Mariama Salifu¹, Melissa Haskell², and Douglas C Noll^1
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ²Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

Oscillating steady state imaging (OSSI) is a novel fMRI acquisition method which produces a high SNR T₂^*-weighted signal. Physiological and drift induced B₀ changes cause undesired signal distortions which can significantly diminish functional contrast in OSSI fMRI. Here, we investigated a method of rapidly estimating quantitative B₀ field changes from OSSI images using an artificial neural network (ANN) model. Our results demonstrated that this technique can be used to rapidly measure field changes, which has the potential to be used for prospective and retrospective B₀ field correction in OSSI. 

Daniel Papp¹, Alexandre D’Astous¹, Julien Cohen-Adad^1,2,3, and Eva Alonso-Ortiz^4
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada, ²Mila - Quebec AI Institute, Montreal, Montreal, QC, Canada, ³Functional Neuroimaging Unit, Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, QC, Canada, ⁴NeuroPoly Lab, Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC, Canada

Respiration-induced changes of the magnetic field are an important source of image artefacts in spinal cord MRI. Here, we introduce realtime dynamic xyz-shimming to fully compensate for these distortions in a time-varying (‘realtime”), slicewise (‘dynamic”) fashion, using both in-plane (‘xy’) and through-plane (‘z’) gradients (‘xyz-shimming’).

Daniel Uher¹, Jacobus F.A. Jansen^1,2, Gerhard S. Drenthen^1,2, Benedikt A. Poser³, Christopher J. Wiggins⁴, Paul A.M. Hofman^2,5, Louis G. Wagner⁵, Rick H.G.J. van Lanen⁶, Christianne M. Hoeberigs^2,5, Albert J. Colon⁵, Olaf E.M.G. Schijns^1,5,6, and Walter H. Backes^1,2
1School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands, ²Department of Radiology & Nuclear Medicine, Maastricht University Medical Centre, Maastricht, Netherlands, ³Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands, ⁴Scannexus, Maastricht, Netherlands, ⁵Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Centre, Heeze/Maastricht, Netherlands, ⁶Department of Neurosurgery, Maastricht University Medical Centre, Maastricht, Netherlands

In this study we aimed to improve the 7T FLAIR image quality, especially within the temporal lobe regions which are often attenuated due to field inhomogeneities. A neural network using MP2RAGE and T₂-weighted images as inputs was set up to generate a new FLAIR-like image. The training was performed on the extratemporal-lobe voxels of the acquired 7T FLAIR image. The deepFLAIR showed a significant improvement in the signal-to-noise ratio and contrast-to-noise ratio in the temporal lobe regions in a number of cases. This study showed the potential to generate FLAIR-like images with reduced inhomogeneity artifacts and improved image quality.

Yael Balbastre^1,2, Divya Varadarajan^1,2, Klara Bas³, John Ashburner³, Robert Frost^1,2, and Bruce Fischl^1,2,4
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Wellcome Center for Human NeuroImaging, Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁴Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States

Low-bandwidth multi-echo acquisitions suffer from spatial distortions between odd and even echoes caused by B₀ homogeneity, causing blurs and loss of effective resolution in sum-of-square images or derived quantitative parameters. We show in this abstract that this curse can also be beneficial, as it allows distortions to be mapped without the need for additional field mapping data. We do so by jointly optimizing a forward model of the data with respect to exponential decay parameters and a smooth distortion map.

Ronal Coronado^1,2, Carlos Castillo-Passi^1,2, Gabriel della Maggiora^1,2, Sergio Manuel Uribe^1,2, Cristian Tejos¹, Claudia Prieto^1,2,3, Cecilia Besa⁴, and Pablo Irarrazaval^1,2
1Biomedical Imaging Center-Universidad Católica de Chile, Santiago, Chile, ²Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, ³King's College London, London, United Kingdom, ⁴Departamento de Radiologia-Universidad Católica de Chile, Santiago, Chile

Magnetic Resonance Fingerprinting (MRF) acquisitions with balanced Steady State Free Precession (bSSFP) and spiral trajectories are prone to off-resonance artifacts. These artifacts affect the reconstruction of the tissue maps (T1 and T2). We propose to use a UNet CNN feed with fingerprints corrupted by off-resonance to generate corrected fingerprints with only aliasing in the bSSFP-MRF sequence. The feasibility of the proposed approach was evaluated in simulations and in-vivo brain data. Our method improved the NRMSE values for both quantitative maps T1 and T2. Considerably reducing the effects of the off-resonance by UNet-MRF in comparison to classical bSSFP-MRF. 

