Biology subjects

Franceschiello, B.

Publications and source records attributed to Franceschiello, B..

8 recordsLinked to original sources

bioRxiv2026

Improved 3D Radial Phyllotaxis Trajectories for Uniform Density Distribution of Readout Directions and Sequential Binning

PurposeTo develop 3D radial spiral phyllotaxis trajectories that provide a uniform density distribution of readout directions and support retrospective sequential binning, thereby reducing ringing artifacts and improving image quality. MethodsUPhy trajectory redefines the polar angle to achieve uniform density distribution of readout directions. FlexiPhy further decouples the azimuthal and polar ordering of interleaves through a randomized permutation, improving robustness to sequential binning. The proposed trajectories were evaluated in vivo on 10 healthy volunteers using two gradient-echo sequences on a 3T MRI scanner. Sequential temporal reconstructions were compared with reference reconstructions using structural similarity and relative L2 error metrics. ResultsUPhy presents analytically demonstrated uniform density distribution of readout directions. Quantitative analysis shows significantly higher SSIM values and lower relative L2 errors for FlexiPhy compared with both the original phyllotaxis and UPhy trajectories after Bonferroni correction (pcorrected < 0.05). ConclusionFlexiPhy enables more reliable sequential binning reconstructions by reducing trajectory-induced ringing artifacts and temporal inconsistencies. Moreover, its randomized construction is not tied to a specific binning strategy, making it broadly compatible with retrospective binning approaches used in dynamic and motion-resolved MRI.

bioinformatics↗

bioRxiv2025

Monalisa: An Open Source, Documented, User-Friendly MATLAB Toolbox for Magnetic Resonance Imaging Reconstruction

PurposeAn open-source, user-friendly MATLAB framework for Magnetic Resonance Imaging (MRI) reconstruction was developed to simplify the reconstruction process, with a specific focus on non-Cartesian imaging and dynamic applications in the presence of motion. MethodsMonalisa is decomposing the reconstruction pipeline into clear modular steps, including raw data reading with flexible file-type abstraction, trajectory computation, density compensation, advanced coil sensitivity mapping, and tailored binning strategies through its "mitosius" preprocessing stage. The framework supports a suite of reconstruction methods, including iterative-SENSE (also named CG-SENSE), GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) reconstructions, and regularized reconstructions supporting both spatial and temporal regularization using l1 (Compressed Sensing (CS)) and l2 techniques, accommodating both Cartesian and non-Cartesian acquisitions. We performed benchmark experiments comparing Monalisa with the Berkeley Advanced Reconstruction Toolbox (BART) toolbox on simulated 2D radial acquisitions. ResultsResults of the comparison demonstrate competitive performance, yielding higher Structural Similarity Index (SSIM) and lower l2 error. Notably, Monalisa reconstructions exhibited fewer visible artifacts than BART. ConclusionBy providing comprehensive documentation, Monalisa serves not only as a powerful tool for research and clinical imaging but also as an educational platform to facilitate innovation in MRI reconstruction.

bioinformatics↗

bioRxiv2025

A-eye: Automated 3D Segmentation of Healthy Human Eyeand Orbit Structures and Axial Length Extraction

This study addresses the need for accurate 3D segmentation of the human eye and orbit from MRI to improve ophthalmic diagnostics. Past efforts focused on small sample sizes and varied imaging methods. Here, two techniques (atlas-based registration and supervised deep learning) are tested for automated segmentation on a large T1-weighted MRI dataset. Results show accurate segmentations of the lens, globe, optic nerve, rectus muscles, and fat. Additionally, the study automates the estimation of axial length, a key biomarker.

bioengineering↗

bioRxiv2024

Eye-Opening Advances: Automated 3D Segmentation, Key Biomarkers Extraction, and the First Large-Scale MRI Eye Atlas.

In this study we introduce an automated 3D segmentation of the healthy human adult eye and orbit from Magnetic Resonance Images, to improve ophthalmic diagnostics and treatments. Past efforts primarily focused on small sample sizes and varied imaging modalities. Here, we leverage a large-scale dataset of T1-weighted MRI of 1245 subjects and the use of the deep learning-based nnU-Net for MR-Eye segmentation tasks. The results showcase robust and accurate 3D segmentations of lens, globe, optic nerve, rectus muscles, and orbital fat. We also present the automated estimation of key ophthalmic morphometry biomarkers such as axial length and volumetry, while benchmarking correlations between body mass index and eye structure volumes. Quality control protocols are introduced through the pipeline to ensure the reliability of the segmented large-scale data, further enhancing the applicability of our algorithm in clinical research. As major outcome we provide the first large-scale unbiased eye atlases (female, male and combined) towards standardization of spatial normalization tools for MR-Eye.

bioengineering↗

bioRxiv2023

Hi-Fi fMRI: High-resolution, fast-sampled and sub-second whole-brain functional MRI at 3T in humans

