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David Schote^1,2, Johannes Behrens², Lukas Winter¹, Christoph Kolbitsch¹, and Christoph Dinh^2
1Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, ²Brain-Link UG (haftungsbeschränkt), Landau i.d. Pfalz, Germany

Keywords: Software Tools, Software Tools

ScanHub (https://github.com/brain-link/scanhub-ui) is an open, generic solution for cloud-based medical data acquisition and processing. Functionalities are subdivided into microservices supporting use cases in the clinical as well as in the research context. The platform capabilities are demonstrated with an exemplary MRI workflow.  As a proof of principle MR data acquisition was simulated with an open-source Bloch solver. A reconstruction of the simulated raw data is provided by a microservice. The whole acquisition process is controlled via a web-based UI; from deploying a pulse sequence to the organization and visualization of reconstructed and DICOMized results.

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Stefano Moia^1,2, Julia Brügger^1,3, Philip Egger^1,3, Giorgia Giulia Evangelista^1,3, Friedhelm Cristoph Hummel^1,3,4, Maria Giulia Preti^1,2, and Dimitri Van De Ville^1,2
1Neuro-X Institute, Ecole polytechnique fédérale de Lausanne, Geneva, Switzerland, ²Department of Radiology and Medical Informatics (DRIM), Faculty of Medicine, University of Geneva, Geneva, Switzerland, ³Neuro-X Institute, EPFL Valais, Clinique Romande de Réadaptation, Sion, Switzerland, ⁴Department of Clinical Neurosciences, Geneva University Hospital (HUG), Geneva, Switzerland

Keywords: Software Tools, Brain Connectivity

NiGSP is a python-based toolbox aimed at facilitating the adoption of graph signal processing with an emphasis on multimodal brain imaging data. We present its standard workflow, that allows basic filtering operations and metrics computations, and we introduce a novel application to cerebral stroke consisting in the creation of a subject-specific anatomical lesion-based filter to be applied on functional MRI timeseries.

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Jonathan Weine¹, Charles McGrath¹, and Sebastian Kozerke^1
1University and ETH Zurich, Zurich, Switzerland

Keywords: Software Tools, Simulations, Cardiovascular

We present CMRsim, a new Python package for MR simulations efficiently incorporating complex organ motion and flow. The MR signal can be calculated using both Bloch simulations as well as analytical signal models. The package leverages TensorFlow2 for GPU acceleration and thereby facilitates fast simulations while also providing a compilation-free framework for prototyping and community contributions. Significant effort has been put on maintainability and software engineering best practices. The API reference as well as information on installation and how to get started is publicly available at: https://people.ee.ethz.ch/~jweine/cmrsim/latest/index.html

Keywords: Open-source, Simulation, Cardiovascular, Motion, Reproducible science, Digital twin

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Bilal Tasdelen¹, Rajiv Ramasawmy², Kathryn E Keenan³, Adrienne Campbell-Washburn², and Krishna S Nayak^1
1Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, ²Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States, ³NIST: National Institute of Standards and Technology, Boulder, CO, United States

Keywords: Software Tools, Software Tools, open-source

We demonstrate quantitative T1 and T2 mapping tools, built from open-source components Pulseq and qMRLab, suitable for multi-center multi-platform studies. The end-to-end solution includes acquisition, reconstruction, ROI analysis, and repeatability testing. The tools are demonstrated in the context of 0.55T relaxometry at two different sites and three scanners with two different hardware and software specifications. Relaxometry results are validated against results derived from vendor-provided sequences.

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Gehua Tong¹, Rishi Ananth², Jason Stockmann³, Vlad Negnevitsky⁴, Benjamin Menküc⁵, Akbar Alipour⁶, John Thomas Vaughan, Jr.^1,7, Sairam Geethanath^7,8, Gaurav Verma⁹, and Gaurav Verma^9
1Biomedical Engineering, Columbia University, New York, NY, United States, ²College of Arts and Sciences, University of Washington, Seattle, WA, United States, ³A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Oxford Ionics Ltd, Oxford, United Kingdom, ⁵University of Applied Sciences and Arts Dortmund, Dortmund, Germany, ⁶BioMedical Engineering and Imaging Institute, Dept. of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mt. Sinai, New York, NY, United States, ⁷Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States, ⁸Accessible MR Laboratory, BioMedical Engineering and Imaging Institute, Dept. of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mt. Sinai, New York, NY, United States, ⁹Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Keywords: Software Tools, Simulations, Education

Access to MRI in low-resource regions is often limited by the lack of local expertise required to sustain long-term MR operation. To help develop interest and understanding in a wider audience from a younger age, we developed Virtual Scanner Games. This web application can be distributed by USBs to run locally and provides eight educational interactive games to introduce MR fundamentals to students at the high school level or higher.
 

