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Julien Lamy¹, Flavy Savigny¹, and Paulo Loureiro de Sousa^1
1ICube, Université de Strasbourg-CNRS, Strasbourg, France

Keywords: Signal Modeling, Simulations

Phase-based electrical conductivity mapping requires high SNR due to the instability of the reconstruction. Electrical conductivity can be measured by the phase-cycled bSSFP, a high-SNR sequence which is also weighted by other biophysical parameters but for which no complete analytical model exist. In this work, we use EPG-based simulations to investigate how the bSSFP signal varies with the flip angle, the phase step and the pulse duration in a diffusive, two-pools model of the brain white matter at 3 T. We show that our simulations agree with in-vivo acquisitions and establish guidelines to optimize the SNR of the phase-cycled bSSFP.

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Iman Aganj^1,2, Matthew Vera¹, Thorsten Feiweier³, Andre J. van der Kouwe^1,2, John E. Kirsch^1,2, and Bruce R. Fischl^1,2
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, ²Radiology Department, Harvard Medical School, Boston, MA, United States, ³Siemens Healthcare GmbH, Erlangen, Germany

Keywords: Signal Modeling, Diffusion Tensor Imaging

In the course of diffusion, water molecules experience varying values for the relaxation-time property of the underlying tissue, a factor that has not been accounted for in diffusion MRI (dMRI) modeling. Accordingly, we derive a relationship between the diffusion profile measured by dMRI and the spatial gradient of the image, and subsequently estimate the latter from the former. We test our hypothesized relationship via dMRI of the human brain (a public in vivo image and an acquired ex vivo stimulated-echo image), showing statistically significant results that may be due to our model and/or the confounding factor of “fiber continuity”.

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Stephen E. Ogier^1,2, Annabel Sorby-Adams³, Kalina Jordanova¹, Andrew M. Dienstfrey⁴, Zydrunas Gimbutas⁴, Jennifer Guo³, John Kirsch⁵, Steven Schiff⁶, Ronald Mulondo⁷, Johnes Obungoloch⁸, Megan E Poorman⁹, John Pitts⁹, W. Taylor Kimberly³, and Kathryn E. Keenan^1
1Magnetic Imaging Group, National Institute of Standards and Technology, Boulder, CO, United States, ²Department of Physics, University of Colorado Boulder, Boulder, CO, United States, ³Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, ⁴Mathematical Analysis and Modeling Group, National Institute of Standards and Technology, Boulder, CO, United States, ⁵A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁶Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, United States, ⁷CURE Children's Hospital of Uganda, Mbale, Uganda, ⁸Faculty of Applied Sciences and Technology, Mbarara University of Science and Technology, Mbarara, Uganda, ⁹Hyperfine, Inc., Guilford, CT, United States

Keywords: Artifacts, Low-Field MRI

The assessment of brain morphology requires geometric accuracy, and the monitoring of pathologies like dementia requires geometric stability over time and between systems. We aim to assess the geometric accuracy and stability of a head-only 64 mT system by imaging the cylindrical phantom provided by the manufacturer, positioned with a 3D-printed holder for stability, and analyzing the geometric fidelity of the images produced by a variety of product sequences. The initial assessment examines the perimeter of the phantom and how it varies along the S-I direction.

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Dylan Archer Dingwell^1,2 and Charles H Cunningham^1,2
1Medical Biophysics, University of Toronto, Toronto, ON, Canada, ²Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada

Keywords: Signal Modeling, Hyperpolarized MR (Non-Gas), 13C Signal Dynamics, 13C MRI, Enzyme Kinetics, Reaction Kinetics, Metabolism

In this study, we used a particle-based Brownian dynamics simulator with concurrent MR modeling to characterize diffusion-related effects on 13C signal dynamics. We conducted a spectrophotometric lactate dehydrogenase (LDH) activity assay with and without the presence of glass beads to measure the influence of diffusion constraints and spatial compartmentalization on the reaction kinetics of LDH-mediated conversion of pyruvate to lactate. We also investigated the relationship between diffusion and RF refocusing in a typical fast GRE sequence by comparing simulated signal intensities for RF spoiled and non-spoiled GRE sequences applied under varying diffusion conditions.

