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Markus Huemer¹, Clemens Stilianu¹, and Rudolf Stollberger^1,2
1Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria, ²BioTechMed Graz, Graz, Austria

Keywords: Data Analysis, CEST & MT

In Chemical Exchange Saturation Transfer (CEST) MRI one common analysis technique is Multipool-Lorentzian fitting of the resulting Z-Spectrum. Multiple Lorentzian lineshapes are fitted to the measured data. The lineshape of the direct water saturation (DS) can deviate substantially from a Lorentzian depending on the saturation scheme and $$$B_1^+$$$. To prevent a cross-influence of the DS on the CEST and increase the fit accuracy we propose the use of a Pseudo-Voigt lineshape for the water line.

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Haiyang Chen¹ and Chenxi Hu^1
1The Institute of Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University (SJTU), Shanghai, China

Keywords: Data Analysis, Data Processing, registration

We propose a deformable groupwise registration method using a locally low-rank (LLR) dissimilarity to estimate myocardial strain from cine MRI images. The proposed method eliminates the drift effect commonly observed in the optical flow and sequentially pairwise registration, facilitating more accurate strain estimation in the diastolic phase. Compared to the globally low-rank dissimilarity, LLR dissimilarity shows slightly better tracking accuracy by imposing the low-rank property in local image regions rather than the whole image. Experiments on a large public cine MRI dataset demonstrates the accuracy of the proposed method on tracking and strain estimation.

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Nick Scholand^1,2, Martin Schilling³, Martin Heide³, and Martin Uecker^1,2,3
1Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria, ²German Centre for Cardiovascular Research (DZHK), Partner site Göttingen, Göttingen, Germany, ³Department of Interventional and Diagnostic Radiology, University Medical Center Göttingen, Göttingen, Germany

Keywords: Software Tools, Software Tools

Digital Reference Objects (DRO) are important for developing imaging and reconstruction techniques especially in medical imaging applications. They allow a stepwise increase in complexity during testing of methods by simulating reproducible datasets.

In this work we present a variety of tools the BART toolbox provides for the creation and simulation of DROs. We present a workflow for designing DROs with Bézier curves using vector graphics applications which are then imported into BART.
Furthermore, we show simulation of analytical k-space data with 2D and 3D coil sensitivities and combining them with simulated signal curves to obtain k-space data for complex scenarios.

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Wen Da Lu^1,2, Mark Cameron Nelson^2,3, Ilana Ruth Leppert², Pietro Bontempi⁴, Simona Schiavi^4,5, Christopher Dennis Rowley^1,2, Alessandro Daducci⁴, and Christine Lucas Tardif^1,2,3
1Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada, ²McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada, ³Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada, ⁴Department of Computer Science, University of Verona, Verona, Italy, ⁵Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genova, Italy

Keywords: Data Analysis, Tractography & Fibre Modelling, Magnetization Transfer, Diffusion-Weighted Imaging, Tractometry

Tractometry is a technique used to investigate the microstructure variations along white matter tracts or to reconstruct microstructure-weighted connectivity matrices. However, partial volume effects from crossing fibers bias the individual fiber measurements and conceal subtle differences. Here, we compare MTR tractometry to a novel approach using dual-encoded MT-weighted DWI analyzed using COMMIT to disentangle the MTR signal of individual white matter fibers. The results show a broader distribution in edge MTR values for the dual-encoding approach in comparison to tractometry. Both techniques show similar spatial MTR patterns, however the dual-encoded MT-weighted DWI approach shows a higher pattern of inter-individual variability.

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Mohammadreza Rezaei-Dastjerdehei¹ and Pierre LeVan^2
1Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada, ²Department of Radiology, University of Calgary, Calgary, AB, Canada

Keywords: Data Analysis, Segmentation, EEG Electrodes Localization, T1-weighted MRI

The localization of EEG sources is one of the fundamental approaches to facilitate the interpretation of EEG data. However, the accuracy of source localization depends on the exact knowledge of the position of the electrodes on the scalp, which currently requires time-consuming and/or expensive approaches. Here, an automatic method is proposed that retrieves the electrode positions by localizing the curvature changes in T1-weighted MRI images caused by electrodes. The results show an average detection sensitivity of ~96.4%, with an average position error of 4.23 mm for all subjects.

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Chenhui Li¹, Jinhuan Xie¹, Liling Long¹, Huiting Zhang², and Yang Song^2
1The First Affiliated Hospital of Guangxi Medical University, Nanning, China, ²MR Scientific Marketing, Siemens Healthineers, Shanghai, China

Keywords: Data Analysis, Segmentation, Habitats

The method of delineating the ROI of whole lesions on quantitative parameter images and then averaging them for comparison did not accurately quantify heterogeneity. In this study, we adopted a habitats analysis method combined with tissue cellularity and blood flow information from IVIM model to segment whole tumor to four subregions to predict microvascular invasion (MVI) in hepatocellular carcinoma. The results show that habitats analysis predicts MVI positivity with an accuracy of 70.19%, and the averaged value of each parameter in whole tumor was not predictive for MVI. This provides a good starting point for further application of this method.

