Ashley Stewart^1,2, Simon Daniel Robinson^2,3,4, Kieran O'Brien^1,2,5, Jin Jin^1,2,5, Angela Walls⁶, Aswin Narayanan², Markus Barth^1,2,7, and Steffen Bollmann^1,2,7
1Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia, ²Centre for Advanced Imaging, University of Queensland, Brisbane, Australia, ³High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, ⁴Department of Neurology, Medical University of Graz, Graz, Austria, ⁵Siemens Healthcare Pty Ltd, Brisbane, Australia, ⁶Clinical & Research Imaging Centre, South Australian Health and Medical Research Institute, Adelaide, Australia, ⁷School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia

We developed an open-source QSM processing framework, QSMxT, that provides a full QSM workflow including converting DICOM data to BIDS, a variety of robust masking strategies, phase unwrapping, background field correction, dipole inversion and region-of-interest analyses based on automated anatomical segmentations. We make all required external dependencies available in a reproducible and scalable analysis environment enabling users to process QSM data for large groups of participants on any operating system in a robust way.

Katherine Louise Bottenhorn¹, Daniel Alcalà-Lopez², Apoorva Ayyagari³, Molly G Bright⁴, César Caballero-Gaudes², Inés Chavarria², Vicente Ferrer², Soichi Hayashi⁵, Vittorio Iacovella⁶, François Lespinasse⁷, Ross Davis Markello⁸, Stefano Moia², Robert Oostenveld^9,10, Taylor Salo¹, Rachael Stickland⁴, Eneko Uruñuela², Merel Margaretha van der Thiel¹¹, and Kristina M Zvolanek^12
1Department of Psychology, Florida International University, Miami, FL, United States, ²Basque Center on Cognition, Brain and Language, Donostia, Spain, ³Northwestern University, Chicago, IL, United States, ⁴Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, United States, ⁵Indiana University, Bloomington, IN, United States, ⁶CIMeC - Center for Mind / Brain Sciences, The University of Trento, Trento, Italy, ⁷Psychology, Université de Montréal, Montréal, QC, Canada, ⁸McGill University, Montréal, QC, Canada, ⁹Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, ¹⁰NatMEG, Karolinska Institutet, Stockholm, Sweden, ¹¹Department of Radiology & Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands, ¹²Biomedical Engineering, Northwestern University, Chicago, IL, United States

Physiopy is a community-driven effort that aims to offer a full data preparation pipeline for non-neural physiological recordings in neuroimaging research and to build consensus on “best practices” among researchers of this specific domain. Its primary goal is to facilitate physiological data collection and sharing, according to an existing data standard and ontology. Corollary goals of the community are to provide recommendations and tools for (1) physiological data acquisition in the MR environment, (2) appropriate processing of these data, (3) organization of resulting datasets and meta-data, and (4) computing metrics for the removal of confounding physiological signals from fMRI data.

Brian J Soher¹, Dinesh K Deelchand², Sandeep Ganji³, Ralph Noeske⁴, Adam Berrington⁵, James Joers², and Gulin Oz^2
1Radiology, Duke University Medical Center, Durham, NC, United States, ²University of Minnesota, Minneapolis, MN, United States, ³Philips Healthcare, Rochester, MN, United States, ⁴GE Healthcare, Berlin, Germany, ⁵University of Nottingham, Nottingham, United Kingdom

The minimal spectral analysis methods described by MRS community consensus are not available in manufacturers’ MRS software. MRS data is still transferred off the scanner for advanced analysis leading to more variability in MRS results and impediments for adoption within a clinical DICOM workflow. We describe the Vespa Inline Engine (VIE), a flexible add-in module for the open-source Vespa spectral analysis package. VIE provides equivalent, inline MRS processing on GE, Siemens and Philips platforms. Raw MRS data sent to the VIE analysis returns results to the DICOM workflow as graphical and tabular DICOM images.

Istvan N Huszar¹, Karla L Miller¹, and Mark Jenkinson^1
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

An open-source framework (TIRL) and a novel 3-stage pipeline are presented to automate the registration between sparsely sampled small histology sections and 3-D MRI data for validating imaging biomarkers. In addition to affine registration, deformable slice-to-volume registration is employed to compensate for both in-plane and through-plane distortions of the histology sections. Each stage of the pipeline is shown to achieve submillimetre precise alignments, surpassing the accuracy of previous methods. With photographic intermediaries the pipeline is fully automatic and does not depend on serial histological sectioning or specialist cutting and stain automation hardware. The tools are provided as part of FSL.

Marten Veldmann¹, Philipp Ehses¹, Kelvin Chow², Maxim Zaitsev³, and Tony Stöcker^1,4
1MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, ²MR R&D Collaborations, Siemens Medical Solutions USA Inc., Chicago, IL, United States, ³Department of Radiology - Medical Physics, University Medical Center Freiburg, Freiburg, Germany, ⁴Department of Physics & Astronomy, University of Bonn, Bonn, Germany

We demonstrate an open-source MR imaging workflow for MR sequence development and online image reconstruction. The open-source approach improves vendor-independent reproducibility of new or modified MR sequences and reconstruction algorithms. An extensible online reconstruction pipeline allows for fast evaluation of the acquired data on the scanner directly. The pipeline supports implementation of advanced open-source image reconstruction techniques and is not restricted to a specific sequence. We successfully implemented an open-source spiral sequence with Pulseq for execution at a 7T MR scanner. A non-Cartesian image reconstruction was done with the BART toolbox using GIRF trajectory prediction.

