Radka Klepochová^1,2, Fabian Niess², Martin Meyerspeer³, Siegfried Trattnig^2,4, Michael Krebs¹, Alexandra Kautzky-Willer¹, Michael Leutner¹, and Martin Krššák^1,2
1Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria, ²High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, ³High-Field MR Center, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria, ⁴Christian Doppler Laboratory for Clinical Molecular MR Imaging (MOLIMA), Christian Doppler Laboratory, Vienna, Austria

The study aimed to explore the behavior of skeletal muscle acetylcarnitine and phosphocreatine during submaximal plantar flexion exercise using interleaved ¹H/³¹P MRS at 7T.  Acetylcarnitine decreased to steady state during the exercise, increased back to initial levels in early recovery period and started to decline during later phase of recovery.  Studies including more volunteers and patients with broader range of metabolic conditions and physical fitness will be necessary for detailed quantitative analysis.  

Marco Barbieri*¹, Lauren Watkins*¹, Bryan Haddock², Garry E. Gold^1,3, and Feliks Kogan^1
1Radiology, Stanford University, Stanford, CA, United States, ²Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark, ³Bioengineering, Stanford University, Stanford, CA, United States

Molecular information derived from dynamic [¹⁸F]NaF PET imaging holds promise to study bone remodeling in bone and joint disorders. The porosity index (PI), based on ultra-short echo-time (uTE) MRI, has been proposed to study bone porosity in clinically valuable acquisition times. We explored the association between bone metabolic and porosity information in the tibial tuberosity in a cohort of 10 subjects with knee OA using hybrid PET-MRI imaging. We found a moderate negative correlation between the metabolic bone remodeling (K_i) and the porosity index. These promising results may highlight a new tool to unveil unknown pathways in musculoskeletal disease pathophysiology.

Trevor Chan^1,2, Nada Kamona^1,2, Brian-Tinh Vu^1,2, Felix Wehrli^1,2, and Chamith Rajapakse^1,2
1Radiology, University of Pennsylvania, Philadelphia, PA, United States, ²Bioengineering, University of Pennsylvania, Philadelphia, PA, United States

We apply a probabilistic deep learning model to perform image super-resolution on magnetic resonance (MR) images. Our results show that the model is capable of high performance in MR; we upsample low resolution images of the distal tibia to 2x initial spatial resolution–equivalent to capturing 4x fewer samples in K-space–with the goal of reconstructing details in the trabecular architecture. We validate our results by comparing trabecular bone microstructure metrics across high-resolution ground truth, model-reconstructed, and low-resolution input images. By drastically reducing scan time for high-resolution imaging, these methods have the potential to make MRI assessment of bone strength clinically viable.

Esha Baidya Kayal¹, Jayendra Tiru Alampally², Raju Sharma³, Sameer Bakhshi³, Amit Mehndiratta^1,3, Rakesh Kumar³, Chandrashekhara SH³, Manisha Jana³, Ashu Seith Bhalla³, Mehar Chand Sharma³, Asit Ranjan Mridha³, Sreenivas Vishnubhatla³, and Devasenathipathy Kandasamy^3
1Indian Institute of Technology Delhi, Delhi, India, ²Krishna Imaging & Diagnostics, Telengana, India, ³All India Institute of Medical Sciences Delhi, Delhi, India

The objective of this prospective study was to assess the role of diffusion weighted MRI for early evaluation of chemotherapy response in patients with Ewing sarcoma with      RECIST 1.1 criteria correlation. Absolute apparent diffusion coefficient (ADC), normalized ADC (nADC) and tumor diameter at baseline and after 1^st cycle of chemotherapy were assessed for early detection of chemotherapy response in 16 patients. Results showed, baseline nADC and after 1^st chemotherapy cycle, relative percentage change (Δ%) in nADC & tumor-size were significant and observed to be useful markers of early chemotherapeutic response (AUC=0.700, 0.750, 0.938) in Ewing sarcoma.

Ravi Prakash Reddy Nanga¹, Mark Elliott¹, Neil Wilson¹, Sophia Swago², Walter Witschey¹, and Ravinder Reddy^1
1Radiology, Center for Advance Metabolic Imaging in Precision Medicine, Perelman School of Medicine at The University of Pennsylvania, Philadelphia, PA, United States, ²Bioengineering, University of Pennsylvania, Philadelphia, PA, United States

We have detected a resonance that was not reported previously, with a chemical shift of ~9.7ppm occurring in down-field MRS (DFMRS) from the human calf muscle in vivo. Based on phantom data, we speculate that the contribution to this broad peak might be from nicotinamide riboside (NR) and nicotinamide mononucleotide (NMN). This is for the first time that this peak is observed and reported from the down-field spectra. It seems to be very specific to calf muscle and has not been identified from the down-field spectra of human brain in vivo, suggesting that it is a feature of muscle metabolism.

Marco Barbieri¹, Melissa T. Hooijmans², Garry E. Gold^1,3, Feliks Kogan¹, and Valentina Mazzoli^1
1Radiology, Stanford University, Stanford, CA, United States, ²Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands, ³Bioengineering, Stanford University, Stanford, CA, United States

Muscle T₂ relaxometry can be used to monitor disease activity in neuromuscular disorders. Dictionary matching of  multi-echo-spin-echo (MESE) data is the gold-standard method to estimate the T₂ of the myocitic component (T₂-water) because of its ability to correct for multiple confounding factors, but suffers from a high computational burden. This work proposes a neural network (NN) approach for fast muscle T₂-water mapping with subject-specific T₂-fat calibration to overcome computational limitations of the dictionary method. The method was validated in-vivo against the standard dictionary approach. The NN application outperformed the dictionary approach in computational resources (x140 faster) while retaining quantitative accuracy.

Ek T. Tan¹, Yan Wen², Kang Wang², Jake A. Fiore¹, R. Marc Lebel², Suryanarayanan Kaushik², Maggie Fung², and Darryl B. Sneag^1
1Hospital for Special Surgery, New York, NY, United States, ²GE Healthcare, Chicago, IL, United States

Deep learning reconstruction (DLRecon) was applied to shorter acquisition 2D Dixon and 3D short-tau-inversion recovery (STIR) brachial plexus MR neurography (MRN) sequences and were compared both qualitatively and quantitatively to standard clinical sequences. DLRecon 2D and 3D images demonstrated similar quality to standard images. DLRecon may facilitate implementation of shorter MRN protocols, thereby helping streamline MRN’s incorporation into busy clinical practices and providing the option of acquiring additional imaging planes.  

Kihwan Kim¹, Yuran Zhu², Yudu Li³, Zhi-Pei Liang ^3,4, and Xin Yu^2,5
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, ²Biomedical Engineering, Case Western Reserve University, cleveland, OH, United States, ³Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴Beckman Institute for advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁵Case Center for Imaging Research, Case Western Reserve University, cleveland, OH, United States

In this study, ³¹P-MRS was employed to evaluate the effects of aging and muscle degeneration on metabolic response to stimulation-induced, high-intensity muscle contraction in two age groups of C57BL/6 and mdx mice, a mouse model of moderate muscular dystrophy. Significant, age-dependent differences in mitochondrial oxidative capacity and creatine kinase activity after muscle stimulation were observed between C57BL/6 and mdx mice, suggesting a positive metabolic response to muscle contraction that can be modulated by aging and muscle degeneration.