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Tianjia Zhu^1,2, Minhui Ouyang^1,3, Kay Sindabizera¹, Juri Kim², and Hao Huang^1,3
1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, ²Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States, ³Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

Keywords: Data Processing, Pediatric, Neuro

High-resolution infant diffusion MRI (dMRI) data poses specific challenges such as severe motion artifacts, long acquisition time for multi-shell data, and low signal-to-noise (SNR). We present a robust image analysis pipeline for high fidelity kurtosis and tensor fitting of 1.2mm isotropic infant brain dMRI that allows us to efficiently analyze in-vivo dMRI data despite the considerable technical challenges specific to infant imaging. State-of-the-art preprocessing including slice-to-volume motion correction and susceptibility-by-movement correction is combined with advanced self-supervised learning-based denoising to produce high fidelity diffusion tensor and diffusion kurtosis fitting.

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Yutaka Hoshiyama¹, Chunqi Wang², Hong Yang², Hideki Ota^3,4, Atsuro Masuda⁴, Masahiro Kawabata⁴, Hideaki Kutsuna¹, Kensuke Shinoda¹, and Yoshimori Kassai^1,3
1MRI Systems Division, Canon Medical Systems Corporation, Ohtawara, Japan, ²Research & Development Center, Canon Medical Systems (China) Co., Ltd, Beijing, China, ³Department of Advanced MRI Collaboration Research, Tohoku University Graduate School of Medicine, Sendai, Japan, ⁴Department of Diagnostic Radiology, Tohoku University Hospital, Sendai, Japan

Keywords: Data Analysis, Contrast Agent, Dynamic

We propose an automated method to present candidates of the arterial phase as the optimal time points from the liver contrast-enhanced MRI images based on the time intensity curve (TIC) analysis of the several ROIs. In clinical situation, there may be some differences of TIC due to a speed of uptake and washout and a quality of the image. In this study, the robustness of our proposed automated method was investigated by utilizing stack-of-stars dynamic images with various pseudo contrast enhancement effects. The results showed that our proposed automated method was applicable for such effects.

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Ho-Ching Yang¹, Mario Dzemidzic^1,2, Qiuting Wen¹, Larry D Riggen Jr³, Steven P Broglio⁴, Michael A McCrea⁵, Thomas W McAllister⁶, Jaroslaw Harezlak⁷, and Yu-Chien Wu^1,8
1Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States, ²Department of Neurology, Indiana University School of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States, ³Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, India, ⁴Michigan Concussion Center, University of Michigan, Ann Arbor, MI, United States, ⁵Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States, ⁶Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States, ⁷Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, United States, ⁸Stark Neurosciences Research Initiative, Indiana University School of Medicine, Indianapolis, IN, United States

Keywords: Data Analysis, Traumatic brain injury, Subject-specific analysis, Heterogeneous, Concussion

Sport-related concussion (SRC) injury inclines to cause subject-specific brain region abnormalities. This study applied subject-specific analysis that accounts for inter-subject variation to investigate heterogeneity of white matter alterations in a sample from a large national multicenter study, the Concussion Assessment, Research and Education Consortium. Our results demonstrated that subject-specific white matter abnormalities in SRC can be uncovered by calculating the extreme Z-score maps based on a template generated from normally distributed diffusion tensor imaging metric values in non-contact sports controls. Our finding indicates that the SRC-induced white matter abnormalities show heterogeneous spatial distribution across participants.

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Khoi Minh Huynh¹, Sang Hun Chung¹, Yueh Lee¹, and Pew-Thian Yap^1
1Department of Radiology and Biomedical Research Imaging Center, UNC Chapel Hill, Chapel Hill, NC, United States

Keywords: Data Processing, Lung

Denoising methods can leverage common information from multiple image volumes for better noise reduction. However, performance can degrade with inter-volume misalignment or contrast changes. We proposed a block-matching denoising method for effective noise removal and robustness to inter-volume differences.

