Francesco Grussu

Scopus Publications

Simulation-Informed Evaluation of Microvascular Parameter Mapping for Diffusion MR Imaging of Solid Tumours
Anna Kira Voronova, Olivia Prior, Athanasios Grigoriou, Francesc Salvà, Elena Elez, et al.
Magnetic Resonance in Medicine, 2026
Purpose We aim to inform the design of new diffusion MRI (dMRI) approaches for microvasculature quantification that enhance the biological specificity of imaging towards cancer. Methods We adopted simulation‐informed modelling of the vascular dMRI signal. We synthesised signals from 1500 synthetic vascular networks, for a variety of protocols (flow‐compensated [FC], non‐compensated [NC], hybrid), featuring different samplings and diffusion times. We estimated the number of independent, recoverable signal degrees of freedom in presence of noise (signal‐to‐noise ratio of 5), and ranked 12 microvascular metrics depending on the quality of their estimation. Lastly, we demonstrated the feasibility of estimating the top‐ranking metrics on 3T dMRI of a healthy volunteer and of a metastatic colorectal cancer (CRC) patient. Results Both NC and FC synthetic vascular signals exhibited complex behaviour as, for example, non‐zero kurtosis and diffusion time dependence. Two independent degrees of freedom appeared recoverable from directionally‐averaged vascular signals (SNR of 5). Mean volumetric flow rate and an Apparent Network Branching (ANB) index maximised correlations between ground truth and estimated values in silico. In the patient, both and detected re‐vascularisation after 3 months of targeted therapy against liver metastases, consistently with Intra‐Voxel Incoherent Motion (IVIM) metrics. Conclusions Simulation‐based modelling of the vascular dMRI signal suggests and as the most promising metrics for tissue microvasculature characterisation. Their estimation in vivo appears feasible to capture general trends, and demonstrates contrasts that are biologically plausible, encouraging their usage in future studies.
ESR Essentials: diffusion-weighted MRI—practice recommendations by the European Society for Magnetic Resonance in Medicine and Biology
Marco Palombo, Benedetta Bodini, Francesco Grussu, Denis Le Bihan, Markus Nilsson, et al.
European Radiology, 2026
Diffusion-weighted imaging (DWI) offers critical insights into tissue microstructure through the assessment of water molecule random displacements and plays a central role in the assessment of neoplastic and non-neoplastic diseases. To successfully implement and use DWI in clinical practice, guidelines for acquisition, interpretation of image contrast and of artefacts should be followed, taking the disease process and body part into account. We recommend covering a b-value range of 0–1000 s/mm2 in the brain (along at least six directions for white matter), and 50–800 s/mm2 in the body. Available acquisition acceleration options should be used to reduce repetition time (TR), echo time (TE), and echo-planar imaging (EPI) distortions, while considering the penalty in signal-to-noise ratio (SNR) and image sharpness. DW images and the apparent diffusion coefficient (ADC) map should be read jointly for the clinical interpretation. Areas of slower diffusion are hyperintense on DW images and hypointense on the ADC map, and vice versa. Magnetic susceptibility distortions and signal drop-outs or pile-ups are particularly pronounced at air-tissue or metal-tissue interfaces and may obscure areas of interest or hinder the co-localisation with structural scans. By following these guidelines and recommendations, radiologists and imaging professionals can enhance diagnostic accuracy, reduce variability, and maximise the clinical value of DWI across diverse applications. Key Points This article provides an overview of DWI principles, clinical applications, potential pitfalls, and emerging advances, alongside expert recommendations for optimal implementation. We provide key considerations tailored to specific applications (neuro and whole-body imaging), including protocol optimisation, adherence to established guidelines, and quality assurance measures to minimise artefacts and ensure reproducibility. By following the guidelines and recommendations summarised in this work, radiologists and imaging professionals can enhance diagnostic accuracy, reduce variability, and maximise the clinical value of DWI across diverse applications.
