Biology subjects

Simegn, G. L.

Publications and source records attributed to Simegn, G. L..

5 recordsLinked to original sources

bioRxiv2025

Gradient scheme optimization for PRESS-localized edited MRS using weighted pathway suppression

This study aimed to design and implement an optimized gradient scheme for PRESS-localized edited magnetic resonance spectroscopy (MRS) to enhance suppression of out-of-voxel (OOV) artifacts. These artifacts, which originate from insufficient crushing of unwanted coherence transfer pathways (CTPs), are particularly challenging in editing schemes for metabolites like gamma-aminobutyric acid and glutathione. To address this, a volume-based likelihood model was developed to guide gradient scheme optimization, prioritizing suppression of CTPs based on likelihood. A volume-based likelihood model for CTP weighting was integrated into a Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient optimization. Using a genetic algorithm with a new dual-penalty cost function, gradient schemes were optimized to maximize pathway-specific suppression. Hardware and sequence constraints, maximum gradient amplitudes and delay durations respectively, informed the optimization. Validation of the optimized scheme was performed with simulations by calculating the k-space crushing efficiency analytically with k-space trajectory. and in vivo using an edited MRS sequence in three brain regions (posterior cingulate cortex PCC, thalamus, and medial prefrontal cortex (mPFC)), with particular focus on OOV artifact reduction and spectral quality improvements. A three-way Analysis of Variance was used to assess the significance level of OOV artifact reduction. The optimized gradient scheme demonstrated improved k-space crushing efficiency (by an average of 197%). OOV artifacts were reduced in all brain regions, particularly in highly OOV-susceptible regions (thalamus and mPFC). Improvements were most notable around 4.3 ppm with significant OOV artifact amplitude reductions (p < 0.001). By using a volume-based likelihood model for CTP prioritization, the optimized DOTCOPS scheme ensures robust and region-agnostic performance in reducing OOV artifacts.

neuroscience↗

bioRxiv2025

Identifying out-of-voxel echoes in edited MRS with phase cycle inversion

1.PurposeTo identify the origin of out-of-voxel (OOV) signals based on the coherence transfer pathway (CTP) formalism using signal phase conferred by the acquisition phase cycling scheme. Knowing the CTP driving OOV artifacts enables optimization of crusher gradients to improve their suppression without additional data acquisition. Theory and MethodsA phase cycle systematically changes the phase of RF pulses across the transients of an experiment, encoding phase shifts into the data that can be used to suppress unwanted CTPs. We present a new approach, phase cycle inversion (PCI), which removes the receiver phase originally applied to the stored transients, replacing it with new receiver phases, matching the phase evolutions associated with each unwanted CTP, to identify the OOV signals. We demonstrated the efficacy of PCI using the MEGA-edited PRESS sequence in simulations, phantom and in vivo experiments. Based on these findings, the crusher gradient scheme was optimized. ResultsThe simulation results demonstrated that PCI can fully separate signals originating from different CTPs using a complete phase cycling scheme. PCI effectively identified the CTP responsible for OOV signals in phantom experiments and in vivo, though with reduced specificity in vivo due to phase instabilities. Re-optimization of the gradient scheme based on the identified OOV-associated CTP to suppress these signals, resulted in cleaner spectra in six volunteers. ConclusionPCI can be broadly applied across pulse sequences and voxel locations, making it a flexible and generalizable approach for diagnosing the CTP origin of OOV signals.

neuroscience↗

bioRxiv2024

Age dependency of neurometabolite T1 relaxation times

PurposeTo measure T1 relaxation times of metabolites at 3T in a healthy aging population and investigate age dependence. MethodsA cohort of 101 healthy adults were recruited with approximately 10 male and 10 female participants in each decade band: 18-29, 30-39, 40-49, 50-59, and 60+ years old. Inversion-recovery PRESS data (TE/TR: 30/2000 ms) were acquired at 8 inversion times (TIs) (300, 400, 511, 637, 780, 947, 1148 and 1400 ms) from voxels in white-matter-rich centrum semiovale (CSO) and gray-matter-rich posterior cingulate cortex (PCC). Modeling of TI-series spectra was performed in Osprey 2.5.0. Quantified metabolite amplitudes for total N-acetylaspartate (tNAA2.0), total creatine at 3.0 ppm (tCr3.0) and 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI), and the sum of glutamine and glutamate (Glx) were modeled to calculate T1 relaxation times of metabolites. ResultsT1 relaxation times of tNAA2.0 in CSO and tNAA2.0, tCr3.0, mI and Glx in PCC decreased with age. These correlations remained significant when controlling for cortical atrophy. T1 relaxation times were significantly different between PCC and CSO for all metabolites except tCr3.0. We also propose linear models for predicting metabolite T1s at 3T to be used in future aging studies. ConclusionMetabolite T1 relaxation times change significantly with age, an effect that will be important to consider for accurate quantitative MRS, particularly in studies of aging.

neuroscience↗

bioRxiv2024

A Water Relaxation Atlas for Age and Region specific Metabolite Concentration Correction

Metabolite concentration estimates from Magnetic Resonance Spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time differences between metabolites and water. One common approach is to correct the water reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T1 and T2) vary between brain locations, in pathology, and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location- and age-appropriate relaxation values into the MRS analysis workflow. 101 volunteers (51 men, 50 women; [~]10 male and 10 female participants per decade (from the 20s to 60s), were recruited. T1-weighted MPRAGE images ((1-mm)3 isotropic resolution) were acquired. Whole-brain water T1 and T2 measurements were made with DESPOT ((1.4 mm)3 isotropic resolution). T1 and T2 maps were registered to the JHU MNI-SS/EVE atlas using affine and LDDMM transformation. The atlass 268 parcels were reduced to 130 by combining homologous parcels. Mean T1 and T2 values were calculated for each parcel in each subject. Linear models of T1 and T2 as functions of age were computed, using age - 30 as the predictor. Reference atlases of "age-30-intercept" and age-slope for T1 and T2 were generated. The atlas-based workflow was integrated into Osprey, which co-registers MRS voxels to the atlas and calculates location- and age-appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location- and subject-appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water-referenced tissue correction, especially for studies of aging.

neuroscience↗

bioRxiv2024

Comparison of test-retest reproducibility of DESPOT and 3D-QALAS for water T1 and T2 mapping

PurposeRelaxometry, specifically T1 and T2 mapping, has become an essential technique for assessing the properties of biological tissues related to various physiological and pathological conditions. Many techniques are being used to estimate T1 and T2 relaxation times, ranging from the traditional inversion or saturation recovery and spin-echo sequences to more advanced methods. Choosing the appropriate method for a specific application is critical since the precision and accuracy of T1 and T2 measurements are influenced by a variety of factors including the pulse sequence and its parameters, the inherent properties of the tissue being examined, the MRI hardware, and the image reconstruction. The aim of this study is to evaluate and compare the test-retest reproducibility of two advanced MRI relaxometry techniques (Driven Equilibrium Single Pulse Observation of T1 and T2, DESPOT, and 3D Quantification using an interleaved Look-Locker acquisition Sequence with a T2 preparation pulse, QALAS), for T1 and T2 mapping in a healthy volunteer cohort. Methods10 healthy volunteers underwent brain MRI at 1.3 mm3 isotropic resolution, acquiring DESPOT and QALAS data ([~]11.8 and [~]5 minutes duration, including field maps, respectively), test-retest with subject repositioning, on a 3.0 Tesla Philips Ingenia Elition scanner. To reconstruct the T1 and T2 maps, we used an equation-based algorithm for DESPOT and a dictionary-based algorithm that incorporates inversion efficiency and B1-field inhomogeneity for QALAS. The test-retest reproducibility was assessed using the coefficient of variation (CoV), intraclass correlation coefficient (ICC) and Bland-Altman plots. ResultsOur results indicate that both the DESPOT and QALAS techniques demonstrate good levels of test-retest reproducibility for T1 and T2 mapping across the brain. Higher whole-brain voxel-to-voxel ICCs are observed in QALAS for T1 (0.84 {+/-} 0.039) and in DESPOT for T2 (0.897 {+/-} 0.029). The Bland-Altman plots show smaller bias and variability of T1 estimates for QALAS (mean of -0.02 s, and upper and lower limits of -0.14 and 0.11 s, 95% CI) than for DESPOT (mean of -0.02 s, and limits of -0.31 and 0.27 s). QALAS also showed less variability (mean 1.08 ms, limits -1.88 to 4.04 ms) for T2 compared to DESPOT (mean of 2.56 ms, and limits -17.29 to 22.41 ms). The within-subject CoVs for QALAS range from 0.6% (T2 in CSF) to 5.8% (T2 in GM), while for DESPOT they range from 2.1% (T2 in CSF) to 6.7% (T2 in GM). The between-subject CoVs for QALAS range from 2.5% (T2 in GM) to 12% (T2 in CSF), and for DESPOT they range from 3.7% (T2 in WM) to 9.3% (T2 in CSF). ConclusionOverall, QALAS demonstrated better reproducibility for T1 and T2 measurements than DESPOT, in addition to reduced acquisition time.

neuroscience↗