Kadir Simsek^1,2, André Döring³, André Pampel⁴, Harald E. Möller⁴, and Roland Kreis^1,2
1Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland, ²Translational Imaging Center, sitem-insel, Bern, Switzerland, ³Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, ⁴Max-Planck Institution for Human Cognitive and Brain Sciences, Leipzig, Germany

Diffusion-weighted MRS was successfully implemented at short T_E, reaching ultra-high b values >20 ms/μm² on a 3T Siemens Connectom system. With simultaneous fitting for different b-value spectra, macromolecular background patterns are estimated using different approaches to model metabolite diffusion and different macromolecular signal parameterization in gray matter and white matter. 

Philipp Lazen¹, Bernhard Strasser¹, Cornelius Cadrien^1,2, Sukrit Sharma¹, Lukas Hingerl¹, Eva Niess¹, Stanislav Motyka¹, Alexandra Lipka¹, Benjamin Spurny-Dworak³, Christoph Brandner⁴, Stephan Gruber¹, Wolfgang Bogner¹, Rupert Lanzenberger³, Karl Rössler², Siegfried Trattnig^1,5, and Gilbert Hangel^1,2
1Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ²Department of Neurosurgery, Medical University of Vienna, Vienna, Austria, ³Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria, ⁴Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria, ⁵Institute for Clinical Molecular MRI, Karl Landsteiner Society, St. Poelten, Austria

We implemented B₁⁺ correction for our metabolite concentration estimation and analyzed the influence of B₁⁺ inhomogeneities. Since the B₁⁺ field is relatively homogeneous in the center of the brain anyways, the biggest effects were observed closer to the fringes of the brain. The concentration estimates increased between 1.0% and 5.6% for different metabolites when comparing B₁⁺ correction to simple T1 correction, remaining within the spectrum of values established by previous literature.

Maria Yanez Lopez¹, Georg Oeltzschner², Anthony N Price¹, Nicolaas AJ Puts³, Emer J Hughes¹, Grainne M McAlonan³, Tomoki Arichi¹, Richard AE Edden², and Enrico De Vita^4
1Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom, ²Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ³Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom, ⁴Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK, London, United Kingdom

The doublet nature of the 3 ppm peak previously reported in neonates indicates that a lower macromolecular contribution to the GABA+ 3 ppm signal is likely to be present in this population. Detailed characterisation of age‐related MM contribution rates is required to improve the MRS fitting process, and therefore, to further increase the accuracy of metabolic measurements in neonates. As a first step, we here measure the macromolecular baseline using metabolite-nulling MRS in healthy term neonatal participants.

Amir Seginer¹, Graeme A. Keith², David A. Porter², and Rita Schmidt^3
1Siemens Healthcare Ltd, Rosh Ha'ayin, Israel, ²Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, Scotland, ³Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel

Readout-Segmented COKE (RS-COKE) is an Echo Planar Spectroscopic Imaging (EPSI) variant supporting larger spectral-widths and is therefore useful for spectral imaging at 7T. However, mismatches between the segments at the overlaps lead to artifacts, especially if the lipids signal is not suppressed (to avoid adversely affecting the metabolites signal). We developed a procedure to measure and reduce the readout segment mismatches – through signal and trajectory corrections – leading to improved spectral images and spectra. The procedure was tested by scanning both a special 3D head-shaped phantom – which includes a lipid layer – and human volunteers.

Duanghathai Pasanta^1,2, David J. White³, Jason L. He¹, Nicolaas A. Puts^1,4, and Talitha Ford^3,5
1Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom, ²Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand, ³Centre for Human Psychopharmacology & Swinburne Neuroimaging, School of Health Sciences, Swinburne University of Technology, Melbourne, Australia, ⁴MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom, ⁵Cognitive Neuroscience Unit, Faculty of Health, Deakin University, Geelong, Australia

We performed functional magnetic resonance spectroscopy (fMRS) to measure the dynamic response of GABA and Glutamate in the superior temporal sulcus (STS) and visual cortex (V1) while viewing social stimuli. MEGA-PRESS fMRS spectra were analyzed in both block and event-related design. Sliding window analyses were used to investigate GABA and Glutamate dynamics at higher temporal resolution. A small decrease in GABA level was observed during stimulus presentation in V1, but no change was observed in STS. We highlight the feasibility of using fMRS to assess changes in metabolite response during social processing in health and disease.

Robyn Walker¹, Parker La², Tiffany Bell ², Julie M Joyce ², Miriam Beauchamp^3,4, William Craig^5,6, Quynh Doan ^7,8, Roger Zemek^9,10, Keith Yeates ¹¹, and Ashley Harris^2
1University of Calgary, Calgary, AB, Canada, ²Radiology, University of Calgary, Calgary, AB, Canada, ³Psychology, University of Montreal, Montreal, QC, Canada, ⁴Psychology, Ste Justine Hospital, Montreal, QC, Canada, ⁵Pediatrics, University of Alberta, Edmonton, AB, Canada, ⁶Pediatrics, Stollery Children's Hospital, Edmonton, AB, Canada, ⁷Pediatrics, University of British Columbia, Vancouver, BC, Canada, ⁸Pediatrics, BC Children's Hospital, Vancouver, AB, Canada, ⁹Pediatrics and Emergency Medicine, University of Ottawa, Ottawa, ON, Canada, ¹⁰Pediatrics and Emergency Medicine, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada, ¹¹Psychology, University of Calgary, Calgary, AB, Canada

This study uses 1H magnetic resonance spectroscopy (MRS) to determine metabolite differences in the chronic phase (~3-month) of pediatric concussion compared to orthopedic injury (OI) controls. In the first analysis, significant differences in N-acetylaspartate (NAA), choline (Cho) and inositol (Ins) were seen between concussion and OI controls. Secondly, in a series of 3-way ANCOVAs across OI, symptomatic and asymptomatic concussion groups showed group differences in NAA, Cho and Ins depending on the symptom scale. When metabolites were different in this analysis by symptoms, it was generally driven by the asymptomatic group. 

