08:15 | 0386.
| AI-based motion estimation in k-space using guidance lines enables scoutless prospective motion correction. Julian Hossbach1,2, Daniel Splitthoff2, Bryan Clifford3, Daniel Polak2,4, Wei-Ching Lo3, Stephen Cauley3, Tobias Kober5, Min Lang4,6, Azadeh Tabari4,6, Jeremy Ford4,6, Komal Manzoor4,6, Lawrence Wald4,6,7, Otto Rapalino4,6, Pamela Schaefer4,6, John Conklin4,6, Susie Huang4,6,7, Heiko Meyer2, and Andreas Maier1 1Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany, 2Siemens Healthcare GmbH, Erlangen, Germany, 3Siemens Medical Solutions, Boston, MA, United States, 4Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States, 5Advanced Clinical Imaging Technology Group, Siemens Healthcare International AG, Lausanne, Switzerland, 6Harvard Medical School, Boston, MA, United States, 7Harvard-MIT Health Sciences and Technology, Boston, MA, United States Keywords: Motion Correction, Motion Correction, Prospective, motion estimation Motivation: Novel research reduced the acquisition of motion navigators to a few guidance lines. A prospective correction is not yet possible due to reconstruction and optimization times. Goal(s): We determine the feasibility of a fast AI-based motion estimation for prospective correction in a 3D MPRAGE research sequence. Approach: A DL network to prospectively estimate the head pose from seamlessly integrated guidance lines in a 3D MPRAGE research sequence was trained on simulated data and used for a rapid adaption of the FOV to achieve a prospective correction. Results: In-vivo experiments showed greatly reduced motion artifacts. The motion estimation is accurate and stable. Impact: Prospectively adapting
the FOV using the proposed AI-based method greatly improves the image quality
of 3D MPRAGE acquisitions. This unique application of ML enables promising research
of prospectively mitigating motion artifacts with minimal changes to the
sequence. |
08:27 | 0387.
| Best of both MoCo worlds: combining fast pilot tone motion sensing with retrospective SAMER correction Yantu Huang1, Huixin Tan1, Ce Wang1, Nan Xiao1, Daniel Nicolas Splitthoff2, Daniel Polak2, Dominik Nickel2, Tom Hilbert3,4,5, and Tobias Kober3,4,5 1Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, 2Siemens Healthcare GmbH, Erlangen, Germany, 3Siemens Healthineers International AG, Lausanne, Switzerland, 4Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 5École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland Keywords: Motion Correction, Motion Correction, pilot tone Motivation: Model-based retrospective motion correction has shown good results but sometimes lacks enough information to accurately derive motion parameters. Goal(s): To use the pilot tone to address the drawbacks of retrospective methods by providing them with high-frequency motion information for more robust and efficient motion correction. Approach: We use pilot tone to refine and increase the temporal resolution of motion parameters for the Scout Accelerated Motion Estimation and Reduction (SAMER) method. Results: Motion phantom and volunteer tests show improved image quality of pilot tone + SAMER compared to SAMER-only while the reconstruction takes clinically acceptable 20s. Impact: Our results demonstrate that pilot tone can be
used to improve the precision and temporal resolution of a model-based
retrospective motion correction method, while being robust and fast. This will help to further mitigate motion artifacts in clinical
routine. |
08:39 | 0388.
