Hannah Parish¹, Shu-Fu Shih^2,3, Shahnaz Ghahremani³, Sevgi Gokce Kafali^2,3, Kara Calkins⁴, and Holden Wu^2,3
1David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States, ²Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States, ³Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States, ⁴Department of Pediatrics, University of California Los Angeles, Los Angeles, CA, United States

Lean body mass (LBM) is an important measure of metabolic ability. Decreased LBM during infancy may be associated with impaired growth and increased risk for future metabolic disease. This study measured skeletal muscle area as a surrogate for LBM in infants using free-breathing MRI. Psoas and paraspinal muscle areas were measured in 14 infants at the L4-L5 intervertebral disc level. Cross-sectional area of the thigh muscles was measured at the right midthigh in 8 infants. Infants with higher birth weights and those born large for gestational age had larger muscle areas than infants born lighter and/or small for gestational age.

Ricardo Flores¹, Fei Tan¹, Michael Ohliger¹, and Peder Larson^1
1Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States

In this abstract, we conducted a clinical evaluation of a prototype lightweight, flexible pediatric RF Coil. We evaluated the image quality on phantoms, an adult volunteer, and four pediatric clinical patients. For the phantom scans, identical 2D GRE protocols were prescribed between arrays and scanners for study reproducibility and accurate image comparisons. For the pediatric clinical patients, different anatomical studies were clinically ordered. Each study was reviewed clinically and found to achieve diagnostic quality with excellent tissue contrast, coverage, and SNR. 

Junhao Zhang¹, Nicolas Acosta¹, Vishwanatha Mitnala Rao¹, Pin-Yu Lee¹, Sabrina Gjerswold-Selleck¹, Zihan Wan¹, Xinyang Feng¹, Jeffrey A. Lieberman², Scott A. Small³, and Jia Guo^2
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, ²Department of Psychiatry, Columbia University, New York, NY, United States, ³Department of Neurology, Columbia University, New York, NY, United States

Jay Fu¹, William Hou², Muge Karaman^3,4, and Xiaohong Joe Zhou^3,4,5
1Watchung Hills Regional High School, Warren, NJ, United States, ²Montgomery High School, Skillman, NJ, United States, ³Center for MR Research, University of Illinois at Chicago, Chicago, IL, United States, ⁴Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States, ⁵Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States

Imaging-based determination of tumor grade is highly desirable, particularly for pediatric brain tumors where surgical biopsy carries considerable risks. We have applied a novel diffusion model – varying diffusion curvature (VDC) model, with two b-value ranges (0-2000 sec/mm² and 0-4000 sec/mm²), to differentiate between low- and high-grade pediatric brain tumors on 70 patients. Our results showed that the combination of the VDC parameters substantially outperformed the ADC for differentiating low- from -high grade pediatric brain tumors. Increasing the maximal b-value from 2000 to 4000 sec/mm² did not noticeably improve the performance.

Xiaoyun Liang^1,2, Chun-Hung Yeh^3,4, and Peter J. Anderson^1,5
1Victorian Infant Brain Study (VIBeS), Murdoch Children's Research Institute, Melbourne, Australia, ²Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia, ³Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan, ⁴Department of Psychiatry, Chang Gung Memorial Hospital, Taoyuan, Taiwan, ⁵Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia

In this study, we investigated whether prenatal alcohol exposure (PAE) disrupts the coupling strength between SC and static FC or dynamic FC using multimodal connectomes. Greater coupling strengths were identified in the PAE T1 and PAE T1-T3 groups within males, indicative of brain structural and functional changes in children with PAE. Distinct age effects on coupling strength were also revealed. Further, the reduction of assortative coefficients indicated that PAE could compromise the robustness of brain networks. These findings suggest that multimodal connectomes, especially integrating network dynamics, could be advantageous to revealing brain structural and functional changes.

Ilaria Gabusi^1,2, Giuseppe Pontillo^2,3, Simona Schiavi^1,4, Sara Bosticardo^1,5, Maria Petracca⁶, Matteo Battocchio^1,7, Antonio Pisani⁸, Arturo Brunetti², Alessandro Daducci¹, and Sirio Cocozza^2
1Department of Computer Science, University of Verona, Verona, Italy, ²Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy, ³Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy, ⁴Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy, ⁵Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, ⁶Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy, ⁷University of Sherbrooke, Sherbrooke, QC, Canada, ⁸Department of Public Health, University of Naples "Federico II", Naples, Italy

Central nervous system involvement in Fabry disease (FD) patients is known, but the effects on brain connectivity have never been explored at macro-scale level. Here, we investigate both structural (SC) and functional connectivity (FC) by analyzing diffusion and resting-state functional MRI. We applied a graph theory approach to compare connectomes of patients and controls and we explored how altered global network metrics are related to patients’ outcome in neuropsychological tests. Our results show that FD patients present a loss of axonal integrity which leads to a widespread reorganization of brain structural and functional architecture, related to their clinical performances.

Daniela Junker¹, Selina Rupp¹, Jessie Han¹, Mingming Wu¹, Anna Reik², Meike Wiechert², Hans Hauner^2,3, Marcus M. Makowski¹, Christina Holzapfel², and Dimitrios C. Karampinos^1
1Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany, ²Institute for Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany, ³Else Kroener-Fresenius-Center of Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany

Risk stratification strategies in patients with metabolic diseases are urgently needed for the selection of appropriate lifestyle interventions. Pancreatic fat content has been shown to be associated with the metabolic syndrome and type 2 diabetes mellitus. MRI-based proton density fat fraction (PDFF) mapping based on a multi-echo gradient echo acquisition is the method of choice for non-invasive pancreatic fat quantification. This study evaluates how the PDFF of the pancreas correlates to adipose tissue PDFF and evolves after an 8-week weight loss intervention.