Javier Montalt-Tordera¹, Jennifer Steeden¹, and Vivek Muthurangu^1
1University College London, London, United Kingdom

We present TensorFlow MRI, a new open-source library of TensorFlow operators for MR image reconstruction and processing. Its goal is to enable fast prototyping of modern MRI applications within a single computing framework. It is intended for researchers working in MR image reconstruction and/or processing, especially those interested in ML applications. The library is primarily Python-based and is easy to use, understand and extend. It has a single-command installation procedure and extensive documentation. Thanks to the use of a TensorFlow backend, it has excellent performance and runs on heterogenous and distributed systems.

Daniel Christopher Hoinkiss¹, Simon Konstandin^1,2, and Matthias Günther^1,2,3
1Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany, ²mediri GmbH, Heidelberg, Germany, ³University of Bremen, Bremen, Germany

We demonstrate a constraint-based approach of MRI sequence development in the vendor-independent MRI pulse sequence development framework gammaSTAR and demonstrate this concept in arterial spin labeling by optimizing the timings of the background suppression pulses to minimize the signal contribution of chosen T1 values in the human brain. This concept can raise MRI sequence development to a new abstraction level by, instead of providing exact timings and parameter values, defining physical constraints and conditions to be satisfied by the MRI sequence during an automated sequence generation process.

Matteo Cencini^1,2, Luca Peretti^2,3, and Michela Tosetti^1,2
1IRCCS Stella Maris, Pisa, Italy, ²Imago7 Foundation, Pisa, Italy, ³Università di Pisa, Pisa, Italy

Modern approaches to iterative imaging, such as model-based reconstruction, requires efficient implementations of Non-Uniform Fourier Transform to reach feasible reconstruction times. In addition, low-rank subspace projection is often used to reduce computational burden. While many implementations of NUFFT currently exists, they are not optimized for this kind of problems. Here, we propose a fast and memory-efficient NUFFT operator with embedded low-rank subspace projection. We demonstrate an order of magnitude of speed-up with comparable image quality compared to other high-level implementations.

Liu Pan^1,2, Sidy Fall¹, and Olivier Baledent^1,2
1CHIMERE UR 7516, Jules Verne University, Amiens, France, ²Medical Image Processing Department, Jules Verne University Hospital, Amiens, France

Real-time phase contrast sequences (RT-PC) has potential value as a scientific and clinical tool in quantifying the effects of respiration on cerebral circulation. To simplify its complicated post-processing process, we developed Flow 2.0 software, which provides a complete post-processing workflow including converting DICOM data, image segmentation, image processing, data extraction, background field correction, anti-aliasing filter, signal processing and analysis and a novel time-domain method for quantifying the effect of respiration on the cerebral circulation. This end-to-end software allows us to quickly, robustly and accurately perform batch process RT-PC and multivariate analysis of the effects of respiration on cerebral circulation.

Steve C.N. Hui^1,2, Muhammad G. Saleh³, Helge J. Zöllner^1,2, Georg Oeltzschner^1,2, Hongli Fan^1,4, Yue Li⁵, Yulu Song^1,2, Hangyi Jiang¹, Jamie Near^6,7, Susumu Mori^1,2, Hanzhang Lu^1,2, and Richard A.E. Edden^1,2
1Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ³Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, ⁴Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁵AnatomyWorks, LLC, Ellicott City, MD, United States, ⁶Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, ⁷Sunnybrook Research Institute, Toronto, ON, Canada

MRSCloud is an online-based spectral simulation tool for brain metabolites. Up to 32 metabolites can be simulated to generate a basis set for linear combination modeling. The 1D projection method, coherence pathway filters and pre-calculation of propagators have been implemented for density-matrix simulations to run on a high-performance cloud server. Results indicated simulations were comparable with FID-A and difference between simulations of spatial points (21x21/41x41/101x101) were small. It allows community users to generate vendor, sequence, editing experimental-specific basis sets that are appropriate for their studies.

Hannes Dillinger¹, Eva Peper¹, Christian Guenthner¹, and Sebastian Kozerke^1
1ETH and University Zurich, Zurich, Switzerland

This work presents a web-based framework for the identification of mechanical resonances of MRI gradients systems based exclusively on mobile phone audio recordings. frequencies are used to guide beneficial combinations of TE/TR for PC-MRI and readout bandwidth settings for EPI sequences. Results demonstrate that the background phase in PC-MRI as well as beat phenomena during EPI readouts can be reduced. The web app can be found at https://mr-radio.herokuapp.com and its source code is publicly available at https://github.com/hdillinger/mr-radio in the spirit of reproducible research.