Functional magnetic resonance imaging (fMRI) is a methodological cornerstone of neuroscience. Most studies measure blood-oxygen-level-dependent (BOLD) signal using echo-planar imaging (EPI), Cartesian sampling, and image reconstruction with a one-to-one correspondence between the number of acquired volumes and reconstructed images. However, EPI schemes are subject to trade-offs between spatial and temporal resolutions. We make strides in overcoming these limitations by measuring BOLD with a gradient recalled echo (GRE) with a 3D radial-spiral phyllotaxis trajectory at a high sampling rate (28.49ms) on standard 3T field strength. The framework enables the reconstruction of 3D signal time courses with whole-brain coverage at simultaneously higher nominal spatial (1mm3) and temporal (up to 250ms) resolutions, as compared to optimized EPI schemes. Additionally, we apply motion correction directly to the k-space raw data, enabling flexible motion-corrected reconstructions; the desired temporal resolution to observe hemodynamic responses can be chosen after scanning. By showing activation in the calcarine sulcus of 20 participants completing an ON-OFF visual paradigm, we demonstrate the reliability of our method for applications in cognitive neuroscience research.

neuroscience↗

bioRxiv2020

Topological Features of Electroencephalography are Reference-Invariant

Electroencephalography (EEG) is among the most widely diffused, inexpensive, and applied neuroimaging techniques. Nonetheless, EEG requires measurements against a reference site(s), which is typically chosen by the experimenter, and specific pre-processing steps precede analysis. It is therefore valuable to obtain quantities that are reference-independent and minimally affected by pre-processing choices. Here, we show that the topological structure of embedding spaces, constructed either from multi-channel EEG timeseries or from their temporal structure, are subject-specific and robust to re-referencing and pre-processing pipelines. By contrast, the shape of correlation spaces, that is, discrete spaces where each point represents an electrode and the distance between them that is in turn related to the correlation between the respective timeseries, were neither significantly subject-specific nor robust to changes of reference. Our results suggest that the shape of spaces describing the observed configurations of EEG signals holds information about the individual specificity of the underlying individuals brain dynamics, and that temporal correlations constrain to a large degree the set of possible dynamics. In turn, these encode the differences between subjects space of resting state EEG signals. Finally, our results and proposed methodology provide tools to explore the individual topographical landscapes and how they are explored dynamically. We propose therefore to augment conventional topographic analyses with an additional - topological - level of analysis, and to consider them jointly. More generally, these results provide a roadmap for the incorporation of topological analyses within EEG pipelines.

neuroscience↗

bioRxiv2020

A psychophysically tuned computational model of human primary visual cortex produces geometric optical illusions

Geometrical optical illusion (GOIs) are mismatches between physical stimuli and perception. GOIs provide an access point to study the interplay between sensation and perception, yet there is scant quantitative investigation of the extent to which different GOIs rely on similar or distinct brain mechanisms. We addressed this knowledge gap. First, 30 healthy adults reported quantitatively their perceptual biases with three GOIs, whose physical properties parametrically varied on a trial-by-trial basis. Biases observed with one GOI were unrelated to those observed with another GOI, suggestive of (partially) distinct underlying mechanisms. Next, we used these psychophysical results to tune a computational model of primary visual cortex that combines parameters of orientation, selectivity, intra-cortical connectivity, and long-range interactions. We showed that similar biases could be generated in-silico, mirroring those observed in humans. Such results provide a roadmap whereby computational modelling, informed by human psychophysics, can reveal likely mechanistic underpinnings of perception.

neuroscience↗

bioRxiv2020

3-Dimensional Magnetic Resonance Imaging of the Freely Moving Human Eye

Abstract Eye motion is a major confound for magnetic resonance imaging (MRI) in neuroscience or ophthalmology. Currently, solutions toward eye stabilisation include participants fixating or administration of paralytics/anaesthetics. We developed a novel MRI protocol for acquiring 3-dimensional images while the eye freely moves. Eye motion serves as the basis for image reconstruction, rather than an impediment. We fully reconstruct videos of the moving eye and head. We quantitatively validate data quality with millimetre resolution in two ways for individual participants. First, eye position based on reconstructed images correlated with simultaneous eye-tracking. Second, the reconstructed images preserve anatomical properties; the eye’s axial length measured from MRI images matched that obtained with ocular biometry. The technique operates on a standard clinical setup, without necessitating specialized hardware, facilitating wide deployment. In clinical practice, we anticipate that this may help reduce burden on both patients and infrastructure, by integrating multiple varieties of assessments into a single comprehensive session. More generally, our protocol is a harbinger for removing the necessity of fixation, thereby opening new opportunities for ethologically-valid, naturalistic paradigms, the inclusion of populations typically unable to stably fixate, and increased translational research such as in awake animals whose eye movements constitute an accessible behavioural readout.Author contributions B.F., L.D.S., M.S., and M.M.M. conceptualised the problem. B.F. and L.D.S. developed, implemented, and tested the protocol. A.M. provided optometry assessments and assisted with eye movement analysis. S.I., D.Z., and M.P.N. assisted with installation of the eye-tracking system within the MRI scanner. J.A.M.B., J.J. and J.Y. contributed with the MRI sequences and compressed sensing framework. B.F., L.D.S. and M.M.M. drafted the manuscript, and all authors contributed to internal review.Competing interests B.F., L.D.S., J.A.M.B., J.Y., M.S., and M.M.M. declare the following competing financial interest: a patent application for the protocol described in this manuscript has been filed (patent application: EP19160832). A.M., S.I., D.Z., M.P.N. and J.J. declare no competing financial interests.Competing Interest StatementB.F., L.D.S., J.A.M.B., J.Y., M.S., and M.M.M. declare the following competing financial interest: a patent application for the protocol described in this manuscript has been filed (patent application: EP19160832). A.M., S.I., D.Z., M.P.N. and J.J. declare no competing financial interests.View Full Text

bioengineering↗