Alejandro Morales Martinez^1,2, Francesco Caliva¹, Claudia Iriondo^1,2, Sarthak Kamat¹, Sharmila Majumdar¹, and Valentina Pedoia^1,2,3
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Graduate Program in Bioengineering, University of California, Berkeley, Berkeley, CA, United States, ³Center for Digital Health Innovation (CDHI), University of California San Francisco, San Francisco, CA, United States

Bone and cartilage segmentation models were trained and validated with a segmented dataset of 40 and 176 3D DESS MRI volumes respectively. The trained models were used to run inference on 20,989 3D DESS MRI volumes from the Osteoarthritis Initiative dataset. Biomarkers such as femoral bone shape, cartilage thickness and cartilage T2 average values were extracted from the segmentations. Point clouds representing each biomarker were transformed into spherical coordinates and merged using different fusion strategies. The spherical maps were used to train an OA diagnosis model with a test specificity, sensitivity and AUC was 84.1%, 78.7%, and 89.7% respectively.

Benjamin Marty^1,2
1NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France, ²NMR Laboratory, CEA/DRF/IBFJ/MIRCen, Paris, France

In this study, a fast 3D MR fingerprinting sequence with water and fat separation (3D MRF T1-FF) was developed for simultaneous measurement of FF and water T1 in the skeletal muscles of patients with fat infiltrations. The precision and accuracy of the sequence was evaluated on a multi-vial phantom and in vivo proofs of concept were obtained in the legs of a healthy volunteer before and after plantar dorsi-flexions and at rest in a patient suffering from inclusion body myositis.

Melissa Tamara Hooijmans¹, Jithsa R. Monte², Martijn Froeling³, Jos Oudeman⁴, Johannes L. Tol⁵, Mario Maas², Gustav J. Strijkers¹, and Aart J. Nederveen^2
1Department of Biomedical Engineering & Physics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands, ²Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands, ³Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands, ⁴Department of Orthopaedic Surgey, University Medical Center Utrecht, Utrecht, Netherlands, ⁵Department of Orthopaedic Surgey, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands

41 athletes with an acute hamstring injury underwent MRI examination of their injured leg and the uninjured contralateral leg at three different time points: (1) within one week after the index injury (baseline), (2) two weeks after baseline, and at (3) Return to Play (RTP). Baseline DTI values (MD, RD and the three eigenvalues) were elevated compared to control hamstring muscles and decreased during the RTP phase. qT2 values were elevated after the index injury and did not change over time. DTI is promising for monitoring recovery of hamstring injuries.

Xiufeng Li¹, Casey P. Johnson¹, and Jutta Ellermann^1
1Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

Perfusion imaging can provide critical information to assess vasculature status and tissue viability of knee bone marrow. Arterial spin labeling, as a non-invasive and non-contrast-enhanced perfusion imaging approach, is well-suited for routine assessment of bone marrow perfusion, longitudinal monitoring of disease progression and repeated evaluation of therapy response. Recently, knee epiphyseal bone marrow ASL imaging has been demonstrated at 3T with promising results by using FAIR ss-FSE method. However, the ss-FSE image readout only supports single-slice acquisitions. To overcome this limitation, we implemented and evaluated FAIR RESOLVE for multi-slice knee epiphyseal bone marrow perfusion imaging.

Po-hung Wu¹, Misung Han¹, Roland Krug¹, Jing Liu¹, Gabby B. Joseph¹, Thomas Link¹, and Galateia Kazakia^1
1Department of Radiology and Biomedical Imaging, University of California - San Francisco, San Francisco, CA, United States

Type 2 diabetes is known to increase fracture risk, possibly through the development of pathological cortical bone porosity. However, the mechanisms of pathological pore growth are not understood. We hypothesize that T2D patients will display altered vascularization within cortical pores due to microvascular disease. In this study, 15 T2D patients and 22 controls were imaged by HR-pQCT and DCE-MRI to analyze vessel and perfusion metrics (eg. vessel density, transition time). The study results suggest that T2D patients have altered vessel distribution and perfusion characteristics, and that microvascular disease may be a factor in diabetic bone disease.

