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Clemens Diwoky¹, Christina Graf², Armin Rund³, and Rudolf Stollberger^2
1Institute of Molecular Biosciences, University of Graz, Graz, Austria, ²Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria, ³Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria

Keywords: Pulse Sequence Design, CEST & MT

Herein, we show for the first time the application of optimal control (OC) to the design of RF pulses for dual-angle T₁ measurements as needed for ³¹P magnetization transfer spectroscopy. Phantom measurements highlight that OC RF pulses are able to produce precise T₁ maps within the relevant coil's sensitivity range. The resulting enlarged spin ensemble leads to high SNR and excellent T₁ prediction if used in non-selective spectroscopic application. In addition, OC pulses were optimized for operation at a large bandwidth suitable for ³¹P spectroscopy (± 15 ppm), which is often a problem when adiabatic BIR-4 pulses are used.

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David Jing Ma¹, Yanting Yang¹, Natalia Harguindeguy¹, Ye Tian¹, Scott A. Small^2,3,4, Feng Liu^2,5, Douglas L. Rothman⁶, and Jia Guo^2,7
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, ²Department of Psychiatry, Columbia University, New York, NY, United States, ³Department of Neurology, Columbia University, New York, NY, United States, ⁴Taub Institute Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, United States, ⁵New York State Psychiatric Institute, Columbia University, New York, NY, United States, ⁶Radiology and Biomedical Imaging of Biomedical Engineering, Yale University, New Haven, CT, United States, ⁷Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States

Keywords: Data Processing, Machine Learning/Artificial Intelligence

A deep learning-based registration method has been a successful image processing tool adopted in medical image registration but there is a lack of learning-based registration tools for spectral registration protocols. A novel CNN-based unsupervised deep learning spectral registration  model was developed and trained on a simulation dataset. The model was then further evaluated on a simulated test set with more extreme conditions and on an in vivo dataset and was compared performances to published frequency-and-phase correction models. An unsupervised deep learning-based spectral registration approach was found to demonstrate state-of-the-art performance in frequency-and-phase correction.

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Ajin Joy¹, Andres Saucedo¹, Melissa Joines¹, Stephanie Lee-Felker¹, Manoj K Sarma¹, James Sayre¹, Maggie Dinome², and M. Albert Thomas^1
1Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Surgery, University of California, Los Angeles, Los Angeles, CA, United States

Keywords: Data Analysis, Breast

Five-dimensional (5D) echo-planar correlated spectroscopic imaging (EP-COSI) combines 2 spectral and 3 spatial dimensions to record two dimensional (2D) correlated spectroscopy (COSY) spectra in multiple regions in multiple slices. In this study, multiple 2D COSY spectra were recorded from breast cancer patients by 5D EP-COSI within practical scan time durations using non-uniform undersampling of one spectral and two spatial dimensions, and compressed sensing-based reconstruction. Different metabolite and lipid ratios were quantified and its association with Ki-67 metric was studied. Findings of this study showed statistically significant association of metabolite and lipid levels with Ki-67 measures in breast cancer patients.

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Annelise Lemaire^1,2, Hui Jun Liao¹, Inga Koerte³, David Howell⁴, Alexander Lin^1,5, and Katherine Breedlove^1,5
1Brigham and Women’s Hospital, Boston, MA, United States, ²University of Michigan, Ann Arbor, MI, United States, ³Ludwig-Maximilians-Universitat, Munich, Germany, ⁴University of Colorado-Anschutz, Aurora, CO, United States, ⁵Harvard Medical School, Boston, MA, United States

Keywords: Data Analysis, Contrast Mechanisms, Traumatic Brain Injury

A population of collegiate athletes, along with matched age and gender-matched student-athlete controls, were evaluated post-concussion, for neurometabolic levels in the PCG by magnetic resonance spectroscopy (MRS) and Post-Concussion Symptom Scale (PCSS) following injury.  Evaluations were conducted 72 hours post-diagnosis. For the purpose of this work, correlations between total Glutamate (Glu+Gln), total Glutamate to total Creatine ratios, and symptoms on the PCS scale associated with cognitive fatigue - such as mental fog, drowsiness, fatigue, and feeling slowed - were explored. 

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Federico Turco¹ and Johannes Slotboom^2
1Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland, ²Support Center for Advanced Neuroimaging (SCAN), Institute of Diagnostic and Interventional Neuroradiology, Bern, Switzerland

Keywords: Data Processing, Spectroscopy, Deep learning frameworks, Deep learning, Optimization

We implemented a model fitting algorithm for magnetic resonance spectroscopy quantification using Pytorch. This network fit the spectra by minimizing the error between the spectrum modeled by the newtwork and the desired target. The implementation can fit multiple spectras in parallel using Pytorch GPU acceleration. We compared the results with a parallel version of TDFDFit and found it to be up to 12 time faster fitting 2048 spectras in 50 seconds.