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Yongquan Ye^1
1United Imaging, Houston, TX, United States

Keywords: Signal Modeling, Data Processing

A dual multi-dimensional integration (dMDI) method was proposed for reliable image noise masking. By adopting a state-of-the-art MDI method, a signal ratio and a reciprocal signal ratio were constructed in the proposed dMDI method. The product of the two signal ratios display a highly reliable dependency of the intrinsic SNR of the signals, which can serve as a voxel-wise noise mask.

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Irene Guadilla^1,2, Ana Fouto¹, Álvaro Planchuelo-Gómez³, Antonio Tristán-Vega³, Amparo Ruiz-Tagle¹, Inês Esteves¹, Gina Caetano¹, Nuno Silva⁴, Pedro Vilela⁵, Raquel Gil-Gouveia^6,7, Santiago Aja-Fernández³, Patrícia Figueiredo¹, and Rita Nunes^1
1Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal, ²Universidad Autónoma de Madrid, Madrid, Spain, ³Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain, ⁴Learning Health, Hospital da Luz, Lisbon, Portugal, ⁵Imaging Department, Hospital da Luz, Lisbon, Portugal, ⁶Neurology Department, Hospital da Luz, Lisbon, Portugal, ⁷Center for Interdisciplinary Research in Health, Universidade Católica Portuguesa, Lisbon, Portugal

Keywords: Signal Modeling, Diffusion/other diffusion imaging techniques

Menstrual migraine affects about 25% of female migraine patients. However, the diagnosis of migraine is particularly difficult because the brain changes associated with migraine are challenging to detect with imaging techniques. Diffusion-weighted MRI (dMRI) permits the detection of alterations in the microenvironment of the brain tissues. We investigate whether removing the contribution of the free water component from the diffusion-signal can provide increased sensitivity to identify white matter changes in migraine using diffusion tensor metrics.

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Chiara Hartmann¹, Mélina Bouldi², and Jan M Warnking^1
1U1216, Grenoble Institut Neurosciences, Univ. Grenoble Alpes, Inserm, Grenoble, France, ²ZMT Zurich MedTech AG, Zürich, Switzerland

Keywords: Signal Modeling, Modelling

Safe access to MRI for patients with active implants may be possible by measuring RF currents in implants in individual patients with low-SAR sequences. We present a simple theoretical model of the B₁⁺-field close to a locally straight wire at any angle to B₀. MRI signals fitted by our model closely match magnitude and complex signals from electromagnetic simulations with a wire parallel to B₀ or tilted at 45° and magnitude experimental signals with parallel wire. RF currents reconstructed from simulated data match ground-truth values to within 6% and current profiles reconstructed from experimental signals follow simulated currents in shape.

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Snigdha Sen¹, Saurabh Singh², Hayley Pye³, Caroline Moore⁴, Hayley Whitaker³, Shonit Punwani², David Atkinson², Eleftheria Panagiotaki¹, and Paddy J Slator^1
1Centre for Medical Image Computing, University College London, London, United Kingdom, ²Centre for Medical Imaging, University College London, London, United Kingdom, ³Molecular Diagnostics and Therapeutics Group, University College London, London, United Kingdom, ⁴Department of Urology, University College London Hospitals NHS Foundation Trust, London, United Kingdom

Keywords: Signal Modeling, Microstructure

Microstructure models are traditionally fitted via computationally expensive non-linear least squares. Recent model fitting techniques use supervised deep learning algorithms trained on synthetic datasets, however the training data distribution affects parameter estimates. Self-supervised learning can address this by extracting training labels directly from the input data. We introduce a self-supervised machine learning algorithm for fitting the VERDICT MRI model for prostate to diffusion-weighted MRI. On simulated data, our approach improves parameter estimation compared to non-linear least squares and supervised machine learning. We also reveal plausible tumour contrast on in-vivo prostate data.

