Christoph Michael Schildknecht¹ and Klaas Paul Prüssmann^1
1Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zürich, Switzerland

Current motion tracking modalities operate either in frequency bands that are also occupied by the MRI scanner or interact heavily with the environment. As an alternative, the frequency band of 1-5 MHz can be utilized for motion tracking and has been demonstrated to deliver high performance. Due to the limited sensitivity at a great distance of current broadband systems, we show in this work how short-wave motion tracking can be done at long ranges, enabling independent and robust motion tracking with fewer geometric constraints and greater ease of use.

Anne Oyarzun¹, Rebeca Echeverria-Chasco^2,3, Paloma L. Martin Moreno⁴, Nuria Garcia-Fernandez⁴, Gorka Bastarrika^2,3, María A. Fernández-Seara^1,3, and Arantxa Villanueva^1,3,5
1Electrical, Electronics and Communications Engineering Department, Universidad Pública de Navarra, Pamplona, Spain, ²Radiology, Clínica Universidad de Navarra, Pamplona, Spain, ³IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain, ⁴Nephrology, Clínica Universidad de Navarra, Pamplona, Spain, ⁵ISC, Instituto de Smart Cities, Pamplona, Spain

Motion correction methods are a prerequisite in multiple-image registration tasks. We implemented a non-rigid groupwise registration method and ROI-focused non-rigid groupwise registration method for renal pCASL images. We evaluated the temporal signal variation in the renal cortex after motion correction using both methods. Motion correction technique shows statistically significant improvement on the tSNR and ROI-focused method performs statistically better.  

Radhika Tibrewala¹, Mahesh B Keerthivasan², Kai Tobias Block¹, Jan Paska¹, and Ryan Brown^1
1CAI2R, NYU School of Medicine, New York, NY, United States, ²Siemens Medical Solutions USA, New York, NY, United States

For MRI hindered by motion artifacts sensors are able to provide a surrogate for bulk motion, but may not be tissue specific. In this work, we use deep learning to build a motion model with an auxiliary pilot tone sensor during a fast-image calibration step. A neural network is used to learn the correlation between the pilot tone signal and the pixel-wise liver displacement which is predicted using an automatic segmentation model. The motion model is used to predict displacement on low frame rate images, thus offering the opportunity to perform motion resolved reconstruction.

Gastao Cruz¹, Camila Munoz¹, René M. Botnar¹, and Claudia Prieto^1
1King's College London, London, United Kingdom

Partial Fourier strategies leverage the Hermitian symmetric properties of k-space into higher acceleration factors. Motion corrected (MC) reconstructions incorporate generalized motion into the process. Contrast resolved reconstructions, e.g. low rank inversion (LRI), exploit temporal redundancies in highly accelerated acquisitions. Both the MC and LRI reconstructions are usually ill-posed and can benefit from additional sources of encoding. The Virtual Coil Concept (VCC) has been recently introduced, combining Parallel Imaging with Partial Fourier.  Here we investigate the combination of VCC with MC and VCC with LRI to enable further acceleration of both approaches.

Suzanne Wong^1,2, Claire Wunker^3,4, Ben Keunen², Maryam Siddiqui⁵, Karolina Piorkowska², Yael Babichev⁴, Warren Foltz⁶, Rebecca Gladdy^3,4, Samuel Pichardo⁵, Adam Waspe², and James Drake^1,2
1Biomedical Engineering, University of Toronto, Toronto, ON, Canada, ²Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada, ³Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada, ⁴Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada, ⁵Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada, ⁶Radiation Physics, University Health Network, Toronto, ON, Canada

Magnetic resonance guided high intensity focused ultrasound (MRgHIFU) has gained interest over the past decade due to its ability to administer controlled hyperthermia for localized drug release. One of the main challenges is that MR thermometry is highly susceptible to motion artifacts. A hybrid principal component analysis and projection onto dipole fields motion artifact removal method was applied in real-time during controlled hyperthermia in a murine model using a small-animal MRgHIFU system (Bruker 7T MRI and IGT HIFU). For a target temperature of 40.5°C to be maintained, a significant increase in ultrasound power was required when tissue motion was observed.

Ryan Beckerleg¹, Joseph Whittaker^1,2, Daniel Gallichan³, and Kevin Murphy^1
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom, ²Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ³Cardiff University Brain Research Imaging Centre (CUBRIC), School of Engineering, Cardiff University, Cardiff, United Kingdom

Previously we showed that when global intensity changes (GIC’s) are present in data (e.g., CO2 stimuli during measurement of CVR, ASL tagging), volume registration algorithms misrepresent such signal change as motion. Here we look at motion estimates derived from VRA’s, an external marker-less camera, and novel data-derived techniques, to evaluate if this problem can be overcome. We determined that in most cases where GIC’s are present, the use of an ICA in correction can improve erroneous results from VRA estimates. This isn’t the case in all scans however and the GIC causing this error should be removed prior to correction.

