1
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Lee PK, Zhou X, Wang N, Syed AB, Brunsing RL, Vasanawala SS, Hargreaves BA. Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen. Magn Reson Med 2024; 92:586-604. [PMID: 38688875 DOI: 10.1002/mrm.30067] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/09/2024] [Accepted: 02/09/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts. A respiratory resolved, three-dimensional (3D) diffusion prepared sequence that obtains distortionless diffusion weighted images during free-breathing is presented. Techniques to address the myriad of challenges including: 3D shot-to-shot phase correction, respiratory binning, diffusion encoding during free-breathing, and robustness to off-resonance are described. METHODS A twice-refocused, M1-nulled diffusion preparation was combined with an RF-spoiled gradient echo readout and respiratory resolved reconstruction to obtain free-breathing diffusion weighted images in the abdomen. Cartesian sampling permits a sampling density that enables 3D shot-to-shot phase navigation and reduction of transient fat artifacts. Theoretical properties of a region-based shot rejection are described. The region-based shot rejection method was evaluated with free-breathing (normal and exaggerated breathing), and respiratory triggering. The proposed sequence was compared in vivo with multishot DW-EPI. RESULTS The proposed sequence exhibits no evident distortion in vivo when compared to multishot DW-EPI, robustness to B0 and B1 field inhomogeneities, and robustness to motion from different respiratory patterns. CONCLUSION Acquisition of distortionless, diffusion weighted images is feasible during free-breathing with a b-value of 500 s/mm2, scan time of 6 min, and a clinically viable reconstruction time.
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Affiliation(s)
- Philip K Lee
- Radiology, Stanford University, Stanford, California, USA
| | - Xuetong Zhou
- Radiology, Stanford University, Stanford, California, USA
- Bioengineering, Stanford University, Stanford, California, USA
| | - Nan Wang
- Radiology, Stanford University, Stanford, California, USA
| | - Ali B Syed
- Radiology, Stanford University, Stanford, California, USA
| | | | | | - Brian A Hargreaves
- Radiology, Stanford University, Stanford, California, USA
- Bioengineering, Stanford University, Stanford, California, USA
- Electrical Engineering, Stanford University, Stanford, California, USA
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2
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Ariyurek C, Wallace TE, Kober T, Kurugol S, Afacan O. Prospective motion correction in kidney MRI using FID navigators. Magn Reson Med 2023; 89:276-285. [PMID: 36063497 PMCID: PMC9670860 DOI: 10.1002/mrm.29424] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/30/2022] [Accepted: 08/05/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE Abdominal MRI scans may require breath-holding to prevent image quality degradation, which can be challenging for patients, especially children. In this study, we evaluate whether FID navigators can be used to measure and correct for motion prospectively, in real-time. METHODS FID navigators were inserted into a 3D radial sequence with stack-of-stars sampling. MRI experiments were conducted on 6 healthy volunteers. A calibration scan was first acquired to create a linear motion model that estimates the kidney displacement due to respiration from the FID navigator signal. This model was then applied to predict and prospectively correct for motion in real time during deep and continuous deep breathing scans. Resultant images acquired with the proposed technique were compared with those acquired without motion correction. Dice scores were calculated between inhale/exhale motion states. Furthermore, images acquired using the proposed technique were compared with images from extra-dimensional golden-angle radial sparse parallel, a retrospective motion state binning technique. RESULTS Images reconstructed for each motion state show that the kidneys' position could be accurately tracked and corrected with the proposed method. The mean of Dice scores computed between the motion states were improved from 0.93 to 0.96 using the proposed technique. Depiction of the kidneys was improved in the combined images of all motion states. Comparing results of the proposed technique and extra-dimensional golden-angle radial sparse parallel, high-quality images can be reconstructed from a fraction of spokes using the proposed method. CONCLUSION The proposed technique reduces blurriness and motion artifacts in kidney imaging by prospectively correcting their position both in-plane and through-slice.