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Christian Tönnes¹, Christian Licht¹, Lothar R. Schad¹, and Frank G. Zöllner^1
1Chair for Computer Assisted Clinical Medicine, MIiSM, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

Keywords: Software Tools, Simulations, Education

SimMRI is a Web-Based MRI simulator developed for teaching students. It allows the simulation of multiple sequences for 1H and 23Na imaging on different brain datasets. The software enables users to customize parameters for each sequence, add noise, or change the voxel count for every dimension of the image. Additionally, the software provides  compressed sensing reconstruction with three k-space subsampling schemes. The computation is solely performed on the client and therefore well suited for large student groups. The code, written in JavaScript, HTML, CSS, and c compiled to WebASM, is open source and supports easy inclusion of new sequences.

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Alia Khaled^1,2, Ahmed Bayoumi¹, Joseph Thomas¹, Refaat Gabr³, Khader Hasan³, Jerry Wolinsky¹, and John Lincoln^1
1Department of Neurology, University of Texas Health Sciences Center, Houston, TX, United States, ²Department of Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States, ³Department of Diagnostic and Interventional Imaging, University of Texas Health Sciences Center, Houston, TX, United States

Keywords: Software Tools, Data Processing, Pipeline

Neurologists and researchers rely on several software tools for neuroimaging data analysis. Limitations in these tools often necessitate the use of more than one program, spanning different coding languages and operating systems, which requires time, effort, and programming skills. MRI-PAP is a collection of pipelines with an easy-to-use graphical user interface (GUI) designed for MRI processing and analysis of multiple sclerosis patient images. These automated pipelines accept DICOM images as input and take them through a series of processing steps. MRI-PAP pipelines save time by accessing different packages from a single GUI and can be used without programming skills.

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Jonathan Weine¹, Charles McGrath¹, and Sebastian Kozerke^1
1University and ETH Zurich, Zurich, Switzerland

Keywords: Software Tools, Pulse Sequence Design

We present CMRseq, a new python package for MR sequence definitions. The package builds upon successful concepts of previous frameworks, while leveraging Python functionality to increase usability and maintainability. CMRseq features physical unit checks, adheres to coding style conventions and includes unit-test coverage and continuous integration infrastructure for documentation. Import and export functionality to facilitate compatibility with popular formats is also included. The API reference as well as information on installation and how to get started is publicly available at: https://people.ee.ethz.ch/~jweine/cmrseq/latest/index.html

Keywords: Open source, reproducible science, sequence design, software tools, visualization

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Katsumi Kose¹, Ryoichi Kose¹, Daiki Tamada², and Utaroh Motosugi^3
1MRIsimulations Inc., Tokyo, Japan, ²University of Yamanashi, Chuo, Japan, ³Kofu Kyoritsu Hospital, Kofu, Japan

Keywords: Software Tools, Motion Correction, motion simulation

To investigate the possibility and limitation of MRI simulations based on the Lagrange description, MRI experiments and simulations of moving objects were performed. As a result, we were able to obtain simulated MR images that almost reproduced the experimental results within practical computation time. However, it was found that the T2 coherence effect should be reduced for moving objects. It was also found that more precise simulation is necessary to reproduce the detailed motion artifacts.

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Jun-Hyeok Lee¹, Hyeong-Geol Shin^2,3, Minjun Kim², Jongho Lee², and Se-Hong Oh^1
1Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea, Republic of, ²Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, ³Johns Hopkins University School of Medicine & Kennedy Krieger Institute, Baltimore, MD, United States

Keywords: Software Tools, Quantitative Susceptibility mapping, Chi-separation

The susceptibility maps generated by QSM and $$$\chi$$$-separation show magnetic susceptibility distributions, which have been proposed as important biomarkers for brain disorders. However, susceptibility mapping requires a complicated multi-step procedure that is difficult for inexperienced researchers. There is a need to design a convenient application that can easily conduct susceptibility mapping. In this work, we developed a MATLAB based GUI software, called the ‘$$$\chi$$$-separation toolbox’ that generates high-quality susceptibility maps in just a few clicks. The GUI of our toolbox is intuitive and user-friendly that it helps researchers to conduct QSM and $$$\chi$$$-separation processing without difficulty.

