View Presentation Video

Do-Wan Lee¹, Young Chul Cho², Mi-hyun Kim^3,4, Yeon Ji Chae⁵, Su Jung Ham³, Yousun Ko¹, Seongwon Na², Youngbin Shin², Nari Kim⁶, Dong‐Cheol Woo^5,7, and Kyung Won Kim^2
1Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, Republic of, ²Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea, Republic of, ³Trialinformatics Inc., Seoul, Korea, Republic of, ⁴Department of Radiation Science & Technology, Jeonbuk National University, Jeonju, Korea, Republic of, ⁵Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, Republic of, ⁶Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, Republic of, ⁷Convergence Medicine Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea, Republic of

Keywords: Visualization, Visualization

In nonclinical animal research for new drug development, the US FDA mandates to manage data in accordance with clinical data interchange standards consortium (CDISC) standard for exchange of nonclinical data (SEND). Older nonclinical trial data, i.e., legacy nonclinical trial data, contain highly useful information but those data were created in various data formats by different researchers. The present study specifically describes how to implement the CDISC SEND data standards for the legacy nonclinical trial data with MRI. Standardization of animal MRI data with CDISC SEND enables us to utilize legacy data for next-generation drug development and new knowledge creation.  

View Presentation Video

Sethu K. Boopathy Jegathambal¹, Ilana Ruth Leppert², David A. Rudko², and Amir Shmuel^2
1Department of Biomedical Engineering, McGill University, Montreal, QC, Canada, ²McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, Montreal, QC, Canada

Keywords: Data Acquisition, Ex-Vivo Applications, Brain Ex-Vivo MRI

Depending on the medium, it might be difficult to maintain the brain stable during ex-vivo imaging. In addition, the SNR might not be homogeneous. We propose using brain and container-specific 3D-printed models for holding the brain. The method requires preliminary imaging of the container and the brain. Then, a 3D-printed model is created, with profiles that match the surface of the brain on one side and the inner surface of the container on the other. The holder is designed to maintain the brain stable in space, where the SNR is high and homogeneous, while allowing contact with the immersion fluid.

View Presentation Video

Pranav Sompalle¹, Ansh Roge¹, Michael Sobota¹, Amogh Hiremath², Sree Harsha Tirumani³, Leonardo Kayat Bittencourt³, Ryan Ward⁴, Justin Ream⁴, Andrei Purysko⁴, Anant Madabhushi⁵, Satish Viswanath¹, and Rakesh Shiradkar^5
1Case Western Reserve University, Cleveland, OH, United States, ²Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, ³University Hospitals Cleveland Medical Center, Cleveland, OH, United States, ⁴Cleveland Clinic Foundation, Cleveland, OH, United States, ⁵Emory University, Atlanta, GA, United States

Keywords: Data Processing, Data Processing, Image processing

Radiologists’ ability to detect clinically-significant prostate cancer on MRI is affected by image quality. While subjective guidelines are provided by the prostate imaging reporting and data system (PI-RADS) and other recent methods, there is a need to develop objective and quantitative metrics of MRI quality. In this multi-reader study, we derive MRI quality metrics using an image processing-based open-source software tool and evaluate their association with radiologist-assigned PI-RADS scores to detect clinically-significant prostate cancer (csPCa). We observe that higher quality MRI is associated with improved detection of csPCa on MRI.

View Presentation Video

Chunwei Ying¹, Spencer Waddle², Nkemdilim Igwe³, Niral J. Patel⁴, Alexander K. Song², Charu Balamurugan⁴, Lori C. Jordan⁴, Dengrong Jiang⁵, Hanzhang Lu⁵, Andria L. Ford³, Manus J. Donahue², and Hongyu An^1,3
1Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States, ²Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States, ³Department of Neurology, Washington University School of Medicine, St Louis, MO, United States, ⁴Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, United States, ⁵Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Keywords: Data Analysis, Brain, repeatability; reproducibility

We evaluated dual-site and dual-vendor test-retest repeatability and reproducibility of cerebral oxygen extraction fraction (OEF) measured with asymmetric spin echo (ASE) and T2-Relaxation-Under-Spin-Tagging (TRUST) MRI. The intra-site intra-class correlation coefficient (ICC) of global ASE-OEF were 0.952 and 0.919 for each site. The intra-site ICC of TRUST-OEF based on the bovine and the HbA model were 0.810 and 0.792 at site 1, and 0.928 and 0.924 at site 2. The inter-site inter-vendor correlation was 0.849 for ASE-OEF and 0.814 for TRUST-OEF based on the bovine model. ASE-OEF was significantly associated with TRUST-OEF (p<0.001).

