Greg Hong^1,2,3, Tina Khazaee^1,2,3, Santiago F. Cobos^1,2,3, Spencer D. Christiansen^1,2, Junmin Liu², Maria Drangova^1,2,3, and David W. Holdsworth^1,2,3
1Medical Biophysics, Western University, London, ON, Canada, ²Robarts Research Institute, London, ON, Canada, ³Bone & Joint Institute, London, ON, Canada

Calcium sulfate (CS) is commonly used to deliver antibiotics to treat periprosthetic joint infection, which is a leading cause of early revision. There is an unmet need for non-invasive measurement of antibiotic release from CS, which would improve understanding of antibiotic delivery in-vivo. Through the use of a gadolinium-based contrast agent as a surrogate, this study shows that quantitative MRI (R2* and QSM) can be used to track diffusion-controlled release. We demonstrate this in a phantom study consisting of a cylindrical CS core surrounded by agar, where gadolinium diffuses out of the core and through the agar sample.

Megan F LaMonica¹, Megan E Poorman², David A Hormuth, II¹, Thomas E Yankeelov¹, and Kathryn E Keenan^2
1Biomedical Engineering, UT Austin, Austin, TX, United States, ²NIST, Boulder, CO, United States

Quantitative magnetic resonance imaging (qMRI) could complement qualitative MRI analysis by providing quantitative diagnostic information standardized across patients. However, it is difficult to determine how microscopic tissue properties affect MR signal. A microscopy- and MRI-compatible bioreactor was filled with different concentrations of fluorescent beads in gel to explore this relationship. Higher bead count was associated with lower ADC. A 59% ADC decrease was observed at 100 beads/mm³ when varying gradient pulse separation from 16 to 20 ms, illustrating sensitivity to different length scales per the Einstein-Smoluchowski relationship. This work is a step towards validating qMRI with fluorescence microscopy.

Octavia Bane^1,2, Jorge Daza³, Berengere Salome⁴, John Sfakianos³, Andrew Charap⁴, Kirolos Meilika³, Kennedy Okhawere³, Amir Horowitz⁴, Bachir Taouli^1,2, Ketan Badani³, and Sara Lewis^1,2
1Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ²BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ⁴Division of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States

In this prospective, single-center study, we evaluated the use of intravoxel incoherent motion model of diffusion (IVIM-DWI) and high-throughput urine proteomics associated with inflammation for diagnosis and characterization of small renal masses. IVIM-DWI parameters had good to excellent diagnostic performance in identifying clear-cell renal cell carcinomas (ccRCC), as well as in distinguishing between stages of ccRCC. In a subset of patients with IVIM-DWI and proteomics, we observed significant correlations between IVIM and proteomic analytes which had differential expression between ccRCC and non-ccRCC, and are involved in promoting the recruitment, expansion and activation of innate and adaptive immune cells.

Siamak Nejad-Davarani¹, Michelle Mierzwa¹, Thorsten Feiweier², Keith Casper³, and Yue Cao^1,4
1Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States, ²Siemens Healthcare GmbH, Erlangen, Germany, ³Department of Otolaryngology, University of Michigan, Ann Arbor, MI, United States, ⁴Department of Radiology, University of Michigan, Ann Arbor, MI, United States

In vivo microstructure imaging based on diffusion weighted imaging can provide valuable information on tumor characteristics and response to cancer therapy. An oscillating diffusion gradient spin-echo sequence was used to acquire DW images with short diffusion times, along with those acquired by a pulsed diffusion gradient spin-echo sequence. Using a random walk with barriers model, microstructure/diffusion parameters in primary and nodal head and neck tumors were estimated prior to, and after two weeks of cancer treatment. Results show feasibility of this method to detect significant changes in these parameters, across the two imaging time points.

Julien Lamy¹, Mary Mondino¹, and Paulo Loureiro de Sousa^1
1ICube, Université de Strasbourg-CNRS, Strasbourg, France

Erwin is an open-source Python toolbox dedicated to the computation of quantitative maps from MRI data. It provides a unified interface, through either its Python API or its command-line tool, to well-known qMRI methods (e.g. B₁ mapping using Actual Flip Angle (AFI) or T₁ mapping using Variable Flip Angle (VFA)) and external toolboxes (e.g. MRtrix or MEDI). Erwin also provides tools to create complex pipelines where the dependencies between steps are tracked: a change in a source image or a method parameter will only trigger the recomputation of a minimal set of subsequent steps.

Emanoel R. Sabidussi¹, Stefan Klein¹, Ben Jeurissen², and Dirk H. J. Poot^1
1Radiology, Erasmus Medical Center, Rotterdam, Netherlands, ²Department of Physics, Imec-Vision Lab, University of Antwerp, Antwerp, Belgium

In Diffusion MRI, the large variability of acquisition schemes limits broader use of Deep Learning for parameter estimation, with data-specific models needed for high-quality predictions. To reduce dependency on training data, we present dtiRIM, a recurrent neural network that learns a regularized solution to a model-based inverse problem. Using the diffusion model allows independent parameters (e.g. gradient directions) to also influence the estimation. We show that a single dtiRIM model predicts diffusion parameters for multiple datasets with lower error than the current state-of-the-art. Our study suggests that dtiRIM has the potential to be the first general learning method for DTI.