The Sense of Smell (SoS) Atlas: Its Creation and First Application to Investigate COVID-19 Related Anosmia With a Comprehensive Quantitative MRI Protocol
Marta Gaviraghi, Eleonora Lupi, Elena Grosso, Andrea Fusari, Mattia Baiguera, et al.
Journal of Magnetic Resonance Imaging, 2026
BackgroundThe loss of smell (anosmia) has been noted in numerous diseases, including COVID‐19. Inflammatory and microstructural alterations are possible underlying mechanisms of anosmia in COVID‐19. However, no atlas exists to study olfaction and the associated tissue property changes.PurposeTo develop the sense of smell (SoS) atlas, including gray matter regions and white matter tracts of the olfactory circuit, to investigate the underpinnings of COVID‐19 related anosmia.Study TypeRetrospective.SubjectsFor the SoS atlas, high‐resolution tractograms of 10 healthy controls (HC) of the Human Connectome Project (7 females, 22–35 years) were used. The SoS atlas was applied to 8 subjects with persistent anosmia following COVID‐19 (COVID‐P, 7 females, 52 ± 12 years), 19 subjects that recovered from COVID‐19 anosmia (COVID‐R, 14 females, 38 ± 13 years), and 17 HC (8 females, 39 ± 12 years).Field Strength/Sequence3 T, 3D inversion recovery, 3D fast field echo, and spin‐echo echo‐planar imaging sequences.AssessmentTo create the SoS atlas, regions were identified and tracts were extracted via tractography following biological constraints. MRI metrics sensitive to alterations in neuroinflammation, axonal degeneration, myelin and macromolecular density, and iron were analyzed.Statistical TestsRegion‐based analysis (p‐value < 0.05, false discovery rate (FDR) corrected) and voxel‐based analysis (p‐value < 0.001 uncorrected, FDR‐corrected cluster extent = 5 voxels) were performed on 15 multisequence‐MRI metrics between the three groups.ResultsThe SoS atlas consisted of 35 regions and, after anatomical curation, the initial 506 tracts were refined to 78. Compared to HC, COVID‐P presented alterations in neuroinflammation‐related (mean: 41% of total alterations) and axonal degeneration‐related (31%) MRI metrics, while COVID‐R presented alterations of myelin‐related metrics (68%). COVID‐P alterations mainly affected the hindbrain (56%), while COVID‐R the hindbrain (39%).Data ConclusionA novel tool, the SoS atlas, was developed to study the olfactory system and applied in combination with multisequence‐MRI metrics to investigate the mechanisms of COVID‐19 related anosmia.Evidence Level3.Technical EfficacyStage 1.
Broad Utility of Ultrasensitive Analysis of ctDNA Dynamics across Solid Tumors Treated with Immunotherapy
Elena Garralda, Charles Abbott, Alma Calahorro, Oriol Mirallas, Jason Pugh, et al.
Clinical Cancer Research, 2026
Purpose: Prior studies have suggested the biomarker potential of plasma-derived ctDNA in patients with cancer treated with immune checkpoint inhibitors. This study investigated the ability of ctDNA to predict progression-free and overall survival in a cohort of patients with advanced cancer treated with immunotherapies. Experimental Design: In order to characterize the potential role of ultrasensitive ctDNA detection in the management of these patients, we have performed tumor whole-genome sequencing–informed, ultrasensitive ctDNA analysis—tracking approximately 1,800 tumor-specific mutations per patient—in a retrospective cohort (n = 136) and a prospective validation cohort (n = 66) across 24 cancer types treated with immune checkpoint inhibitors alone or in combination with bispecific antibodies or immune cell engagers. Results: Analyzing 1,455 longitudinal samples, we found that ctDNA molecular response measured as early as 3 weeks after treatment initiation correlated with improved progression-free and overall survival, whereas ctDNA clearance at any time strongly correlated with radiologic response and prolonged survival. Additionally, ctDNA dynamics distinguished true progression from pseudoprogression and predicted outcomes in patients receiving continued immunotherapy beyond initial progression. Conclusions: This study highlights the broader applicability of ultrasensitive ctDNA as a biomarker across cancer types and immunotherapy modalities.
