Simona Schiavi^1,2, Mario Ocampo-Pineda¹, Raphaël Truffet³, Emmanuel Caruyer³, Alessandro Daducci¹, and Marco Palombo^4,5,6
1Department of Computer Science, University of Verona, Verona, Italy, ²Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy, ³CNRS, Inria, Inserm, Univ Rennes, Rennes, France, ⁴Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, United Kingdom, ⁵Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, ⁶School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

The soma and neurite density imaging (SANDI) model has been recently introduced to estimate diffusion MRI indices of apparent neurite and soma density noninvasively in the brain. Here, we introduce SANDI_AMICO a fast (10 seconds for whole brain images) and robust implementation for estimating SANDI parameters using the Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework. Using numerical simulations and in vivo human data, we show excellent performances of SANDI_AMICO in terms of accuracy, precision, robustness to noise and intra-subject reproducibility.

Céline Provins¹, Christopher J. Markiewicz², Rastko Ciric², Mathias Goncalves², Cesar Caballero-Gaudes³, Russell A. Poldrack², Patric Hagmann¹, and Oscar Esteban^1
1Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ²Department of Psychology, Stanford University, Stanford, CA, US, Stanford, CA, United States, ³Basque Center on Cognition, Brain and Language, Donostia, Spain, Donostia, Spain

Quality control of functional MRI data is essential as artifacts can have a critical impact on subsequent analysis. Yet, visual assessment of a dataset is tedious and time-consuming. By extending the carpet plot with the voxels located on a closed band (or “crown”) around the brain, we showed that fMRI data quality can be assessed more effectively. This new feature has been incorporated into MRIQC and fMRIPrep. In addition, a new nuisance regressor has been added to the latter, calculated from timeseries within this new “crown”.

Wilburn E Reddick¹, Jordan Teague¹, Ruitian Song¹, John O Glass¹, and Lisa M Jacola^2
1Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States, ²Psychology, St. Jude Children's Research Hospital, Memphis, TN, United States

Heatmap differences between 336 patients treated for ALL and 59 age-similar controls were calculated for a limited set of reproducible edges and clustered using an agglomerative hierarchical clustering that identified four clusters with unique connectivity. Differences in edges demonstrated patterns of lower (group 2), higher (group 4), or mixed (group 1 & 3) connectivity relative to controls. Neurocognitive performance was substantially below normal on measures of memory (DSF – groups 2 & 3; DSB - group 1; Visual matching – group 2). This data driven approach was able to identify four distinct groups of patients with unique connectivity profiles and cognitive performance.

María Paula Del Pópolo^1,2, Leandro Enrique Salcedo^1,2,3, Clara Lisazo^1,2, Federico Julián González Nicolini^2,4,5, Trinidad González Padin^1,2, Rodrigo Nahuel Alcalá Marañón^1,2,6, Rocío Paula Boschi^2,7, Mercedes Paula Caspi^2,7, Ezequiel F Petra⁸, Pedro Pablo Ariza^1,2, Valdir Fialkowski⁹, and Daniel Fino Villamil^1,2,5
1MRI Department, Fundación Argentina para el Desarrollo en Salud, Mendoza, Argentina, ²MRI Department, Fundación Escuela de Medicina Nuclear, Mendoza, Argentina, ³FI, Universidad de Mendoza, Mendoza, Argentina, ⁴FGQNYCS, Comisión Nacional de Energía Atómica, Buenos Aires, Argentina, ⁵Instituto Balseiro, Universidad Nacional de Cuyo, San Carlos de Bariloche, Argentina, ⁶FCEN, Universidad Nacional de Cuyo, Mendoza, Argentina, ⁷MRI Department, Fundación Argentina para el Desarrollo en Salud, Mendoza, Argentina, ⁸Hemodynamics, Fundación Argentina para el Desarrollo en Salud, Mendoza, Argentina, ⁹Research Department, Philips Healthcare, Barueri/San Paulo, Brazil

The abscence of an angiographic functional analysis of the head and neck regions opens the possibility of applying the 4D Flow technique to quantify hemodynamics and blood flow parameters. Through a Python algorithm using Finite Elements Method the dynamic blood behavior was analyzed, obtaining a complete correlation with the clinical diagnosis. 

Andrew Hoopes¹, Jocelyn S. Mora¹, Adrian Dalca^1,2,3, Bruce Fischl*^1,2,3, and Malte Hoffmann*^1,2
1Martinos Center for Biomedical Imaging, Boston, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States

The removal of non-brain signal from MR data is an integral component of neuroimaging streams. However, popular skull-stripping utilities are typically tailored to isotropic T1-weighted scans and tend to fail, sometimes catastrophically, on images with other MRI contrasts or stack-of-slices acquisitions that are common in the clinic. We propose SynthStrip, a flexible tool that produces highly accurate brain masks across a landscape of neuroimaging data with widely varying contrast and resolution. We implement our method by leveraging anatomical label maps to synthesize a broad set of training images, optimizing a robust convolutional network agnostic to MRI contrast and acquisition scheme.