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Xin Wang¹, Yin Guo², Kaiyu Zhang², Gador Canton³, Niranjan Balu³, Thomas S. Hatsukami⁴, Mahmud Mossa-Basha⁵, and Chun Yuan^3
1Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States, ²Department of Bioengineering, University of Washington, Seattle, WA, United States, ³Department of Radiology, University of Washington, Seattle, WA, United States, ⁴Department of Surgery, University of Washington, Seattle, WA, United States, ⁵Department of Radiology, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States

Keywords: Data Processing, Visualization

This work aims to develop an algorithm for straightened curved planar reformation (CPR), i.e., extracting the 2D cross-sectional images along a vessel centerline in a vessel wall MRI scan. Previous methods determine the orientation of CPR images in a fixed way, leading to undesired rotation along the centerline. Our method instead calculates the local coordinate system with respect to previous slices, thus keeping the continuity of cross-sections. Our CPR results show a better readability of the straightened vessel image, especially when one wants to utilize the spatial information among adjacent CPR slices for a more comprehensive and accurate medical analysis.

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Josephine H Naish^1,2, Marta Tibiletti¹, Christopher Short^3,4, Tom Semple^3,4, Simon Padley^3,4, Jane C Davies^3,4, and Geoff JM Parker^1,5
1Bioxydyn Ltd, Manchester, United Kingdom, ²MCMR, Manchester University NHS Foundation Trust, Manchester, United Kingdom, ³National Heart & Lung Institute, Imperial College London, London, United Kingdom, ⁴Royal Brompton Hospital, Guy's & St Thomas’ Trust, London, United Kingdom, ⁵Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom

Keywords: Data Analysis, Lung, Bayes

Lung oxygen-enhanced MRI can provide regional information relating to lung function, but voxel-wise parameter estimation is hampered by low SNR. Here we present a hierarchical Bayesian approach to voxel-wise parameter estimation implemented in R and the probabilistic programming language Stan.

In both simulations and in OE-MRI data acquired in patients with cystic fibrosis, the Bayesian approach results in substantially less noisy parameter maps compared to conventional non-linear least squares estimation.

Run Xu¹, Guanwu Li¹, Dan YU², Yongming Dai², and Xinyue Liang^2
1Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China, ²Central Research Institute, United Imaging Healthcare, Shanghai, China

Keywords: Data Analysis, Cancer

This study aimed to develop multiparametric physiologic MRI-based spatial habitat analysis and to validate the association between the habitats and the molecular subtypes. The distinct tumor habitats were identified using multiple MRI-parameter maps, then applied to calculate the volume fraction and fractal dimension (FD) and to investigate their diagnosis value. The combination of FD and volume fraction improves the discriminatory ability for TNBC from non-TNBC with an AUC to 0.951 from 0.853.

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Fangxu Xing¹, Riwei Jin², Imani Gilbert³, Georges El Fakhri¹, Jamie L. Perry³, Bradley P. Sutton², and Jonghye Woo^1
1Radiology, Harvard Medical School, Boston, MA, United States, ²University of Illinois at Urbana-Champaign, Champaign, IL, United States, ³East Carolina University, Greenville, NC, United States

Keywords: Data Analysis, Data Analysis, Speech, motion analysis, atlas

Dynamic magnetic resonance imaging has become increasingly efficient at capturing speech deformations of the velopharyngeal region in real time. With previously developed dynamic vocal tract atlases, quantification of group deformation statistics of a population in speech has become possible in a common atlas space. However, subject-specific deformation characteristics as an underlying property tend to be hidden in the spatial and temporal alignment process of atlas construction. We present a registration-based deformation characterization method that extracts subject-specific motion variations in two layers of registration steps. A dataset of fifteen human subjects is processed to reveal unique deformation patterns of each subject.

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Marta Vuozzo¹, Max Zimmermann², Mystele Tendonge³, Petros Martirosian², Manfred Kneilling⁴, Fritz Schick², Bernd Pichler³, and Andreas Schmid^2
1Werner Siemens Imaging Center, University Hospital Tübingen, Tübingen, Germany, ²University Hospital Tübingen, Tübingen, Germany, ³Univeristy Hospital Tübingen, Tübingen, Germany, ⁴Univerity Hospital Tübingen, Tübingen, Germany

Keywords: Data Analysis, Cancer, Multimodal

Solid tumors exhibit intratumoral heterogeneity, which is related to therapy efficacy. Since biopsies represent a small part of the tumor, multimodal imaging techniques provide panoptic cancer characterization. We developed an acquisition and analysis protocol based on multiparametric MRI to classify and provide a holistic characterization of intratumoral heterogeneity and correlate it with positron emission tomography and histology. We applied MRI to characterize phenotypic changes induced by antiPD-L1 therapy response. Results reveal that models trained exclusively with MRI data provide biologically relevant maps of phenotypes showing intratumoral heterogeneity, but also allow non-invasive identification of tumors that respond or resist to therapy.