Integrating C-reactive protein flare and early MRI dynamics for enhanced prediction of immunotherapy response
Daniel Navarro-Garcia, Francesco Grussu, Christina Zatse, Niklas Klümper, Carlos Macarro, et al.
Journal for Immunotherapy of Cancer, 2025
Background The C-reactive protein (CRP) flare response, an indicator of early immune activation, has emerged as a promising and cost-effective biomarker for predicting response to immune checkpoint inhibitors (ICIs) across various tumor types. This study evaluates the utility of CRP dynamics as a tumor-agnostic biomarker and integrates systemic inflammatory markers with advanced multiparametric MRI metrics to uncover the biological mechanisms underlying the CRP flare phenomenon and its relationship with treatment response. Methods Patients were stratified into three groups based on CRP kinetics: (1) flare-responders, characterized by an initial doubling of baseline CRP followed by a decrease below baseline; (2) CRP responders, defined as patients with no flare increase but a CRP reduction of at least 30% below baseline and (3) CRP non-responders. Multiparametric MRI was performed at baseline, early (1–3 weeks), and intermediate (6–8 weeks) time points to assess tumor size and microstructural features, including cell density and vascularization. Clinical benefit and survival outcomes, including progression-free survival (PFS) and overall survival (OS), were analyzed using Kaplan-Meier curves and log-rank tests. Cox regression analyses were performed to identify independent predictors of clinical outcomes, while intergroup differences in MRI metrics were assessed using Wilcoxon rank-sum and Kruskal-Wallis tests. Results Among the 121 evaluable patients with solid tumors enrolled in the PREDICT trial, CRP flare-responders demonstrated significantly longer PFS (5.6 months) and OS (12.1 months) compared with responders (PFS: 3.4 months, OS: 8.0 months) and non-responders (PFS: 3.2 months, OS: 6.7 months; p=0.01 and p<0.01, respectively). Additionally, clinical benefit was achieved in 50% of flare-responders, compared with 13% of responders (p=0.05) and 23% of non-responders (p<0.01). Tumor growth was interrupted early after treatment initiation in CRP flare-responders, whereas non-responders exhibited marked increases in tumor size. In the pilot subset of 33 patients with MRI data, diffusion MRI revealed stable or increased apparent diffusion coefficient values in CRP flare-responders, indicative of reduced tumor cellularity just after 1–3 weeks of treatment. Conclusions This study highlights the potential of combining early CRP dynamics with non-invasive imaging metrics to identify ICI responders as early as 2 weeks after treatment initiation. By integrating systemic inflammatory biomarkers with MRI-derived insights into tumor size and microstructural changes, these findings optimize therapeutic strategies and advance understanding of immunotherapy-driven tumor dynamics.
Evaluation of magnetic resonance spectroscopy total sodium concentration measures, and associations with microstructure and physical impairment in cervical myelopathy
Bhavana S. Solanky, Ferran Prados, Carmen Tur, Francesco Grussu, Selma Al-Ahmad, et al.
Scientific Reports, 2025
Spinal cord injury causes a cascade of physiological responses, which may trigger a subsequent neurotoxic increase in intracellular sodium. This can lead to neurodegeneration, both at and beyond the site of injury, causing clinical symptoms and loss of function. However, in vivo measurements of tissue sodium remain challenging. Here we utilise sodium magnetic resonance spectroscopy (23Na-MRS) at 3T to measure tissue sodium concentration (TSC) and its association with microstructural measures and macromolecular MRI metrics in the cervical spinal cord, distal to the site of injury. Twenty people with cervical myelopathy and twenty healthy controls, were studied. Associations with motor and sensory impairments were explored using ASIA and jOAMEQ scores. No significant difference in TSC in the cervical myelopathy group (39 ± 10 mM) relative to healthy controls (35 ± 13 mM) was found. However, patients had a significantly lower cord-cross-sectional area than controls (70 ± 9 mm2 vs. 82 ± 9 mm2, p < 0.001). Lower-extremity function positively correlated with intracellular volume fraction (p = 0.031). In conclusion, using 23Na-MRS, TSC in cervical myelopathy patients was successfully measured. Differences in TSC relative to healthy controls did not reach significance, despite a significant reduction in cord-cross-sectional area. However, lower intracellular volume fraction, indicating reduced neurite density distal to the site of injury, was associated with physical impairment.