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Markus Huemer¹, Oliver Maier¹, Moritz Simon Fabian², Manuel Schmidt², Arnd Dörfler², Kristian Bredies³, Moritz Zaiss^2,4, and Rudolf Stollberger^1,5
1Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria, ²Institute of Neuroradiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany, ³Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria, ⁴High-Field Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, ⁵BioTechMed Graz, Graz, Austria

Keywords: Data Analysis, CEST & MT

This work investigates an improved quantification algorithm for CEST spectra using a joint Frobenius TGV regularization. The performance is assessed with simulations, phantom and in-vivo measurement with respect to systematic errors and robustness. For comparison, a pixel-wise fit using the Levenberg-Marquardt algorithm and the IDEAL algorithm were implemented. The $$$TGV_j$$$ algorithm shows the best performance in the main parameter fitting stability and parameter SNR.

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Tianzhe Li^1,2, Julio Cardenas-Rodriguez³, and Marty David Pagel^4
1Cancer Systems Imaging, UT MD Anderson Cancer Center, Houston, TX, United States, ²Medical Physics Program, UT Health, Houston, TX, United States, ³Data Translators LLC, Oro Valley, AZ, United States, ⁴Cancer Systems Imaging, MD Anderson Cancer Center, Houston, TX, United States

Keywords: Data Analysis, CEST & MT, pH imaging

AcidoCEST MRI can measure the extracellular pH of the tumor microenvironment.  We have further refined our “Bloch fitting” method, and shown that this analysis method can accurately and precisely measure pH without additional MRI information, with an accuracy of 0.03 pH units.  In addition, we have developed a machine learning method that can classify pH as > 7.0 or < 6.5 pH units (PPV=0.94, NPV=096), and a machine learning regression method that can estimate pH with a mean absolute error of 0.031 pH units.

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Lauren Gao¹ and Phillip Sun^1
1Emory University, Atlanta, GA, United States

Keywords: Data Analysis, CEST & MT

CEST quantification is challenging because the measurement depends on the scan protocols. Also, T1 normalization is not straightforward under non-equilibrium conditions. Using multi-pool simulations and phantom experiments, our study evaluated quasi-steady-state(QUASS) algorithm-boosted CEST quantification. The 3-pool CEST simulation showed significant T1 dependencies due to complex interactions among Ts, TR, and T1. Such dependencies were corrected with QUASS reconstruction. In addition, a multi-T1 phantom was used to evaluate the quantification. Whereas the apparent CEST MRI showed significant dependence on Ts, TR, and  T1, accurate CEST quantification was demonstrated with the spinlock-model-based fitting of QUASS CEST MRI.

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Zhen Hu¹, Dan Zhu^2,3, and Qin Qin^2,3
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ³Kirby Center, Kennedy Krieger Institute, Baltimore, MD, United States

Keywords: Signal Modeling, Brain, Relaxometry

The dual flip angle (DFA) method is widely used for brain T1 quantification. In addition to the well-known RF field effect, this study investigated the overlooked magnetization transfer (MT) effect for the accuracy of in-vivo T1 mapping. Based on the two-pool Bloch simulation, the DFA derived T1 estimation revealed FA and TR dependence, which was confirmed by the in-vivo brain T1 mapping using the same set of sequence parameters. Longer TR (30 ms) reduced the variation between FA pairs and were closer to the literature values, but at the cost of longer acquisition time.

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İpek Özdemir¹, Sandeep Ganji², Joseph Gillen^1,3, Michal Považan⁴, and Peter B Barker^1,3
1Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, United States, ²Philips Healthcare, Best, Netherlands, ³F.M. Kennedy Krieger Institute, Baltimore, MD, United States, ⁴Danish Research Centre for MR, Copenhagen, Denmark

Keywords: Data Acquisition, Spectroscopy, Downfield MRSI

A 3D downfield-MRSI sequence with whole brain coverage, 22-minute scan time, and 0.7 cm³ nominal spatial resolution has been developed at 3T. The sequence was tested in 5 normal volunteers; LCModel analysis showed CRLB average values of 3-4% for protein amide resonances in 3 selected gray and white matter regions.