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Affiliation(s)
- Cemre Ariyurek
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Tess E Wallace
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sila Kurugol
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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3
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Liu J, Wang C, Wang J, Zhang C, Wu Y, Balu N, Qi H, Zhang Q, Yuan C, Chen H. Motion detection and correction for carotid MRI using a markerless optical system. Magn Reson Imaging 2022; 94:161-167. [PMID: 36191857 DOI: 10.1016/j.mri.2022.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 04/29/2022] [Accepted: 09/27/2022] [Indexed: 10/14/2022]
Abstract
PURPOSE Motion related artifact is a challenge for MRI, especially when imaging regions like the carotid artery where complex motion (abrupt and bulk motion) may occur. This study aims to develop a non-contact motion detection and correction system for carotid MRI using a markerless optical tracking system. METHODS The proposed markerless optical tracking system consisted of a cross-line laser, an MRI-compatible camera and plastic holders mounted inside the scanner bore. The neck motion of the subject can be captured by monitoring the change of the projected laser position in real-time. The system was used to correct both abrupt motion and bulk motion for carotid MRI. The abrupt motion (e.g. coughing) was compensated by discarding the corrupted k-space lines and re-estimating the missing lines using SPIRiT algorithm. The bulk motion was corrected by phase adjustment of k-space lines according to the measured 1D-translational bulk motion (along anterior-posterior direction) and optimized in-plane translation parameters. Ten volunteers underwent carotid MRI with real-time neck motion detection and retrospective motion correction. Artery sharpness, vessel wall thickness and overall image quality score were compared between the motion-corrupted image and motion-corrected images of different correction strategies. RESULTS Both the abrupt motion and the bulk motion during carotid scanning were successfully detected and corrected. The results of ten volunteers demonstrated significant improvement in carotid artery sharpness, vessel wall thickness measurement, and overall image quality score using the proposed markerless optical tracking system and motion correction strategies. CONCLUSION The proposed markerless structured light based motion detection and correction system can sensitively detect both abrupt and bulk motion during carotid MR scans. By correcting for both abrupt and bulk motion, vessel wall delineation was improved in carotid MR images, which could potentially facilitate carotid plaque identification and atherosclerosis diagnosis in the future.
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Affiliation(s)
- Jin Liu
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America
| | - Chunyao Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Jinnan Wang
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America.
| | - Chen Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Yifan Wu
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America
| | - Niranjan Balu
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America.
| | - Haikun Qi
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Qiang Zhang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
| | - Chun Yuan
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America.
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
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4
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Mossa-Basha M, Zhu C, Wu L. Vessel Wall MR Imaging in the Pediatric Head and Neck. Magn Reson Imaging Clin N Am 2021; 29:595-604. [PMID: 34717847 DOI: 10.1016/j.mric.2021.06.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Vessel wall MR imaging (VWI) is a technique that progressively has gained traction in clinical diagnostic applications for evaluation of intracranial and extracranial vasculopathies, with increasing use in pediatric populations. The technique has shown promise in detection, differentiation, and characterization of both inflammatory and noninflammatory vasculopathies. In this article, optimal techniques for intracranial and extracranial VWI as well as applications and value for pediatric vascular disease evaluation are discussed.
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Affiliation(s)
- Mahmud Mossa-Basha
- Department of Radiology, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, USA.
