Collin J. Buelo^1,2, Ruiyang Zhao^1,2, Julia V. Velikina¹, Steffen Bollmann³, Ante Zhu⁴, Qing Yuan⁵, Mounes Aliyari Ghasabeh⁶, Stefan Ruschke⁷, Dimitrios C. Karampinos⁷, David T. Harris¹, Ryan J. Mattison⁸, Michael R. Jeng⁹, Ivan Pedrosa⁵, Ihab R. Kamel⁵, Shreyas Vasanawala¹⁰, Takeshi Yokoo^5,11, Scott B. Reeder^1,2,8,12,13, and Diego Hernando^1,2
1Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ³3School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia, ⁴GE Global Research, Niskayuna, NY, United States, ⁵Radiology, University of Texas Southwestern Medical Center, Dallas, TX, United States, ⁶Radiology, The Johns Hopkins University, Baltimore, MD, United States, ⁷Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany, ⁸Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁹Pediatrics - Hematology & Oncology, Stanford University, Palo Alto, CA, United States, ¹⁰Radiology, Stanford University, Palo Alto, CA, United States, ¹¹Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States, ¹²Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ¹³Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

Quantitative susceptibility mapping (QSM) of the liver is an emerging method to quantify liver iron concentration (LIC). Multiple new methods have recently been introduced for QSM in the liver. These methods include CobraChi, a deep learning-based method, as well as an L2-optimization based method. This work evaluates the performance of these liver QSM methods across multiple centers and MRI vendors.

Miha Fuderer^1,2, Oscar van der Heide¹, Hongyan Liu¹, Cornelis A.T. van den Berg¹, and Alessandro Sbrizzi^1
1Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ²Department of Radiology, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, Netherlands

In non-steady-state multi-parametric quantitative MRI (e.g. MR-STAT or MR Fingerprinting (MRF)), it is a non-trivial task to devise good sequences of time-varying flip angles. Recent work addresses this issue, albeit assuming a single-voxel approach, i.e. the k-space encoding is not taken into account when optimizing. In this work we show, using examples from MR-STAT reconstructions and using a Cramer-Rao based BLAKJac analysis, that two apparently similar sequences can have vastly different outcomes when applied in an actual (2-dimensional) measurement setup - showing that the context of encoding is very relevant.

Ivar J.H.G. Wamelink¹, Beatriz Padrela¹, Joost P.A. Kuijer¹, Yi Zhang², Frederik Barkhof^1,3, Henk J.M.M. Mutsaerts¹, Elsmarieke van de Giessen¹, and Vera C. Keil^1
1Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam Medisch Centrum, Amsterdam, Netherlands, ²Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China, ³UCL Institutes of Neurology and Healthcare Engineering, London, United Kingdom

Amide proton transfer (APT) chemical exchange saturation transfer (CEST) MRI is a potentially useful clinical technique to image brain tumors, but its reproducibility was not yet investigated in detail. Our 3T scan-rescan protocol on volunteers and glioma patients revealed high within-session and slightly lower in-between-session and between-day reproducibility, with highest reproducibility occipitally and centrally in the brain and lowest at the skull base. The within-session reproducibility for patients was also good, both intratumoral and extratumoral. These results show that APT-CEST at 3T MRI renders reproducible values allowing for clinical monitoring of metabolic brain tumors.

siya sherif¹, Ali Aghaeifar^2,3, Kerrin Pine⁴, Belinda Ding⁵, Maryam Seif⁶, Evelyne Balteau¹, Daniel Nanz^7,8, Christine Bastin¹, Eric Salmon^1,9, Pierre Maquet^1,9, Gilles Vandewalle¹, Christopher T Rodgers ⁵, Patrick Freund⁶, Nikolaus Weiskopf^4,10, Christophe Phillips^1,11, and Martina F Callaghan^3
1GIGA-Cyclotron Research Centre - In Vivo Imaging, University of Liège, Liège, Belgium, ²MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom, ³Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom, ⁴Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ⁵Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom, ⁶Spinal cord injury Center, University of Zurich, Zurich, Switzerland, ⁷Swiss Center for Musculoskeletal Imaging, Zurich, Switzerland, ⁸University of Zurich, Zurich, Switzerland, ⁹Department of Neurology, University Hospital of Liège, Liège, Belgium, ¹⁰Felix Bloch Institute for Solid State Physics, Leipzig University, Leipzig, Germany, ¹¹GIGA - In Silico Medicine, University of Liège, Liège, Belgium

Repeatability is key to the utility of quantitative MRI, which promises standardised measures with biological relevance. Here we tested a candidate ultra-high resolution (0.6mm isotropic)  multi-parameter mapping protocol at five 7T sites. Repeatability was good, with B0 and B1+ field inhomogeneities being the limiting factor. MTsat had the lowest repeatability and PD the highest. R1 and R2* were intermediate. Repeatability was also lowest in temporal and occipital cortices.  These observations were consistent across sites.

