Biology subjects

Starcuk, Z.

Publications and source records attributed to Starcuk, Z..

3 recordsLinked to original sources

bioRxiv2025

Metabolic modelling and time-resolved mapping of glucose oxidative metabolism in rats brain by indirect deuterium detection with 1H-FID-MRSI at 9.4T

ObjectThe present study exploits newly developed dynamic indirect 1H-[2H]-FID-MRSI at 9.4T, combined with a dedicated metabolic model, to enable regional and quantitative characterization of glucose oxidative metabolism flux in the rat brain with minimal metabolic assumptions, by measuring both 2H-labelled Glx turnover and pool size along a controlled 2H-Glc infusion protocol. Materials and MethodsSeven rats underwent dynamic 2D 1H-FID-MRSI during a 2-hour infusion of [6,6-2H2] glucose. Consecutive 13-min acquisitions quantified Glx-C4 1H-signal decay, converted to 2H-Glx concentrations using baseline metabolite pool sizes. A three-pool kinetic model including 2H-label loss was fitted to regional turnover curves to estimate oxidative flux (Vgt) and pyruvate dilution (Kdil). Model performance and parameter robustness were finally assessed with Monte-Carlo simulations. ResultsIn vivo 2H-Glx turnover showed a saturated exponential rise ([~]60 min), with a labelling plateau higher in striatum (1.85 mol/g) than hippocampus (1.55 mol/g). Metabolic modelling provided region-specific oxidative fluxes: Vgt = 0.27 {+/-} 0.07 mol/g/min (hippocampus) and Vgt = 0.40 {+/-} 0.06 mol/g/min (striatum), with consistent Kdil across regions. Simulations confirmed a good model robustness in retrieving Vgt over a large range of experimental conditions. DiscussionThis work shows the appropriateness of indirect dynamic 1H-[2H]-FID-MRSI for quantitative metabolic flux mapping of cerebral glucose oxidative metabolism.

neuroscience↗

bioRxiv2022

Model-Constrained Self-supervised Deep Learning Approach to the Quantification of Magnetic Resonance Spectroscopy Data Based on Linear-combination Model Fitting

PurposeWhile the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. MethodWe present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. ResultThe validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. ConclusionThe proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra. HighlightsO_LIA novel self-supervised deep learning method for quantifying metabolite concentrations in MR spectroscopy signals. C_LIO_LIProviding a unique opportunity to quantify complex-valued MRS data in the time domain. C_LIO_LIFaster MR spectroscopy quantification with comparable accuracy to traditional methods. C_LIO_LIInvestigating the impacts of the dataset size and neural network design on our proposed model C_LI

neuroscience↗

bioRxiv2022

Model-Informed Unsupervised Deep Learning Approaches to Frequency and Phase Correction of MRS Signals

PurposeA supervised deep learning (DL) approach for frequency-and-phase Correction (FPC) of MR spectroscopy (MRS) data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasibility and efficiency of unsupervised DL-based FPC. MethodTwo novel DL-based FPC methods (deep learning-based Cr referencing [dCrR] and deep learning-based spectral registration [dSR]) which use a priori physics domain knowledge are presented. The proposed networks were trained, validated, and evaluated using simulated, phantom, and publicly accessible in-vivo MEGA-edited MRS data. The performance of our proposed FPC methods was compared to other generally used FPC methods, in terms of precision and time efficiency. A new measure was proposed in this study to evaluate the FPC method performance. The ability of each of our methods to carry out FPC at varying SNR levels was evaluated. A Monte Carlo (MC) study was carried out to investigate the performance of our proposed methods. ResultThe validation using low-SNR manipulated simulated data demonstrated that the proposed methods could perform FPC comparably to other methods. The evaluation showed that the dCrR method achieved the highest performance in phantom data. The applicability of the proposed method for FPC of GABA-edited in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. ConclusionThe proposed physics-informed deep neural networks trained in an unsupervised manner with complex data can offer efficient FPC of MRS data in a shorter time.

neuroscience↗