Agazi Samuel Tesfai¹, Andreas Vollmer², Moritz Braig¹, Johannes Fischer¹, Ute Ludwig¹, and Michael Bock^1
1Dept. of Radiology, Medical Physics, Medical Center - University of Freiburg, Freiburg, Germany, ²Dept. of Oral and Craniomaxillofacial Surgery, Center for Dental Medicine, Medical Center - University of Freiburg, Freiburg, Germany

Detection of root canals is vital for dental diagnosis, however it is difficult to locate these anatomies within sub-millimeter dimension. To determine the ability of MRI to display such structures, a bovine tooth with different sized artificial cavities was prepared. It was evaluated with a preclinical 7T system and a clinical 3T MR system against cone beam CT. 7T measurements with UTE offer precise distinction of cavities up to 200µm. 3T UTE allows only differentiation of 1000µm cavities due to blurring. Tooth immersed in contrast agent solution allows localization up to 200µm cavity with spin echo sequence and negative contrast.

Casey P. Johnson^1,2, Ferenc Toth¹, Alexandra R. Armstrong¹, Harry K. W. Kim^3,4, and Jutta M. Ellermann^2,5
1Veterinary Clinical Sciences Department, University of Minnesota, Saint Paul, MN, United States, ²Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, ³Texas Scottish Rite Hospital for Children, Dallas, TX, United States, ⁴Department of Orthopaedic Surgery, UT Southwestern Medical Center, Dallas, TX, United States, ⁵Department of Radiology, University of Minnesota, Minneapolis, MN, United States

This study tested whether T2 and T1ρ relaxation times are sensitive in detecting early-stage osteonecrosis of the femoral head in piglet model under in vivo conditions and at clinical 3T MRI. This study builds on recent ex vivo 9.4T studies assessing T2 and T1ρ in the piglet model. We also evaluated apparent diffusion coefficient for comparison. We found that T2, T1ρ, and ADC were all significantly increased in the ischemic vs. contralateral control femoral heads in n=6 piglets one week after onset of ischemia. These methods may be clinically useful to detect and characterize early-stage osteonecrosis to inform treatment decisions.

Daniel Smith¹, Andrea Zonnino¹, Fabrizio Sergi¹, and Curtis Johnson^1
1University of Delaware, Newark, DE, United States

In this study, we propose to use a technique called MM-MRE to identify states of contraction of individual forearm muscles based on the measurement of muscle shear wave speed. Using a custom protocol and passive driver device, we scanned four subjects through 45 MRE scans each in three wrist positions during five torque application states. We found significant correlations between increased wave speed and higher applied torques, in both agonist and antagonist motions. The results indicate that MM-MRE is an effective measurement tool for analyzing the contractile state of individual forearm muscle during isometric contractions of the wrist joint.

Arjun D. Desai^1,2, Francesco Caliva³, Claudia Iriondo³, Naji Khosravan⁴, Aliasghar Mortazi⁴, Sachin Jambawalikar⁵, Drew Torigian⁶, Jutta Ellerman⁷, Mehmet Akçakaya⁸, Ulas Bagci⁴, Radhika Tibrewala³, Io Flament³, Matt O'Brien³, Sharmila Majumdar³, Mathias Perslev⁹, Akshay Pai⁹, Christian Igel⁹, Erik B. Dam⁹, Sibaji Gaj¹⁰, Mingrui Yang¹⁰, Kunio Nakamura¹⁰, Xiaojuan Li¹⁰, Cem M. Deniz¹¹, Vladimir Juras¹², Ravinder Regatte¹¹, Garry E. Gold², Brian A. Hargreaves², Valentina Pedoia³, and Akshay S. Chaudhari^2
1Electrical Engineering, Stanford University, Stanford, CA, United States, ²Radiology, Stanford University, Stanford, CA, United States, ³Radiology, University of California San Francisco, San Francisco, CA, United States, ⁴University of Central Florida, Orlando, FL, United States, ⁵Radiology, Columbia University Medical Center, New York, NY, United States, ⁶Radiology, University of Pennsylvania, Philadelphia, PA, United States, ⁷Radiology, University of Minnesota, Minneapolis, MN, United States, ⁸Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, ⁹Computer Science, University of Copenhagen, Copenhagen, Sweden, ¹⁰Biomedical Engineering, Cleveland Clinic, Cleveland, OH, United States, ¹¹Radiology, New York University Langone Health, New York, NY, United States, ¹²Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria

Cartilage thickness can be predictive of joint health. However, manual cartilage segmentation is tedious and prone to inter-reader variations. Automated segmentation using deep-learning is promising; yet, heterogeneity in network design and lack of dataset standardization has made it challenging to evaluate the efficacy of different methods. To address this issue, we organized a standardized, multi-institutional challenge for knee cartilage and meniscus segmentation. Results show that CNNs achieve similar performance independent of network architecture and training design and, given the high segmentation accuracy achieved by all models, only a weak correlation between segmentation accuracy metrics and cartilage thickness was observed.

Peter Cipriano¹, Daehyun Yoon¹, Ryan Penticuff¹, Yingding Xu², Ian Carroll³, and Sandip Biswal^1
1Stanford University, Stanford, CA, United States, ²Stanford University, Palo Alto, CA, United States, ³Stanford University, Redwood City, CA, United States

Six patients with suspected cerebrospinal fluid leak but in whom no site of leakage had been identified and six controls underwent simultaneous, whole-body [18F]FDG PET/MRI imaging. Increased [18F]FDG uptake was found in paraspinal structures in all six patients and was significantly greater than in the corresponding areas of controls. Temporary but significant relief in symptoms resulted from blood patches placed at locations coinciding with PET/MRI abnormalities.

Yongye Chen¹, Yang Zhang², Enlong Zhang¹, Xiaoying Xing¹, Qizheng Wang¹, Huishu Yuan¹, Min-Ying Su², and Ning Lang^1
1Department of Radiology, Peking University Third Hospital, Beijing, China, ²Department of Radiological Sciences, University of California, Irvine, CA, United States

For patients suspected to have spinal metastasis, a confirmed pathological diagnosis is needed to proceed with appropriate treatment. This study applied quantitative radiomics to differentiate 5 groups of patients with metastatic cancers in the spine, including 28 lung, 11 breast, 7 kidney, 11 prostate and 18 thyroid. The analysis was done on post-contrast images. A total of 107 features, including 32 first order and 75 texture, were extracted for each case by using PyRadiomics. The group differentiation was done by using multi-class support vector machine (SVM). The overall accuracy was 80%, with the highest accuracy of 27/28=96% for lung mets.

Yu Song¹, Qingwei Song¹, Aibo Wang², Ailian Liu¹, Yanwei Miao¹, Nan Zhang¹, Haonan Zhang¹, and Lizhi Xie^3
1Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China, ²Department of Radiology, Peking University Third Hospital, Beijing, China, ³GE Healthcare, MR Research, Beijing, China

Diabetes is a metabolic disease that leads to a high risk of fracture related to osteoporosis. Noninvasive and reliable assessment of osteoporosis is essential for clinical practice. The aim of this study was to explore the agreement of the Proton Density Fat Fraction (PDFF) values of lumbar vertebra measured by Magnetic Resonance Imaging (MRI) IDEAL-IQ sequences at different field strengths and to investigate the value of IDEAL-IQ in the assessment of osteoporosis risk in diabetic.

James MacKay^1,2, Lauren Watkins³, Garry Gold³, and Feliks Kogan^4
1Radiology, University of East Anglia, Norwich, United Kingdom, ²Radiology, University of Cambridge, Cambridge, United Kingdom, ³Radiology, Stanford University, Stanford, CA, United States, ⁴Stanford University, Stanford, CA, United States

Synovial inflammation is hypothesised to play a role in the development and progression of osteophytes in osteoarthritis (OA).

 

Here we use hybrid 3T PET-MRI to perform simultaneous bilateral knee MR imaging of 11 participants (22 knees) with knee OA. We use ¹⁸F-NaF PET to quantify osteophyte metabolic activity and dynamic contrast enhanced MR imaging to quantify synovitis.

 

We demonstrate that synovitis adjacent to osteophytes is more intense (as quantified by the DCE parameter K^trans) than the whole-joint average, and that there is a significant association between increased osteophyte metabolic activity (as quantified by PET SUV_max) and intensity of adjacent synovitis.