ISSN# 1545-4428 | Published date: 19 April, 2024
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At-A-Glance Session Detail
   
Cardiac & Abdominal Motion Correction: Freeze, Don’t Move!
Oral
Acquisition & Reconstruction
Wednesday, 08 May 2024
Hall 606
08:15 -  10:15
Moderators: Thomas Küstner & Thomas Olausson
Session Number: O-07
CME Credit

08:15 Introduction
Thomas Küstner
University Hospital Tuebingen, Germany
08:270749.
Dynamic Mode Decomposition (DMD) Cardiac Phase Estimation for adult and fetal real-time MRI
Ecrin Yagiz1, Bilal Tasdelen1, Ibrahim K. Ozaslan1, Mihailo R. Jovanovic1, Ye Tian1, and Krishna S Nayak1
1Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States

Keywords: Motion Correction, Fetus, retrospective-gating

Motivation: Cardiac synchronization in adult and fetal imaging requires external devices (electrocardiogram, Doppler-ultrasound), which may compromise image quality and increase scan time. Self-gating with real-time imaging can mitigate this but may be less reliable for irregular motions and limited in fetal applications.

Goal(s): To develop a fast image-based cardiac phase estimation method with no assumption on the heart rate and minimal user input.

Approach: Dynamic Mode Decomposition is used to estimate cardiac motion signal for retrospective-gating.

Results: DMD cardiac phase estimation captures cardiac motion despite the irregularities and other bulk motions, as demonstrated in real-time adult and fetal cardiac imaging, including a twin gestation.

Impact: The proposed technique, Dynamic Mode Decomposition cardiac phase estimation, constructs cardiac signal with no assumption on periodicity, no iterations, and only minimal user input. This may be valuable in fetal cardiac imaging, where the cardiac signal is not readily available.

08:390750.
TR-resolved Real-Time Low-Field CMR using Hermitian Motion Corrected Reconstructions
Gastao Cruz1, Jesse Hamilton1,2, Evan Cummings1,2, Vikas Gulani1, and Nicole Seiberlich1,2
1Radiology, University of Michigan, Ann Arbor, MI, United States, 2Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

Keywords: Motion Correction, Low-Field MRI, Real-time

Motivation: Real-time cardiac MR could provide new insights into function of the myocardium while avoiding artefacts arising from cardiac and/or respiratory motion. 

Goal(s): High temporal resolution real-time cardiac MR is particularly challenging at low field (0.55T) due reduced SNR and coil elements available on commercial systems. 

Approach: Here, we leverage motion compensated reconstructions and Hermitian symmetry to enable the highly undersampled reconstructions required for real-time cardiac MR at low field. 

Results: Experiments at 0.55T show that the proposed approach enables imaging with a temporal resolution of 6 ms (R~48x) with minimal aliasing, outperforming conventional compressed sensing (considerable aliasing) and parallel imaging (aliasing dominated).

Impact: TR-resolved (6ms temporal resolution) real-time cardiac is demonstrated at 0.55T where SNR is limited. Such highly accelerated imaging may reveal finer details in myocardial function. Additionally, the high acceleration factors achieved here could also be leveraged for 3D real-time imaging.

08:510751.
Pilot Tone Navigated Motion Correction in DCE-MRI
Cemre Ariyurek1, Jeanne Chow1, Onur Afacan1, and Sila Kurugol1
1Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States

Keywords: Motion Correction, DSC & DCE Perfusion, Motion Correction

Motivation: Addressing the challenge of respiratory motion in abdominal DCE-MRI, especially in pediatric patients, to improve image quality and enhance quantitative DCE-MRI analysis.

Goal(s): To develop a motion correction method using PilotTone navigators (PTnavs) to enhance DCE-MRI quality and reliability.

Approach: We extract PTnav, create a linear motion model using binning based reference motion parameters. We then apply the motion model to the PTnav for each spoke to estimate its motion and correct for it. We evaluate the method on non-contrast volunteer and pediatric DCE-MRI data.

Results: Successful elimination of motion artifacts and improved image quality, reduced image alignment and improved signal-time-intensity curves.

Impact: The proposed PT-based motion correction effectively overcomes the challenges of previous motion correction methods, eliminating respiratory motion artifacts and enhancing image quality and misalignment in high-temporal-resolution DCE-MRI. This advancement improves diagnostic accuracy, particularly in pediatric cases with unpredictable breathing patterns.