| | - Chengcheng Zhu
- Department of Radiology, University of Washington, 325 9th Avenue, Seattle, WA 98104, USA
| | - Lei Wu
- Department of Radiology, University of Washington, 1660 South Columbian Way, Seattle, WA 98108, USA
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5
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Hu Z, van der Kouwe A, Han F, Xiao J, Chen J, Han H, Bi X, Li D, Fan Z. Motion-compensated 3D turbo spin-echo for more robust MR intracranial vessel wall imaging. Magn Reson Med 2021; 86:637-647. [PMID: 33768617 DOI: 10.1002/mrm.28777] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 01/31/2021] [Accepted: 02/27/2021] [Indexed: 12/31/2022]
Abstract
PURPOSE (1) To investigate the effect of internal localized movement on 3DMR intracranial vessel wall imaging and (2) to develop a novel motion-compensation approach combining volumetric navigator (vNav) and self-gating (SG) to simultaneously compensate for bulk and localized movements. METHODS A 3D variable-flip-angle turbo spin-echo (ie, SPACE) sequence was modified to incorporate vNav and SG modules. The SG signals from the center k-space line are acquired at the beginning of each TR to detect localized motion-affected TRs. The vNavs from low-resolution 3D EPI are acquired to identify bulk head motion. Fifteen healthy subjects and 3 stroke patients were recruited in this study. Overall image quality (0-poor to 4-excellent) and vessel wall sharpness were compared among the scenarios with and without bulk and/or localized motion and/or the proposed compensation strategies. RESULTS Localized motion reduced wall sharpness, which was significantly mitigated by SG (ie, outer boundary of basilar artery: 0.68 ± 0.27 vs 0.86 ± 0.17; P = .037). When motion occurred, the overall image quality and vessel wall sharpness obtained with vNav-SG SPACE were significantly higher than those obtained with conventional SPACE (ie, basilarartery outer boundary sharpness: 0.73 ± 0.24 vs 0.94 ± 0.24; P = .033), yet comparable to those obtained in motion-free scans (ie, basilarartery outer boundary sharpness: 0.94 ± 0.24 vs 0.96 ± 0.31; P = .815). CONCLUSION Localized movements can induce considerable artifacts in intracranial vessel wall imaging. The vNav-SG approach is capable of compensating for both bulk and localized motions.
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Affiliation(s)
- Zhehao Hu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Brookline, Massachusetts, USA
| | - Fei Han
- Siemens Medical Solutions USA, Inc., Los Angeles, California, USA
| | - Jiayu Xiao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Junzhou Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Xiaoming Bi
- Siemens Medical Solutions USA, Inc., Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Radiology, University of Southern California Keck School of Medicine, Los Angeles, California, USA
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6
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Wallace TE, Afacan O, Jaimes C, Rispoli J, Pelkola K, Dugan M, Kober T, Warfield SK. Free induction decay navigator motion metrics for prediction of diagnostic image quality in pediatric MRI. Magn Reson Med 2021; 85:3169-3181. [PMID: 33404086 PMCID: PMC7904595 DOI: 10.1002/mrm.28649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/05/2020] [Accepted: 11/25/2020] [Indexed: 12/23/2022]
Abstract
Purpose To investigate the ability of free induction decay navigator (FIDnav)‐based motion monitoring to predict diagnostic utility and reduce the time and cost associated with acquiring diagnostically useful images in a pediatric patient cohort. Methods A study was carried out in 102 pediatric patients (aged 0‐18 years) at 3T using a 32‐channel head coil array. Subjects were scanned with an FID‐navigated MPRAGE sequence and images were graded by two radiologists using a five‐point scale to evaluate the impact of motion artifacts on diagnostic image quality. The correlation between image quality and four integrated FIDnav motion metrics was investigated, as well as the sensitivity and specificity of each FIDnav‐based metric to detect different levels of motion corruption in the images. Potential time and cost savings were also assessed by retrospectively applying an optimal detection threshold to FIDnav motion scores. Results A total of 12% of images were rated as non‐diagnostic, while a further 12% had compromised diagnostic value due to motion artifacts. FID‐navigated metrics exhibited a moderately strong correlation with image grade (Spearman's rho ≥ 0.56). Integrating the cross‐correlation between FIDnav signal vectors achieved the highest sensitivity and specificity for detecting non‐diagnostic images, yielding total time savings of 7% across all scans. This corresponded to a financial benefit of $2080 in this study. Conclusions Our results indicate that integrated motion metrics from FIDnavs embedded in structural MRI are a useful predictor of diagnostic image quality, which translates to substantial time and cost savings when applied to pediatric MRI examinations.