Cristina Sainz Martinez^1,2, Mathieu Lemay¹, Meritxell Bach Cuadra^2,3,4, and João Jorge^1,2
1Systems Division, Swiss Center for Electronics and Microtechnology (CSEM), Nêuchatel, Switzerland, ²Medical Image Analysis Laboratory (MIAL), Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, ³Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁴Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

With the increasing importance of ultra-high field systems, suitable simulation platforms are needed for the development of high-resolution imaging methods. Here, we propose a realistic computational brain phantom at 100μm resolution, by mapping fundamental MR properties (e.g., T1, T2, coil sensitivities) from existing brain MRI data to the fine-scale anatomical space of BigBrain, a publicly-available 100μm-resolution ex-vivo image obtained with optical methods. We propose an approach to map image contrast from lower-resolution MRI data to BigBrain, retaining the latter’s fine structural detail. We then show its value for methodological development in two applications: super-resolution, and reconstruction of highly-undersampled k-space acquisitions.

Yudu Li^1,2, Jiahui Xiong^1,2, Rong Guo^1,2, Yibo Zhao^1,2, Yao Li^3,4, and Zhi-Pei Liang^1,2
1Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁴Med-X Research Institute, Shanghai, China

Myelin water fraction (MWF) mapping can substantially improve our understanding of several demyelinating diseases. While MWF maps can be obtained from multi-exponential fitting of multi-echo imaging data, current solutions are often very sensitive to noise and modeling errors. This work addresses this problem using a new model-based method. This method has two key novel features: a) an improved signal model capable of compensating practical signal errors, and b) incorporation of parameter distributions and low-rank signal structures. Both simulation and experimental results show that the proposed method significantly outperforms the conventional methods currently used for MWF estimation.

Martins Otikovs¹, Ankit Basak¹, and Lucio Frydman^1
1Weizmann Institute of Science, Rehovot, Israel

The benefits of performing locally low-rank (LLR) reconstruction on subsampled diffusion weighted (DW) data employing spatiotemporal encoding (SPEN) methods, is investigated. SPEN allows for self-referenced correction of motion-induced phase errors in case of interleaved DW acquisitions, and allows to overcome distortions otherwise observed along EPI’s phase-encoded dimension. In combination with LLR regularization and with joint subsampling of b-weighted and interleaved images, additional improvements are demonstrated. The method is tested on phantoms, and improvements in in-vivo brain and prostate scans on volunteers are demonstrated.

Chen Liu¹, David Jing Ma², Nanyan Zhu³, Kay Igwe², Jochen Weber², Roshell Li⁴, Emily Turner Wood⁵, Wafae Labriji⁶, Vasile Stupar⁶, Yanping Sun⁷, Neil Harris⁸, Antoine Depaulis⁶, Florence Fauvelle ⁹, Scott A. Small^10,11,12, Douglas L. Rothman¹³, and Jia Guo^10,14
1Department of Electrical Engineering and the Taub Institute, Columbia University, New York, NY, United States, ²Columbia University, New York, NY, United States, ³Department of Biological Sciences and the Taub Institute, Columbia University, New York, NY, United States, ⁴Department of Biomedical Engineering, Columbia University, New York, NY, United States, ⁵University of California, Los Angeles, Los Angeles, CA, United States, ⁶Grenoble Institut Neurosciences (GIN), Grenoble, France, ⁷Herbert Irving Comprehensive Cancer Centre, Columbia University, New York, NY, United States, ⁸Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States, ⁹Grenoble MRI Facility IRMaGe, France, France, ¹⁰Department of Psychiatry, Columbia University, New York, NY, United States, ¹¹Department of Neurology, Columbia University, New York, NY, United States, ¹²Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, United States, ¹³Radiology and Biomedical Imaging and of Biomedical Engineering, Yale University, New Haven, CT, United States, ¹⁴Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States

Spectral editing studies in mice brains have been limited due to difficulty in spectrum processing and lack of software package analysis. However, in preclinical studies, mouse models play an important role in understanding effects of drugs and its impact on the nervous system. JET is a fully automated software that performs raw data conversion, spectrum registration, spectral quality assessment and metabolite quantification of MEGA-PRESS mouse data at 9.4T. In this work, we first introduce the automated spectra processing pipeline of JET and further demonstrate its utilities in mouse studies.

Hélène Lajous^1,2, Tom Hilbert^1,3,4, Christopher W. Roy¹, Sébastien Tourbier¹, Priscille de Dumast^1,2, Yasser Alemán-Gómez¹, Thomas Yu⁴, Patric Hagmann¹, Mériam Koob¹, Vincent Dunet¹, Tobias Kober^1,3,4, Matthias Stuber^1,2, and Meritxell Bach Cuadra^1,2,4
1Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ²CIBM Center for Biomedical Imaging, Lausanne, Switzerland, ³Advanced Clinical Imaging Technology (ACIT), Siemens Healthcare, Lausanne, Switzerland, ⁴Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Accurate characterization of in utero human brain maturation is critical. However, the limited number of exploitable magnetic resonance acquisitions not corrupted by motion in this cohort of sensitive subjects hinders the validation of advanced image processing techniques. Numerical simulations can mitigate these limitations by providing a controlled environment with a known ground truth. We present a flexible framework that simulates magnetic resonance acquisitions of the fetal brain in a realistic setup including stochastic motion. From simulated images comparable to clinical acquisitions, we assess the accuracy and robustness of super-resolution fetal brain magnetic resonance imaging with respect to noise and motion.