Aparna Karnik¹, Xin Shen², Humberto Monsivais³, Antonia Sunjar², Ali Caglar Özen⁴, Serhat Ilbey⁴, Mark Chiew⁵, Mukerrem Cakmak⁶, Joseph Rispoli^1,2, and Uzay Emir^3
1Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States, ²Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States, ³School of Health Sciences, Purdue University, West Lafayette, IN, United States, ⁴Department of Radiology, Medical Physics, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, ⁵Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, ⁶School of Materials Engineering, Purdue University, West Lafayette, IN, United States

We developed a method of acquiring high-resolution MRI for the generation of Quantitative Susceptibility Mapping (QSM) using Ultra-short echo time (UTE) MRI with a novel 3D rosette k-space trajectory. This method was used to generate high-resolution (0.94 mm³ isotropic) magnetic susceptibility maps in an Iron-Chloride phantom and the human brain.  Generated maps were then compared with the susceptibility maps reconstructed from low-resolution data acquired using multi-echo UTE acquisition. Susceptibility values were comparable with current literature demonstrating the promise of this novel method in the generation of QSM.

Padriac Hooper^1,2, Monique Tourell^1,2, Kieran O'Brien^2,3, Jin Jin^2,3, Simon Daniel Robinson^1,4,5,6, and Markus Barth^1,2,7
1Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, ²ARC Training Centre for Innovation in Biomedical Imaging Technology, Brisbane, Australia, ³Siemens Healthcare Pty Ltd, Brisbane, Australia, ⁴High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, ⁵Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria, ⁶Department of Neurology, Medical University of Graz, Graz, Austria, ⁷School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

The phantom was developed to cover the full range of physiological and pathological magnetic susceptibility seen in iron- and calcium-containing tissue, using 4 materials within the one phantom. The phantom facilitates quality assurance for acquisition strategies and post-processing tools.

Siyun Jung¹, Soohyun Jeon¹, Yoonho Nam², Hyun Gi Kim³, and Dong-Hyun Kim^1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, ²Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea, Republic of, ³Department of Radiology, Catholic University of Korea, Eunpyeong St. Mary's Hospital, Seoul, Korea, Republic of

In conventional QSM methods, it is difficult to analyze gray matter subcortical in pediatrics, because of small contrast difference between gray matter and white matter. In addition, there are no deep learning approaches that reflect the characteristics of the pediatric brain. In this study, we propose an unsupervised model-based joint-loss deep learning network for pediatric QSM, PedQ-NET. The proposed method achieved better edge preservation on the deep gray matter subcortical areas in pediatric QSM. According to the evaluation of in-vivo pediatric data, QSM generated by PedQ-NET shows enhanced edges in the subcortical areas and clear details with reduced artifacts.

Javier Silva^1,2,3, Carlos Milovic⁴, Mathias Lambert^1,2,3, Cristian Montalba^2,3, Cristobal Arrieta^1,2,3, Sergio Uribe^2,3,5, and Cristian Tejos^1,2,3
1Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, ²Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, ³Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, ⁴Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ⁵Department of Radiology, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile

Compared to Quantitative Susceptibility Mapping (QSM) in the brain, abdominal QSM faces additional issues due to the presence of gas and fatty tissue. Recent works in abdominal QSM are more focused on its feasibility as a biomarker for disease diagnosis than improving or assessing the robustness and quality of the reconstructions. In this work, we present an abdominal QSM phantom with realistic tissue textures. Our flexible simulation pipeline allows emulating different stages of diseases and MRI signal contributions. Our reconstruction experiments show the potential of our phantom to compare QSM algorithms in different scenarios.

Patrick Kinz¹ and Lothar Schad^1
1Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

MRI-based mapping of oxygen extraction fraction with QSM and qBOLD is a non-invasive diagnostic tool with many possible applications. But current reconstruction methods based on quasi-Newton (QN) methods are very dependent on accurate parameter initialization. Artificial Neural Networks showed a lot of potential in our previous works. Using a Convolutional Neural Network improves the reconstruction, since neighboring voxels can provide additional information. Using a GESFIDE sequence to sample the qBOLD signal instead of a standard mGRE that samples only the FID, improves the reconstruction accuracy of R₂, Y and χ_nb a lot.

Daniel Ridani^1,2, Noée Ducros-Chabot^1,2, Mathieu Dehaes^2,3, and Benjamin De-Leener^1,2,4
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montreal, QC, Canada, ²Research Center, Ste-Justine Hospital University Centre, Montreal, QC, Canada, ³Department of Radiology, Radio-oncology, and Nuclear Medicine, Université de Montréal, Montreal, QC, Canada, ⁴Department of Computer and Software Engineering, Polytechnique Montréal, Montreal, QC, Canada

Neonatal asphyxia is a condition that causes lack of oxygen in the infant brain which lead to adverse neurodevelopmental outcomes. MRI provides a very clear image of the brain which  lead to a detection of a  variety of brain abnormalities. Quantitative susceptibility mapping (QSM) is a MRI tool to quantify brain oxygenation. In this study, we developed QSM reconstruction algorithms and tested them in data acquired in  patients following neonatal asphyxia. Results include the comparison of five methods to generate QSM images in the brain specific segmented regions. Our preliminary data suggest that image intensity depends on the reconstruction method.