Mackenzie Carlson¹, Phillip DiGiacomo¹, Brian Burns², Pascal Spincemaille³, Yi Wang³, Julian Maclaren^1,4, Murat Aksoy⁴, Brian Rutt¹, and Michael Zeineh^1
1Stanford University, Stanford, CA, United States, ²GE Healthcare, Stanford, CA, United States, ³Weill Cornell Medicine, New York City, NY, United States, ⁴Hobbitview Inc., San Jose, CA, United States

Correction of head motion during MRI scanning is critical to achieving high-resolution images to study and quantify small regions such as the hippocampus in Alzheimer’s disease. Our optical prospective motion correction prototype system has previously demonstrated improved image quality in volunteers at 7T, but clinical translatability has been lacking. In this work, we implemented an integrated power supply and applied prospective correction to a broad set of sequences, including mGRE and FSE, for a clinically relevant research protocol with emphasis on hippocampal iron imaging. Metrics achievable using our protocol include high-resolution cortical QSM, hippocampal subfield QSM and volume.

Saikat Sengupta^1,2, Mishek Musa³, and Yue Chen^4
1Vanderbilt University Institute of Imaging Science, Nashville, TN, United States, ²Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, United States, ³Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR, United States, ⁴Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States

In this abstract we present initial feasibility results of a novel pneumatic sensor-based head motion detection and tracking system for MRI. The system comprises of head pad with a built in matrix of air pressure sensors, which replaces the standard head pad in an MRI scan and allows fast sequence agnostic tracking of head motions without the need for navigators, head markers or camera systems. Here, we show initial results for motion detection and head pose estimation in phantom studies using a motion model trained outside the scanner.

Hannah Eichhorn¹, Simon Chemnitz-Thomsen^1,2, Evangelos Vouros^1,3, Nitesh Shekhrajka⁴, Robert Frost^5,6, André van der Kouwe^5,7, and Melanie Ganz^1,8
1Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark, ²Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark, ³Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark, ⁴Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark, ⁵Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁶Department of Radiology, Harvard Medial School, Boston, MA, United States, ⁷Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁸Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

Currently no image quality metric, used for evaluating the performance of artifact correction or image reconstruction methods, is sensitive to all possible image artifacts. This complicates the choice of a proper quantitative quality measure. To provide assistance with this choice, we investigated the correlation of commonly used metrics with radiological evaluation on a dataset acquired with and without motion. The full-reference metrics SSIM and PSNR correlated most strongly with observer scores. Among the reference-free metrics Image Entropy, Average Edge Strength and Tenengrad measure showed a consistent correlation for all investigated sequences. 

Oscar Albert Dabrowski¹, Sébastien Courvoisier², Jean-Luc Falcone³, Antoine Klauser², Julien Songeon³, Michel Kocher², Bastien Chopard³, and Francois Lazeyras^2
1Computer science, University of Geneva, Geneva, Switzerland, ²CIBM, Geneva, Switzerland, ³University of Geneva, Geneva, Switzerland

In the age of artificial intelligence, there is a need for simple and inexpensive frameworks aimed at performing standardized reproducible motion-controlled experiments allowing the creation of datasets of motion corrupted in vivo MRI scans. Focused on human brain imaging, we propose ChoCo: a choreography controlled protocol for reproducible head motion and an ad-hoc hardware device for synchronization between an MRI scanner and a movie displaying motion to be performed. A proof of concept demonstrated that 6 participants were able to reproduce head choreographies accurately. The resulting motion corrupted brain images show qualitatively similar artifacts, confirming consistent motion reproduction among subjects.

Tamsin Edwards Lambourne^1,2, André J. W. van der Kouwe^1,3, and Robert Frost^1,3
1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ²Northeastern University, Boston, MA, United States, ³Department of Radiology, Harvard Medical School, Boston, MA, United States

Short <5 ms cloverleaf navigators have been demonstrated for prospective motion correction at a rate of 71 Hz in steady-state FLASH. The original cloverleaf approach required a 12 second k-space mapping pre-scan to reliably deal with out-of-plane rotations. Here we investigate simulation of a k-space map based on a low spatial resolution pre-scan. The approach shows potential for real-time detection of rigid-body rotation in the context of real-time self-correction of the cloverleaf orientation.