Yajing Zhang¹, Zhigang Wu², Xiuquan Hu³, Guangyu Jiang⁴, Jing Zhang³, Yan Zhao⁵, Marc Van Cauteren⁶, and Jiazheng Wang^3
1BU-MR Clinical Science, Philips Healthcare, Suzhou, China, ²Philips Healthcare (China), Shenzhen, China, ³Philips Healthcare (China), Beijing, China, ⁴BU-MR Clinical Application, Philips Healthcare, Suzhou, China, ⁵BU-MR R&D, Philips Healthcare, Suzhou, China, ⁶BU-MR Clinical Science, Philips Healthcare, Tokyo, Japan

Brain diffusion imaging by single shot EPI has remained challenging for the local B0 field variation, which leads to geometry distortion and signal inhomogeneity in the resulting images. In this study, we show that the brain single shot EPI image quality could be improved by correction of the geometric distortion and increased resolution, when compared to the traditional acquisition scheme. In-plane resolution of 1.2x1.2 mm² was achieved and the distortion correction was effectively reduced.

Milena Capiglioni¹, Claus Kiefer², and Roland Wiest^2
1Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging, University of Bern, Bern, Switzerland, ²Institute for Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), Inselspital, Bern, Bern, Switzerland

We evaluated the performance of stimulus-induced rotational saturation contrast (SIRS) to assess the possibility of detecting neuronal currents. We analyzed composite signals with multiple frequencies to evaluate the ability of the technique to be used as a filter of spectral components. We conclude that the method can separate and distinguish frequency components and reconstruct the distribution of spectral components between individually acquired signals. These tests are the basis for moving towards the clinical application of this sequence to locate fields with frequencies associated with specific pathologies.

Alexandra Grace Roberts¹, Pascal Spincemaille², Thanh Nguyen², and Yi Wang^2,3
1Electrical and Computer Engineering, Cornell University, Ithaca, NY, United States, ²Radiology, Weill Cornell Medicine, New York, NY, United States, ³Biomedical Engineering, Cornell University, Ithaca, NY, United States

Morphology Enabled Dipole Inversion (MEDI) is an iterative reconstruction algorithm for Quantitative Susceptibility Mapping (QSM) that is effective in suppressing streaking artifacts by exploiting the magnitude image as a morphological prior. However, contiguous areas of dipole-incompatibility (such as noise) induce shadow artifacts whose spatial frequency components are not sufficiently regularized by the gradient based regularization in MEDI.  Regularizing these spatially connected regions reduces shadow artifacts in Morphology Enabled Dipole Inversion based Quantitative Susceptibility Mapping reconstructions.

Zaineb Amor¹, Chaithya G.R.^1,2, Caroline Le Ster¹, Guillaume Daval-Frérot^1,2,3, Nicolas Boulant¹, Franck Mauconduit¹, Christian Mirkes⁴, Philippe Ciuciu^1,2, and Alexandre Vignaud^1
1Université Paris-Saclay, CEA, Neurospin, CNRS, Gif-sur-Yvette, France, ²Université Paris-Saclay, Inria, Parietal, Palaiseau, France, ³Siemens Healthcare SAS, Saint Denis, France, ⁴Skope Magnetic Resonance Technologies AG, Zurich, Switzerland

The benefit of retrospective correction of static and dynamic $$$B_{0}$$$ field imperfections was studied on non-Cartesian compressed sensing-based acquisition patterns by taking as an example 3D-SPARKLING encoding scheme. Long-TR dynamic acquisitions of T2*-weighted MRI (fMRI-like) volumes  were acquired at 7T, retrospectively corrected, and the evaluation of the corrections was based on the image quality and the gain in temporal signal-to-noise ratio (tSNR). We found that correcting for the static and dynamic $$$0^{th}$$$ order contributions is enough to enhance the image quality and tSNR significantly (up to 22% gain in median tSNR).