| Computer # |
| 2639.
 | 1 | Dynamic distortion correction of ME-EPI using learning-based single-scan multi-echo blip up-down acquisition (ME-BUDA) Nuowei Ge1, Qinqin Yang1, Zejun Wu1, Jianfeng Bao2, Zhigang Wu3, Congbo Cai1, and Shuhui Cai1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China, 3Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China Keywords: Artifacts, Artifacts, Distortion correction, ME-EPI Motivation: Multi-echo (ME) fMRI approaches based on ME-EPI acquisition achieve higher BOLD sensitivity and reproducibility than traditional EPI. However, ME-EPI suffers from severe image distortion. Goal(s): To develop a technique to correct ME-EPI distortion artifacts in single scan. Approach: We developed a deep learning-based technique to correct ME-EPI distortion artifacts using single-scan multi-echo blip up-down acquisition (ME-BUDA). The proposed method was suitable for both spin- and gradient-echo-based EPI and was validated in simulation, human brain and additional dynamic imaging experiments. Results: For simulation experiment, the PSNR and SSIM were 33.31 and 0.98, respectively. For in vivo dynamic imaging, the temporal SNR increased by 75%. Impact: The ME-BUDA method can reliably correct the geometric distortion of dynamic ME-EPI images without additional information, ensuring the distortion-free, real-time, and high-quality ME-fMRI analysis of important function regions. |
| 2640.
 | 2 | Development of Cervical Spine Mimicking Phantom for Diffusion MRI Near Metal Thammathida Ketsiri1, Richard D. Dortch1, and Zhiqiang Li1
1Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States Keywords: Artifacts, Phantoms, Diffusion, Metal, Spine Motivation: Diffusion-MRI (dMRI) is increasingly used to evaluate neurological disorders and injuries of the spinal cord. Unfortunately, high-quality dMRI for post-surgical evaluation of the spinal cord is often limited due to the distortion artifacts from metal implants. Goal(s): The goal of this work is to assist in the development of novel imaging protocols to overcome this challenge. Approach: To do this, a cervical spine phantom was developed to replicate the spine’s geometric and MRI properties along with the image artifacts generated from metal implants. Results: Preliminary data demonstrated that the model is helpful for visualizing and developing novel dMRI protocols near metal implants. Impact: The proposed cervical spine phantom, designed to characterize the dMRI performance of the spinal cord post-surgery, including artifacts from metallic implants, is potentially helpful for developing novel imaging techniques for post-surgical spinal cord injuries. |
| 2641.
 | 3 | Reducing pulsation artifacts in 3D time-of-flight angiography at 7T using locally-scrambled ordering of the acquisition Rita Schmidt1, Amir Seginer2, Dana Niry3,4, and Edna Furman-Haran2
1Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel, 2Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel, 3Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel, 4Department of Radiology, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel Keywords: Artifacts, Velocity & Flow, acquisition order; angiography Motivation: Non-contrast-enhanced time-of-flight (TOF) at 7T greatly improves the delineation of small vessels but is impaired by pulsation artifacts. Goal(s): Our goal was to reduce pulsation artifacts in 3D TOF to better delineate vessels. Approach: We recently developed “local-scrambling” which generates semi-random acquisition ordering to reduce artifacts from semi-periodic local signal fluctuations. This local-scrambling was tested in 3D TOF to reduce pulsatile blood flow artifacts in human scanning at 7T. Results: Artifacts from pulsatile blood flow were significantly reduced using the new local-scrambling (of the 2D phase encodes), both for line-by-line and center-out acquisitions. The method can be of special interest for high-resolution angiography. Impact: Increased
resolution of non-contrast-enhanced time-of-flight (TOF) can provide more
accurate vessels delineation. A new local-scrambling acquisition scheme can
significantly improve 3D angiography by reducing interference noise and pulsation artifacts without requiring any changes to the reconstruction scheme. |
| 2642.