View Presentation Video

Ahmad Algohary¹, Evangelia I. Zacharaki¹, Mohammad Alhusseini¹, Adrian Breto¹, Veronica Wallaengen¹, Isaac Xu¹, Sandra Gaston¹, Patricia Castillo¹, Sanoj Punnen¹, Benjamin Spieler¹, Matthew Abramowitz¹, Alan Dal Pra¹, Oleksandr Kryvenko¹, Alan Pollack¹, and Radka Stoyanova^1
1The University of Miami, Miami, FL, United States

Keywords: Machine Learning/Artificial Intelligence, Prostate

In this work, we introduce a novel automated triple-reference intensity normalization method for T2W images with the aim of obtaining consistent longitudinal measurements leading to improved quantitative assessment of radiation treatment outcome for prostate cancer patients.

View Presentation Video

Lucas McCullum¹, John Wood², Maria Gule-Monroe³, Ho-Ling Anthony Liu⁴, Melissa Chen³, Komal Shah³, Noah Nathan Chasen³, Vinodh Kumar³, Ping Hou⁴, Jason Stafford⁴, Caroline Chung⁵, Moiz Ahmad⁴, Christopher Walker⁴, and Joshua Yung^4
1Medical Physics, MD Anderson Cancer Center, Houston, TX, United States, ²Enterprise Development and Integration, MD Anderson Cancer Center, Houston, TX, United States, ³Neuroradiology, MD Anderson Cancer Center, Houston, TX, United States, ⁴Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States, ⁵Data Governance & Provenance, MD Anderson Cancer Center, Houston, TX, United States

Keywords: Machine Learning/Artificial Intelligence, Data Analysis, Image Quality

A dataset of 3D-GRE and 3D-TSE brain 3T post contrast T1-weighted images as part of a quality improvement project were collected and shown to five neuro-radiologists who evaluated each sequence for image quality and artifacts. The same scans were processed using the MRQy tool for objective, quantitative image quality metrics. Using the combined radiologist and quantitative metrics dataset, a decision tree classifier with a bagging ensemble approach was trained to predict radiologist assessment using the quantitative metrics. The resulting AUCs for each classification task were above 0.7 for all combinations of sufficiently represented classes and qualitative image metrics.

View Presentation Video

Navid Reyhanian¹, Michael Mullen², Taylor Froelich², Michael Garwood², and Jarvis Haupt^1
1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, ²Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

Keywords: Data Analysis, Magnets (B0), optimization; static field inhomogeneity; artifact correction

Many methods deployed for Magnetic Resonance Imaging (MRI) suffer from degraded image quality when the static magnetic field ($$$B_0$$$) is nonuniform, as $$$B_0$$$ inhomogeneity causes different types of image artifacts and distortions. Image correction methods require a prior estimate of the field map. In this work, we study the problem of static $$$B_0$$$ inhomogeneity estimation from distorted and undistorted image pairs, despite undistorted images being noisy. The estimated field map is later utilized to correct distorted images. The efficacy of the proposed approach is demonstrated for different encoding methods in the presence of large magnetic field inhomogeneity via extensive simulations.

View Presentation Video

Bo-Jhen Su¹, Pei-Hsin Wu¹, Rajiv S Deshpande², and Felix W Wehrli^2
1Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, ²Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

Keywords: Data Analysis, Oxygenation

SvO₂ and OEF were estimated via susceptometry-based oximetry (SBO). The impact of user-defined ROI was investigated in this study. The results show that (1) the consistency of SvO₂ estimation depends on operators’ comprehension of MR-oximetry, (2) SvO₂ might be under-estimated with manual ROI selection, and (3) the operator-induced bias can be eliminated by the semi-automatic analysis. The preliminary data indicate the feasibility of the constructed semi-automatic analysis for consistent estimation of SvO₂ among operators and its potential for bulk analysis and clinical use.