Gehua Tong¹, John Thomas Vaughan, Jr. ², and Sairam Geethanath^2
1Biomedical Engineering, Columbia University, New York, NY, United States, ²Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States

Virtual Scanner is an open-source MR platform for end-to-end simulation and serves as a digital twin for real MR scanners in hardware and software aspects. In this update, we present new functions including RF pulse design and simulation, hardware-incorporated Bloch simulation that takes into account B₀ and B₁^- fields, and non-Cartesian reconstruction. Three Google Colab notebooks are provided to demonstrate these functions and enable users to perform virtual experiments in a zero-footprint manner. Furthermore, we demonstrate the digital twin concept in a development iteration case using the SPGR sequence. 

Stefan Ruschke¹ and Dimitrios C. Karampinos^1,2
1Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany, ²Munich Institute of Biomedical Engineering, Technical Universit of Munich, Munich, Germany

Multi-dimensional MR spectroscopy is frequently used for the probing of MR properties including the characterization of the water–fat environment in body applications. Previously, many studies used an ad-hoc multi-step approach where the fitting of the spectral content and the signal modelling was performed in separate steps albeit the theoretically compromised precision when compared to a joint fitting and modelling approach. ALFONSO allows the convenient and yet flexible definition of joint fitting and modelling strategies. Its utility and supremacy are demonstrated for the quantification of fat fraction in the liver, ADC in bone marrow and lipid droplet size in a phantom

Gehua Tong¹, John Thomas Vaughan, Jr. ², and Sairam Geethanath^2
1Biomedical Engineering, Columbia University, New York, NY, United States, ²Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States

Ultrashort Echo Time (UTE) sequences are clinically useful in short T₂ tissues such as bone, cartilage, and lung parenchyma. To help harmonize UTE protocols across clinical sites, we present an open-source repository implementing three basic radial minimal TE sequences using PyPulseq: 2D UTE, 3D UTE, and COncurrent Dephasing and Excitation (CODE). Functions for customized sequences and reconstruction scripts were provided for a complete pipeline. Echo Times (TEs) from 0.12 to 0.82 ms were achieved, and 27.6% more signal was captured in the bone and connective tissue in 2D ex vivo images compared to the GRE reference. 

Anaïs Artiges¹, Franck Mauconduit¹, Ivy Uszynski¹, Baptiste Mulot², Elodie Chaillou³, Philippe Ciuciu⁴, and Cyril Poupon^1
1BAOBAB, NeuroSpin, Paris-Saclay University, CNRS, CEA, Gif-sur-Yvette, France, ²Beauval Nature, ZooParc de Beauval, Saint-Aignan, France, ³UMR 85 Physiologie de la Reproduction et des Comportements, INRAE Centre Val-de-Loire, Nouzilly, France, ⁴Parietal, NeuroSpin, Paris-Saclay University, Inria, CEA, Gif-sur-Yvette, France

We created a new object-oriented environment for MR pulse-sequence development based on IDEA VE11C and above versions using an Open Science philosophy. This Ginkgo toolkit uses a modular structure to facilitate the design of pulse sequences using the aggregation of basic open-source sequence blocks available from the toolkit. Proofs of concept of the productivity gain reached using Ginkgo are provided through the implementation of a series of sequence models including a diffusion-weighted PGSE 3D EPI sequence.

Ecrin Yagiz¹, Sophia X. Cui², Nam G. Lee¹, Bilal Tasdelen¹, Ye Tian¹, and Krishna S. Nayak^1,3
1Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, ²Siemens Medical Solutions USA, Inc.,, Los Angeles, CA, United States, ³Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States

MRI is used extensively for body composition assessment in large part due to the increasing prevalence of metabolic disease and obesity. In this work, we provide a framework for simulating body composition MRI, including non-Cartesian sequences and emerging low field strengths. This framework can be utilized to investigate the optimality of acquisition parameters (e.g., resolution, TE, sampling trajectory) and the performance of fat/water separated reconstruction algorithms.

