| 13:45 | 1247.
 | A Novel Deep Learning Denoising Algorithm for Neural Signal Recovery in fMRI Scanning Bo-Wei Chen1, Zhuyuan Lyu2,3, Xiao Yu2,3, Tingting He2,3, Boyi Qu2,3,4, Haiming Wang2,3,4, Zheng Tang2,3, Mingfeng Ye2,3, You-Yin Chen*1, and Hsin-Yi Lai*2,3,4,5
1Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, 2Department of Neurology of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China, 3MOE Frontier Science Center for Brain Science and Brain-Machine Integration, State Key Laboratory of Brain-machine Intelligence, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China, 4College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, 5Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310000, China Keywords: Artifacts, Artifacts Motivation: While fMRI infers neural activity from hemodynamic changes, the relationship between the two remains to be further clarified. Simultaneous electrophysiological recordings (Ephy) and fMRI can provide additional insights into neurovascular coupling and brain function. Goal(s): Our objective is to address the electromagnetic interference (EMI) noise in the simultaneous Ephy and fMRI recording. Approach: A deep learning-based fully convolutional neural network (FCNN) was proposed to effectively eliminate EMI noise. Simulated neural signals and tactile-evoked neural signals were implemented for training and testing. Results: FCNN significantly reducing EMI noises, maintaining spike waveform consistency and successfully retaining the most neural signals. Impact: This
research proposed a universal and robust denoising approach to address electromagnetic
interference during simultaneous recording of neural signals and fMRI data, which
will be relevant for understanding of neurovascular coupling and brain function. |
| 13:57 | 1248.
 | Dynamic Mode Decomposition enables low-latency high temporal resolution reconstruction for golden-angle spiral real-time MRI Ecrin Yagiz1, Ibrahim K. Ozaslan1, Bilal Tasdelen1, Mihailo R. Jovanovic1, Ye Tian1, and Krishna S Nayak1
1Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States Keywords: Image Reconstruction, Cardiovascular, fetal, low-field, online reconstruction Motivation: Real-time MRI methods with higher spatiotemporal resolution employ undersampled non-Cartesian trajectories combined with a computationally intense reconstruction to mitigate aliasing. However, often, a low-latency, coarse-temporal resolution, low-quality reconstruction is provided online. This may hinder the scan quality in interventional and fetal imaging.
Goal(s): To develop a low-latency, high-temporal resolution online reconstruction for real-time MRI.
Approach: We introduce a novel method using Dynamic Mode Decomposition for low-latency, high-temporal resolution reconstruction that removes spiral aliasing. The online version is achieved by predicting and removing aliasing artifacts in upcoming frames.
Results: We evaluate DMD Filtering in the context of real-time adult and fetal cardiac function assessment.
Impact: The proposed technique, Dynamic Mode Decomposition filtering, achieves low-latency (<20ms/frame), high-temporal resolution reconstruction with negligible spiral aliasing artifact, and no iterations. This may be valuable for online reconstruction during interventional and fetal cardiac imaging.
|
| 14:09 | 1249.
 | Banding insensitive DESPO for T1 and T2 mapping applied to 3D prostate at 3T. Ronal Coronado1,2, Carlos Castillo-Passi3,4, Cecilia Besa2,5, and Pablo Irarrazaval1,2,6
1Pontificia Universidad Catolica de Chile, Santiago, Chile, 2Millenium Institute for Intelligent Healthcare Engineering, Santiago, Chile, 3Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, 4School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 5Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile, 6Institute for Biological and Medical Engineering, Santiago, Chile Keywords: Artifacts, Prostate, Banding artifacts, bSSFP mapping Motivation: DESPO is a robust T2 brain mapping technique based on balanced steady-state free precession (bSSFP). However, it is susceptible to off-resonance artifacts, especially in areas with high susceptibility changes, such as the prostate. Goal(s): Our proposed method, DESPO+, integrates the complete bSSFP and spoiled-gradient echo (SPGR) models, using a simulation-based approach for 3D T1/T2 maps in the prostate region. Approach: We employed a simulated-based method of the full bSSFP and SPGR models incluiding off-resonance to reconstruct T1/T2/PD simultaneously. Results: DESPO+ provides off-resonance insensitive with high-resolution 3D T1/T2 mapping, synthesizing T1-weighted/T2-weighted images using a short scan time of 3.6 minutes, similar to DESPO. Impact: DESPO+ provides an
off-resonance insensitive and customizable solution, enabling high-resolution
3D T1/T2 mapping and synthesized T1-weighted/T2-weighted images for the entire
prostate, all achieved within a short scan time of 3.6 minutes, similar to
DESPO.m |
| 14:21 | 1250.