 | 4 | Leveraging Compressed Sensing for Improvement of FIDDLE Image Quality Rather Than for Acquisition Speed Wolfgang G Rehwald1,2, Jianing Pang3, Rafael Rojas2, Julie Swanson2, David Wendell2, Sherilyn Pirela2, Jeana Dement2, Igor Klem2, and Raymond Kim2
1Siemens Medical Solutions USA, Inc., Durham, NC, United States, 2Duke Cardiovascular MR Center, Duke University, Durham, NC, United States, 3Siemens Medical Solutions USA, Inc., Issaquah, WA, United States Keywords: Artifacts, Cardiovascular, FIDDLE Motivation: Imperfect breath holding and cardiac arrhythmia create chest wall and cardiac ghosting in conventional segmented dark-blood LGE images (FIDDLE) often rendering them non-clinical. Goal(s): We aimed to apply compressed sensing ‘CS’ as the solution to this problem, to acquire FIDDLE images without ghosting, even in challenging patients. Approach: CS can acquire multiple high-spatial and excellent-temporal resolution single shots that intrinsically never display ghosting artifacts. Sparsity is created along the shot dimension, enabling CS to reconstruct the generally not-so-sparse FIDDLE images. Single shot averaging further improves SNR. Results: The CS FIDDLE images show high SNR and no ghosting. They should simplify clinical imaging. Impact: The
CS FIDDLE method should improve clinical CMR image quality and alleviate the
need for repeated acquisitions due to poor breath holding. It also enables using
CS for the acquisition of single still-frame images with intrinsically higher
SNR. |
| 2643.
 | 5 | A deep-learning-based framework for spark artifacts detection and correction Li Tong1, Puwei Wang1, Lei Zhu1, Shucheng Qin1, and Zhenkui Wang1
1Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China Keywords: Artifacts, Artifacts Motivation: The occasional occurrence of spark artifacts in MRI could significantly hinder diagnosis. Previous whole K-space segmentation methods are unstable and require stringent intensity normalization. Goal(s): In this study, we aim to improve the accuracy and stability of spark identification, especially for sparks with low intensities and near the K-space center. Approach: We propose a two-step deep learning-based framework consisting of spark patch classification and patch-level spark segmentation, which are further corrected by ESPIRiT. Results: The proposed methods are demonstrated to be effective and robust on various imaging protocols and body parts for different degrees of spark artifacts. Impact: Incidental
spark artifacts in MRI can significantly hinder diagnosis. We developed a deep-learning-based two-step framework for robust
spark detection and correction, which has been validated to be effective on a
variety of imaging protocols for different degrees of spark artifacts. |
| 2644.
 | 6 | A low rank k-space approach to channel-by-channel reduced FOV imaging Fadil Ali1,2, Mark Bydder3, Stefan Zbyn1, Brendan Eck1, Ben Garelick4, Andres Saucedo5, Vahid Ghodrati6, Ajin Joy6, J. Paul Finn6, and Xiaojuan Li1
1Imaging Institute, The Cleveland Clinic Foundation, Cleveland, OH, United States, 2Radiology, University of California, Los Angeles, Los Angeles, CA, United States, 3Medical Research, Matai Medical Research Institute, Gisborne, New Zealand, 4The Pennsylvania State University, University Park, PA, United States, 5The University of Southern California, Los Angeles, CA, United States, 6The University of California, Los Angeles, Los Angeles, CA, United States Keywords: Artifacts, Image Reconstruction, beamforming, phased-array Motivation: To introduce a channel-by-channel reduced field-of-view (FOV) method for an arbitrary region of interest (ROI). Goal(s): With Nc channels as input, we output Nc channels, with the signal outside of the target ROI vectorially nulled while preserving the original channel sensitivity profiles and relative phases for each channel. Approach: Using a full-FOV calibration set, we learned the linear operators needed to be applied on local k-space neighbors across all channels in order to vectorially cancel the signal outside of the target-ROI for any channel. Results: We were able to generate reduced FOV images in phantom and in vivo settings. Impact: A reduced FOV method is introduced that determines the linear operators needed to vectorially cancel the signal outside the target ROI. The output is the Nc channels with the region outside of the target ROI cancelled while preserving channel sensitivities. |
| 2645.
 | 7 | Strain tensor imaging using single-shot multi-slice DENSE in a pediatric population at 7T Merlijn C.E. van der Plas1,2, Elisabeth C. van der Voort1, Jannie P. Wijnen1,2, Alex Bhogal1, Anne E.M. Leenders2, Evita C. Wiegers1, Eelco W. Hoving2, Marita H. Partanen2, and Jaco J.M. Zwanenburg1
1University Medical Center Utrecht, Utrecht, Netherlands, 2Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands Keywords: Artifacts, Artifacts, pediatric, neuro Motivation: DENSE can provide information about the brain pulsations that likely reflect the condition of blood vessels, which may deteriorate following treatment in pediatric brain tumors. Goal(s): The goal of this study is to perform an initial analysis of this single-shot multi-slice DENSE data in a pediatric population to study the robustness of this sequence during motion. Approach: By using a single-shot multi-slice DENSE sequence, brain motion maps were acquired from which strain maps could be derived on a voxel-wise level. Results: Even though the pediatric participants moved during the MR-acquisition, good quality strain maps were obtained with the expected patterns (as in adults). Impact: Single-shot
strain tensor imaging allows evaluation of cardiac-related brain tissue strain,
in a pediatric cohort of posterior fossa tumor, despite the presence of unwanted
head motion. This enables investigating strain as potential new biomarker of neurovascular
integrity in patients. |
| 2646.