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Jia Xu¹, Baolian Yang², Douglas Kelley², and Vincent A. Magnotta^1,3,4
1Radiology, University of Iowa, Iowa City, IA, United States, ²GE Healthcare, Waukesha, WI, United States, ³Psychiatry, University of Iowa, Iowa City, IA, United States, ⁴Biomedical Engineering, University of Iowa, Iowa City, IA, United States

Keywords: Software Tools, Shims

In this work, we proposed an automated High Order Shim procedure for neuroimaging studies. The proposed shimming procedure is fully automated and hence eliminates variability between operators. The procedure performs automated real-time brain extraction to define the region of interest (ROI) of the field map to be used in the shimming algorithm. Automated High Order Shim has fewer image distortions and narrower spectral linewidths than linear shimming and manual high-order shimming, suggesting its superior performance in correcting B0 field homogeneity. The shimming performance was assessed by acquiring EPI-based images and MR spectroscopy at both 3T and 7T field strengths.  

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Prakash Kumar¹, Bilal Tasdelen¹, and Krishna S Nayak^1
1Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States

Keywords: Software Tools, Data Acquisition

This work presents an open-source workflow for dynamic MR acquisition and reconstruction. The entire workflow consists of vendor-agnostic tools to allow for immediate sharing of data, pulse sequence, and reconstruction code across sites. This work is an adaptation of Open-Source MR Imaging and Reconstruction Workflow to dynamic imaging (Veldmann et. al, MRM 88(6):2395-2407). Using the FIRE interface, we demonstrate a Gadgetron loader of acquisition metadata including k-space trajectories and present a pipeline to convert pulse sequences developed through HeartVista’s SpinBench into Pulseq for easy sharing of sequences.

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Anaïs Artiges¹, Éléa Granier¹, Ivy Uszynski¹, Franck Mauconduit¹, Philippe Ciuciu², and Cyril Poupon^1
1BAOBAB, NeuroSpin, Paris-Saclay University, CNRS, CEA, Gif-sur-Yvette, France, ²Mind, NeuroSpin, Paris-Saclay University, INRIA, CEA, Gif-sur-Yvette, France

Keywords: Software Tools, Pulse Sequence Design

Developing MRI pulse sequences demands to access proprietary sequences, to open up parts of the code provided by the manufacturer, and to deal with intellectual property issues. To address these limitations, we have provided the community with GinkgoSequence, a modular and open-source MRI pulse sequence development framework for Siemens devices. In this work,  we present the addition of a new diffusion-weighted sequences development toolbox in the existing GinkgoSequence framework. It includes trapezoidal and free-waveform diffusion gradients, fat saturation, echo-planar reading, partial Fourier and GRAPPA accelerations, and stimulated echo acquisition mode (STEAM). To assess its quality, it has been tested in-vivo.

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Cemre Ariyurek¹, Aziz Koçanaoğulları¹, Can Taylan Sari¹, Serge Vasylechko¹, Onur Afacan¹, and Sila Kurugol^1
1Quantitative Intelligent Imaging Lab (QUIN), Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States

Keywords: Software Tools, DSC & DCE Perfusion

Dynamic contrast enhanced MRI (DCE-MRI)  is capable of quantitative assessment of renal function and evaluation of the detailed anatomy of the urinary tract and renal arteries in patients. To reconstruct the dynamic volumes in DCE-MRI, Golden-angle RAdial Sparse Parallel (GRASP) reconstruction algorithm is commonly used. In this software, we have developed an efficient open-source, purely Python-based, standalone GRASP reconstruction library called pyGRASP that allows researchers to access the source code for development, facilitating flexible deployment with readable code and no compilation, and easy utilization without requirement of a steep learning curve.

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Matteo Cencini¹, Marta Lancione¹, Luca Peretti¹, and Michela Tosetti^1
1IRCCS Stella Maris, Pisa, Italy

Keywords: Software Tools, Software Tools

Quantitative MRI methods improve tissue characterization and allow for better longitudinal assessment, but need efficient and reliable fitting routines. Currently available qMR frameworks, while covering a wide range of applications, suffer either from a relatively low efficiency or require non-trivial compilation steps. Here, we propose an open-source framework for qMR quantification which maintains a good computational efficiency without sacrificing the readability and customizability of the code.

