The VOXEL group at ARC
We are specialised in neuroimaging techniques and methods pertaining to magnetic resonance imaging and positron emission tomography.
We particularly focus on novel imaging techniques and methods, and multimodal imaging.
Aim
We would like to contribute to the development of neuroimaging techniques and methods and to create opportunities and establish new collaborations.
Current group members
Farshad Falahati
Research Infrastructure Specialist, EngineerGrégoria Kalpouzos
Associate Professor, Senior LecturerMartin Nilsson
Doctoral studentJavier Oltra
Postdoctoral ResearcherGoran Papenberg
Associate Professor, Principal ResearcherBárbara Avelar Pereira
Assistant ProfessorAlireza Salami
Associate Professor, Principal ResearcherFrida Smids
Doctoral studentWhat we do
Below are some examples of what we currently do. We collaborate
- Brain iron imaging using quantitative susceptibility mapping
- MR Elastography to assess stiffness and viscoelastic properties of the brain tissue, in collaboration with KTH Royal Institute of Technology
- PET imaging of the dopaminergic system, in collaboration with Umeå University
- Development of a high-resolution T2 sequence on 3T MRI for the assessment of the olfactory bulbs in collaboration with SUBIC, at Stockholm University
- Development of a deep learning tool for the automated segmentation of midbrain nuclei (see section Available codes)
- Contribution to the Multilayer Integration of Networks Toolbox (see section Available codes)
- MR spectroscopy in collaboration with SUBIC and Örebro University
| MRI | Summary measures/assessment |
|---|---|
| T1-weighted | Morphometrics, perivascular spaces |
| Diffusion Tensor Imaging (DTI) | White-matter integrity, structural connectome |
| Functional MRI (task rest) | Activation, functional connectome |
| T2-FLAIR | White-matter hyperintensities |
| Multi-echo Gradient Recalled Echo )meGRE) | Relaxometry R2*, Quantitative Susceptibility Mapping (QSM) for iron quantification |
| High-resolution T2 | Olfactory bulbs' anatamy |
| MR Elastography | Cerebral stiffness, viscosity-elasticity |
| Multidimensional diffusion (MUDI) | Subvoxel diffusion metrics (e.g., microanisotropy) |
| Pseudo-continuous Arterial Spin Labeling (pCASL) | Quantitative cerebral blood flow (perfusion) |
| Susceptibility Weighted Imaging (SWI) | Microbleeds |
| Neurite Orientation Dispersion and Density Imaging (NODDI) | Neurite Density Index, Orientation Dispersion Index |
| 1H MR Spectroscopy | Metabolite concentrations (e.g., NAA, Cr, Cho, mI, Glx, GABA) |
| PET | |
|---|---|
| 11C-raclopride | Dopamine D1 receptors |
| 11C-SCH23390 | Dopamine D2 receptors |
| 11C-PBR28 | TSPO density, microglia |
Reference: Falahati F. Gustavsson J, Kalpouzos G (2024). Automated segmentation of midbrain nuclei using deep learning and multisequence MRI: A longitudinal study on iron accumulation with age. Imaging Neuroscience, 2: 1-20. Doi: 10.1162/imag_a_00304
Reference: Sarraf S, Avelar-Pereira B, Hosseini SMH; Alzheimer’s Disease Neuroimaging Initiative (2025). Multilayer Integration of Networks Toolbox (MINT). Communications Biology 8(1):894. doi: 10.1038/s42003-025-08269-4.