Marina Manso Jimeno^1,2, John Thomas Vaughan Jr.^1,2, and Sairam Geethanath^2
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, ²Columbia Magnetic Resonance Research Center, New York, NY, United States

Spiral trajectories are suitable for fMRI as they provide high temporal resolution and motion tolerance. However, they are not typically available on scanners as the associated reconstruction is more involved. We have demonstrated the feasibility of an open-source spiral sequence end-to-end pipeline and compared it to the vendor-supplied EPI sequence. Although the spiral images showed signal loss near the frontal sinus, both datasets presented similar image quality and contrast. Importantly, the spiral sequence had a 2.3-fold advantage on temporal SNR and achieved analogous functional connectivity maps of the DMN, demonstrating the sequence BOLD sensitivity and viability.

 

Rafael Neto Henriques¹, Sune Nørhøj Jespersen^2,3, and Noam Shemesh^1
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, ²Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Clinical Institute, Aarhus University, Aarhus, Denmark, ³Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark

PCA denoising based on the Marchenko-Pastur (MP) distribution has become the state-of-the-art procedure to suppress thermal noise in multi-dimensional MRI. Here we developed a Hybrid-PCA strategy that combines a-priori noise variance estimation and the random matrix theory for PCA eigenvalue classification, to overcome shortcomings of contemporary MP-PCA denoising. Our results show that, while the MP-PCA denoising fails to classify the noise PCA components in data with spatially correlated noise, the Hybrid-PCA algorithm maintains its denoising performance. The Hybrid-PCA denoising can thus be a useful procedure for data corrupted by spatially correlated noise, as typically arises in vendor reconstructed data.

Alex A Bhogal^1
1Radiology, UMC Utrecht, Utrecht, Netherlands

The seeVR toolbox is a series of Matlab functions designed help analyze CVR through rapid implementation of processing pipelines. Depending on the type of stimulus used and the properties of the data acquisition, a number of options are available. All code is open-source and freely downloadable from the seeVR github repository (https://github.com/abhogal-lab/seeVR).

Marcel Zwiers¹, Cyril Pernet², Anthony Galassi³, and Robert Oostenveld^1,4
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, ²Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark, ³Center for Multimodal Neuroimaging, National Institute of Mental Health, Bethesda, MD, United States, ⁴NatMEG, Karolinska Institutet, Stockholm, Sweden

Sharing neuroimaging data is an important development that has the potential to be scaled up with the new Brain Imaging Data Structure (BIDS) standard. Existing tools to converting data to BIDS often require programming skills or are tailored to specific institutes, datasets or data formats. Here we introduce BIDScoin, a cross-platform, flexible, free and open-source converter that provides a graphical user interface to help users finding their way in the BIDS standard, and supports plugins to extend its functionality. We show its design and demonstrate how it can be applied to a downloadable tutorial dataset.

Petra J van Houdt¹, Sudarshan Ragunathan², Michael Berks³, Zaki Ahmed⁴, Lucy Kershaw⁵, Oliver Gurney-Champion⁶, Sirisha Tadimalla⁷, Jesper Kallehauge⁸, Jonathan Arvidsson⁹, Ben Dickie¹⁰, Simon Lévy¹¹, Laura Bell¹², Steven Sourbron¹³, and Michael Jonathan Thrippleton^14
1Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, Netherlands, ²Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States, ³Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom, ⁴Radiology, Mayo Clinic, Rochester, MN, United States, ⁵Edinburgh Imaging and Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom, ⁶Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands, ⁷Institute of Medical Physics, The University of Sydney, Sydney, Australia, ⁸Department of Oncology, Aarhus University, Aarhus, Denmark, ⁹Institute of Clinical Sciences, Department of Radiation Sciences, The University of Gothenburg, Gothenburg, Sweden, ¹⁰Manchester Academic Health Science Centre, Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom, ¹¹Institute of Radiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, ¹²Clinical Imaging, Genentech, Inc, South San Francisco, CA, United States, ¹³Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom, ¹⁴Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom

A lack of validated, open-source code reduces the reliability of perfusion MRI, resulting in duplicate development. To address this problem, the Open Science Initiative for Perfusion Imaging (OSIPI) established a taskforce to collect, validate and harmonise such code. To date, 74 code contributions have been collected, with 14 of these tested. Source code and tests are published in an open-access repository. The OSIPI DCE/DSC-MRI code collection constitutes a valuable resource for researchers, and will ultimately be developed into a standardised, community-driven open-source code library.

Michiel Cottaar¹, Nicole Eichert¹, Hossein Rafipoor¹, Amy Howard¹, Daniel Kor¹, Jiewon Kang¹, Pavan Chaggar^1,2, Ying-Qiu Zheng¹, Sean Fitzgibbon¹, and Saad Jbabdi^1
1University of Oxford, OXFORD, United Kingdom, ²Mathematics, University of Oxford, Oxford, United Kingdom

We introduce FSL-DIVE, an interactive visualisation software for diffusion microstructure modelling. This software allows the user to (i) flexibly set up a multi-compartment microstructure model, including fibre orientation modelling and restricted diffusion, (ii) choose different acquisition parameters, (iii) change the model parameters and analyse the model behaviour through multiple bespoke plots, (iv) save the predicted data (including noise) for any specified acquisition scheme. We hope this tool may be useful for teaching diffusion microstructure modelling, for developing new models, or for getting a better understanding of existing models.