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Douglas C Dean III^1,2,3, Nagesh Adluru³, and Jose Guerrero^3
1Pediatrics, University of Wisconsin–Madison, Madison, WI, United States, ²Medical Physics, University of Wisconsin–Madison, Madison, WI, United States, ³Waisman Center, University of Wisconsin–Madison, Madison, WI, United States

Keywords: Software Tools, Data Processing

Quantitative MRI provides a unique opportunity to characterize the underlying tissue and establish measurements that canserve as biomarkers. However, many of these methods require specialized workflows and and tools which limit their broader adoption. Here, we present QMRI-neuropipe, an open-source, flexible framework that provides a wide selection of methods, algorithms, and tools for processing and analyzing multiple qMRI datatypes. QMRI-neuropipe supports the Brain Imaging Datset Standard (BIDS) and currently supports processing of diffusion and relaxometry datasets. Future developments will aim to incorporate alternative quantitative MRI methods (e.g. MT, QSM, etc.) into the QMRI-neuropipe framework.

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Carina Graf¹ and Christopher T Rodgers^2
1Wolfson Brain Imaging Centre, Department of Clinical Neuroscience, University of Cambridge, Cambridge, United Kingdom, ²University of Cambridge, Cambridge, United Kingdom

Keywords: Software Tools, Spectroscopy, non-cartesian MRSI

With the wider availability of ultra-high field MR systems, metabolic imaging using MRSI is a fast-developing area of research. Frequently used sequences use non-uniformly sampled trajectories to achieve high-acceleration factors e.g. concentric-ring trajectories (CRT). In this work, we demonstrate the from-scratch implementation of a simulation and reconstruction tool for CRT-MRSI using (1) a regridding, (2) an iterative L2 linear solver as well as (3) applying the non-uniform FFT implementation from the BART-toolbox to simulated non-cartesian MRSI data with a known Fourier-transform pair. The exact sampled data permits the evaluation of the impact of different reconstruction implementations.

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Aaron T. Gudmundson^1,2, Helge J. Zöllner^1,2, Christopher W. Davies-Jenkins^1,2, Erik G. Lee^3,4, Timothy J. Hendrickson^3,4, Richard A. E. Edden^1,2, and Georg Oeltzschner^1,2
1The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²Kennedy Krieger Institute, F. M. Kirby Research Center for Functional Brain Imaging, Baltimore, MD, United States, ³Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, United States, ⁴Informatics Institute, University of Minnesota, Minneapolis, MN, United States

Keywords: Software Tools, Spectroscopy, Automation

Large-scale application of magnetic resonance spectroscopy (MRS) is limited by demanding data processing and reliance on expert knowledge. We have developed an open-source continuous automated analysis workflow for MRS data, substantially reducing the effort for manual data analysis and quality control. This workflow is of particular interest for application-oriented non-expert users and large-scale multi-center multi-vendor MRS studies, and should substantially protect against loss of data from acquisition protocol errors.

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Dale H. Mugler^1
1Neuroscience, Medical University of South Carolina, Charleston, SC, United States

Keywords: Software Tools, Spectroscopy, Data Analysis, Metabolism

Brain tumor metabolic maps are one application of non-invasive MR Spectroscopy (MRS), although there are many other areas of patient treatment and surgery planning that would benefit from improved MRS analysis.  A fast, accurate new method for MRS is used here to estimate the ratio of Choline to NAA from simulated spectra modeled on those near a brain tumor, using simple formulas for determining the FID amplitudes that relate to metabolite intensities and concentrations.  No approximate numerical integration is required.  The time of computation of  0.052 seconds per voxel speeds the construction of a metabolic 3D brain map.

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Aizada Nurdinova^1,2, Stefan Ruschke², Jonathan Stelter², Michael Gestrich³, and Dimitrios Karampinos^2
1Biomedical Physics, Stanford University, Stanford, CA, United States, ²Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany, ³Altair Engineering GmbH, Boeblingen, Germany

Keywords: Simulations, Software Tools

Bloch simulations is a powerful tool in MRI research and education as it allows predicting the signal evolutions in magnetic resonance experiments for various applications. However, Bloch simulations require solving ordinary differential equations for the whole spin ensemble and can thus become infeasible for realistic scenarios with large total number of spins. Therefore, the present work investigates the potential of a GPU-based implementation for accelerated Bloch simulations as an extension to the open source Juelich Extensible MRI Simulator (JEMRIS) in comparison to the existing MPI CPU implementation.