Barbara Daria Wichtmann¹, Steffen Albert², Daniel Pinto dos Santos³, Ulrike Irmgard Attenberger¹, and Bettina Baessler^4
1Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany, ²Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ³Department of Radiology, University Hospital Cologne, Cologne, Germany, ⁴Institute of Diagnostic and Interventional Radiology, University Hospital Wuerzburg, Wuerzburg, Germany

Radiomics enables the extraction of quantitative features from medical images, potentially augmenting the characterization of healthy and diseased tissue. Before these features can be routinely used as biomarkers in clinical practice, however, their repeatability and reproducibility must be ensured. This study seeks to investigate feature repeatability in an in-vivo, clinical test-retest dataset of prostate cancer patients. Our results show that the majority (71.8%) of radiomic features extracted from in-vivo, clinical T2-weighted images was not repeatable, emphasizing the need for repeatability and reproducibility studies.

Difei Wang¹, Rüdiger Stirnberg¹, and Tony Stöcker^1,2
1German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, ²Department of Physics and Astronomy, University of Bonn, Bonn, Germany

We investigated the reproducibility of multiparameter mapping (MPM) using multi-echo skipped-CAIPI 3D-EPI at 3T. Compared to >15 minutes of FLASH-MPM, EPI-MPM under 3 minutes achieved considerably high repeatability. In only one fifth of the total scan time of FLASH, the resulting CoVs of EPI were only 1.2-2.2 times larger than FLASH for R₁, PD and MTsat, while the CoV of EPI for R₂* was even smaller. Minor differences of the observed parameter estimates can be attributed to the intrinsic difference between EPI and FLASH sequence timing.

John P. Neri¹, Matthew F. Koff¹, Kevin M. Koch², and Ek T. Tan^1
1Hospital for Special Surgery, New York, NY, United States, ²Medical College of Wisconsin, Milwaukee, WI, United States

Conventional echo-planar imaging (EPI) DWI suffers from substantial artifact when imaging orthopedic hardware. DWI with multi-acquisition with variable resonance image combination (MAVRIC) can reduce susceptibility artifact around metal implants. The purpose of this work was to evaluate the quantitative accuracy of DWI-MAVRIC near metal components using a diffusion phantom. DWI-MAVRIC sequences were acquired in the presence of no metal, cobalt-chromium, titanium, and stainless steel. It was found that DWI-MAVRIC can accurately measure apparent diffusion coefficient (ADC) in the presence of metal. Among tested metals, stainless steel creates the greatest artifact that prevented the acquisition of accurate ADC data.

Lejla Alic¹, Sanneke C Willekens^1,2, Henk-Jan M.M. Mutsaerts², Jan Petr³, Netteke A.Y.N. Schouten-van Meeteren^4,5, Maarten M.H. Lequin⁶, and Evita E.C. Wiegers^2
1Magnetic Detection & Imaging Group, University of Twente, Enschede, Netherlands, ²Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands, ³Helmholtz-Zentrum Dresden-Rossendorf, Institute for Radiopharmaceutical Cancer Research, Dresden, Germany, ⁴Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam, Netherlands, ⁵Department of Neuro-oncology, Princess Máxima Centre, Utrecht, Netherlands, ⁶Radiology, University Medical Center Utrecht, Utrecht, Netherlands

ASL-MRI is reported as an option to assess potentially heterogeneous physiological processes important for tumour treatment. Therefore, we explored the heterogeneity in normalised CBF as an imaging biomarker for assessment of treatment effect in pLGG. There is a noticeable effect of chemotherapy observed as a change in texture of healthy appearing brain tissue. A high difference in texture between treated and non-treated patients for non-enhancing tumour part is observed, suggesting that texture, based on co-occurrence matrices, is suitable as an imaging biomarker for assessment of treatment effect in pLGG.

Ratthaporn Boonsuth¹, Rebecca S Samson¹, Francesco Grussu^1,2, Marco Battiston¹, Torben Schneider³, Masami Yoneyama⁴, Ferran Prados^1,5,6, Carmen Tur^1,7, Sara Collorone¹, Rosa Cortese¹, Claudia AM Gandini Wheeler-Kingshott^1,8,9, and Marios C Yiannakas^1
1NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ²Radiomics Group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, ³Philips Healthcare, Guildford, Surrey, United Kingdom, ⁴Philips Japan, Minatoku, Tokyo, Japan, ⁵Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ⁶E-Heath Centre, Universitat Oberta de Catalunya, Barcelona, Spain, ⁷Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d’Hebron Institute of Research, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain, ⁸Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, ⁹Brain Connectivity Research Centre, IRCCS Mondino Foundation, Pavia, Italy

Advanced diffusion-weighted imaging (DWI) methods are powerful diagnostic and research tools when applied in the central nervous system (CNS). However, the use of DWI methods to study individual nerves outside the CNS in vivo, such as the sciatic nerve, has been hampered by a number of technical challenges. In this study, we explore the feasibility of acquiring multi-shell DWI metrics in the sciatic nerve using reduced field-of-view echo planar imaging and report results from a reproducibility assessment in in healthy controls.

