Non-Cartesian MRI: Novel Techniques and Applications

Neuroimaging has become one of the most powerful tools for understanding brain structure, function, and connectivity. Within magnetic resonance imaging (MRI), non Cartesian k space sampling techniques—such as spiral, radial, PROPELLER, and rosette trajectories—have emerged as promising alternatives to conventional Cartesian acquisitions. These approaches offer improved sampling efficiency, reduced motion sensitivity, and enhanced temporal and spatial resolution, all crucial for capturing the complex dynamics of the human brain. Despite growing evidence of their advantages in fMRI, diffusion, and spectroscopic studies, the integration of non Cartesian strategies into routine neuroscientific workflows remains limited. The field therefore faces important questions about how best to harness these trajectories to advance cognitive, clinical, and computational neuroscience.Recent studies demonstrate the potential of non Cartesian MRI for faster imaging, reduced artifacts, and improved sensitivity to subtle neural activations. For instance, spiral fMRI has enabled higher temporal resolution in tracking resting state networks, while radial and rosette trajectories show robustness against motion and susceptibility artifacts. Emerging machine learning–based reconstructions and model driven calibrations have further expanded their feasibility for in vivo brain studies. However, gaps persist regarding standardized reconstruction pipelines, quantitative signal modeling, and reproducibility across different hardware and acquisition conditions. This Research Topic aims to unite neuroscientists, physicists, and computational scientists to explore the translation of non Cartesian MRI methods into practical brain research tools.This Research Topic aims to elucidate and expand the neuroscientific potential of non Cartesian MRI by showcasing novel acquisition and reconstruction methods, computational modeling approaches, and translational studies that leverage these techniques to probe neural structure and function. The goal is to solidify non Cartesian MRI as a robust methodological framework for exploring dynamic brain processes, from basic circuit mapping to clinical biomarker development in pathology.To gather further insights into the methodological and neuroscientific boundaries of this field, we welcome articles addressing, but not limited to, the following themes:o Development and optimization of non Cartesian trajectories for brain imagingo Neural signal modeling, reconstruction algorithms, and artifact correction in non Cartesian acquisitionso Applications of non Cartesian fMRI, diffusion, and spectroscopic imaging in neuroscienceo Integration of machine learning and compressed sensing for brain data reconstructiono Comparative studies of non Cartesian versus Cartesian methods in cognitive and clinical paradigmso Quantitative evaluation metrics for image quality, reproducibility, and functional sensitivityo Translational and clinical neuroscience studies employing non Cartesian MRI in health and diseaseTopic Editor Michael D. Noseworthy is the CEO and co-founder of Concussion MRI Analytics (https://www.concussionmrianalytics.ca) and has received research funding from GE HealthCare Canada. Topic Editor Steven D. Beyea holds a patent relevant to the Research Topic (a non-revenue-generating method for automated reconstruction of dynamic, non-Cartesian radial MRI data) and has received grant support from GE HealthCare and Synaptive Medical. All other Topic Editors declare no competing interests with regard to the Research Topic subject. Topic editors: Steven D. Beyea, Wietske Van Der Zwaag, Michael D Noseworthy Source: https://www.frontiersin.org/research-topics/85873/non-cartesian-mri-novel-techniques-and-applications