Simon Daniel Robinson^1,2,3, Beata Bachrata³, Korbinian Eckstein³, Barbara Dymerska⁴, Saskia Bollmann², Steffen Bollmann⁵, Shota Hodono², Martijn Cloos², Monique Tourell², Jin Jin⁶, Kieran O'Brien⁶, David Reutens², Siegfried Trattnig³, Christian Enzinger¹, and Markus Barth^5
1Department of Neurology, Medical University of Graz, Graz, Austria, ²Centre for Advanced Imaging, University of Queensland, Brisbane, Australia, ³Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, ⁴UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁵School of Electrical Engineering and Information Technology, University of Queensland, Brisbane, Australia, ⁶Siemens Healthineers, Brisbane, Australia

We evaluate the performance of a recently-proposed dynamic distortion correction (DDC) method for fMRI at 7T, with a task which generates field fluctuations. The Reverse-Encoded First Image and Low resoLution reference scan (REFILL) method generates fieldmaps from the phase of standard, single-echo EPI from fMRI time series, using coil sensitivity information from a fast reference scan and removing other, non-B₀-related contributions to the phase, which are calculated from a readout-reverse EPI volume. In contrast to conventional static distortion correction (SDC) with a GE-based fieldmap, REFILL captured dynamic changes to the field, leading to an accurate correction and increased tSNR.

Jullie W Pan¹, Chan H Moon², and Hoby P Hetherington^3,4
1Radiology, University of Missouri Columbia, Columbia, MO, United States, ²Radiology, University of Pittsburgh, Pittsburgh, PA, United States, ³Resonance Research Inc., Billerica, MA, United States, ⁴University of Missouri Columbia, Columbia, MO, United States

With increasing use of ultra-high field, there is a need for fast and efficient field mapping. Towards this purpose, the non-Cartesian circular rosette trajectory is advantageous due to its low gradient demands and its efficiency due to the simultaneous acquisition of spatial and field encoding information. We describe the implementation of the circular rosette trajectory for field mapping at 7T in the human brain and compare this to the highly accurate although slow 5 echo Bolero field map.

Marina Manso Jimeno^1,2, John Thomas Vaughan Jr.^1,2, and Sairam Geethanath^2
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, ²Columbia Magnetic Resonance Research Center, New York, NY, United States

The trade-off of a shorter MRI magnet design is having to compromise image quality due to reduced field homogeneity. Moreover, the sample induces unique field perturbations during the scan that can only be corrected if the exact field map is known. Here we proposed a DL model that synthesizes sample-specific field maps based on artifact-corrupted acquired images and the system’s field distribution. In the retrospective study, we obtained a mean NRMSE of 0.04 during testing between model output and the true field maps and found that using the model output for correction significantly reduced the artifacts on the images.

Hannah Scholten¹ and Herbert Köstler^1
1Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany

Eddy current induced field perturbations can cause artifacts in diffusion weighted imaging (DWI). Such perturbations may be calculated and corrected for by the gradient system transfer function (GSTF), provided the gradient system behaves linear and time-invariant. We discovered that the linearity assumption can be violated by gradients with high zeroth order moments. We therefore propose a modified measurement scheme for the GSTF including higher gradient moments to better approximate their behavior. Our approach substantially reduces errors in the predicted gradient time courses. We may thus enable the application of GSTF-based correction methods to DWI in the future.

Xubin Chai^1,2, Chuyu Liu¹, Chu Wang¹, Rong Xue², and Xiaolei Song^1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, ²State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

   For imaging small-sized organs like the prostate, reduced-field-of-view (rFOV) technique is useful for shortening scan time, increasing resolution and reducing artifacts caused by field inhomogeneity and motion. Herein on a 3T clinical scanner, rFOV CEST was obtained using an off-resonance saturation preparation (seconds long), followed by readouts at the crossing section of a 90 deg excitation slab and a 180 deg refocus slab that had an angle in between. For saturation powers of 0.7 uT and 2 uT, CEST contrast maps and quantitative curves suggested the rFOV-CEST outperformed the Traditional-FOV acquisitions, which has potential for prostate CEST imaging at 3T.