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Guodong Weng^1,2, Piotr Radojewski^1,2, Federico Turco^1,2, and Johannes Slotboom^1,2
1Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland, ²Translational Imaging Center, sitem-insel, Bern, Switzerland

Keywords: Visualization, Spectroscopy, Spectral editing

In vivo detection of phosphoethanolamine (PE) using spectral editing has recently been shown to be feasible. This study shows whole-brain PE maps using SLOW-EPSI in a healthy subject with a nominal resolution of 4.3*4.3*10 mm. The result shows that the PE level is high in the cerebral cortex and low in the white matter.

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Amirmohammad Shamaei^1,2, Jana Starcukova¹, and Zenon Starcuk Jr^1
1Institute of Scientific Instruments of the Czech Academy of Sciences Research institute in Brno, Brno, Czech Republic, ²Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic

Keywords: Data Processing, Spectroscopy, Singular value decomposition, MR spectroscopic imaging, Denoising

The utility of MR spectroscopic imaging (MRSI) can be limited by a low signal-to-noise ratio (SNR) in practice. Averaging multiple coherent repetitions increases the SNR, but at the cost of time-consuming acquisition. Several computationally expensive approaches based on low-rank matrix approximation for denoising MRSI data have been proposed, which do not take advantage of previously acquired spectra.
This work demonstrates a novel computationally cheap data-driven approach to MRSI denoising, coined EigenMRS, by learning low-rank structures of MRS data. As proof of concept, EigenMRS was tested against the simulated ¹H- MRSI data, and the results showed an increase in denoising performance.

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Jimin Ren^1,2
1Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States, ²Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States

Keywords: Data Analysis, Spectroscopy, brain, phosphocholine, phosphoethanolamine, phosphomonoester

It has been a common practice to quantify brain phosphomonoester (PME) 31P signals using a two-component model composed of phosphoethanolamine (PE) and phosphocholine  (PC). This study demonstrates spectral evidence of the presence of a hidden broad PME (h-PME) signal underneath PE and PC resonances, characterized by a short T1 and potentially contributed by RBC 2,3-DPG in brain blood vessels, though other sources of signal contribution cannot be fully ruled out.  The results have implication in using PE and PC as biomarkers of altered phospholipid metabolism in brain pathologies.

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Tomohisa Okada¹, Hideto Kuribayashi², Yuta Urushibata², Thai Akasaka¹, Ravi Teja Seethamraju³, Sinyeob Ahn³, and Tadashi Isa^1
1Human Brain Research Center, Kyoto University, Kyoto, Japan, ²Siemens Healthcare, Japan, Tokyo, Japan, ³Siemens Medical Solutions, Malvern, PA, United States

Keywords: Data Analysis, Spectroscopy

The effect of baseline flexibility LCModel parameter DKNTMN (0.15, 0.3, 0.6 and 1) on a 7T short-TE STEAM MRS measurement of GABA, glutamate and excitatory-inhibitory ratio (EIR) was investigated using both simulated and measured macromolecular basis sets. Mean (SD) of GABA/tCr was highest, 0.23 (0.02), using simulated MM basis. Using measured MM basis, the ratios decreased from 0.18 (0.04) to 0.12 (0.03) by the increase of DKNTMN values. The GABA/tCr ratio and EIR of a former multi-center study was 0.19 and 8.2 after T2 decay correction. Analysis using DKNTMN of 0.3 conformed best and considered to be the choice.

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Andrea Dell'Orco^1,2, Layla Tabea Riemann², Stephen L. R. Ellison³, Bernd Ittermann², Anna Tietze¹, Michael Scheel¹, and Ariane Fillmer^2
1Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neuroradiology, Berlin, Germany, Berlin, Germany, ²Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Germany., Berlin, Germany, ³LGC Limited Registered, Teddington, Middlesex, United Kingdom

Keywords: Data Analysis, Spectroscopy, macromolecules, single-voxel 1HMRS

Parametric macromolecule (MM) models can improve the quantification of brain ¹H-MRS. We quantified a publicly available repeated-acquisition dataset with LCModel and applied a parametric MM model in conjunction with four different settings for spline node distance and compared the quantification results. Coefficients of variation, average Cramér-Rao lower bound, repeatability, and reproducibility were estimated. A moderate effect of the baseline spline was shown.