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Ken Sakaie¹, Ajay Nemani², Mark Lowe², and Katherine Koenig^2
1The Cleveland Clinic, Cleveland, OH, United States, ²Imaging Institute, The Cleveland Clinic, Cleveland, OH, United States

Keywords: Signal Modeling, Multiple Sclerosis

Diffusion Tensor Imaging of the fornix promises to serve as an imaging biomarker for cognitive decline in multiple sclerosis and Alzheimer’s disease. As the fornix is surrounded by cerebrospinal fluid in the lateral ventricles, partial volume averaging can bias measures of tissue microstructure. Here, we use a high spatial resolution, multishell acquisition to test the methods for combating partial volume averaging in the fornix. 

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Jing Wang¹, Bing Ji¹, Yang Lei¹, Tian Liu², Hui Mao¹, and Xiaofeng Yang^1
1Emory University, Atlanta, GA, United States, ²Icahn School of Medicine at Mount Sinai, New York, NY, United States

Keywords: Signal Modeling, Spectroscopy

This abstract reports a novel and efficient self-supervised deep learning autoencoder network for denoising MRS data and improving signal-to-noise ratio (SNR) of spectra, which may enable rapid MRS data acquisition and improving its clinical workflow and applications.

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Jacob Blum¹, Tsen-Hsuan Lin¹, Donsub Rim², and Sheng-Kwei Song ^1
1Radiology, Washington University School of Medicine in Saint Louis, Saint Louis, MO, United States, ²Department of Mathematics and Statistics, Washington University in Saint Louis, Saint Louis, MO, United States

Keywords: Signal Modeling, Diffusion Tensor Imaging

 Deep Neural Network based Network Estimation Operator (NEO) was developed to differentiate and quantify parallel fibers of different axial diffusivities. Data from both Monte Carlo simulations and an In-Silico imaging phantom were analyzed by both DBSI and NEO. Results reveal that NEO distinguished parallel fibers of different axial diffusivity while DBSI only identified a single fiber. NEO can detect and quantify injured vs. non-injured axons in a coherent fiber bundle. Thus, NEO can quantify the extent of axonal injury and loss more accurately than DBSI and other advanced diffusion MRI models.

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Xiaoben Li¹, Mengyuan Ma¹, Jinyang Wang¹, Junying Cheng², Wenjun Yao³, and Changqing Wang^1
1School of Biomedical Engineering, Anhui Medical University, Hefei, China, ²Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ³Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China

Keywords: Signal Modeling, Liver

The R₂' relaxation rate is expected to become a more specific biomarker in hepatic iron overload  because it is not affected by pathological factors. However, calibrating the relationship between R₂' relaxation rate and hepatic iron concentration (HIC) at different field strengths is time-consuming. In this work, we develop Monte Carlo simulations to investigate relationship between R₂' relaxation rate and HIC in hepatic iron overload at both 1.5T and 3.0T. Results show strong linear relationships between R₂' predictions and HIC. The Monte Carlo simulations may greatly shorten the clinical calibration time of MRI relaxation rates and HIC.

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Stefania Oliviero¹, Cosimo Del Gratta¹, Andrada Costantina Traeba², Tommaso Boccato³, Caterina Mainero², and Nicola Toschi^2,3
1Neuroscience, Imaging and Clinical Sciences, and Institute for Advanced Biomedical Technologies, University of Chieti-Pescara G. D'Annunzio, Chieti, Italy, ²A.A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, United States, ³Biomedicine and Prevention, University of Rome Tor Vergata, Roma, Italy

Keywords: Signal Modeling, White Matter, myelin, diffusion, in silico, MR

An accurate, noninvasive measure of axonal radii could play a crucial role in the understanding of healthy and diseased neural processes. Several diffusion-weighted MRI (dMRI) methods report on the axon radii distribution or mean axon radii. However, these measurements systematically overestimate histologically derived values. In this preliminary study, we address this limitation by formulating a model which explicitly includes the myelin contribution to the cerebral dMR signal. In detail, we introduce a myelinic compartment in both the AxCaliber and ActiveAx models and demonstrate a significant improvement of axonal radius estimation using synthetic data simulation.