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Yonni Friedlander^1,2, Dante P.I. Capaldi³, Norm Konyer¹, Melanie Kjarsgaard², Carmen Venegas^2,4, Parameswaran Nair^2,4, and Sarah Svenningsen^1,2,4
1Imaging Research Centre, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada, ²Firestone Institute of Respiratory Health, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada, ³Deparment of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States, ⁴Division of Respirology, McMaster University, Hamilton, ON, Canada

Keywords: Data Analysis, Lung

Using free-breathing ¹H MRI, diaphragm excursion was measured in 9 patients with severe asthma at two visits 4-months apart. At each visit, patients with asthma were imaged before and after administration of a bronchodilator. Diaphragm excursion measured at the two visits were not significantly different from one another (mean bias=0.34cm, p=0.07) and were well correlated (r=0.53, p=0.004), demonstrating repeatability of the measurement. In addition, diaphragm excursion was significantly increased following bronchodilator administration (p=0.01).

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Jasmine D Cakmak¹, Reza Azarpazhooh², Alexander Khaw², and Udunna Anazodo^1,2
1McGill University, Montreal, QC, Canada, ²Western University, London, ON, Canada

Keywords: Data Processing, Segmentation, Synthetic MRI

Recycling large-scale clinical data to retrospectively study associations of brain imaging variables with emerging environmental risk factors is a sustainable approach in the growing field of neuroepidemiology. Here, we evaluated the utility of a deep learning tool to increase the resolution of clinical (1.5T) T1-weighted MRI by comparing global assessment of gray matter volume (GMV) and cortical thickness (CT) to 3T research scans. Overall, the resolved 1.5T images had higher (18%) GMV and CT compared to 3T and were more biased than unresolved images. Further analysis in larger cohorts using improved segmentation approaches could validate recycling of enhanced clinical scans.  

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Ken Sakaie¹, Katherine Koenig¹, and Mark J. Lowe^2
1The Cleveland Clinic, Cleveland, OH, United States, ²Imaging Institute, The Cleveland Clinic, Cleveland, OH, United States

Keywords: Data Analysis, Alzheimer's Disease

Diffusion Tensor Imaging measures in fornix may serve as a biomarker in Alzheimer’s disease. However, partial volume averaging between the fornix and surrounding cerebrospinal fluid imposes systematic bias that can conflate tissue microstructure with atrophy. Free Water Elimination methods have been proposed as a solution, but comparison between these methods is limited. We perform direct comparison between the approaches in terms of repeatability and bias.

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Nico J J Arezza^1,2, Mohammad Omer¹, and Corey A Baron^1,2
1Medical Biophysics, Western University, London, ON, Canada, ²Centre for Functional and Metabolic Mapping, Robarts Research Institute, London, ON, Canada

Keywords: Data Processing, Neuro

Water diffusion anisotropy is a diffusion MRI metric that is sensitive to brain injury and neurodegeneration. Microscopic fractional anisotropy (μFA) is a new metric of diffusion anisotropy that is immune to crossing fiber effects, unlike traditional FA, which enables probing of axon integrity in gray matter and crossing white matter tracts. However, μFA is underestimated  in voxels adjacent to cerebrospinal fluid (CSF) due to partial volume contamination, which is particularly problematic in cortical gray matter and tissue adjacent to ventricles. Here, we demonstrate a free water elimination method to remove the CSF signal from μFA measurements in four healthy volunteers.

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Joanna Chappell¹, Nada Mufti^1,2, Hassna Irzan¹, Pat O’Brien³, George Attilakos³, Magda Sokolska⁴, Priya Narayanan³, Trevor Gaunt⁵, Paul Humpheries⁵, Premal Patel⁵, Elspeth Whitby⁶, Eric Jauniaux^2,3, J. Ciaran Hutchinson⁵, Neil Sebire⁵, David Atkinson⁷, Giles Kendall^2,4, Sebastien Ourselin ¹, Tom Vercauteren¹, Anna L David^2,3, and Andrew Melbourne^1
1School of Biomedical Engineering and Imaging Sciences (BMEIS), Kings College London, London, United Kingdom, ²Elizabeth Garret Anderson Institute for Women’s Health, University College London, London, United Kingdom, ³University College London Hospital, London, United Kingdom, ⁴Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, United Kingdom, ⁵Great Ormond Street Hospital for Children, London, United Kingdom, ⁶Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom, ⁷Centre for Medical Imaging, University College London, London, United Kingdom

Keywords: Data Processing, Placenta

Feature Selection Models provide a ranking of pathological MRI markers able to predict the outcome of Placenta Accreta Spectrum Disorder, which could be used to aid in clinical decision-making and improve maternal outcome. The potential being to reduce the workload of radiologists by establishing the most clinically relevant pathological MRI markers that predict outcome. Our results found three pathological markers to have the highest ranking to the outcomes with an average accuracy of 75% using a Random Forest Selection Model and Boruta algorithm.