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Samuel Rot^1,2, Matthew Clemence³, Bhavana S Solanky^1,4, and Claudia AM Gandini Wheeler-Kingshott^1,5,6
1NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, ²Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ³Philips Healthcare, Best, Netherlands, ⁴Quantitative Imaging Group, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ⁵Department of Brain & Behavioural Sciences, University of Pavia, Pavia, Italy, ⁶Brain Connectivity Centre Research Department, IRCCS Mondino Foundation, Pavia, Italy

Keywords: New Trajectories & Spatial Encoding Methods, Non-Proton

Modern sodium MRI (²³Na-MRI) uses efficient, non-Cartesian k-space sampling strategies to acquire 3D volumes in clinically feasible scan times. 3D Seiffert spirals are a novel k-space sampling scheme with improved efficiency to existing methods. ²³Na-MRI, and particularly temporally resolved functional ²³Na-MRI, could be an ideal application for Seiffert spirals. We present initial, in silico simulations of 3D Seiffert spiral k-space sampling for a highly undersampled 1.8 second functional ²³Na-MRI protocol. Seiffert spirals generate images of improved quality and SNR in direct comparison to 3D-cones. Essential further work will involve compressed sensing reconstruction, further trajectory optimisation and in vivo imaging.

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Tatsuhiro Wada^1,2, Osamu Togao³, Chiaki Tokunaga¹, Kazufumi Kikuchi⁴, Koji Yamashita⁵, Masami Yoneyama⁶, Masahiro Oga¹, Koji Kobayashi¹, Toyoyuki Kato¹, Kousei Ishigami⁴, and Hidetake Yabuuchi^7
1Division of Radiology, Department of Medical Technology, Kyushu university hospital, Fukuoka, Japan, ²Department of Health Sciences, Graduate school of Medical Sciences, Kyushu University, Fukuoka, Japan, ³Department of Molecular Imaging & Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, ⁴Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, ⁵Department of Radiology Informatics & Network, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, ⁶Philips Japan, Tokyo, Japan, ⁷Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan

Keywords: Tumors, CEST & MT

Glioma grading using chemical exchange saturation transfer (CEST) imaging is often performed in a single cross-section. However, CEST imaging of multiple cross-sections is desirable for intra-tumor heterogeneity. Compressed sensing and sensitivity encoding (CS-SENSE) was applied to CEST imaging to obtain multi-slice CEST imaging in a clinically appropriate scan time. The diagnostic performance of three-dimensional (3D) CEST imaging was comparable to that of a two-dimensional CEST imaging. The evaluation of the entire tumors by multi-slice CEST imaging was important in the gliomas' grading because the signal intensities differed among the tumor slices.

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Maria Sedykh¹, Stefano Casagranda², Patrick Liebig³, Christos Papageorgakis², Laura Mancini^4,5, Sotirios Bisdas^4,5, Manuel Schmidt¹, Arnd Doerfler¹, and Moritz Zaiss^1
1Institut of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany, ²Department of R&D Advanced Applications, Olea Medical, La Ciotat, France, ³Siemens Healthcare GmbH, Erlangen, Germany, ⁴Lysholm Department of Neuroradiology, University College of London Hospitals NHS Foundation Trust, London, United Kingdom, ⁵Institute of Neurology UCL, London, United Kingdom

Keywords: Tumors, CEST & MT

Fluid suppression has an inestimable value in improving the readability of Amide Proton Transfer weighted imaging in the neuro-oncological field, by removing contamination effects in the fluid compartments. With this work we wanted to justify the derivation of this metric and to show different clinical examples. Our new fluid-suppressed APTw metric for 3T MR imaging, based on the principles of Spillover Correction for CEST imaging, can be then derived through the Bloch McConnell Equations.

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Minghao Wu¹, Tongling Jiang², Cong Xie³, Xianchang Zhang⁴, Yi Zhang⁵, and Yaou Liu^1
1Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, beijing, China, ²Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China, Hangzhou, China, ³Beijing Tiantan Hospital, Capital Medical University, Beijing, China, beijing, China, ⁴MR Collaboration, Siemens Healthineers Ltd., Beijing, China, beijing, China, ⁵College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China, Hangzhou, China

Keywords: Radiomics, Brain, amide proton transfer weighted (APTw) imaging

The fifth edition of the 2021 WHO Classification of Tumors of the Central Nervous System (WHO CNS5) has introduced significant changes in classifying glioma subtypes based on molecular profiles. This study investigated the feasibility of amide proton transfer weighted (APTw)-based radiomics for predicting molecular subtypes and WHO grade of adult-type diffuse gliomas. APTw-based radiomics achieved satisfactory performance in distinguishing three molecular subtypes and WHO grade (High (Ⅳ grade)/Low (Ⅱ-Ⅲ grade)). This application may be an effective and promising quantitative approach for better noninvasive characterization and  classification of gliomas.