View Presentation Video

Zhuoyang Gu¹, Lianghu Guo¹, Qing Yang¹, Xinyi Cai¹, Tianli Tao¹, Sifan He¹, Qian Wang¹, He Qiang², Dinggang Shen¹, and Han Zhang^1
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, ²United Imaging Healthcare Co., Ltd., Shanghai, China

Keywords: Parallel Imaging, Data Acquisition, Quality Assessment

MRI acquisition from certain populations is difficult. For instance, infant MRI without sedation during wakefulness has a high failure rate due to head motion. Thus, a shorter scan time is required. Increasing studies focused on fast MRI acquisition but few studies examined their qualities. We argue that only calculating common image quality metrics (e.g., SNR) may not meet the satisfaction of brain MRI analysis with fast acquisition techniques. A whole-process quality assessment method is thus proposed comparing different fast acquisition techniques with practical guidelines. In future, comprehensive quality assessment on fast MRI acquisitions can become a study routine.

Thuy Thanh Dao¹, Angela Renton^1,2, Aswin Narayanan³, Markus Barth¹, and Steffen Bollmann^1
1School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia, ²Queensland Brain Institute, The University of Queensland, Brisbane, Australia, ³Australian National Imaging Facility, The University of Queensland, Brisbane, Australia

Keywords: Software Tools, Data Processing

Glatard et al [1] found that the operating system version affected the computational results of neuroimaging analysis pipelines. Their work demonstrated that this reproducibility issue is caused by the accumulated floating-point differences across operating systems. Neurodesk (www.neurodesk.org) approaches this problem by packaging every pipeline into a software container and thereby can control the underlying dependencies for each application regardless of the operating system. In this work, a brain tissue segmentation pipeline from the FMRIB Software Library (FSL) was compared with a containerised version from Neurodesk. The comparison revealed that Neurodesk provides computational reproducibility compared with local installations.

View Presentation Video

Remika Mito^1,2, Heath Pardoe¹, Robert Smith¹, Jacques-Donald Tournier^3,4, David Vaughan^1,2,5, Mangor Pedersen⁶, and Graeme Jackson^1,2,5
1Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, ²Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia, ³Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁴Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁵Department of Neurology, Austin Health, Melbourne, Australia, ⁶Auckland University of Technology, Auckland, New Zealand

Keywords: Data Analysis, Diffusion/other diffusion imaging techniques, Data harmonization

Diffusion MRI data are known to be sensitive to scanner and site effects, for which harmonization methods have been developed. However, harmonization methods have not yet been applied to many advanced diffusion imaging metrics. In this work, we apply the ComBat harmonization approach to fixel-based analysis. ComBat successfully minimizes scanner-related differences in fibre density and cross-section, and performs similarly to the inclusion of scanner as a nuisance regressor during whole-brain fixel-based analysis. Importantly, ComBat can now readily be used within the fixel-based framework, which will enable large multi-centre studies to implement this approach in the future.

View Presentation Video

Woojin Jung¹, Seongjae Mun¹, Jingyu Ko¹, and Koung Mi Kang^2
1AIRS Medical, Seoul, Korea, Republic of, ²Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of

Keywords: Machine Learning/Artificial Intelligence, Brain

We validated the brain volumetric results with 3D T1 weighted images with accelerated scans reconstructed by FDA-cleared deep learning-based software (SwiftMR, AIRS Medical). Acceleration scans with three different acceleration levels were simulated using k-space undersampling, and the image quality and brain volume measures were evaluated.  In addition, we acquired conventional and accelerated scans from each participant to evaluate the reliability between conventional and accelerated scans reconstructed by SwiftMR and inter-method reliability between different brain segmentation software.  As a result, brain volume measures with accelerated scans with deep learning-based reconstruction were in good agreement with those of the corresponding conventional scan.