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David G.J. Heesterbeek¹, Max H.C. van Riel¹, Cornelis A.T. van den Berg¹, and Alessandro Sbrizzi^1
1Department of Radiotherapy, Computational Imaging Group for MR Therapy and Diagnostics, University Medical Center Utrecht, Utrecht, Netherlands

Keywords: Signal Representations, Elastography, Motion estimation, Dynamic system

We propose a framework for data-driven discovery of the governing biomechanical equations for soft tissue directly from k-space data. This framework is based on principles from Spectro-dynamics, a novel method to infer dynamical information directly from severely undersampled spectral data with high spatiotemporal resolution, and sparse regression. As a proof-of-concept, the underlying equations of motion are identified for an actively forced linear elastic system using experimentally obtained k-space data. The systems studied in this abstract are successfully identified with errors of less than 2.5% on the system parameters, and their respective displacement fields are reconstructed accurately.

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Jinyu Wu¹, Tianyu Qiu¹, Zhangren Tu¹, Di Guo², and Xiaobo Qu^1
1Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, ²School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

Keywords: Data Processing, PET/MR

Nuclear Magnetic Resonance (NMR) has been a frequently-used analytical tool in many areas of modern biology, chemistry and medicine for decades. However, it is usually limited by a low Signal-to-Noise ratio (SNR). In practical applications, Signal Averaging (SA) with repeated samplings is required to improve the signal-to-noise ratio, which greatly increases the scanning time. In this paper, based on the characteristic that NMR time-domain signals can be decomposed exponentially, a model for denoising NMR spectroscopy based on exponential decomposition constraints is proposed. It can effectively improve the denoising ability and therefore save the scanning time.

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Chang-Le Chen¹, Mahbaneh Eshaghzadeh Torbati², Weiquan Luo³, Davneet Minhas³, Charles Laymon³, Seong Jae Hwang⁴, Ciprian Crainiceanu⁵, Pauline Maillard⁶, Evan Fletcher⁶, Charles DeCarli⁶, Howard Aizenstein^1,7, and Dana Tudorascu^7,8
1Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States, ²Intelligent System Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States, ³Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States, ⁴Department of Artificial Intelligence, Yonsei University, Seoul, Korea, Republic of, ⁵Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States, ⁶Department of Neurology, University of California Davis, Davis, CA, United States, ⁷Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States, ⁸Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States

Keywords: Data Processing, Data Processing, Harmonization

To broaden our knowledge of inter-scanner variability, we established a pipeline to linearly estimate the site effect by incorporating ComBat modeling and superpixel parcellation. We found that the variation prominently manifested in tissue contrast, noise level, and field inhomogeneity with respect to cross-site data. We further used the estimated parameters to harmonize images. The image quality and structural similarity of cross-scanner data can be improved after the harmonization procedure, and the variation in volumetric measures can also be reduced. This study provides further insight for the research focusing on the development of image harmonization methods.

