Digital Poster
Motion-Robust Neuroimaging
ISMRM & ISMRT Annual Meeting & Exhibition • 10-15 May 2025 • Honolulu, Hawai'i
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Computer Number: 1
4427. Centric
View-ordering Variable-density Sampling with Motion Correction
for Quantitative Susceptibility Mapping
Y. Meng, D. Qiu
Emory University, Atlanta, United States
Impact: This work provides a fast robust QSM acquisition
protocol for reducing measurement sensitivity to motion.
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Computer Number: 2
4428. Innovative
FE-DIC for Robust Motion and Intensity Correction in Dynamic
CEST-MRI
Y. Zheng, H. Liu, Z. Liu, Y. Jin, Z. Li, W. Cui, Y. Wu, Z.
Hu, D. Luo
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China, Shenzhen, China
Impact: The proposed method enhances the reliability and
precision of CEST-MRI, facilitating its applications in
clinical and research settings.
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Computer Number: 3
4429. Head
motion correction based on Pilot Tone signals – a referenceless
method
Y. Li, C-C Cheng, J. Dubey, J. Guenette, L. Qin, B. Madore
Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, China
Impact: The proposed approach is effective at reducing
motion artifacts, and it was designed to be translatable to
clinical practice, as it does not affect workflow.
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Computer Number: 4
4430. High-Temporal
Resolution Direct Motion Parameter Estimation via Inertial
Sensors
M. T. Arslan, F. Calakli, S. Warfield
Computational Radiology Laboratory, Boston Children's Hospital, Boston, United States
Impact: We developed computationally cheap novel
algorithms that utilize a low-cost wearable inertial sensor
to deliver accurate motion information with very high
temporal resolution without requiring modifications to the
sequence.
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Computer Number: 5
4431. A
Radial Based Multi-spatial, Multi-temporal Resolution Aligned
Reconstruction Scheme for Accelerated Motion Correction in Brain
MRI
B. Li, H. She
Shanghai Jiao Tong University, Shanghai, China
Impact: A flexible and time-efficient method based on
aligned reconstruction framework was developed for
rigid-body motion correction in accelerated brain MRI, which
may be beneficial to the exams of clinical uncooperative
patients as well as brain MRI research community.
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Computer Number: 6
4432. Highly
accelerated and motion-robust 2D TSE brain MRI: Combining SAMER
retrospective MoCo with a data-driven regularizer
R. Andujar Lugo, Y. Juli, D. Nickel, B. Clifford, D.
Splitthoff, W-C Lo, S. Y. Huang, J. Conklin, L. Wald, S.
Cauley, D. Polak
Friedrich-Alexander University, Erlangen, Germany
Impact: We integrate retrospective motion correction
into a data-driven deep learning network to facilitate fast
and motion-robust 2D TSE imaging in the brain.
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Computer Number: 7
4433. vNav-QALAS:
Motion robust 3D multi-parametric brain mapping with volumetric
navigators
P. Xu, S. Fujita, Y. Jun, B. Gagoski, O. Afacan, H. Liu, B.
Bilgic
Zhejiang University, Hangzhou, China
Impact: We presented a motion-robust 3D multiparametric
brain mapping technique that requires no external hardware
for motion tracking and minimal increase (~9%) of the
overall acquisition time.
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Computer Number: 8
4434. Fast
and accurate motion-corrected reconstruction with
motion-correcting Implicit GROG (motion-iGROG)
Y. Lin, D. Abraham, N. Wang, Z. Zhou, X. Cao, A. Nurdinova,
K. Setsompop
Stanford University, Stanford, United States
Impact: Motion-iGROG enables rapid and accurate
motion-corrected and background-phase-changed
reconstruction, which could be employed synergistically with
emerging high-temporal motion-tracking methods, such as
pilot tone and advanced motion navigators, where 100s of
motion states are obtained across each imaging scan.