09:030752.
Learning non-rigid registration in k-space from highly-accelerated cardiac and respiratory MR data
Aya Ghoul1, Kerstin Hammernik2, Daniel Rueckert2,3,4, Sergios Gatidis1,5, and Thomas Küstner1
1Medical Image And Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen, Germany, 2School of Computation, Information and Technology, Technical University of Munich, Munich, Germany, 3Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany, 4Department of Computing, Imperial College London, London, United Kingdom, 5Department of Radiology, Stanford University, Stanford, CA, United States

Keywords: Motion Correction, Motion Correction, Image registration, motion estimation, Cardiovascular, Lung, MR-Guided Radiotherapy, motion-compensated reconstruction, Multimodal motion correction

Motivation: Time-resolved motion estimation from accelerated MR data enables high-quality imaging, intra-modality motion correction and real-time tracking during MR-guided radiotherapy. Conventionally, image registration is solved in the image domain and, therefore, remains susceptible to aliasing artifacts for highly-accelerated acquisitions.

Goal(s): We aim to propose a robust non-rigid image registration framework from highly-accelerated data without additional information.

Approach:  We introduce a novel Local-All-Pass Attention Network (LAPANet) that performs accurate motion estimation directly from the acquired k-space.

Results: LAPANet provides reliable estimates for fully-sampled and undersampled data, up to 104-fold for cardiac motion and 148-fold for respiratory motion, and outperforms established image-based registrations in different trajectories.

Impact: Our framework can reliably estimate non-rigid motion from highly-accelerated data without a-priori information. This enables faster acquisition through integration into motion-compensated reconstructions, intra-modality motion correction for other imaging methods and real-time motion characterization and tracking for guided radiotherapy and interventions.

09:150753.
Free-breathing SMS-bSSFP myocardial perfusion imaging with prospective slice-tracking and AI-based reconstruction
Naledi Lenah Adam1, Ronald Mooiweer1,2,3, Andrew Tyler1, Karl Kunze1,2, Peter Speier4, Daniel Stäb5, Amedeo Chiribiri1, and Sébastien Roujol1
1School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom, 2MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom, 3MR Physics, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom, 4Cardiovascular predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, 5MR Research Collaborations, Siemens Healthcare Limited, Melbourne, Australia

Keywords: Motion Correction, Perfusion, free breathing, myocardial perfusion, simultaneous multi-slice, prospective motion-correction, machine learning/artificial intelligence

Motivation:  Simultaneous multi-slice-bSSFP shows promise for myocardial perfusion imaging with high spatial coverage/resolution. Free-breathing acquisitions are desirable but currently result in large through-plane motion. 

Goal(s): To develop a free-breathing SMS-bSSFP myocardial perfusion technique with high spatial coverage/resolution and prospective through-plane motion correction. 

Approach: Prospective slice-tracking using fastNAV was implemented into an SMS-bSSFP perfusion sequence. Image reconstruction used TGRAPPA combined with a deep learning-based complex-value image denoiser. This technique was evaluated in 10 patients undergoing two rest SMS perfusion scans with/without fastNAV. 

Results: The proposed approach resulted in significant motion reduction, low noise-level reconstruction, and no degradation of myocardial sharpness. 

Impact: This study demonstrates the feasibility of prospective slice tracking in an SMS perfusion sequence. Combined with the proposed deep learning-based reconstruction, it provides a myocardial perfusion protocol with increased spatial coverage, high spatial resolution, and feasible under free-breathing conditions.

09:270754.
Frequency Modulated Continuous Wave Radar-based respiratory motion correction for cardiac MRI: Initial Results
Jemon Diao1, Yang Liu1, Jiayu Zhu2, Jian Xu3, Zijian Zhou1, Haikun Qi1,4, and Peng Hu1,4
1ShanghaiTech University, Shanghai, China, 2United Imaging Healthcare, Shanghai, China, 3UIH America, Inc., Houston, TX, United States, 4Shanghai Clinical Research and Trial Center, Shanghai, China

Keywords: Motion Correction, Motion Correction

Motivation: Cardiac MRI is susceptible to motion-induced artifacts because of the sequential data acquisition process. 

Goal(s): Evaluate the feasibility of utilizing a Frequency Modulated Continuous Wave (FMCW) Radar as a quantitative respiratory motion correction signal for free-breathing cardiac MRI.  

Approach: A short calibration scan was performed to establish a motion model relating the FMCW Radar signal to the respiratory-induced heart motion. The established model was then applied during the imaging scan to perform retrospective motion correction for each k-space readout line.

Results: The FMCW radar showed good correlation to the respiratory-induced heart motion, and the proposed method effectively improved the image quality.

Impact: This study demonstrated the feasibility of utilizing FMCW radar as a surrogate to accomplish motion correction in free-breathing cardiac MRI.  

