Biology subjects

Alcicek, S.

Publications and source records attributed to Alcicek, S..

3 recordsLinked to original sources

bioRxiv2024

Absolute quantification of cerebral metabolites using 2D 1H-MRSI with quantitative MRI-based water reference

PurposeMetabolite concentrations are valuable biomarkers in brain tumors (BT). However, correction of water relaxation effects often requires time-consuming quantitative MRI (qMRI) sequences on top of a lengthy spectroscopic water reference acquisition. The goal of this work was to develop and validate a fast metabolite quantification method where a 2D spectroscopic water reference acquisition is obtained using a fast qMRI protocol and single-voxel STEAM sequence. MethodsA 2D sLASER sequence was acquired for MRSI. An 8-minute qMRI protocol was also acquired. A single-voxel unsuppressed water signal was acquired using a STEAM sequence. The H2O map, obtained from qMRI, was calibrated based on the STEAM-signal to obtain the spectroscopic water reference (proposed method). Five healthy volunteers and one BT patient were scanned at 3T. Concentrations obtained using the proposed and two reference methods, one where water relaxation effects were corrected using literature values (Reference method) and one where they were corrected using qMRI-derived values (Reference method with qMRI) were compared. ResultsIn healthy subjects, WM metabolite concentrations obtained using water relaxation using literature values (Reference method) significantly differed from those using individual-specific corrections (Reference method with qMRI and proposed method). Bland-Altman analyses revealed a very low bias and SD of the differences between the Reference method with qMRI and the proposed-method (Bias<0.5% and SD<10%). The BT regions showed a [~]15% underestimation of metabolite concentrations using the Reference method. ConclusionFor metabolite quantification, accurate water referencing with individual-specific corrections for water relaxation times was obtained in 8 minutes using the proposed method.

neuroscience↗

bioRxiv2024

Data-driven determination of 1H-MRS basis set composition

PurposeMetabolite amplitude estimates derived from linear combination modeling of MR spectra depend upon the precise list of constituent metabolite basis functions used (the "basis set"). The absence of clear consensus on the "ideal" composition or objective criteria to determine the suitability of a particular basis set contributes to the poor reproducibility of MRS. In this proof-of-concept study, we demonstrate a novel, data-driven approach for deciding the basis-set composition using Akaike information criteria (AIC). MethodsWe have developed an algorithm that iteratively adds metabolites to the basis set using iterative modeling, informed by AIC scores. We investigated two quantitative "stopping conditions", referred to as max-AIC and zero-amplitude, and whether to optimize the selection of basis set on a per-spectrum basis or at the group level. The algorithm was tested using two groups of synthetic in-vivo-like spectra representing healthy brain and tumor spectra, respectively, and the derived basis sets (and metabolite amplitude estimates) were compared to the ground truth. ResultsAll derived basis sets correctly identified high-concentration metabolites and provided reasonable fits of the spectra. At the single-spectrum level, the two stopping conditions derived the underlying basis set with 84-88% accuracy. When optimizing across a group, basis set determination accuracy improved to 89-92%. ConclusionData-driven determination of the basis set composition is feasible. With refinement, this approach could provide a valuable data-driven way to derive or refine basis sets, reducing the operator bias of MRS analyses, enhancing the objectivity of quantitative analyses, and increasing the clinical viability of MRS.

neuroscience↗

bioRxiv2024

Multi-site Ultrashort Echo Time 3D Phosphorous MRSI repeatability using novel Rosette Trajectory (PETALUTE)

PurposeThis study aims 1) to implement an operator-independent acquisition, reconstruction, and processing pipeline using a novel rosette k-space pattern for UTE 31P 3D MRSI and 2) to evaluate the clinical applicability and replicability at different experimental setups. MethodsA multicenter repeatability study was conducted for the novel UTE 31P 3D Rosette MRSI at three institutions with different experimental setups. Non-localized 31P MRSI data of 5 healthy subjects at each site were acquired with an acquisition delay of 65 s and a final resolution of 10 x 10 x 10 mm3 in 9 min. Spectra were quantified using the LCModel package. The potential acceleration was achieved using compressed sensing on retrospectively undersampled data. Reproducibility at each site was evaluated using the inter-subject coefficient of variance. ResultsThis novel acquisition and advanced processing techniques yielded high-quality spectra and enabled the detection of the critical brain metabolites at three different sites with different hardware specifications. In vivo, feasibility with an acceleration factor of 4 in 6.75 min resulted in a mean Cramer-Rao lower bounds below 20% for PCr, ATPs, PME, and the mean CoV of ATP/PCr resulted in below %20. ConclusionWe demonstrated that UTE 31P 3D Rosette MRSI acquisition, combined with compressed sensing and LCModel analysis, allows patient-friendly, operator-independent, high-resolution 31P MRSI to be acquired at clinical setups.

neuroscience↗