Histology-informed microstructural diffusion simulations for MRI cancer characterisation—the Histo-μSim framework
Athanasios Grigoriou, Carlos Macarro, Marco Palombo, Daniel Navarro-Garcia, Anna Kira Voronova, et al.
Communications Biology, 2025
Clinically feasible liver tumour cell size measurement through histology-informed in vivo diffusion MRI
Francesco Grussu, Athanasios Grigoriou, Kinga Bernatowicz, Marco Palombo, Irene Casanova-Salas, et al.
Communications Medicine, 2025
Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3—Ex vivo imaging: Data processing, comparisons with microscopy, and tractography
Kurt G. Schilling, Amy F. D. Howard, Francesco Grussu, Andrada Ianus, Brian Hansen, et al.
Magnetic Resonance in Medicine, 2025
Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non‐invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting‐edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three‐part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre‐processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open‐source software and databases specific to small animal and ex vivo imaging.
Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging
Ileana O. Jelescu, Francesco Grussu, Andrada Ianus, Brian Hansen, Rachel L. C. Barrett, et al.
Magnetic Resonance in Medicine, 2025
Small‐animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model‐fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2—Ex vivo imaging: Added value and acquisition
Kurt G. Schilling, Francesco Grussu, Andrada Ianus, Brian Hansen, Amy F. D. Howard, et al.
Magnetic Resonance in Medicine, 2025
Body size and intracranial volume interact with the structure of the central nervous system: A multi-center in vivo neuroimaging study
René Labounek, Monica T. Bondy, Amy L. Paulson, Sandrine Bédard, Mihael Abramovic, et al.
Imaging Neuroscience, 2025
SpinFlowSim: A blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer
Anna Kira Voronova, Athanasios Grigoriou, Kinga Bernatowicz, Sara Simonetti, Garazi Serna, et al.
Medical Image Analysis, 2025
Enhancing Tumor Microstructural Quantification With Machine Learning and Diffusion-Relaxation MRI
Carlos Macarro, Kinga Bernatowicz, Alonso Garcia‐Ruiz, Garazi Serna, Camilo Monreal‐Agüero, et al.
Journal of Magnetic Resonance Imaging, 2025
Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction
Kinga Bernatowicz, Ramon Amat, Olivia Prior, Joan Frigola, Marta Ligero, et al.
Journal for Immunotherapy of Cancer, 2025
Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology
Ella Fokkinga, Juan A. Hernandez‐Tamames, Andrada Ianus, Markus Nilsson, Chantal M. W. Tax, et al.
Journal of Magnetic Resonance Imaging, 2024
Whole-body Magnetic Resonance Imaging as a Treatment Response Biomarker in Castration-resistant Prostate Cancer with Bone Metastases: The iPROMET Clinical Trial
Alonso Garcia-Ruiz, Carlos Macarro, Francesca Zacchi, Rafael Morales-Barrera, Francesco Grussu, et al.
European Urology, 2024
Investigating the relationship between thalamic iron concentration and disease severity in secondary progressive multiple sclerosis using quantitative susceptibility mapping: Cross-sectional analysis from the MS-STAT2 randomised controlled trial
Thomas Williams, Nevin John, Alberto Calvi, Alessia Bianchi, Floriana De Angelis, et al.
Neuroimage Reports, 2024
Erratum for: Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer (Radiology: Artificial Intelligence 2024;6(2) https://doi.org/10.1148/ryai.230118)
Olivia Prior, Carlos Macarro, Víctor Navarro, Camilo Monreal, Marta Ligero, et al.