View Presentation Video

Yuya Saito¹, Koji Kamagata¹, Toshiaki Taoka², Christina Andica¹, Wataru Uchida¹, Kaito Takabayashi¹, Mana Owaki^1,3, Seina Yoshida^1,3, Keigo Yamazaki^1,3, Shohei Fujita^1,4, Akifumi Hagiwara¹, Junko Kikuta¹, Toshiaki Akashi¹, Akihiko Wada¹, Keigo Shimoji¹, Masaaki Hori⁵, Shinji Naganawa⁶, and Shigeki Aoki^1
1Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan, ²Department of Innovative Biomedical Visualization (iBMV), Nagoya University Graduate School of Medicine, Aichi, Japan, ³Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan, ⁴Department of Radiology, University of Tokyo, Tokyo, Japan, ⁵Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan, ⁶Department of Radiology, Nagoya University Graduate School of Medicine, Aichi, Japan

Keywords: Data Processing, Data Processing, Diffusion MRI, ALPS-index, DTI-ALPS, Harmonization, multisite study

Diffusion tensor image analysis along the perivascular space (DTI-ALPS) is a promising noninvasive method for indirectly evaluating the glymphatic system. However, the ALPS-index calculated from diffusion MRI data collected at multiple sites should be harmonized to avoid site-related effects. We applied the combined association test (ComBat), which uses regression of covariates with empirical Bayes, for harmonizing the ALPS-index. ComBat mitigated site-related effects, increased statistical power to differentiate Alzheimer’s disease and cognitive normal, and improved the correlation between the ALPS-index and cognitive function. Thus, ComBat harmonization can be applied to evaluate the glymphatic system using the ALPS-index in large multisite studies.

View Presentation Video

Daniel L Schwartz¹, Xin Li², Fred Lesage³, Andreas Linninger⁴, Mohammad Jamshidi⁴, Becky Stone⁵, and William Rooney^5
1Advanced Imaging Research Center/Neurology, OHSU, PORTLAND, OR, United States, ²Advanced Imaging Research Center, OHSU, PORTLAND, OR, United States, ³Electrical Engineering, Polytechnique Montreal, Montreal, QC, Canada, ⁴Bioengineering, UIC, Chicago, IL, United States, ⁵Advanced Imaging Research Center, OHSU, Portland, OR, United States

Keywords: Data Processing, DSC & DCE Perfusion, Whole-brain bolus arrival times

Dynamic susceptibility contrast imaging has used bolus arrival time (BAT) in tissue, relative to an arterial input function (AIF), as a nuisance variable when deriving perfusion metrics (e.g. blood flow). However, BAT reflects physiological changes in both fluid dynamics of feeding vasculature and capillary inflow. This work presents a reproducible approach for reliable and repeatable quantification of an AIF and whole brain BAT, which itself may be germane to chronic and acute cerebrovascular disease (CVD), such as white matter hyperintensities, age-associated CVD, and stroke.

View Presentation Video

Miguel Guevara¹, Davy Cam², Jacques Badagbon², Stephane Roche², Michel Bottlaender¹, Yann Cointepas¹, Jean-François Mangin¹, Ludovic de Rochefort², and Alexandre Vignaud^1
1Neurospin, Paris, France, ²Ventio, Marseille, France

Keywords: Data Processing, Quantitative Susceptibility mapping, Cloud-computing

Introduction of a pipeline for computing the Quantitative Susceptibility Mapping (QSM) from non-optimal MRI data in a secure cloud-infrastructure. The pipeline allows the computation of clean and exploitable QSM, thanks to a phase pre-processing to reduce the presence of artifacts in the 7T MRI data.