Chen Solomon¹, Omer Shmueli¹, Tamar Blumenfeld-Katzir¹, Dvir Radunsky¹, Noam Omer¹, Neta Stern¹, Shai Shrot^2,3, Moti Salti^4,5, Hayit Greenspan¹, and Noam Ben-Eliezer^6,7
1Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, ²Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel, ³Tel Aviv University, Tel Aviv, Israel, ⁴Brain Imaging Research Center, Soroka Medical Center, Beer Sheva, Israel, ⁵University Medical Center, Ben Gurion University, Beer Sheva, Israel, ⁶Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States, ⁷Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

Computer assisted detection (CAD) of pathology in MRI scans may provide higher sensitivity to tissue changes. We present rigorous comparison of CAD vs. conventional radiologic evaluation of multiple sclerosis (MS) lesions. A psychophysical experiment was performed, where radiologists and a deep neural-network were asked to detect artificial MS lesions, synthetically simulated on T₂-weighted FLAIR images, and at 8 levels of severity. Odds ratio analysis indicated that the human vision is less sensitive to low-severity lesions. This suggests that CAD can improve early detection of tissue abnormalities in the brain.

Maarten Naeyaert¹, Tim Vanderhasselt¹, and Hubert Raeymaekers^1
1Radiology, Universitair Ziekenhuis Brussel, Brussels, Belgium

Six volunteers were scanned two times on two different 3T scanners (GE, Philips) using the 3D-QALAS sequence. The intracranial and brain parenchymal volume and myelin content were determined and the brain segmented in cerebrospinal fluid and white and grey matter using synthetic MRI. Bland-Altman plots, Dice coefficient and coefficient of variation indicate that the test-retest variability is very small, but there is an inter-vendor bias for CSF, GM and WM. MyC, BPV and ICV have only a very limited inter-vendor bias. At least for GM and BPV, the expected inter-scanner variation, besides the bias, is below the clinically relevant threshold.
 

Oscar van der Heide^1,2, Miha Fuderer^1,2, Hongyan Liu^1,2, Cornelis A.T. van den Berg^1,2, and Alessandro Sbrizzi^1,2
1Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ²Department of Radiology, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, Netherlands

MR-STAT is a quantitative MRI technique that obtains multiple tissue parameter maps (e.g. T1, T2) from a single, short scan. In this study we perform an accuracy and repeatability study of MR-STAT using a Cartesian FISP-based sequence by repeatedly scanning Eurospin gel tubes and comparing the results against reference measurements. We observe coefficients of variation between 0.5 % and 1.5 % for T1 and between 2.0 % and 7.2 % for T2. The mean relative errors as compared to the reference measurements are -1.5 % for T1 and 5.8 % for T2.

Laura Carretero^1,2, Maggie Fung³, Pablo García-Polo¹, Daniel Litwiller⁴, Marc Lebel⁵, Graeme C McKinnon⁶, and Mario Padrón^7
1GE Healthcare, Madrid, Spain, ²Rey Juan Carlos University, Madrid, Spain, ³GE Healthcare, New York, NY, United States, ⁴GE Healthcare, Colorado, CO, United States, ⁵GE Healthcare, Calgary, AB, Canada, ⁶GE Healthcare, Waukesha, WI, United States, ⁷Clínica Cemtro, Madrid, Spain

MRI-based cartilage imaging shows biochemical and microstructural changes at early stages of osteoarthritis before changes become visible with structural MRI and arthroscopy. However, clinical application of T2 mapping is driven by high variability and suboptimal reproducibility, besides long examination times. The purpose of this study is to accelerate T2 mapping technique and apply a novel DL reconstruction method to develop a fast and robust way to access T2 relaxometry. Phantom and in-vivo testing demonstrated that DL reconstructed accelerated images provide increased consistency compared to conventional reconstruction and implies a great step into an extensive clinical adoption.

Karthik R Sreenivasan¹, Dietmar Cordes¹, Virendra Mishra¹, and Le H Hua^1
1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States

New advances in quantitative MRI (qMRI) techniques provide basic MRI properties such as proton density (PD) that can act as a direct surrogate of the tissue integrity in the voxel. PD may help demonstrate differences on an individual level in patients with multiple sclerosis. In this study, we wanted to compare the within-subject reproducibility of qMRI-based PD measures. Variability in the estimated PD was low and fell within the limits for reliability. These results suggest that in-vivo estimates of PD using qMRI are reproducible within the subject in the same scanner and could play a vital role in clinical studies.