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Affiliation(s)
- Tess E Wallace
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Camilo Jaimes
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Joanne Rispoli
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Kristina Pelkola
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Monet Dugan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
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7
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Frost R, Biasiolli L, Li L, Hurst K, Alkhalil M, Choudhury RP, Robson MD, Hess AT, Jezzard P. Navigator-based reacquisition and estimation of motion-corrupted data: Application to multi-echo spin echo for carotid wall MRI. Magn Reson Med 2020; 83:2026-2041. [PMID: 31697862 PMCID: PMC7065122 DOI: 10.1002/mrm.28063] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 10/10/2019] [Accepted: 10/11/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE To assess whether artifacts in multi-slice multi-echo spin echo neck imaging, thought to be caused by brief motion events such as swallowing, can be corrected by reacquiring corrupted central k-space data and estimating the remainder with parallel imaging. METHODS A single phase-encode line (ky = 0, phase-encode direction anteroposterior) navigator echo was used to identify motion-corrupted data and guide the online reacquisition. If motion corruption was detected in the 7 central k-space lines, they were replaced with reacquired data. Subsequently, GRAPPA reconstruction was trained on the updated central portion of k-space and then used to estimate the remaining motion-corrupted k-space data from surrounding uncorrupted data. Similar compressed sensing-based approaches have been used previously to compensate for respiration in cardiac imaging. The g-factor noise amplification was calculated for the parallel imaging reconstruction of data acquired with a 10-channel neck coil. The method was assessed in scans with 9 volunteers and 12 patients. RESULTS The g-factor analysis showed that GRAPPA reconstruction of 2 adjacent motion-corrupted lines causes high noise amplification; therefore, the number of 2-line estimations should be limited. In volunteer scans, median ghosting reduction of 24% was achieved with 2 adjacent motion-corrupted lines correction, and image quality was improved in 2 patient scans that had motion corruption close to the center of k-space. CONCLUSION Motion-corrupted echo-trains can be identified with a navigator echo. Combined reacquisition and parallel imaging estimation reduced motion artifacts in multi-slice MESE when there were brief motion events, especially when motion corruption was close to the center of k-space.
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Affiliation(s)
- Robert Frost
- Wellcome Centre for Integrative NeuroimagingFMRIB DivisionNuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownMassachusetts
- Department of RadiologyHarvard Medical SchoolBostonMassachusetts
| | - Luca Biasiolli
- Oxford Centre for Clinical Magnetic Resonance ResearchDivision of Cardiovascular MedicineRadcliffe Department of MedicineUniversity of OxfordOxfordUnited Kingdom
- Acute Vascular Imaging CentreDivision of Cardiovascular MedicineRadcliffe Department of MedicineUniversity of OxfordOxfordUnited Kingdom
| | - Linqing Li
- Laboratory of Brain and CognitionNational Institute of Mental HealthBethesdaMaryland
| | - Katherine Hurst
- Nuffield Department of Surgical SciencesUniversity of OxfordOxfordUnited Kingdom
| | - Mohammad Alkhalil
- Acute Vascular Imaging CentreDivision of Cardiovascular MedicineRadcliffe Department of MedicineUniversity of OxfordOxfordUnited Kingdom
| | - Robin P. Choudhury
- Acute Vascular Imaging CentreDivision of Cardiovascular MedicineRadcliffe Department of MedicineUniversity of OxfordOxfordUnited Kingdom
| | - Matthew D. Robson
- Oxford Centre for Clinical Magnetic Resonance ResearchDivision of Cardiovascular MedicineRadcliffe Department of MedicineUniversity of OxfordOxfordUnited Kingdom
| | - Aaron T. Hess
- Oxford Centre for Clinical Magnetic Resonance ResearchDivision of Cardiovascular MedicineRadcliffe Department of MedicineUniversity of OxfordOxfordUnited Kingdom
| | - Peter Jezzard
- Wellcome Centre for Integrative NeuroimagingFMRIB DivisionNuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
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8
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Nguyen TD, Wen Y, Du J, Liu Z, Gillen K, Spincemaille P, Gupta A, Yang Q, Wang Y. Quantitative susceptibility mapping of carotid plaques using nonlinear total field inversion: Initial experience in patients with significant carotid stenosis. Magn Reson Med 2020; 84:1501-1509. [DOI: 10.1002/mrm.28227] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 01/30/2020] [Accepted: 02/03/2020] [Indexed: 12/13/2022]