Evita Wiegers¹, Alex Bhogal¹, Sarah Jacobs¹, Mark Gosselink¹, Jeanine Prompers¹, and Dennis Klomp^1
1Radiology, University Medical Center Utrecht, Utrecht, Netherlands

Short TE STEAM is an appealing MRS sequence especially for the quantification of low-concentration J-coupled spin systems. However, in comparison to sLASER, STEAM is associated with larger chemical shift displacement errors in all three directions. With an inhomogeneous B₁⁺-field, slice profiles degrade, resulting in artefacts from extracranial lipids. Here we demonstrate that lipid artefacts can be removed through the use of an external crusher coil. Without use of the crusher coil, large extracranial lipid artefacts are present when selecting a large voxel. Upon activation of the crusher coil, these signals are eliminated, yielding ¹H spectra free of large baseline distortions.

Mark Stephan Widmaier^1,2, Song-I Lim^1,2, and Lijing Xin^1,3
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland, ²Laboratory for Functional and Metabolic Imaging, EPFL, Lausanne, Switzerland, ³Animal Imaging and Technology, EPFL, Lausanne, Switzerland

In this abstract, we report the reproducibility of the new ³¹P-MRF technique on healthy volunteers. We show that relaxation and concentration rates can be estimated fast and accurate in good agreement with state-of-the-art methods in the human brain and in phantoms at 7T. This novel efficient technique is able to achieve a 5-fold acquisition time reduction of ³¹P relaxation parameter measurements, using only 6 min scan time.

Martyna Dziadosz^1,2, Rudy Rizzo^1,2, Sreenath P Kyathanahally³, and Roland Kreis^1,2
1Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland, ²Translational Imaging Center, sitem-insel, Bern, Switzerland, ³Department Systems Analysis, Integrated Assessment and Modelling, Data Science for Environmental Research group, Dübendorf, Switzerland

The main limitation of MRS is low SNR. Several approaches for denoising have been proposed. However, it is debatable, whether denoising can reduce estimate uncertainties. In this work, we investigate denoising using deep learning (DL) in time-frequency representations. The results were assessed using two methods: first, an adjusted noise score was used and second, the outcome of traditional fitting was evaluated. We found that time-frequency domain denoising through DL produces a visually appealing spectrum but mean residuals for the relevant spectral regions and variance in fit results remained as high as without denoising.  

Ayhan Gursan¹, Arjan D. Hendriks¹, Dimitri Welting¹, Dennis W.J. Klomp¹, and Jeanine J. Prompers^1
1Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands

Gastric emptying abnormalities are frequently observed in diabetes patients. Here we explored whether dynamic 3D DMI could be a radiation-free alternative for scintigraphy to measure the gastric emptying rate. One volunteer was scanned after oral intake of deuterated glucose and proximal and distal gastric emptying was monitored. The distribution of the glucose load in the proximal and distal parts of the stomach and the rate of gastric emptying agreed well with scintigraphy results. DMI has potential to investigate gastric emptying abnormalities in patients with diabetes, while at the same time providing information on glucose uptake and metabolism in the liver.

Sneha Vaishali Senthil^1,2, Brenden Toshihide Kadota ², and Jamie Near^2,3
1Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada, ²Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada, ³Medical Biophysics Department, Sunnybrook Research Institute, Toronto, ON, Canada

MRSI is a non-invasive in-vivo technique for mapping tissue concentrations in clinical and neuro-scientific research. Although, in vivo MRSI has made progress with respect to spatial resolution, acquisition time and the number of detectable metabolites; frequency and phase drifts in the acquired data are still a persisting problem, resulting in SNR losses, broadening of spectral peaks and deformities in line spectra of metabolites. In this abstract, we show how rosette MRSI sampling trajectories offer the possibility to perform frequency and phase drift correction which is otherwise not possible to do in most other commonly used cartesian and non-cartesian sampling trajectories.

Mahrshi Jani^1,2, Shengyue Su¹, Manoj Kumar Sarma¹, Ariane Fillmer³, and Anke Henning^1,4
1Advance Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, United States, ²Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States, ³Medical Metrology, Physikalisch Technische Bundesanstalt, Berlin, Germany, ⁴Max Planck Institute for Biological Cybernetics, Tuebingen, Germany

In this work, we compare the spectroscopy results and metabolite maps for different shimming routine. We use vendor default shimming technique and our own shimming technique. We tried to do shimming in different regions of brain (i.e. prefrontal cortex, occipital, and insula), also we tried to do multivoxel shimming. We compared the frequency shift maps between vendor’s implemented shim routine and our own shim algorithm. Also, we compared metabolite maps we got from different shimmed region after shimming using vendors and our own shimming routine.