| Navigator based prospective motion correction in short TR sequences with minimal scan time penalty Adam van Niekerk1, Henric Rydén1,2, Sophie Shauman1, Ola Norbeck1,2, Tim Sprenger3, Enrico Avventi1,2, and Stefan Skare1,2 1Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden, 2Clinical Neuroscience, Karolinska University Hospital, Stockholm, Sweden, 3MR Applied Science Laboratory Europe, GE Healthcare, Munich, Germany Keywords: Motion Correction, Motion Correction Motivation: Inserting navigators into short-TR gradient echo pulse sequences increases scan duration as the TR needs to be extended to maintain a steady-state. Goal(s): Periodically interleave navigators without disrupting stead-state. Approach: A packed pulse sequence encoding was implemented that divides the phase encoding table into partitions. Each partition begins with two blank-TRs that contain identical RF and spoiling to the parent sequence. The first blank-TR readout is replaced with a navigator acquisition. The second is replaced with computation time to perform a field of view update. Results: The packed sequences reduced the time penalty from 1-minute to 3-13 seconds, without impacting motion correction efficacy. Impact: The packed sequence structure allows researchers to augment the acquisition of short-TR GRE sequences with fast (<TR) navigators for almost no additional scan time without affecting motion correction efficacy, and is applicable to all sequences where the TR is minimised. |
08:51 | 0389.
| Integrating scout and guidance line-based retrospective motion correction into a 3D deep learning reconstruction for fast and robust brain MRI Daniel Polak1, Marcel Dominik Nickel1, Daniel Nicolas Splitthoff1, Jeanette Deck1, Bryan Clifford2, Yantu Huang3, Wei-Ching Lo2, Susie Y. Huang4, John Conklin4, Lawrence L. Wald5, and Stephen F. Cauley2 1Siemens Healthineers, Erlangen, Germany, 2Siemens Medical Solutions, Boston, MA, United States, 3Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, 4Massachusetts General Hospital, Boston, MA, United States, 5A. A. Martinos Center for Biomedical Imaging, Boston, MA, United States Keywords: Alzheimer's Disease, MR Value Motivation: Rising medical imaging utilization and increasing use of automated support systems demand high-quality, fast, and reproducible/robust MRI techniques. Despite rapid scanning afforded by deep learning, motion remains a common source of artifacts. Goal(s): Integrate retrospective motion correction into a deep learning reconstruction to facilitate high-quality, fast, and motion-robust brain imaging. Approach: Scout and guidance line-based motion correction was implemented into MPRAGE, SPACE and SWI to enable rapid motion trajectory estimation. A data-consistency driven neural network reconstruction was adapted to perform network regularized motion correction. Results: Improved SNR and reduced motion artifacts are demonstrated in vivo using 4-6-fold accelerated scans with instructed subject motion. Impact: Retrospective
motion correction was integrated into a deep learning reconstruction to
facilitate fast and motion-robust 3D brain imaging across T1, T2, T2 FLAIR and
T2*/SWI. This should add clinical value to routine brain exams and emerging
neuro-degenerative screening protocols (ARIA). |
09:03 | 0390.
| Versatile motion-corrected brain MRI leveraging ERIC-PT: Efficient, Robust and Instruction-free Calibrated Pilot Tone Yannick Brackenier1,2,3, Lucilio Cordero-Grande1,2,4, Sarah McElroy1,2,5, Raphael Tomi-Tricot1,2,6, Philippa Bridgen1,3,7, Shaihan J Malik1,2,3, and Joseph V Hajnal1,2,3 1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 3London Collaborative Ultra high field System (LoCUS), London, United Kingdom, 4Biomedical Image Technologies, Universidad Politécnica de Madrid and CIBER-BNN, ISCIII, Madrid, Spain, 5Siemens Healthcare Limited, London, United Kingdom, 6Siemens Healthcare Limited, Frimley, United Kingdom, 7Guys and St Thomas’ NHS Foundation Trust, King's College London, London, United Kingdom Keywords: Motion Correction, Motion Correction, Pilot Tone Motivation: Robust motion correction relies on sequence modifications, either adding navigators or re-ordering the k-space sampling. These modifications might not be possible for every sequence. Goal(s): To leverage motion-sensitive Pilot Tone (PT) signals to guide motion correction for any standard 3D acquisition. Approach: We propose the ERIC calibration protocol, which distributes short self-navigated (DISORDER) acquisitions across the whole examination. Combined with data-driven motion correction reconstructions, we can achieve robust PT calibration. Results: We show the potential to correct standard MPRAGE acquisitions with a linear phase encoding scheme in 4 healthy volunteers (HV) even when using 54 seconds worth of calibration data. Impact: Correcting motion in any 3D acquisition is an
unsolved problem. Combining pre-calibrated PT signals with data-driven optimizations
explores a promising avenue. To this end, building a robust calibration model
by acquiring ~1min worth of data would easily integrate into examinations. |
09:15 | 0391.