Affiliation(s)
| | - Yan Wen
- Radiology Weill Cornell Medicine New York NY
- Meinig School of Biomedical Engineering Cornell University Ithaca NY
| | - Jingwen Du
- Xuanwu Hospital Capital Medical University Beijing China
| | - Zhe Liu
- Radiology Weill Cornell Medicine New York NY
- Meinig School of Biomedical Engineering Cornell University Ithaca NY
| | | | | | - Ajay Gupta
- Radiology Weill Cornell Medicine New York NY
| | - Qi Yang
- Xuanwu Hospital Capital Medical University Beijing China
| | - Yi Wang
- Radiology Weill Cornell Medicine New York NY
- Meinig School of Biomedical Engineering Cornell University Ithaca NY
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9
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Rigie D, Vahle T, Zhao T, Czekella B, Frohwein LJ, Schäfers K, Boada FE. Cardiorespiratory motion-tracking via self-refocused rosette navigators. Magn Reson Med 2019; 81:2947-2958. [PMID: 30615208 DOI: 10.1002/mrm.27609] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 09/27/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop a flexible method for tracking respiratory and cardiac motions throughout MR and PET-MR body examinations that requires no additional hardware and minimal sequence modification. METHODS The incorporation of a contrast-neutral rosette navigator module following the RF excitation allows for robust cardiorespiratory motion tracking with minimal impact on the host sequence. Spatial encoding gradients are applied to the FID signal and the desired motion signals are extracted with a blind source separation technique. This approach is validated with an anthropomorphic, PET-MR-compatible motion phantom as well as in 13 human subjects. RESULTS Both respiratory and cardiac motions were reliably extracted from the proposed rosette navigator in phantom and patient studies. In the phantom study, the MR-derived motion signals were additionally validated against the ground truth measurement of diaphragm displacement and left ventricle model triggering pulse. CONCLUSION The proposed method yields accurate respiratory and cardiac motion-state tracking, requiring only a short (1.76 ms) additional navigator module, which is self-refocusing and imposes minimal constraints on sequence design.
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Affiliation(s)
- David Rigie
- Bernard and Irene Schwartz Center for Biomedical Imaging, NYU School of Medicine, New York, New York
| | | | - Tiejun Zhao
- Siemens Medical Solutions, New York, New York
| | - Björn Czekella
- European Institute for Molecular Imaging, Münster, Germany
| | | | - Klaus Schäfers
- European Institute for Molecular Imaging, Münster, Germany
| | - Fernando E Boada
- Bernard and Irene Schwartz Center for Biomedical Imaging, NYU School of Medicine, New York, New York
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10
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Wallace TE, Afacan O, Waszak M, Kober T, Warfield SK. Head motion measurement and correction using FID navigators. Magn Reson Med 2018; 81:258-274. [PMID: 30058216 DOI: 10.1002/mrm.27381] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 04/18/2018] [Accepted: 05/08/2018] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop a novel framework for rapid, intrinsic head motion measurement in MRI using FID navigators (FIDnavs) from a multichannel head coil array. METHODS FIDnavs encode substantial rigid-body motion information; however, current implementations require patient-specific training with external tracking data to extract quantitative positional changes. In this work, a forward model of FIDnav signals was calibrated using simulated movement of a reference image within a model of the spatial coil sensitivities. A FIDnav module was inserted into a nonselective 3D FLASH sequence, and rigid-body motion parameters were retrospectively estimated every readout time using nonlinear optimization to solve the inverse problem posed by the measured FIDnavs. This approach was tested in simulated data and in 7 volunteers, scanned at 3T with a 32-channel head coil array, performing a series of directed motion paradigms. RESULTS FIDnav motion estimates achieved mean absolute errors of 0.34 ± 0.49 mm and 0.52 ± 0.61° across all subjects and scans, relative to ground-truth motion measurements provided by an electromagnetic tracking system. Retrospective correction with FIDnav motion estimates resulted in substantial improvements in quantitative image quality metrics across all scans with intentional head motion. CONCLUSIONS Quantitative rigid-body motion information can be effectively estimated using the proposed FIDnav-based approach, which represents a practical method for retrospective motion compensation in less cooperative patient populations.