 | Joint suppression of cardiac bSSFP cine banding and flow artifacts based on twofold phase-cycling and a dual-encoder neural network Zhuo Chen1, Juan Gao1, Haiyang Chen1, Xin Tang2, Yixin Emu1, and Chenxi Hu1
1Shanghai Jiao Tong University, Shanghai, China, 2United Imaging Healthcare Co., Ltd, Shanghai, China Keywords: Artifacts, Artifacts, Cardiac function, Cine Motivation: Cardiac bSSFP cine imaging suffers from banding and flow artifacts caused by the off-resonance. Although fourfold phase cycling suppresses the banding artifacts, it invokes flow artifacts and prolongs the scan. Goal(s): To develop a twofold phase-cycling sequence with a neural-network-based reconstruction for a fast and joint suppression of banding and flow artifacts in cardiac cine imaging. Approach: We compared the method with standard bSSFP and regular phase cycling in the left ventricle and atrium in 10 healthy subjects. Results: Needing only 10 heartbeats, the proposed method robustly suppressed both artifacts in the presence of anatomical variations. Impact: Banding and flow artifacts are common in bSSFP cine
imaging, especially with cardiac devices or high-field MR. The proposed method
provides a robust and practical tool for suppression of them and improves the
reliability of cine MRI. |
| 14:33 | 1251.
 | Dynamic Estimation of Respiration-Induced B0 Inhomogeneities in OSSI fMRI: A Novel Framework Using FIDNavs and SENSE Maps Mariama Salifu1 and Douglas C Noll1
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States Keywords: Artifacts, System Imperfections: Measurement & Correction, Artifacts, Physiological noise correction, FID navigators, fMRI Motivation: In OSSI fMRI, temporal fluctuations in the B0 field, predominantly resulting from respiration, can induce changes to the steady state, subsequently diminishing the temporal signal-to-noise ratio (tSNR) Goal(s): Our objective is to develop an efficient technique for estimating respiration-induced B0 variations in OSSI fMRI Approach: We have used Free Induction Decay (FID) frequency offset to estimate the first-order field inhomogeneities. A novel element in our approach lies in adopting a spatial encoding strategy for these FIDs, drawing on the geometric centroids of each coil's sensitivity profile Results: Our initial findings indicates comparable results between our FIDNavs method and image-based field map method. Impact: This approach allows for a rapid measurement of B0 variations, thus facilitating faster real-time corrections. This method bypasses the lengthy process of calibration or the need for reference images. |
| 14:45 | 1252.
 | RF Power Amplifier Drift Compensation for Reliable B1 Mapping Using Bloch-Siegert Technique Ali Aghaeifar1 and Klaus Scheffler 1,2
1Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 2Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany Keywords: Artifacts, Reproductive, RFPA, Drift, Bloch-Siegert, B1 Motivation: RFPA drift affects reproducibility of B1+ mapping Goal(s): The goal of study is to investigate factors influencing RFPA output drift and introducing a real-time feedback mechanism to compensate for drift Approach: B1+ with Bloch-Siegert shift technique is measured in a repetitive manner and reproducibility of maps is assessed. The RFPA output is continuously monitored through the use of DICOs, and adjustments to the transmit voltage are made to compensate for drift Results: The findings indicate that RFPA output drift is more noticeable with extended RF pulses and shorter TR. Implementing real-time drift correction effectively minimizes drift, leading to enhanced stability in B1+ maps Impact: Our demonstration of real-time feedback to mitigate RFPA drift enhances measurement accuracy and reproducibility. This approach can be advantageous to achieve the consistency and reliability of research, particularly in the context of multi-center studies. |
| 14:57 | 1253.
 | Model-based frequency-and-phase correction of 1H-MRS data with 2D linear-combination modeling Dunja Simicic1,2, Helge Jörn Zöllner1,2, Christopher William Davies-Jenkins1,2, and Georg Oeltzschner1,2
1Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 2F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States Keywords: Signal Modeling, Spectroscopy, MRS; Frequency-and-phase correction; linear-combination modeling; 2D-modeling Motivation: Retrospective frequency-and-phase correction (FPC) methods like spectral registration struggle at low SNR. Goal(s): To develop model-based correction FPC with simultaneous 2D fitting of all transients. To compare its performance to conventional FPC. Approach: Inclusion of all transients (without prior FPC) into a 2D linear-combination model with frequency and phase parameters for each transient. Comparison with conventional approach (spectral registration, averaging and 1D modeling). Outcome measures: frequency/phase/amplitude ground truth error & standard deviation, amplitude CRLB. Results: Model-based FPC is feasible and retrieves frequency/phase variations with high fidelity. At low SNR, frequency and metabolite amplitude estimation is more accurate and precise. Impact: Direct integration of frequency-and-phase correction into 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, e.g., long TEs, strong diffusion weighting, etc. |
| 15:09 | 1254.
