Biology subjects

Shamaei, A.

Publications and source records attributed to Shamaei, A..

3 recordsLinked to original sources

bioRxiv2024

SMART MRS: A Simulated MEGA-PRESS Artifacts Toolbox for GABA-edited MRS

PurposeTo create a Python-based toolbox to simulate commonly occurring artifacts for single voxel Gamma-Aminobutyric Acid (GABA)-edited Magnetic Resonance Spectroscopy (MRS) data. MethodsThe toolbox was designed to maximize user flexibility and contains artifact, applied, input/output (I/O), and support functions. The artifact functions can produce spurious echoes, eddy currents, nuisance peaks, line broadening, baseline contamination, linear frequency drifts, and frequency and phase shift artifacts. Applied functions combine or apply specific parameter values to produce recognizable effects such as lipid peak and motion contamination. I/O and support functions provide additional functionality to accommodate different kinds of input data (MATLAB FID-A .mat files, nifti-mrs files), which vary by domain (time vs. frequency), collection type (edited vs. non-edited) and scale. ResultsUsers may provide some or all of the required artifact function parameter values which will result in artifacts of different appearances. Visual assessment confirms the resemblance of simulated compared to in vivo produced artifacts. ConclusionOur pip installable Python artifact simulated toolbox SMART_MRS is meant to enhance the diversity and quality of existing simulated edited-MRS data and is complementary to existing MRS simulation software.

bioengineering↗

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↗

bioRxiv2021

NIfTI-MRS: A standard format for magnetic resonance spectroscopic data

PurposeThe use of multiple data formats in the MRS community currently hinders data sharing and integration. NIfTI-MRS is proposed as a standard MR spectroscopy data format, which is implemented as an extension to the neuroimaging informatics technology initiative (NIfTI) format. Using this standardised format will facilitate data sharing, ease algorithm development, and encourage the integration of MRS analysis with other imaging modalities. MethodsA file format based on the NIfTI header extension framework was designed to incorporate essential spectroscopic metadata and additional encoding dimensions. A detailed description of the specification is provided. An open-source command-line conversion program is implemented to enable conversion of single-voxel and spectroscopic imaging data to NIfTI-MRS. To provide visualisation of data in NIfTI-MRS, a dedicated plugin is implemented for FSLeyes, the FSL image viewer. ResultsAlongside online documentation, ten example datasets are provided in the proposed format. In addition, minimal examples of NIfTI-MRS readers have been implemented. The conversion software, spec2nii, currently converts fourteen formats to NIfTI-MRS, including DICOM and vendor proprietary formats. ConclusionThe proposed format aims to solve the issue of multiple data formats being used in the MRS community. By providing a single conversion point, it aims to simplify the processing and analysis of MRS data, thereby lowering the barrier to use of MRS. Furthermore, it can serve as the basis for open data sharing, collaboration, and interoperability of analysis programs. It also opens possibility of greater standardisation and harmonisation. By aligning with the dominant format in neuroimaging, NIfTI-MRS enables the use of mature tools present in the imaging community, demonstrated in this work by using a dedicated imaging tool, FSLeyes, as a viewer.

neuroscience↗