View Presentation Video

Steven Sourbron¹, Joao Almeida e Sousa¹, Alexander Daniel², Charlotte Buchanan², Ebony Gunwhy¹, Eve Lennie¹, Kevin Teh¹, Steve Shillitoe¹, David Morris³, Andrew Priest⁴, David Thomas⁵, and Susan Francis^2
1University of Sheffield, Sheffield, United Kingdom, ²University of Nottingham, Nottingham, United Kingdom, ³University of Edinburgh, Edinburgh, Scotland, ⁴University of Cambridge, Cambridge, United Kingdom, ⁵University College London, London, United Kingdom

Keywords: Software Tools, Data Processing

DICOM is the universally recognized standard for medical imaging, but reading and writing from DICOM databases remains a challenging task for most data scientists. The dbdicom package was developed to provide an intuitive programming interface for reading and writing data from entire DICOM databases, and replaces confusing DICOM-native concepts by language and notations that will be more familiar to data scientists working in python. The package is available under an open license but is in the early stages of development and currently rolled out in 3 multi-vendor, multi-center studies.

View Presentation Video

Patricia S. Lan¹, Xinzeng Wang², Alessandro Scotti³, Praveen Jayapal⁴, Pingni Wang¹, Arnaud Guidon⁵, and Andreas M. Loening^4
1GE Healthcare, Menlo Park, CA, United States, ²GE Healthcare, Houston, TX, United States, ³GE Healthcare, Columbus, OH, United States, ⁴Department of Radiology, Stanford University, Stanford, CA, United States, ⁵GE Healthcare, Boston, MA, United States

Keywords: Diffusion/other diffusion imaging techniques, Diffusion/other diffusion imaging techniques

Diffusion-weighted imaging (DWI) is a key component of identifying prostate tumors on MRI. Multi-shot DWI techniques (e.g. MUSE) have been shown to enable high resolution prostate DWI and, compared to single-shot DWI, reduce distortion artifacts due to rectal gas and hip implants at the expense of increased scan time. In this study, we evaluated a CNN-based deep learning (DL) image reconstruction method for MUSE. Our results indicate that for high b-value images the DL-based reconstruction improved perceived image quality even with half the original NEX, suggesting potential scan time reduction using DL.

View Presentation Video

Pavan Poojar¹ and Sairam Geethanath^1
1Accessible Magnetic Resonance Laboratory, Biomedical Imaging and Engineering Institute, Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, New York, NY, United States

Keywords: Data Acquisition, Low-Field MRI

Low-field MR scanners are low-cost, portable, and requires lesser space. In this study, we performed in vitro phantom repeatability study on a 48 mT scanner (Multiwave technologies) by scanning the phantom 3 sessions in a day (11 AM, 2 PM, and 5 PM) at 3 locations. During each scan, we measured the temperature, humidity, and F0 to see their effect on the image. We scanned 9 sequences in the study to monitor noise, F0 and B0. The difference in temperature and humidity was determined for each session.The line intensity profilesrevealed that VLF scanner produces consistent B0 maps and qualitative images.

Renjie Yang¹, Changsheng Liu¹, Weiyin Vivian Liu², Liang Li¹, and Yunfei Zha^1
1Renmin Hospital of Wuhan University, Wuhan, China, ²GE Healthcare, Beijing, China

Keywords: Artifacts, Surgery

Subsidence assessment is an important indicator for postoperative follow-ups for ACDF patients and normally performed on radiographs. It is still challenging to image ACDF patients using conventional MRI for the measure of segment height is influenced by the metal artifacts. In this study, MAVRIC SL showed better inter-rater agreement on measurements of segment height compared to FSE T1WI. 20 cases on MAVRIC SL and 6 cases on FSE T1W1 among 22 patients showed the measurements of segment height was less than the maximum allowed difference (2mm). This study demonstrated that MAVRIC SL has a strong potential in the subsidence assessment.

View Presentation Video

Hai Nan Ren¹, Li Jun Qian¹, Xu Hua Gong¹, Yan Zhou¹, and Yang Song^2
1Radiology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China, ²MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Quality control

This study investigated the feasibility and performance of quality assessment of hepatic magnetic resonance (MR) images using a deep-learning-based segmentation and radiomics approach. We used a pre-trained deep learning model to segment the liver on different contrast-enhanced MR phases and then extracted quantitative features to assess the image quality by a machine learning method. The results showed that the radiomics model had a high performance for image quality identification in both training and test sets. This suggests that it was feasible to automate the identification of image quality by using radiomics approaches.