Radiology Artificial Intelligence, 2024
What contributes to disability in progressive MS? A brain and cervical cord–matched quantitative MRI study
Carmen Tur, Marco Battiston, Marios C Yiannakas, Sara Collorone, Alberto Calvi, et al.
Multiple Sclerosis Journal, 2024
An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI
Alonso Garcia-Ruiz, Albert Pons-Escoda, Francesco Grussu, Pablo Naval-Baudin, Camilo Monreal-Aguero, et al.
Cell Reports Medicine, 2024
Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer
Olivia Prior, Carlos Macarro, Víctor Navarro, Camilo Monreal, Marta Ligero, et al.
Radiology Artificial Intelligence, 2024
Feasibility of in vivo multi-parametric quantitative magnetic resonance imaging of the healthy sciatic nerve with a unified signal readout protocol
Ratthaporn Boonsuth, Marco Battiston, Francesco Grussu, Christina Maria Samlidou, Alberto Calvi, et al.
Scientific Reports, 2023
Multimodal Analysis of the Visual Pathways in Friedreich's Ataxia Reveals Novel Biomarkers
Gilbert Thomas‐Black, Daniel R. Altmann, Harry Crook, Nita Solanky, Ferran Prados Carrasco, et al.
Movement Disorders, 2023
Differentiating Multiple Sclerosis From AQP4-Neuromyelitis Optica Spectrum Disorder and MOG-Antibody Disease With Imaging
Rosa Cortese, Ferran Prados Carrasco, Carmen Tur, Alessia Bianchi, Wallace Brownlee, et al.
Neurology, 2023
A deep learning network from downsampled diffusion-weighted MRI k-space to image-space
Convegno Nazionale Di Bioingegneria, 2023
Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset
Antonio Ricciardi, Francesco Grussu, Baris Kanber, Ferran Prados, Marios C. Yiannakas, et al.
Frontiers in Neuroinformatics, 2023
Myelin quantification in Magnetic Resonance Imaging
Convegno Nazionale Di Bioingegneria, 2023
Multi-echo quantitative susceptibility mapping: how to combine echoes for accuracy and precision at 3 Tesla
Emma Biondetti, Anita Karsa, Francesco Grussu, Marco Battiston, Marios C. Yiannakas, et al.
Magnetic Resonance in Medicine, 2022
SENSE EPI reconstruction with 2D phase error correction and channel-wise noise removal
Elizabeth Powell, Torben Schneider, Marco Battiston, Francesco Grussu, Ahmed Toosy, et al.
Magnetic Resonance in Medicine, 2022
Diffuse Large B-Cell Epstein-Barr Virus-Positive Primary CNS Lymphoma in Non-AIDS Patients: High Diagnostic Accuracy of DSC Perfusion Metrics
A. Pons-Escoda, A. García-Ruíz, P. Naval-Baudin, F. Grussu, M. Viveros, et al.
American Journal of Neuroradiology, 2022
Non-invasive biomarkers for response and survival prediction in patients with advanced solid tumours treated with immune checkpoint inhibitors (ICIs)
K. Bernatowicz, M. Vieito, R. Berche, G. Alonso, V. Galvao, et al.
European Journal of Cancer, 2022
Comparison of multicenter MRI protocols for visualizing the spinal cord gray matter
Julien Cohen‐Adad, Eva Alonso‐Ortiz, Stephanie Alley, Maria Marcella Lagana, Francesca Baglio, et al.
Magnetic Resonance in Medicine, 2022
Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology
Francesco Grussu, Kinga Bernatowicz, Irene Casanova‐Salas, Natalia Castro, Paolo Nuciforo, et al.
Magnetic Resonance in Medicine, 2022
Voxel-level analysis of normalized DSC-PWI time-intensity curves: a potential generalizable approach and its proof of concept in discriminating glioblastoma and metastasis
Albert Pons-Escoda, Alonso Garcia-Ruiz, Pablo Naval-Baudin, Francesco Grussu, Juan Jose Sanchez Fernandez, et al.