 | 8 | Mitigating Distortion Artifacts in Accelerated-EPI Using an Ensemble of k-t GRAPPA Kernels (EnKT-GRAPPA) Yimeng Lin1, Daniel Raz Abraham2,3, Nan Wang2,3, and Kawin Setsompop2,3
1Center for Biomedical imaging Research, Tsinghua University, Beijing, China, 2Electrical Engineering, Stanford University, Stanford, CA, United States, 3Radiology, Stanford University, Stanford, CA, United States Keywords: Artifacts, Artifacts Motivation: EPI suffers from field-inhomogeneity distortions. Employing large inplane acceleration (Rinplane) can mitigate this issue at a cost of increase noise and artifacts, while postprocessing correction can lead to resolution-loss. Goal(s): Develop a reconstruction method based on an ensemble of k-t GRAPPA-kernels (EnKT-GRAPPA) for use on moderately-accelerated EPI, to both fill missing-kspace and reduce distortion. Approach: EnKT-GRAPPA kernels are trained using k-t calibration data to fill missing-kspace and correct for cumulative-phase of field-inhomogeneity in one step, where phase/distortion correction level can be flexibly tuned. Results: For the same distortion mitigation level, EnKT-GRAPPA-reconstructed-images exhibit higher-SNR compared to those from conventional GRAPPA-reconstructed-images of a higher-Rinplane acquisition. Impact: EnKT-GRAPPA enables moderately accelerated EPI to achieve a high level of distortion mitigation while preserving SNR. This method should be useful in many applications such as fMRI, diffusion and perfusion imaging. |
| 2647.
 | 9 | Quantification of breathing-induced B0 field variations along the human spinal cord at 3T Laura Beghini1, Gergely David2,3, Martina D. Liechti3, Silvan Büeler3, Alexander Jaffray4, and S. Johanna Vannesjo1
1Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway, 2Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland, 3Department of Neuro-Urology, Balgrist University Hospital, University of Zurich, Zurich, Switzerland, 4Department of Physics, University of British Columbia, Vancouver, BC, Canada Keywords: Artifacts, Spinal Cord, Breathing Motivation: Breathing-induced B0 field variations cause artefacts in spinal cord MRI. Characterizing the variations is useful for optimizing correction strategies, but has not yet been done below the thoracic region. Goal(s): To quantify breathing-induced fields in the cervical, thoracic, and lumbar spinal cord, and to investigate their effect on image quality in multi-echo GRE. Approach: Field measurements were obtained from navigator projection lines in 2D slices, and were quantified by taking their standard deviation over time. Results: Breathing-induced field variations were detected in every subject, with a similar profile across vertebral levels. Levels with more intense field variations showed higher artifact load. Impact: Breathing-induced B0 fields impact image quality and exhibit a consistent pattern of variation along the spinal cord that is highly similar between subjects. Characterizing these variations lays the foundation for future optimized corrections to achieve consistently high image quality. |
| 2648.
 | 10 | Comparison of data-driven approaches for gradient delay corrections in PDFF mapping using a radial stack-of-stars acquisition Philipp Braun1, Christoph Zoellner1, Jonathan K. Stelter1, Johannes M. Peeters2, Kilian Weiss3, Rickmer Braren1, Daniela Junker1, and Dimitrios C. Karampinos1
1School of Medicine and Health, Technical University of Munich, Munich, Germany, 2Philips Healthcare, Best, Netherlands, 3Philips GmbH Market DACH, Hamburg, Germany Keywords: Artifacts, System Imperfections: Measurement & Correction, Fat & Fat/Water Separation Motivation: Data-driven eddy current compensation would allow corrections to be performed retroactively on scanned data without the need for calibration scans or direct measurements during the scan.
Goal(s): This work aims to evaluate data-based eddy current correction techniques for self-gated free-breathing radial SOS sequences in the liver.