Eros Montin¹, Ioannis P Georgakis^1,2, Bei Zhang³, and Riccardo Lattanzi^1,4
1Center for Advanced Imaging Innovation and Research (CAI2R) Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ²Corsmed, Stockolm, Sweden, ³Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States, ⁴Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine,, New York, NY, United States

This work introduces POIROT (Performance Observer In Receive or Transmit), a web-based tool for the assessment of receive and transmit coil designs against ultimate intrinsic signal-to-noise ratio and transmit efficiency, respectively. It enables engineers, for the first time, to evaluate how good is a design and whether there is further room for improvement before building a prototype. POIROT currently includes ultimate intrinsic data for three numerical head models at different resolutions and magnetic field strengths. POIROT could be integrated with rapid numerical EM modeling tools to develop a pipeline for coil design optimization that uses ultimate performance as the benchmark.

Mark Mikkelsen¹, Dickson Wong², Wolfgang Bogner³, Yaroslav O. Halchenko⁴, Damon G. Lamb^5,6,7,8,9, Paul G. Mullins¹⁰, Georg Oeltzschner^11,12, and Martin Wilson^13
1Department of Radiology, Weill Cornell Medicine, New York, NY, United States, ²Schulich School of Medicine and Dentistry, Western University, London, ON, Canada, ³High-field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ⁴Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States, ⁵Departments of Psychiatry, Neuroscience, and Biomedical Engineering, University of Florida, Gainesville, FL, United States, ⁶Center for OCD, Anxiety, and Related Disorders, University of Florida, Gainesville, FL, United States, ⁷Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, United States, ⁸McKnight Brain Institute, University of Florida, Gainesville, FL, United States, ⁹Brain Rehabilitation Research Center, Malcom Randall Veterans Affairs Medical Center, Gainesville, FL, United States, ¹⁰Bangor Imaging Unit, School of Psychology, Bangor University, Bangor, United Kingdom, ¹¹Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ¹²F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ¹³Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom

Magnetic resonance spectroscopy (MRS) studies can generate extensive and complex datasets. Datasets can be organized idiosyncratically across (and even within) labs. However, sharing data and reproducing results without the added challenge of parsing lab-specific file organization is paramount to open science. Sharing of MRS data that is independent of lab-specific practices would greatly aid transparency and reusability. Here, we present an extension to the Brain Imaging Data Structure (BIDS) specification for MRS data. MRS-BIDS adopts the established tenets of standardization of BIDS and applies them to MRS data while considering the nuances relevant to MRS methodology.

Peter van Gelderen¹, Jacco A de Zwart¹, and Jeff H Duyn^1
1Advanced MRI section, LFMI, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States

A recent study has shown rapid MRI signals in response to whisker pad stimulation in mice that track electrical activity. We used rapid, high SNR imaging at 7 T to look for a possible direct effect of visual stimulation on MRI signal in human visual cortex. Two rapid stimulation protocols and variation of T₁-weighting failed to generate observable signal changes above the 0.01% detection threshold, well below the previously reported effect size of 0.15%. This confirms the well-established difficulty in developing more direct measures of neuronal activity than available with BOLD-fMRI.

Chuyu Liu¹, Yishi Wang², Zhensen Chen³, Yajing Zhang⁴, and Xiaolei Song^1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, ²Philips Healthcare, Beijing, China, ³Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ⁴MR Clinical Science, Philips Healthcare, Suzhou, China

We developed a real-time software for CEST post-processing and visualization on Philips 3T scanner, termed as PV-CEST. PV-CEST runs in the vendor provided PRIDE 2.0 environment, providing image view, Z-spectra analysis and quantitative maps calculation functionalities during scanning procedure, which could have great clinical utility.

Luca Peretti^1,2,3, Matteo Cencini³, Paolo Cecchi^2,4, Graziella Donatelli^2,4, Mauro Costagli^3,5, and Michela Tosetti^3
1University of Pisa, Pisa, Italy, ²Imago7 Research Foundation, Pisa, Italy, ³IRCCS Stella Maris, Pisa, Italy, ⁴Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy, ⁵University of Genoa, Genoa, Italy

Synthetic MR imaging allows reconstruction of different image contrasts from a single acquisition, reducing scan times. Here we introduce PySynthMRI, an open-source tool which provides a user-friendly and fluid interface to synthesize and modify contrast-weighted images. The tool allows the user to add and adjust virtual scanner parameters showing, in real time, how the resulting synthesized images vary accordingly, which makes PySynthMRI a valuable tool for both research and teaching. The generated images can be exported in the preferred format.