European Radiology, 2022
Histo-MRI map study protocol: a prospective cohort study mapping MRI to histology for biomarker validation and prediction of prostate cancer
Saurabh Singh, Manju Mathew, Thomy Mertzanidou, Shipra Suman, Joey Clemente, et al.
BMJ Open, 2022
Progressive Subsampling for Oversampled Data - Application to Quantitative MRI
Stefano B. Blumberg, Hongxiang Lin, Francesco Grussu, Yukun Zhou, Matteo Figini, et al.
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2022
Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique
Carmen Tur, Francesco Grussu, Floriana De Angelis, Ferran Prados, Baris Kanber, et al.
Neuroimage Clinical, 2022
Robust imaging habitat computation using voxel-wise radiomics features
Kinga Bernatowicz, Francesco Grussu, Marta Ligero, Alonso Garcia, Eric Delgado, et al.
Scientific Reports, 2021
Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers (Scientific Data, (2021), 8, 1, (219), 10.1038/s41597-021-00941-8)
Julien Cohen-Adad, Eva Alonso-Ortiz, Mihael Abramovic, Carina Arneitz, Nicole Atcheson, et al.
Scientific Data, 2021
Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers
Julien Cohen-Adad, Eva Alonso-Ortiz, Mihael Abramovic, Carina Arneitz, Nicole Atcheson, et al.
Scientific Data, 2021
Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers (Scientific Data, (2021), 8, 1, (219), 10.1038/s41597-021-00941-8)
Julien Cohen-Adad, Eva Alonso-Ortiz, Mihael Abramovic, Carina Arneitz, Nicole Atcheson, et al.
Scientific Data, 2021
Assessing Lumbar Plexus and Sciatic Nerve Damage in Relapsing-Remitting Multiple Sclerosis Using Magnetisation Transfer Ratio
Ratthaporn Boonsuth, Rebecca S. Samson, Carmen Tur, Marco Battiston, Francesco Grussu, et al.
Frontiers in Neurology, 2021
Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging
Francesco Grussu, Stefano B. Blumberg, Marco Battiston, Lebina S. Kakkar, Hongxiang Lin, et al.
Frontiers in Physics, 2021
Diffusion-Weighted Imaging: Recent Advances and Applications
Eloy Martinez-Heras, Francesco Grussu, Ferran Prados, Elisabeth Solana, Sara Llufriu
Seminars in Ultrasound CT and MRI, 2021
Generic acquisition protocol for quantitative MRI of the spinal cord
Julien Cohen-Adad, Eva Alonso-Ortiz, Mihael Abramovic, Carina Arneitz, Nicole Atcheson, et al.
Nature Protocols, 2021
Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis
Daniel Johnson, Antonio Ricciardi, Wallace Brownlee, Baris Kanber, Ferran Prados, et al.
Frontiers in Neurology, 2021
Brain microstructural and metabolic alterations detected in vivo at onset of the first demyelinating event
Sara Collorone, Ferran Prados, Baris Kanber, Niamh M Cawley, Carmen Tur, et al.
Brain, 2021
Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI
Ryutaro Tanno, Daniel E. Worrall, Enrico Kaden, Aurobrata Ghosh, Francesco Grussu, et al.
Neuroimage, 2021
Deep Learning Model Fitting for Diffusion-Relaxometry: A Comparative Study
Francesco Grussu, Marco Battiston, Marco Palombo, Torben Schneider, Claudia A. M. Gandini Wheeler-Kingshott, et al.
Mathematics and Visualization, 2021
Reduced neurite density in the brain and cervical spinal cord in relapsing–remitting multiple sclerosis: A NODDI study
Sara Collorone, Niamh Cawley, Francesco Grussu, Ferran Prados, Francesca Tona, et al.
Multiple Sclerosis Journal, 2020
Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results
Lipeng Ning, Elisenda Bonet-Carne, Francesco Grussu, Farshid Sepehrband, Enrico Kaden, et al.