09:390755.
Respiratory Triggered MRI using an NMR-on-a-chip Sensor
Fabian Bschorr1, Thomas Hüfken1, Tobias Lobmeyer1, Frederik Dreyer2, Jianyu Zhao2, Jens Anders2, and Volker Rasche1
1Internal Medicine II, Ulm University Medical Center, Ulm, Germany, 2Institute of Smart Sensors, University of Stuttgart, Stuttgart, Germany

Keywords: Motion Correction, Motion Correction

Motivation: We propose an NMR-on-a-chip sensor as contactless und hysteresis-free alternative for conventional respiratory belts in respiratory-triggered MRI.

Goal(s): The objective of this work was to demonstrate the feasibility of a local field probe for monitoring respiratory motion induced magnetic field changes as respiratory motion surrogate.

Approach: Respiratory belt and field probe signal were recorded simultaneously clearly showing the accurate identification of the respiratory stage by the field probe.

Results: The field probe signal was analysed and fed back to the MR scanner in real-time for proofing its applicability for triggered lung MRI, yielding a sharp lung-liver interface compared with the non-triggered version.

Impact: The feasibility of NMR-on-a-chip sensors for monitoring physiologically-induced magnetic field variations is shown. They enable contactless, hysteresis-free and easy-to-use monitoring of physiologically-induced field variations, which can be fed back to the scanner for real-time respiratory motion monitoring and triggering.

09:510756.
Motion Correction with Combination of Disentangled Cycle-GAN and k-space Subsampling for the gadoxetic acid-enhanced liver MRI
Gang Chen1,2, Xinglong Rao1, Martins Otikovs3, Yang Lian4, Peng Sun5, Xin Zhou1,2,6, Chaoyang Liu1,2,6, and Qingjia Bao1,2
1State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China, 2University of Chinese Academy of Sciences, Beijing, China, 3Weizmann Institute of Science, Rehovot, Israel, 4Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, 5Clinical & Technical Support, Philips Healthcare, Beijing, China, 6Optics Valley Laboratory, Hubei, China

Keywords: Motion Correction, Motion Correction

Motivation: The gadoxetic acid-enhanced liver MRI is often accompanied by significant motion artifacts due to drug side effects.

Goal(s): Motion correction by integrating Disentangled Cycle-GAN with the k-space Subsampling (DCGAN-kS) method.

Approach: Convert motion correction to the image domain transfer problem resolved by DCGAN with the aid of the k-space subsampling strategy for reducing features and simplifying the domain transfer problem.

Results: The method can effectively remove artifacts for the arterial phase imaging of the gadoxetic acid-enhanced liver MRI.

Impact: The proposed scheme outperforms the other state-of-the-art methods for motion correction in the gadoxetic acid-enhanced liver MRI, which could enhance the image quality and reduce failed scanning.

10:030757.
High resolution 3D isotropic non-rigid motion compensated T1 Dixon of Liver at a hybrid PET-MR scanner
Jake Penney1,2, Khalid Ambarki1, Aurélien Monnet1, Hatem Necib3,4, Valérie Vilgrain5,6, Karl-Philipp Kunze7,8, René Michael Botnar7,9, Claudia Prieto7,9, and Ralph Sinkus2,7
1Siemens Healthcare France, Courbevoie, France, 2INSERM U1148 Laboratory for vascular translational science, Paris, France, 3CHU de Nantes, Nantes, France, 4UMR_S 1307 Centre de Recherche en Cancérologie et Immunologie Intégrée Nantes Angers, Nantes, France, 5Hôpital Beaujon AP-HP, Clichy, France, 6INSERM U1149 Centre de Recherche sur l'Inflammation, Paris, France, 7School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom, 8MR Research Collaborations, Siemens Healthcare GmbH, Frimley, United Kingdom, 9Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile

Keywords: Motion Correction, Motion Correction, MRI, PET-MRI, liver, navigator, HCC, cancer, free-breathing MRI

Motivation: In current clinical practice, liver MRI scans often suffer from motion artifacts. This issue typically arises because patients struggle to maintain breath-holding. 

Goal(s): Our goal is to produce a high-quality, isotropic 3D Dixon T1 scan without the need for breath-holding.

Approach: . Our method involves using a navigator to estimate liver motion, enabling us to calculate non-rigid motion fields for image reconstruction

Results: This approach yields high-quality free-breathing isotropic T1 3D Dixon liver data with a voxel size of 1.3mm³, surpassing the quality of the gold standard non-isotropic breath-hold Dixon T1 liver scan.

Impact: This work aims to deliver previously unseen high-quality free-breathing isotropic 3D Dixon liver data that can surpass the current clinical standard breath-hold non-isotropic T1 3D Dixon liver scans.