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Affiliation(s)
- Tess E Wallace
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Maryna Waszak
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
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11
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Waszak M, Falkovskiy P, Hilbert T, Bonnier G, Maréchal B, Meuli R, Gruetter R, Kober T, Krueger G. Prospective head motion correction using FID-guided on-demand image navigators. Magn Reson Med 2016; 78:193-203. [DOI: 10.1002/mrm.26364] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Revised: 06/19/2016] [Accepted: 07/11/2016] [Indexed: 12/30/2022]
Affiliation(s)
- Maryna Waszak
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare AG; Lausanne Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL); Lausanne Switzerland
- Department of Radiology, University Hospital (CHUV); Lausanne Switzerland
| | - Pavel Falkovskiy
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare AG; Lausanne Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL); Lausanne Switzerland
- Department of Radiology, University Hospital (CHUV); Lausanne Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare AG; Lausanne Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL); Lausanne Switzerland
- Department of Radiology, University Hospital (CHUV); Lausanne Switzerland
| | - Guillaume Bonnier
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare AG; Lausanne Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL); Lausanne Switzerland
- Department of Radiology, University Hospital (CHUV); Lausanne Switzerland
| | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare AG; Lausanne Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL); Lausanne Switzerland
- Department of Radiology, University Hospital (CHUV); Lausanne Switzerland
| | - Reto Meuli
- Department of Radiology, University Hospital (CHUV); Lausanne Switzerland
| | - Rolf Gruetter
- Department of Radiology, University Hospital (CHUV); Lausanne Switzerland
- Centre d'Imagerie BioMedicale (CIBM), École Polytechnique Fédérale de Lausanne (EPFL); Lausanne Switzerland
- Department of Radiology, University of Geneva; Geneva Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology (HC CMEA SUI DI BM PI), Siemens Healthcare AG; Lausanne Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL); Lausanne Switzerland
- Department of Radiology, University Hospital (CHUV); Lausanne Switzerland
| | - Gunnar Krueger
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL); Lausanne Switzerland
- Department of Radiology, University Hospital (CHUV); Lausanne Switzerland
- Siemens Medical Solutions USA, Inc; Boston MA USA
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Koppal S, Warntjes M, Swann J, Dyverfeldt P, Kihlberg J, Moreno R, Magee D, Roberts N, Zachrisson H, Forssell C, Länne T, Treanor D, de Muinck ED. Quantitative fat and R2* mapping in vivo to measure lipid-rich necrotic core and intraplaque hemorrhage in carotid atherosclerosis. Magn Reson Med 2016; 78:285-296. [PMID: 27510300 DOI: 10.1002/mrm.26359] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Revised: 06/29/2016] [Accepted: 07/07/2016] [Indexed: 11/08/2022]
Abstract
PURPOSE The aim of this work was to quantify the extent of lipid-rich necrotic core (LRNC) and intraplaque hemorrhage (IPH) in atherosclerotic plaques. METHODS Patients scheduled for carotid endarterectomy underwent four-point Dixon and T1-weighted magnetic resonance imaging (MRI) at 3 Tesla. Fat and R2* maps were generated from the Dixon sequence at the acquired spatial resolution of 0.60 × 0.60 × 0.70 mm voxel size. MRI and three-dimensional (3D) histology volumes of plaques were registered. The registration matrix was applied to segmentations denoting LRNC and IPH in 3D histology to split plaque volumes in regions with and without LRNC and IPH. RESULTS Five patients were included. Regarding volumes of LRNC identified by 3D histology, the average fat fraction by MRI was significantly higher inside LRNC than outside: 12.64 ± 0.2737% versus 9.294 ± 0.1762% (mean ± standard error of the mean [SEM]; P < 0.001). The same was true for IPH identified by 3D histology, R2* inside versus outside IPH was: 71.81 ± 1.276 s-1 versus 56.94 ± 0.9095 s-1 (mean ± SEM; P < 0.001). There was a strong correlation between the cumulative fat and the volume of LRNC from 3D histology (R2 = 0.92) as well as between cumulative R2* and IPH (R2 = 0.94). CONCLUSION Quantitative mapping of fat and R2* from Dixon MRI reliably quantifies the extent of LRNC and IPH. Magn Reson Med 78:285-296, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Sandeep Koppal