 | Sequence Adaptive B1+ and B0 Field-imperfections Estimation (SAFE) for enhanced MRF quantification Mengze Gao1, Xiaozhi Cao1,2, Daniel Abraham2, Zihan Zhou1, and Kawin Setsompop1,2
1Department of Radiology, Stanford University, Stanford, CA, United States, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States Keywords: Artifacts, Machine Learning/Artificial Intelligence Motivation: B1+ and B0 field-inhomogeneities can significantly reduce accuracy and robustness of MRF’s quantitative parameter estimates. Additional B1+ and B0 calibration scans can mitigate this but add scan time and cannot be applied retrospectively to previously collected data. Goal(s): Here, we proposed a calibration-free sequence-adaptive deep-learning framework, to estimate and correct for B1+ and B0 effects of any MRF sequence. Approach: We demonstrate its capability on arbitrary MRF sequences at 3T, where no training data were previously obtained. Results: Such approach can be applied to any previously-acquired and future MRF-scans. The flexibility in directly applying this framework to other quantitative sequences is also highlighted. Impact: Proposed method can estimate B1+ and B0 maps without calibration scan and be applied to arbitrary MRF sequence without new training data. It can be used retrospectively to improve quality of parameter maps of any previously-acquired or future MRF data. |
| 15:21 | 1255.
 | Basis function compression for compact representation of high spatial orders of field variation using field monitoring Paul I. Dubovan1,2, Gabriel Varela-Mattatall1,3, Ravi S. Menon1,2, Adam B. Kerr4,5, and Corey A. Baron1,2
1Medical Biophysics, Western University, London, ON, Canada, 2Centre for Functional and Metabolic Mapping, Western University, London, ON, Canada, 3Lawson Health Research Institute, London, ON, Canada, 4Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, United States, 5Department of Electrical Engineering, Stanford University, Stanford, CA, United States Keywords: Artifacts, Artifacts, Field Monitoring Motivation: Field monitoring using field probes has shown to inaccurately estimate higher order field variations on a high-performance gradient system using the conventional fitting procedure. Goal(s): To develop and validate a new fitting approach for field monitoring measurements for improved higher order field characterizations on complex MRI systems. Approach: Perform a calibration scan by moving probes around imaging volume to accurately characterize field variations, then compress this data to preserve important field information, with the purpose of applying this information to new scans. Results: Quantitative phantom results and qualitative in-vivo diffusion images show significantly improved image quality when using the proposed fitting method. Impact: This work
presents a new method for accurately calculating higher order field monitoring
measurements on a head-only MRI scanner, resulting in substantially improved
image quality. This may be useful for other research centers that also utilize complex,
high-performance MRI systems. |
| 15:33 | 1256.
 | Field-Correcting GRAPPA (FCG) for improved mitigation of even-odd and field-related artifacts in EPI Nan Wang1, Daniel Abraham2, Adam B Kerr2,3, Hua Wu3, Congyu Liao1, Xiaozhi Cao1, Jonathan R Polimeni4,5,6, Renzo Huber7, and Kawin Setsompop1
1Radiology Department, Stanford University, Stanford, CA, United States, 2Electrical Engineering, Stanford University, Stanford, CA, United States, 3Cognitive and Neurobiological Imaging Center, Stanford University, Stanford, CA, United States, 4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 5Department of Radiology, Harvard Medical School, Boston, MA, United States, 6Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, 7Functional Magnetic Resonance Facility (FMRIF), National Institutes of Health, Bethesda, MD, United States Keywords: Artifacts, Artifacts Motivation: Field perturbation from gradient error and eddy current has been a long-standing issue for EPI, especially with high gradient performance or ramp sampling Goal(s): To develop a MLP based Field-Correcting GRAPPA (FCG) that accounts for spatial-varying field and produces artifacts-mitigated images Approach: Calibration data with dual polarity were acquired. A MLP based kernel was trained to take single-polarity data as input and output the clean averaged data. It was applied for both phantom and in vivo undersampled EPI with ramp-sampling. Results: FCG produced best correction results for ramp-sampling EPI, with potentials for wide applications including fMRI. Impact: A Field-Correcting GRAPPA (FCG) technique was developed to correct the field perturbation induced image artifacts for EPI, which accounts for spatial varying field and produced promising resulting on ramp-sampling cases, with great potentials for wide applications including fMRI. |