Approach: PDFF liver maps were obtained using free-breathing radial SoS scans from four volunteers and the eddy current corrected results were compared to a clinically established breath-hold cartesian sequence.
Results: Data-driven eddy current corrections improve PDFF map homogeneity for radial SoS sequences with the RING method outperforming the spoke alignment.
Impact: Prior studies analyzed various data-driven eddy current corrections in
radial imaging, but never quantitatively assessed in vivo PDFF maps. This work
is the first to apply multiple data-driven correction techniques in vivo,
comparing them to clinically established breath-hold cartesian sequences. |
| 2649.
 | 11 | Highly segmented skipped-CAIPI 3D-EPI with a modified interleaved Flyback and partial Fourier acquisition for fast MR angiography Simon Blömer1, Rüdiger Stirnberg1, and Tony Stöcker1,2
1MR Physics, DZNE, Bonn, Germany, 2Department of Physics and Astronomy, University of Bonn, Bonn, Germany Keywords: Artifacts, Velocity & Flow, 3D-EPI Motivation: As of yet, ghosting and signal dropouts limit the usability of EPI sequences for MRA-TOF compared to well-established methods such as GRE-TOF. Goal(s): Our goal was to mitigate flow artifacts in MRA-TOF images at short acquisition times whilst improving the efficiency of previous techniques Approach: We introduced a novel Flyback sampling approach in a highly segmented skipped-CAIPI 3D-EPI sequence with phase partial Fourier to acquire in vivo data at a resolution of 0.6mm isotropic at 7T within about one minute. Results: The in vivo measurements show a significant reduction in ghosting and signal dropout and improved resolution of small vessels in TOF MIPs. Impact: Reduced acquisition times and flow artifacts of highly segmented skipped-CAIPI 3D-EPI with a modified, interleaved Flyback acquisition and partial Fourier may increase the applicability of EPI for fast MRA-TOF. |
| 2650.
 | 12 | Imaging Near Metallic Implants: Initial 0.55T vs. 3T MRI Comparison in Hip Implant Patients Kübra Keskin1, Sophia X. Cui2, Bochao Li3, Jordan S. Gross4, Zorica Buser5, Jay R. Lieberman6, Brian A. Hargreaves7,8,9, and Krishna S. Nayak1,3
1Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, 2Siemens Healthineers, Los Angeles, CA, United States, 3Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, 4Diagnostic Radiology, University of California Los Angeles, Los Angeles, CA, United States, 5Gerling Institute, New York, NY, United States, 6Orthopaedic Surgery, University of Southern California, Los Angeles, CA, United States, 7Radiology, Stanford University, Stanford, CA, United States, 8Electrical Engineering, Stanford University, Stanford, CA, United States, 9Bioengineering, Stanford University, Stanford, CA, United States Keywords: Artifacts, Low-Field MRI, Artifacts, MSK, Metallic Implants, Susceptibility, Multi-Spectral Imaging, SEMAC, Metal artifacts Motivation: Patients with orthopedic metallic implants often require diagnostic imaging to evaluate adjacent tissues. MRI performance, including artifact levels and SNR, varies with field strength. Goal(s): To compare 0.55T and 3T MRI for imaging patients with total hip arthroplasty (THA). Approach: Patients with THA were scanned with similar protocols at 0.55T and 3T, including multi-spectral imaging (MSI). We qualitatively compared the images from both scanners. Results: Metal artifact severity was reduced at 0.55T compared to 3T at the expense of SNR. Diagnostic imaging of patients with titanium hip implants at 0.55T is possible without MSI. Impact: We qualitatively compared 0.55T and 3T image quality in patients with hip replacements. Our findings indicate that 0.55T MRI offers substantially reduced metal artifacts and advanced multi-spectral techniques may not be required in many cases. |
| 2651.
 | 13 | A deep-learning-based Signal-to-Noise Ratio (SNR) adaptive uniformity correction method Li Tong1, Puwei Wang1, and Zhenkui Wang1
1Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China Keywords: Artifacts, Machine Learning/Artificial Intelligence Motivation: Intensity normalization in MRI is crucial for consistent image analysis. However, naïve uniformity correction (UC) using pre-collected coil sensitivity distribution may over-compensate low-SNR regions and reduce visual quality. Goal(s): This study aims to develop an SNR-adaptive UC method to prevent over-compensation in low-SNR regions. Approach: We propose using a deep network to learn an SNR-aware uniformity correction map by suppressing over-compensation on low-SNR regions. During training, the original uniformity correction map is used to guide the weighing between consistency loss and over-compensation suppression loss. Results: The proposed method has been demonstrated to effectively suppress over-compensation on low-SNR regions in various head imaging protocols. Impact: The proposed method can improve MRI image quality by adaptively compensating for intensity variations based on the noise level in different regions. It may lead to more accurate diagnoses and better identification of subtle changes in MRI images. |
| 2652.
