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Rizzuti G, Schakel T, Huttinga NRF, Dankbaar JW, van Leeuwen T, Sbrizzi A. Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01161-y. [PMID: 38758490 DOI: 10.1007/s10334-024-01161-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/18/2024]
Abstract
OBJECT In a typical MR session, several contrasts are acquired. Due to the sequential nature of the data acquisition process, the patient may experience some discomfort at some point during the session, and start moving. Hence, it is quite common to have MR sessions where some contrasts are well-resolved, while other contrasts exhibit motion artifacts. Instead of repeating the scans that are corrupted by motion, we introduce a reference-guided retrospective motion correction scheme that takes advantage of the motion-free scans, based on a generalized rigid registration routine. MATERIALS AND METHODS We focus on various existing clinical 3D brain protocols at 1.5 Tesla MRI based on Cartesian sampling. Controlled experiments with three healthy volunteers and three levels of motion are performed. RESULTS Radiological inspection confirms that the proposed method consistently ameliorates the corrupted scans. Furthermore, for the set of specific motion tests performed in this study, the quality indexes based on PSNR and SSIM shows only a modest decrease in correction quality as a function of motion complexity. DISCUSSION While the results on controlled experiments are positive, future applications to patient data will ultimately clarify whether the proposed correction scheme satisfies the radiological requirements.
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Affiliation(s)
- Gabrio Rizzuti
- Utrecht University, Heidelberglaan 8, 3584 CS, Utrecht, The Netherlands
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Tim Schakel
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Niek R F Huttinga
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Jan Willem Dankbaar
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Tristan van Leeuwen
- Utrecht University, Heidelberglaan 8, 3584 CS, Utrecht, The Netherlands
- Centrum Wiskunde & Informatica, Science Park Amsterdam 123, 1098 XG, Amsterdam, The Netherlands
| | - Alessandro Sbrizzi
- Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
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Boroojeni PE, Chen Y, Commean PK, Eldeniz C, Skolnick GB, Merrill C, Patel KB, An H. Deep-learning synthesized pseudo-CT for MR high-resolution pediatric cranial bone imaging (MR-HiPCB). Magn Reson Med 2022; 88:2285-2297. [PMID: 35713359 PMCID: PMC9420780 DOI: 10.1002/mrm.29356] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/06/2022] [Accepted: 05/23/2022] [Indexed: 11/12/2022]
Abstract
PURPOSE CT is routinely used to detect cranial abnormalities in pediatric patients with head trauma or craniosynostosis. This study aimed to develop a deep learning method to synthesize pseudo-CT (pCT) images for MR high-resolution pediatric cranial bone imaging to eliminating ionizing radiation from CT. METHODS 3D golden-angle stack-of-stars MRI were obtained from 44 pediatric participants. Two patch-based residual UNets were trained using paired MR and CT patches randomly selected from the whole head (NetWH) or in the vicinity of bone, fractures/sutures, or air (NetBA) to synthesize pCT. A third residual UNet was trained to generate a binary brain mask using only MRI. The pCT images from NetWH (pCTNetWH ) in the brain area and NetBA (pCTNetBA ) in the nonbrain area were combined to generate pCTCom . A manual processing method using inverted MR images was also employed for comparison. RESULTS pCTCom (68.01 ± 14.83 HU) had significantly smaller mean absolute errors (MAEs) than pCTNetWH (82.58 ± 16.98 HU, P < 0.0001) and pCTNetBA (91.32 ± 17.2 HU, P < 0.0001) in the whole head. Within cranial bone, the MAE of pCTCom (227.92 ± 46.88 HU) was significantly lower than pCTNetWH (287.85 ± 59.46 HU, P < 0.0001) but similar to pCTNetBA (230.20 ± 46.17 HU). Dice similarity coefficient of the segmented bone was significantly higher in pCTCom (0.90 ± 0.02) than in pCTNetWH (0.86 ± 0.04, P < 0.0001), pCTNetBA (0.88 ± 0.03, P < 0.0001), and inverted MR (0.71 ± 0.09, P < 0.0001). Dice similarity coefficient from pCTCom demonstrated significantly reduced age dependence than inverted MRI. Furthermore, pCTCom provided excellent suture and fracture visibility comparable to CT. CONCLUSION MR high-resolution pediatric cranial bone imaging may facilitate the clinical translation of a radiation-free MR cranial bone imaging method for pediatric patients.
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Affiliation(s)
- Parna Eshraghi Boroojeni
- Dept. of Biomedical Engineering, Washington University in
St. Louis, St. Louis, Missouri 63110, USA
| | - Yasheng Chen
- Dept. of Neurology, Washington University in St. Louis, St.
Louis, Missouri 63110, USA
| | - Paul K. Commean
- Mallinckrodt Institute of Radiology, Washington University
in St. Louis, St. Louis, Missouri 63110, USA
| | - Cihat Eldeniz
- Mallinckrodt Institute of Radiology, Washington University
in St. Louis, St. Louis, Missouri 63110, USA
| | - Gary B. Skolnick
- Division of Plastic and Reconstructive Surgery, Washington
University in St. Louis, St. Louis, Missouri 63110, USA
| | - Corinne Merrill
- