Keywords: Signal Modeling, fMRI (resting state), Neuro, brain modeling, The Virtual Brain

Motivation: Brain dynamic simulators rely on the same model for each grey matter region.

Goal(s): We embed region-specific mean field models (MFs) into virtual brains by designing a flexible framework linking input/output signals from different MFs.

Approach: We integrate a recently-developed cerebellar MF into “The Virtual Brain” platform. We simulate brain dynamics with cerebellar MF for cerebellar nodes and connect cerebellar MF input/output with MFs previously used for other regions.

Results: The multi-modEl framework is ready to be used with any number of different MFs; moreover, in the cerebellum, using its realistic MF improves 7-folds the correlation between simulated and empirical functional connectivity.

Impact: Simulations of brain dynamics rely on assigning the same model to all brain regions, not capturing cortical microcircuits diversities. We developed a framework to connect region-specific mean field models and demonstrated an improved performance in the cerebellum, towards personalized simulations.

Keywords: Signal Modeling, Cancer

Motivation: The effectiveness of anti-angiogenesis therapy in patients with advanced nasopharyngeal carcinoma is not optimal, and early monitoring of treatment effectiveness can reduce unnecessary treatment for patients.

Goal(s): The focus of this study was to ascertain whether Amide Proton Transfer- Magnetic Resonance Imaging (APT-MRI) could be effectively used to evaluate tumor response at an early stage. 

Approach: The research involved an examination of the correlation between APTw values and tumor cell proliferation, achieved by subjecting preclinical models to diverse antiangiogenic therapies. 

Results: The results of our study demonstrated a marked positive correlation between APTw and the expression of Ki67, a tumor proliferation marker.

Impact: This study is useful for the clinical application of anti-angiogenesis therapy. The study provides an in-depth exploration of the mechanisms and efficacy of this therapeutic approach, offering a comprehensive understanding that is crucial for its successful implementation in clinical practice. 

Keywords: Signal Modeling, Microstructure, water exchange, quantitative microstructure imaging

Motivation: The diffusion MRI (dMRI) based two-compartmental Kӓrger exchange model is widely used to characterize transcytolemmal water exchange, but the influence of diffusion restriction remains unclear. 

Goal(s): To investigate how diffusion restriction and other acquisition and microstructural parameters impact the estimation of transcytolemmal water exchange rate constants in the Kӓrger model.

Approach: Finite-difference-based numerical simulations were performed to quantitatively investigate time-varying transcytolemmal exchange rates of magnetization. 

Results: When the compartmental size is large, e.g. 15 μm (close to typical cancer cell sizes), Kӓrger-model-derived exchange rate constants will be significantly dependent on time, diffusion gradient waveforms and microstructural features, resulting in overestimation of water exchange.
 

Impact: This work elucidates the influence of diffusion restriction on the estimation of transcytolemmal water exchange rate constants in the two-compartmental Kӓrger model, revealing that water exchange is overestimated in large compartmental sizes such as in tumors. 
 

Keywords: Signal Modeling, Spectroscopy

Motivation: The composition of metabolite basis sets impacts their estimates, but no consensus or objective methods exist to decide which compounds should be included or excluded for a particular dataset.

Goal(s): To develop an objective, data-driven procedure for determining basis-set composition.

Approach: An iterated fitting algorithm uses information criteria scores to select the most appropriate metabolite basis functions directly from the data. We tested two “stopping conditions” using in-vivo-like simulated spectra.

Results: The algorithm correctly, consistently identified large parts of the ground-truth set. Stopping conditions set reliable bounds on the basis-set composition. Refinement for low-concentration compounds is expected to further improve accuracy.

Impact: Model selection for data-driven assessment of basis set composition has the potential to provide objective criteria and remove operator bias of linear-combination modeling. This may reduce analytic variability and help establish practices for low-concentration and pathology-specific metabolites.

Keywords: Signal Modeling, Cell Tracking & Reporter Genes, Machine Learning/Artificial Intelligence, Contrast Agents, Data Acquisition, Data Processing, Modelling, Multi-Contrast, Non-Proton

Motivation: Detection of multiple cell targets separately labeled with different chemically-shifted, paramagnetic ¹⁹F tracers can benefit from radial k-space sampling pulse sequences; however, radial sampling can lead to non-linear smearing chemical shift artifacts.

Goal(s): Our goal is to develop suitable modeling of the radial chemical shifts and a physics-informed deconvolution scheme to unmix multi-spectral components.

Approach: We proposed a novel Radon transform modeling of forward operator for radial chemical shifts and introduced machine learning based method for multi-spectral deconvolution. 

Results: Radial chemically-shifted artifacts are significantly reduced via the deep unrolling learned deconvolution algorithm, especially for low signal-to-noise-ratio (SNR) and highly undersampled acquisitions.

Impact: Unlike Cartesian chemical shifts that result in image displacements, radial chemical shifts produce more complex artifacts. To effectively unmix the multi-spectral ¹⁹F components, we developed an analytical model using Radon transform and a data-driven deconvolution method based on deep unrolling.

Keywords: Signal Modeling, Data Analysis, OEF

Motivation: Quantitative MRI for the measurement of oxygen extraction fraction (OEF) offers the advantage of being radiation-free. However, the quantification of T₂ and T₂^∗ parameters still suffers from long acquisition time, especially in dynamic imaging.

Goal(s): To evaluate the applicability of ultrafast dynamic multiple overlapping-echo detachment (MOLED) imaging for OEF research.

Approach: The MOLED imaging was utilized for rapidly and synchronously mapping T₂ and T₂^∗ to dynamically track OEF changes during both breathing and breath-holding conditions.

Results: The time envelope of mean OEF under both breathing and breath-holding is in agreement with the literature reported.

Impact: Owing to its fast and dynamic imaging capabilities, the multiple overlapping-echo detachment (MOLED) T₂-T₂^* acquisition has shown great potential in the quantitative measurement of the brain's oxygen metabolism, which holds significant relevance in assisting the diagnosis of diseases.

Keywords: Signal Modeling, New Signal Preparation Schemes

Motivation: Mapping current flow during temporal interference stimulation (kHz range) or the application of tumor treating fields (100 kHz - 300 kHz) can improve target localization and dose control for these techniques.

Goal(s): Explore novel encoding strategies to measure high-frequency magnetic field modulations caused by injected currents.

Approach: We use Bloch simulations to explore novel encoding strategies and phantom measurements with currents that create magnetic fields modulations for validation.

Results: We show that with off-resonance preparation pulses it is possible to be sensitive to high frequency field modulations while avoiding SAR and B₁ limits, albeit with a lower sensitivity than previously suggested spin-lock methods.

Impact: This work demonstrates the possibility of measuring time-varying fields at a much greater frequency range than previously possible, which can be crucial for high-frequency brain stimulation techniques such as temporal interference stimulation and tumor treating fields.

Keywords: Signal Representations, Tissue Characterization, Muscle, Elastography

Motivation: Over simplified constitutive relations limit the applicability of in vivo biomechanical analysis of tissue.

Goal(s): To develop a data-driven framework for discovering potentially more accurate in vivo constitutive relations using displacement fields and pressure measurements obtained in a simple acquisition/reconstruction setup. 

Approach: An inflatable pressure cuff is used to deform the thigh muscle during an MRI scan. Time-resolved images and displacement fields are reconstructed directly from k-space and used to extract strain information. This information allows for the discovery of potentially more accurate constitutive relations. 

Results: An anisotropic constitutive relation for the hamstring is found. Numerical tests suggest the validity of the method.

Impact: Data-driven discovery of tissue’s constitutive relations could help to better characterise its mechanical properties. We demonstrated in this proof of concept study that information acquired during  simple dynamic loading experiments allows reconstruction of constitutive relations that include muscle anisotropy. 

Keywords: Signal Representations, Alzheimer's Disease, Myelin, Parkinson's disease, neurodegenerative disease

Motivation: Human brain 31P-MR spectra often show a broad membrane phospholipid (MPL) signal, which has an intensity far exceeding all other sharp signals combined and a linewidth of ~ 15 ppm.   Despite its spectral dominance and distortive effect on the sharp 31P peaks, this MPL signal has received little NMR characterization.  

Goal(s): Measure MPL T2 relaxation time, estimate its concentration, and demonstrate MPL chemical exchange effect. 

Approach: Chemical exchange was measured by inversion-recovery method, and T2 by varying the delayed time of FID-sampling. 

Results: MPL has a short T2 (0.1 ms), and high concentration (1.2 M), and present with internal chemical exchange within its structure.

Impact: MPL signals can be selectively detected using 31P MRS in the human brain.  High concentration of MPL in the order of 1M may provide a valuable surrogate point for studying demyelination process in neurodegenerative diseases such as multiple sclerosis (MS). 

Keywords: Signal Representations, Cardiovascular, Cardiac binning, respiratory binning, motion identification, variational autoencoder (VAE), multidimensional quantitative imaging

Motivation: Respiratory and cardiac motion identification is challenging with changing contrast weightings for self-gated multidimensional techniques like MR multitasking.

Goal(s): To guide VAE latent vector constraints design for representing relaxation and motion.
 

Approach: We evaluated VAE representational fidelity for 16 combinations of constraints on T1/T2 relaxation, cardiac, and respiratory latent dimensions.

Results: The results demonstrate that nonlinear T1/T2 relaxation representations and cardiac phase representations improve VAE performance.

Impact: Latent space design is important for VAE learning in multidimensional cardiac imaging, suggesting avenues for better self-gated cardiac and respiratory binning. 

Keywords: New Signal Preparation Schemes, MR Fingerprinting

Motivation: Can we encode the tissue's frequency by using RF's frequency?

Goal(s): To distinguish tissue components based on their unique frequencies.

Approach: Based on 3D MRF technique, we introduced frequency-sensitive module by varying the RF's frequency TR-to-TR.