| PHIMO: Physics-Informed Motion Correction of GRE MRI for T2* Quantification Hannah Eichhorn1,2, Kerstin Hammernik2, Veronika Spieker1,2, Elisa Saks3,4, Kilian Weiss5, Christine Preibisch3,4,6, and Julia A. Schnabel1,2,7 1Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany, 2School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, 3School of Medicine and Health, Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany, 4School of Medicine and Health, TUM-Neuroimaging Center, Technical University of Munich, Munich, Germany, 5Philips GmbH Market DACH, Hamburg, Germany, 6School of Medicine and Health, Clinic for Neurology, Technical University of Munich, Munich, Germany, 7Biomedical Engineering Department, School of Biomedical Imaging and Imaging Sciences, King’s College London, London, United Kingdom Keywords: Motion Correction, Quantitative Imaging, Motion Correction, Deep Learning, Brain Motivation: T2* quantification from GRE-MRI is particularly impacted by subject motion due to its sensitivity to magnetic field inhomogeneities. The current multi-parametric quantitative BOLD motion correction method depends on additional k-space acquisition, extending overall acquisition times. Goal(s): To develop a learning-based motion correction method tailored to T2* quantification that avoids redundant data acquisition. Approach: PHIMO leverages multi-echo T2* decay information to identify motion-corrupted k-space lines and excludes them from a data-consistent deep learning reconstruction. Results: We are able to correct motion artifacts in subjects with stronger motion, approaching the performance of the current motion correction method, while substantially reducing the acquisition time. Impact: PHIMO reduces strong motion artifacts in T2* maps by utilizing T2* decay information in an unrolled DL reconstruction. PHIMO avoids redundant data acquisition compared to a current correction method and reduces the acquisition time by over 40%, facilitating clinical applicability. |
09:27 | 0392.
| High temporal resolution motion correction in MRF using quantitative-scout-based navigation (QUEEN) and motion-dictionary matching. Aizada Nurdinova1, Xiaozhi Cao1, Julio Oscanoa2, Daniel Raz Abraham3, Nan Wang1, and Kawin Setsompop1,3 1Radiology, Stanford University, Stanford, CA, United States, 2Bioengineering, Stanford University, Stanford, CA, United States, 3Electrical Engineering, Stanford University, Stanford, CA, United States Keywords: Motion Correction, Motion Correction Motivation: Motion correction in MRF using navigators in sequence deadtime improves imaging robustness, however, temporal resolution of the approaches is limited to 6-10 s. Goal(s): We aim to achieve accurate motion tracking at 0.5 s temporal resolution, by integrating the QUantitatively-Enhanced parameter Estimation from Navigators (QUEEN) approach into MRF. Approach: Compact navigators were inserted throughout the MRF acquisition at a minimal encoding efficiency reduction of ~5%. The acquisition of the quantitative scout was integrated into the MRF’s dummy scan, resulting in no added scantime. Results: The estimated in vivo motion parameters have MAE of 0.4 mm and 0.2 deg compared to image registration estimates. Impact: The
proposed method can achieve high temporal resolution motion estimates, and
therefore, is a promising approach for high-precision motion correction in MRF. |
09:39 | 0393.