Neuroimage, 2020
Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising
Francesco Grussu, Marco Battiston, Jelle Veraart, Torben Schneider, Julien Cohen-Adad, et al.
Neuroimage, 2020
A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis
Carmen Tur, Francesco Grussu, Ferran Prados, Thalis Charalambous, Sara Collorone, et al.
Multiple Sclerosis Journal, 2020
Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy
Ferran Prados, Marcello Moccia, Aubrey Johnson, Marios Yiannakas, Francesco Grussu, et al.
Neuroimage, 2020
Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge
Marco Pizzolato, Marco Palombo, Elisenda Bonet-Carne, Chantal M. W. Tax, Francesco Grussu, et al.
Mathematics and Visualization, 2020
Fast bound pool fraction mapping via steady-state magnetization transfer saturation using single-shot EPI
Marco Battiston, Torben Schneider, Francesco Grussu, Marios C. Yiannakas, Ferran Prados, et al.
Magnetic Resonance in Medicine, 2019
Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms
Chantal MW. Tax, Francesco Grussu, Enrico Kaden, Lipeng Ning, Umesh Rudrapatna, et al.
Neuroimage, 2019
Relevance of time-dependence for clinically viable diffusion imaging of the spinal cord
Francesco Grussu, Andrada Ianuş, Carmen Tur, Ferran Prados, Torben Schneider, et al.
Magnetic Resonance in Medicine, 2019
Spatial Characterisation of Fibre Response Functions for Spherical Deconvolution in Multiple Sclerosis
Carmen Tur, Francesco Grussu, Ferran Prados, Sara Collorone, Claudia A. M. Gandini Wheeler-Kingshott, et al.
Mathematics and Visualization, 2019
Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC): Progress and Results
Lipeng Ning, Elisenda Bonet-Carne, Francesco Grussu, Farshid Sepehrband, Enrico Kaden, et al.
Mathematics and Visualization, 2019
Structural cortical network reorganization associated with early conversion to multiple sclerosis
C. Tur, A. Eshaghi, D. R. Altmann, T. M. Jenkins, F. Prados, et al.
Scientific Reports, 2018
An optimized framework for quantitative magnetization transfer imaging of the cervical spinal cord in vivo
Marco Battiston, Francesco Grussu, Andrada Ianus, Torben Schneider, Ferran Prados, et al.
Magnetic Resonance in Medicine, 2018
Fast and reproducible in vivo T1 mapping of the human cervical spinal cord
Marco Battiston, Torben Schneider, Ferran Prados, Francesco Grussu, Marios C. Yiannakas, et al.
Magnetic Resonance in Medicine, 2018
Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?
Francesco Grussu, Torben Schneider, Carmen Tur, Richard L. Yates, Mohamed Tachrount, et al.
Annals of Clinical and Translational Neurology, 2017
Spinal cord grey matter segmentation challenge
Ferran Prados, John Ashburner, Claudia Blaiotta, Tom Brosch, Julio Carballido-Gamio, et al.
Neuroimage, 2017
A framework for optimal whole-sample histological quantification of neurite orientation dispersion in the human spinal cord
Francesco Grussu, Torben Schneider, Richard L. Yates, Hui Zhang, Claudia A.M. Gandini Wheeler-Kingshott, et al.
Journal of Neuroscience Methods, 2016
Reduced field-of-view diffusion-weighted imaging of the lumbosacral enlargement: A pilot in vivo study of the healthy spinal cord at 3t
Marios C. Yiannakas, Francesco Grussu, Polymnia Louka, Ferran Prados, Rebecca S. Samson, et al.
Plos One, 2016
Bayesian image quality transfer
Ryutaro Tanno, Aurobrata Ghosh, Francesco Grussu, Enrico Kaden, Antonio Criminisi, et al.
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2016
Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo
Francesco Grussu, Torben Schneider, Hui Zhang, Daniel C. Alexander, Claudia A.M. Wheeler–Kingshott
Neuroimage, 2015

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