 | 14 | Evaluation of Accelerated SEMAC at 0.55T using Hexagonal Sampling and Parallel Imaging Bahadır Alp Barlas1, Kübra Keskin1, Bochao Li2, Brian A Hargreaves3,4,5, and Krishna S Nayak1,2
1Electrical and Computer Engineering, University of Sourthern California, Los Angeles, CA, United States, 2Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, 3Radiology, Stanford University, Stanford, CA, United States, 4Electrical Engineering, Stanford University, Stanford, CA, United States, 5Bioengineering, Stanford University, Stanford, CA, United States Keywords: Artifacts, Low-Field MRI Motivation: Low-field and mid-field MRI systems have tremendous potential for imaging near metal implants with reduced artifacts, but reduced ability to do parallel imaging limits encoding options. Goal(s): To achieve 50% scan time reduction in addition to conventional parallel imaging at 0.55T. Approach: Hexagonal sampling (in ky-kf space) combined with GRAPPA-2 in SEMAC acquisitions was evaluated qualitatively and quantitatively via phantom and in vivo experiments at 0.55T. Results: GRAPPA-2 hexagonal sampling achieved comparable image quality to conventional GRAPPA-2 SEMAC with slight SNR reduction and a modest increase in artifact area, while allowing a 50% decrease in scan time. Impact: We evaluate the
performance of hexagonal sampling combined with GRAPPA-2 at 0.55T where high
parallel imaging factors are challenging via phantom and in vivo experiments.
50% additional scan time reduction is achieved with a modest increase in
artifact area. |
| 2653.
 | 15 | Self-supervised Contrastive Learning for Automatic Image Quality Assessment in Whole-body MRI: Preliminary results in UK Biobank Veronika Ecker1,2, Marcel Früh1, Bin Yang2, Sergios Gatidis1, and Thomas Küstner1
1University Hospital of Tübingen, Tübingen, Germany, 2University of Stuttgart, Stuttgart, Germany Keywords: Artifacts, Artifacts, Image Quality Assessment, Motion Correction, Self-supervised Contrastive Learning Motivation: MRI is vital for many medical decisions, yet susceptible to motion artifacts. Impairment by motion artifacts can reduce the reliability of diagnoses and a motion‐free reacquisition can become time-/cost‐intensive. Moreover, in large-scale cohorts, manual inspection is impractical. An automated quality assessment is desirable, but collection of motion-free references is challenging or even impractical. Goal(s): We aim for automatic image quality assessment without extensive labeled training data. Approach: We present a self-supervised quality classification framework based on SimCLR operating as zero-shot learning. Results: The framework achieves promising results for binary quality classification, while showcasing its potential for future work as continuous quality score. Impact: By automating MRI quality assessment, our approach helps in preventing artifact propagation into downstream tasks without additional efforts for manual inspection or data labeling. |
| 2654.
 | 16 | Gibbs Ringing Correction for non-Cartesian Acquisitions Jeffery Wong1,2,3, Santiago Coelho1,2, Hong-Hsi Lee4,5, Jingjia Chen1,2, Li Feng1,2, Els Fieremans1,2, and Dmitry S. Novikov1,2
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, 3Department of Biomedical Engineering, New York University, Brooklyn, NY, United States, 4Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 5Department of Radiology, Harvard Medical School, Boston, MA, United States Keywords: Artifacts, Artifacts Motivation: Truncation in k-space leads to Gibbs ringing. Removal of Gibbs artifact for non-Cartesian isotropic sampling remains unaddressed. Goal(s): To develop Gibbs ringing correction method for non-Cartesian isotropic k-space readouts. Approach: We generalize the subvoxel-shift Gibbs ringing correction to isotropic sampling schemes. Results: The developed correction removes Gibbs ringing for isotropic sampling schemes. Impact: Gibbs ringing leads to artifacts and biases in parametric maps, especially in diffusion MRI. We generalize the subvoxel-shift Gibbs ringing correction, previously developed for cartesian EPI acquisitions, to non-Cartesian sampling. The method will increase the reproducibility of MRI processing pipelines. |