Patrick Liebig¹, Juergen Herrler², Raphael Tomi-Tricot^3,4,5, Sydney Williams⁶, Belinda Ding-Yuan^3,6, Majd Hlou⁷, Venkata Chebrolu⁸, Fasil Gadjimuradov^7,9, Tom Hilbert^10,11,12, Tobias Kober^10,11,12, Rene Gumbrecht¹, Robin Martin Heidemann¹, Thomas Benkert⁷, Chris Rodgers¹³, David Andrew Porter⁶, Iulius Dragonu³, Armin Nagel^14,15, and Shaihan Malik^4,5
1Siemens Healthcare GmbH, Erlangen, Germany, ²Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, ³MR Research Collaborations, Siemens Healthcare Limited, London, United Kingdom, ⁴Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom, ⁵Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom, ⁶Imaging Centre of Excellence, University of Glasgow, Glasgow, United Kingdom, ⁷MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ⁸Siemens Medical Solutions USA, Inc., Rochester, MN, United States, ⁹Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, ¹⁰Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, ¹¹Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ¹²LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ¹³Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom, ¹⁴Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, ¹⁵Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

7T MRI is affected by inhomogeneous transmit and receive B1 field that can impede the inherent gains in signal-to-noise ratio. pTx provides excellent results in correcting the transmit field and showed feasibility in a clinical setting as well^1,2. Although multiple algorithms have been developed to correct for the receive profile or signal homogeneities in general, each algorithm has its own shortcomings. Here, we suggest combining prospective correction of the transmit field by pTx with a deep learning network to retrospectively correct for the remaining signal inhomogeneities (mainly receive field variations) in a generalized fashion.

Benson Yang^1,2, Chih-Hung Chen², and Simon J Graham^1,3
1Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada, ²Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, ³Medical Biophysics, University of Toronto, Toronto, ON, Canada

Parallel radiofrequency transmission (pTx) technology continues to demonstrate its potential at addressing MRI safety concerns relating to patients with implanted deep brain stimulation devices. One promising technique involves electromagnetic simulation that determine optimized pTx inputs to generate a safe mode of imaging surrounding the implanted device. However, in practice, instrumentation uncertainty can impact the ability of the pTx system to accurately produce the desired signals. The present work studied the safety implications of system uncertainty and system failure in a 4-channel pTx platform. The preliminary results showed that in a worst-case scenario, temperature elevations that exceed MRI guidelines are possible.

Kevin Moulin^1,2, Tanjib Rahman ³, Magalie Viallon^1,2, Pierre Croisille^1,2, and Luigi Perotti^3
1CREATIS, Lyon, France, ²Department of Radiology, University Hospital Saint-Etienne, Saint Etienne, France, ³Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, United States

Cardiac Diffusion Tensor imaging (cDTI) is a powerful non-contrast technique that can characterize cellular structure in vivo. Despite motion-compensated diffusion encoding designs, cDTI remains sensitive to unwanted cardiac motion, particularly during diastole. The lack of knowledge of the detailed interaction between real cardiac motion and motion-compensated gradients prevents more advanced diffusion encoding designs. The objective of this work is to provide a new simulation framework to study the interaction between cardiac displacements estimated in vivo from DENSE imaging and motion-compensated diffusion encoding waveforms.

Carlos Castillo-Passi^1,2,3, Ronal Coronado^2,3,4, Sergio Uribe^2,3,5, and Pablo Irarrazaval^1,2,3,4
1Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, ²Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile, ³Millennium Nucleus in Cardiovascular Magnetic Resonance, Cardio MR, Santiago, Chile, ⁴Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, ⁵Radiology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile

In this work, we proposed a GPU-enabled MRI simulation framework written in Julia. The primary purpose of this simulator is to evaluate imaging pipelines from the acquisition to the reconstruction. To showcase this, we simulated two variations of an MRF sequence with radial acquisitions and then reconstructed using two methods. We demonstrated that this framework aids users to quickly write and test state-of-the-art MRI techniques and showed its potential to compare imaging pipelines in quantitative mapping. 

Vasit Sirilapanan¹, Yongmann M Chung¹, Richard Harrison², Clare Cameron³, Michael Lynn⁴, Charles Hutchinson⁵, and Farhan Ahmed^4
1School of Engineering, University of Warwick, Coventry, United Kingdom, ²School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom, ³University of Herefordshire, Hatfield, United Kingdom, ⁴Department of Radiology, Royal Berkshire Hospital, Reading, United Kingdom, ⁵Medical School, University of Warwick, Coventry, United Kingdom

MRI-based CFD simulations are compared with ultrasound-based CFD to ascertain the capability of ultrasound.