and width; concentrations of γ-aminobutyric acid, glutathione, and macromolecules; and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with. RESULTS Twenty-six submissions were evaluated. Visually, most fit packages performed well with mostly noise-like residuals. However, striking differences in fit performance were found with bias problems also evident for well-known packages. In addition, often error bounds were not appropriately estimated and deduced confidence limits misleading. Soft constraints as used in LCModel were found to substantially influence the fitting results and their dependence on SNR. CONCLUSIONS Substantial differences were found for accuracy and precision of fit results obtained by the multiple fit packages

In vivo 1H MR spectroscopy with J‐refocusing

Purpose The goal of this study was to propose a novel localized proton MR spectroscopy (MRS) sequence that reduces signal loss due to J‐modulation in the rat brain in vivo. Methods Sprague‐Dawley rats were studied at 9.4 T. A semi‐LASER sequence with evenly distributed echo‐time (TE) was used, and a 90° J‐refocusing pulse was inserted at TE/2. Proton spectra were acquired at two TEs (30 and 68 ms), with and without the J‐refocused pulse. Data were processed in MATLAB and quantified with LCModel. Results The J‐refocused spectrum acquired at TE = 30 ms did not show any signal losses due to J‐modulation and had comparable spectral pattern to the one acquired with semi‐LASER using the minimum achievable TE. Higher signal amplitudes for glutamine, γ‐aminobutyric acid and glutathione led to more reliable quantification precision for these metabolites. The refocused signal intensities at TE = 68 ms were also unaffected by J‐modulation but were smaller than the signals at TE = 30 ms mainly due to transverse T2 relaxation of metabolites. Conclusion The proposed localized MRS sequence will be beneficial in both animal and human MRS studies when using ultra‐short TE is not possible while also providing more reliable quantification precision for J‐coupled metabolites

Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts' consensus recommendations.

Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step

Voxel placement and data quality.

<p>(A) Images of human brains illustrating the position and size of the VOI. (B) Representative ¹H STEAM spectra (4 T, 27 mL, <i>T</i>_R = 4.5 s, number of averages = 4) measured at seven <i>T</i>_Es from the human occipital lobe in one young (left) and one elderly (right) subject. The vertical scale has been adjusted such that the NAA resonance detected at <i>T</i>_E = 10 ms for both young and elderly subjects has the same intensity. Horizontal dashed lines are visual guides to indicate that the intensity of NAA and tCr signals decrease faster in the elderly than the young subject. The faster signal decay reflects a shorter <i>T</i>₂ value. Spectra are shown without line broadening. NAA, N-acetylaspartate, tCr, total creatine = creatine (Cr) + phosphocreatine (PCr), tCho, total choline = choline containing compounds.</p

<i>T</i>₂ fits for metabolites in young and elderly subjects.

<p>Individual exponential fits (represented by decaying lines) of the experimentally measured data for (A) the NAA singlet at 2.01 ppm, (B) the tCr signal at 3.03 ppm, and (C) the tCho singlet at 3.2 ppm in one representative young and one representative elderly subject. The amplitude of all data sets was normalized by setting the first <i>T</i>_E point to unity for both young and elderly subjects. For all metabolites and all subjects, <i>T</i>₂s were fit with R² ≥ 0.918, with the lowest R² for tCho.</p