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Javier Bisbal^1,2,3, Joaquin Mura⁴, Julio Sotelo^1,3,5, Hernán Mella^3,6, Cristóbal Arrieta^1,2,3, Pablo Irarrazaval^1,2,3,7, and Sergio Uribe^1,3,8
1Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, ²Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, ³Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile, ⁴Department of Mechanical Engineering, Universidad Técnica Federico Santa Maria, Santiago, Chile, ⁵School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile, ⁶School of Electrical Engineering, Pontificia Universidad Católica de Valparaiso, Valparaíso, Chile, ⁷Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, ⁸Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

Keywords: Data Processing, Machine Learning/Artificial Intelligence

4D flow MRI suffers from different sources of noise and aliasing artifacts. Recent denoising techniques are time-consuming or dependent of parameter estimation. We developed a physics informed neural network with divergence-free vector potential as a non-parametric denoising technique for 4D flow MRI. Results from simulated pulsatile flow and CFD vascular model shows significant noise reduction and aliasing correction. Future work includes comparison with other techniques on different types of data and uncertainty quantification.

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Berk Can Acikgoz^1,2, Adèle L.C. Mackowiak^1,2,3, Nils M.J. Plähn^1,2, Yasaman Safarkhanloo⁴, Eva S. Peper^1,2, Tom Hilbert^3,5,6, Giulia M.C. Rossi³, and Jessica A.M. Bastiaansen^1,2
1Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland, ²Translation Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, ³Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, ⁴Advanced Clinical Imaging Technology,Department of Cardiology, Inselspital, Bern University Hospital, Bern, Switzerland, ⁵Siemens Healthineers International AG, Lausanne, Switzerland, ⁶LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Keywords: Data Processing, Quantitative Imaging, phase-cycled bSSFP, fat fraction

A novel cluster-based sparse total-variation minimization (CSTV) dictionary matching algorithm for multi-compartment parameter mapping applied to a phase-cycled balanced steady state free precession (PC-bSSFP) signal profile is presented. A dictionary encoding off-resonance frequency and relaxation time ratio is matched to the measured profiles with the proposed method to recover the underlying fat fraction, proton density, and water-fat-separated images simultaneously. The performance of the proposed method is tested and compared with a Laplacian regularized non-negative least squares dictionary matching method in phantom and in vivo experiments.

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Anne Oyarzun-Domeño^1,2, Izaskun Cía ¹, Rebeca Echeverria-Chasco^2,3, María A. Fernández-Seara^2,3, Paloma L. Martin-Moreno^2,4, Nuria Garcia-Fernandez^2,4, Gorka Bastarrika^2,3, Javier Navallas^1,2, and Arantxa Villanueva^1,2,5
1Electrical, Electronics and Communications Engineering, Public University of Navarre, Pamplona, Spain, ²Health Research Insitute of Navarra, IdiSNA, Pamplona, Spain, ³Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain, ⁴Department of Nephrology, Clínica Universidad de Navarra, Pamplona, Spain, ⁵Institute of Smart Cities (ISC), Pamplona, Spain

Keywords: Data Processing, Perfusion

Renal perfusion quantification is of importance in the post-operative surveillance of the allograft in translated patients. Together with cortical perfusion measurement, there is a strong interest in the quantification of medullary perfusion values, which requires an additional segmentation step of renal compartments. We applied Gaussian Mixture Models over renal MRI dataset to automatically extract the labels for each compartment to separately calculate cortical and medullary perfusion values. Proposed method showed performance metrics above 85% against ground truth labels and correlation coefficient above 96% and 58% for cortical and medullary perfusion values comparing with ground truth perfusion values.

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Xin Li¹, Wei Huang¹, William D. Rooney¹, and Charles S Springer^1
1Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States

Keywords: Data Analysis, DSC & DCE Perfusion

Using DCE modeling to quantify prostate transcytolemmal water exchange effect faces the challenges of often insufficient interstitial contrast agent concentration as well as suboptimal temporal resolution. Using ultra-high temporal resolution prostate DCE data, the goal of this work is to investigate the impact of DCE-MRI temporal alignment between the arterial input function (AIF) and the tissue time-courses on water exchange quantification. Results here show that when perfect alignment is not achievable in practice, an earlier AIF bolus arrival bias will generally reduce the capability for water-exchange quantification using DCE-MRI.