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Marcel Warntjes
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.,SyntheticMR AB, Linköping, Sweden
| | - Jeremy Swann
- School of Computing, University of Leeds, Leeds, United Kingdom
| | - Petter Dyverfeldt
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Johan Kihlberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Rodrigo Moreno
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,KTH, Royal Institute of Technology, Stockholm, Sweden
| | - Derek Magee
- School of Computing, University of Leeds, Leeds, United Kingdom
| | - Nicholas Roberts
- Division of Brain Sciences, Department of Medicine, Institute of Neurology, Imperial College, London, United Kingdom
| | - Helene Zachrisson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Claes Forssell
- Department of Thoracic and Vascular Surgery, and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Toste Länne
- Department of Thoracic and Vascular Surgery, and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Darren Treanor
- Department of Pathology and Tumor Biology, Leeds Institute of Molecular Medicine, University of Leeds, Leeds, United Kingdom.,Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Ebo D de Muinck
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
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Zaitsev M, Maclaren J, Herbst M. Motion artifacts in MRI: A complex problem with many partial solutions. J Magn Reson Imaging 2015; 42:887-901. [PMID: 25630632 DOI: 10.1002/jmri.24850] [Citation(s) in RCA: 356] [Impact Index Per Article: 39.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Accepted: 12/22/2014] [Indexed: 01/29/2023] Open
Abstract
Subject motion during magnetic resonance imaging (MRI) has been problematic since its introduction as a clinical imaging modality. While sensitivity to particle motion or blood flow can be used to provide useful image contrast, bulk motion presents a considerable problem in the majority of clinical applications. It is one of the most frequent sources of artifacts. Over 30 years of research have produced numerous methods to mitigate or correct for motion artifacts, but no single method can be applied in all imaging situations. Instead, a "toolbox" of methods exists, where each tool is suitable for some tasks, but not for others. This article reviews the origins of motion artifacts and presents current mitigation and correction methods. In some imaging situations, the currently available motion correction tools are highly effective; in other cases, appropriate tools still need to be developed. It seems likely that this multifaceted approach will be what eventually solves the motion sensitivity problem in MRI, rather than a single solution that is effective in all situations. This review places a strong emphasis on explaining the physics behind the occurrence of such artifacts, with the aim of aiding artifact detection and mitigation in particular clinical situations.
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Affiliation(s)
- Maxim Zaitsev
- Department of Radiology, University Medical Centre Freiburg, Freiburg, Germany
| | - Julian Maclaren
- Department of Radiology, University Medical Centre Freiburg, Freiburg, Germany.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Michael Herbst
- Department of Radiology, University Medical Centre Freiburg, Freiburg, Germany.,University of Hawaii, Department of Medicine, John A. Burns School of Medicine, Honolulu, Hawaii, USA
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