Results: We are able to simultaneously obtain T1, T2 and frequency maps, which help improve the image fedelity and quantitative accuracy. Furthermore, it could provide a tool to differentiate the tissue components based on their frequency.

Impact: If one is interested in quantifying tissues with a frequency shift compared to water, such as fat, myelin water and some amino acids, this paper can offer a brand new angle with its noval mechanism.

Keywords: Quantitative Imaging, MR Fingerprinting

Motivation: For quantitative methods like MRF an accurate yet fast simulation of signal evolutions is essential. For fast simulations, ignoring time-evolutions of RF-pulses or actual diffusion directions are common practice even though these can affect the resulting fingerprints.

Goal(s): We investigate some of these simplifications and their influence on the resulting signal evolutions and quantitative values.

Approach:  For this we simulate and analyze some possible impacts separately and compare the resulting signal evolutions and quantitative values. 

Results: In conclusion, while ignoring diffusion directions has negligible impact on the results, assuming instantaneous RF-pulses or ignoring B1-field variances leads to significant differences in the results. 

Impact: Simplifications like ignoring B1-inhomogeneities, slice profile or time-evolution of RF-pulses lead to significant differences in simulated fingerprints. On the other hand, ignoring the diffusion directions in case of Gaussian diffusion has negligible influence on the signal evolution and quantitative values.

Keywords: Quantitative Imaging, Susceptibility, QSM, Reproducibility, Inversion algorithms, Background Field Removal, Reference region

Motivation: Quantitative Susceptibility Mapping (QSM), an MRI technique used to investigate iron, myelin and calcium in neurology research, necessitates referencing susceptibility values, but the effect of this referencing step on the study outcome is not well understood.

Goal(s): To disentangle the impact of reference region and inversion algorithm on scan-rescan susceptibility variation.

Approach: Three brain reference regions and twenty-one inversion algorithms were studied on DGM susceptibility reproducibility using 5 subjects (4 scan-rescan each).

Results: The choice of the reference region had a more significant impact on reproducibility than the choice of the inversion algorithms. Whole brain and white matter referenced findings were highly reproducible.

Impact: The choice of the reference region affects statistical power and can lead to the masking of significant group differences due to increased variation.

Keywords: Signal Modeling, Microstructure, quantitative microstructure imaging, signal modeling

Motivation:  Incorporating the impact of cell size distribution is challenging in current dMRI-based microstructural imaging. All relevant models fail to provide an analytical signal expression, but instead replace the intracellular signal with the sum of signal terms corresponding to different cell diameters. Although this is intuitive, subsequent equations are usually ill-conditioned and hard to resolve.

Goal(s): To derive the analytical expression for dMRI signals and rebuild a cellular microstructural model.

Approach: We performed theoretical modelling based on simulated signals and validations on numerical simulations and in-vitro cell experiments.

Results: A semi-data-driven cellular microstructural model is proposed and it outperforms the published method.

Impact: This work provides the first analytical expression for dMRI signals while incorporating cell size distribution. The proposed microstructural model can extract not only accurate mean cell size, but also distribution information, which provides an additional biomarker for tumor monitoring.

Keywords: Quantitative Imaging, Quantitative Susceptibility mapping

Motivation: To elucidate the contribution of nondipolar frequency shifts to the anisotropy of phase contrast in brain MRI and decode the underpinning biophysical mechanisms of phase contrast.

Goal(s): To differentiate the roles of magnetic susceptibility and nondipolar frequency shifts across various head orientations in MRI scans.

Approach: Utilizing DEEPOLE QUASAR, the study compares susceptibility estimates and nondipolar frequency shifts in anisotropic brain regions across multiple head orientations.

Results: Nondipolar frequency shifts played a substantial role in the anisotropy of frequency shifts, with DEEPOLE QUASAR providing more stable susceptibility estimates than conventional QSM, regardless of head orientation.

Impact: The study establishes the substantial influence of nondipolar frequency shifts on MRI phase contrast anisotropy, questioning established assumptions from susceptibility tensor imaging and quantitative susceptibility mapping, thereby paving the way for more accurate brain tissue characterization.

Keywords: Software Tools, Software Tools, Extended Phase Graphs

Motivation: Most existing MR simulators either focus on the implementation of multiple physical phenomena or on massive parallelization, but these two aspects are usually not tackled simultaneously. 

Goal(s): To provide a feature-rich, massively parallelized and differentiable MR simulator.

Approach: We built on the Extended Phase Graphs formalism to efficiently simulate all the main MR physical phenomena. We used PyTorch as a backend to enable massive parallelization and efficient differentiation.

Results: Our toolbox, demonstrated on a numerical Fast Spin Echo experiment on an exchanging two-pool system, achieved order of magnitude speed-up compared to existing implementations and efficient differentiation with minimal boilerplate.

Impact: Torch-EPG-X will represent a useful tool for synthetic signal generation for deep learning, parameter fitting, model-based reconstruction and sequence optimization.