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Computer Number: 9
4435. An
in-vivo approach to quantify head motion tracking accuracy:
comparison of markerless optical tracking versus fat-navigators
Z. Zariry, F. Lamberton, R. Frost, T. Gaass, T. Troalen, H.
Rayson, J. Slipsager, N. Richard, J. Bonaiuto, A. Van Der
Kouwe, B. Hiba
Institut des Sciences Cognitives - Marc Jeannerod / CNRS UMR5229, Bron, France
Impact:
The proposed approach enables in-vivo evaluation of head-motion tracking in MRI and, consequently, could contribute to improving motion artifact-correction for brain-MRI. Its impact will be substantial with the advent of ultra-high magnetic-field scanners and the widespread use of high-resolution brain-MRI. |
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Computer Number: 10
4436. 3D-cone
acquisition for improved combined angiography, structural and
perfusion imaging with subspace-based motion correction
Q. Shen, W. Wu, M. Chiew, Y. Ji, J. Woods, T. Okell
University of Oxford, Oxford, United Kingdom
Impact: This work enhances the motion robustness of ASL
imaging by improving navigator reconstruction under varying
contrast and subtraction-based reconstruction with
mismatched k-space. These improvements pave the way for
broader clinical applications and more reliable diagnostic
imaging.
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Computer Number: 11
4437. Self-navigated
motion correction in multi-contrast intracranial vascular
imaging using 3D radial trajectory
X. Chao, X. Ma, K. Zhang, N. Balu, L. Han, P. Wu, H. Wang,
Z. Chen
Fudan University, Shanghai, China
Impact: The proposed retrospective motion correction
approach improves image quality of iSNAP, and enhances its
clinical value.
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Computer Number: 12
4438. Fetal
Brain Volume Reconstruction from Motion-corrupted Stacks Based
on Hybrid Convolution Neural Network and Transformer
L. Ma, Z. He, W. Lin, L. Gang
The University of North Carolina at Chapel Hill, Chapel Hill, United States
Impact: The proposed fetal brain MRI 3D volume
reconstruction method based on CNN and Transformer can solve
arbitrary motion correction of 2D slices and reconstruct
high-resolution fetal brain MRI 3D volumes effectively and
efficiently.
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Computer Number: 13
4439. Retrospective
Motion Artifact Correction Using Refinement U-Nets with Wavelet
Affine Transformations and Adaptive Multi-Loss Normalization
A. Hassan, M. Yaser, I. Mohamed, M. Medhat, M. Ismail, M. M.
Makary, M. A. Al-masni
Cairo University, Cairo, Egypt
Impact: Correcting motion artifacts in MRI scans
enhances image quality, making them more reliable for
clinical diagnosis. Additionally, using this approach as a
preprocessing step for tasks like registration and
segmentation boosts model accuracy and supports improved
diagnostic outcomes.
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Computer Number: 14
4440. Learning-Based
Motion Correction for High-Resolution 3D MRSI of the Brain
without Water Suppression
H. Zhuang, Z. Ke, Y. Zhao, R. Guo, Y. Li, W. Jin, Z. Cheng,
Y. Zhang, W. Tang, M. Zhang, Z-P Liang, Y. Li
Shanghai Jiao Tong University, Shanghai, China
Impact: A novel motion correction method has been
developed for high-resolution, non-water-suppressed MRSI of
the brain. This method has the potential to significantly
improve the robustness and clinical applicability of
non-water-suppressed MRSI.
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Computer Number: 15
4441. Joint
reconstruction and motion correction of the fetal brain T2 maps
S. Bhattacharya, A. Price, A. Uus, H. S. Sousa, M.
Marenzana, K. Colford, M. Lee, L. Cordero-Grande, S. Malik,
M. Deprez
King's College London, London, United Kingdom
Impact: This should improve the usability of the
previously proposed T2 measurement
pipeline and allow for faster scan times as less data is
required as well as improve robustness to motion when all
nine stacks are used.
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Computer Number: 16
4442. Motion-corrected
brain MRI at 64 mT
Y. Brackenier, R. P. Teixeira, L. Cordero-Grande, E.
Ljunberg, N. Bourke, T. Arichi, S. Deoni, S. Williams, J. V.
Hajnal
King's College London, London, United Kingdom
Impact: alignedSENSE, combined with phase correction,
can effectively improve image quality without increasing
scan time in ULF MRI systems without increasing scan time,
making them more valuable for clinical use.
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