| Real-time motion correction and multicoil shim array B0 update for whole-brain MR spectroscopic imaging Ovidiu Cristian Andronesi1, Robert Frost1, Nicolas Sebastian Arango2, Nutandev Bikkamane Jayadev3, Yulin Chang3, Paul Wighton1, Malte Hoffmann1, Jason Stockmann1, and Andre van der Kouwe1 1Radiology, Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States, 2Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 3Siemens Medical Solutions, Boston, MA, United States Keywords: Motion Correction, Motion Correction, Real-time shimming, Multicoil shim array, Metabolic Imaging Motivation: Very high quality of MR spectroscopic imaging (MRSI) data is needed for robust and reproducible metabolite quantification. This critically depends on the B0 shimming and scan stability. Integrated RF-receive/B0-shim arrays significantly improve spectral quality. Goal(s): Real-time motion correction and multicoil shimming update with an integrated RF-receive/B0-shim array for robust whole-brain MRSI. Approach: We developed a rapid navigator for head tracking and B0 fieldmapping in combination with rapid processing for real-time update of multicoil shim currents and MRSI localization. Results: Real-time motion correction and multicoil shimming provides significantly narrower linewidth, higher signal-to-noise, reduced quantification errors and reproducible metabolic imaging. Impact: Whole-brain MRSI is a
unique method for non-invasive mapping of brain neurochemistry, and in
combination with real-time motion correction and multicoil shim array update
provides robust and reproducible quantitative metabolic imaging for clinical use. |
09:51 | 0394.
| Contrast-Optimized Basis Functions for Self-Navigated Motion Correction in 3D quantitative MRI Sebastian Flassbeck1,2, Elisa Marchetto1,2, Andrew Mao1,2,3, and Jakob Assländer1,2 1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 2Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY, United States Keywords: Motion Correction, Motion Correction, MR Fingerprinting, qMT, low rank Motivation: Motion-induced artifacts are a significant barrier to achieving clinically acceptable image quality for multi-compartment quantitative MRI techniques, e.g. a 2-pool magnetization transfer model. Goal(s): To develop a self-navigating approach to estimating motion parameters in an MR-fingerprinting-like acquisition. Approach: We optimize a subspace that maximizes the contrast-to-noise ratio between brain parenchyma and cerebrospinal fluid for a low-resolution, time-segmented low-rank reconstruction used to estimate motion. Results: Compared to the typical SVD basis, the contrast-optimized basis improves the smoothness of the motion estimates and the apparent resolution of the reconstructed coefficient images and quantitative maps. Impact: The proposed retrospective, self-navigating motion correction technique does not require any sequence modifications and/or additional scan time. It can therefore be applied to many quantitative MRI techniques where the signal's variation over time can be well-described in a low-rank subspace. |
10:03 | 0395.
| Motion resolved rapid 3D multiparametric brain mapping with self-navigation Shohei Fujita1,2,3,4, Yohan Jun1,2, Xingwang Yong1,2,5, Jaejin Cho1,2, Borjan Gagoski2,6, and Berkin Bilgic1,2,7 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3Department of Radiology, Juntendo University, Tokyo, Japan, 4Department of Radiology, The University of Tokyo, Tokyo, Japan, 5Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Zhejiang, China, 6Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, 7Harvard/MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States Keywords: Quantitative Imaging, Neuro Motivation: While 3D multiparametric mapping acquisitions can provide rich and quantitative information, their long acquisition time renders them susceptible to motion. Goal(s): To develop a rapid, multiparametric technique for motion-robust brain mapping. Approach: 3D-QALAS acquisition with Cartesian variable-density sampling was implemented to achieve self-navigation while maintaining high scan efficiency. Brain position was estimated for each TR and incorporated in the reconstruction’s forward model. Results: The proposed method enabled reconstruction of motion-resolved datasets at a time resolution of 4.5s with tracking accuracy of <0.2 degrees and <0.5mm, providing T1 and T2 maps with significantly reduced artifacts and improved agreement with measurements from motion-free scans. Impact: We propose an efficient, whole-brain quantitative scan at 1mm3 resolution in 3:36min and incorporate self-navigated motion-correction, thereby obviating the need for navigators or external hardware. This benefits clinical translation especially for imaging unsedated children in clinical and research settings. |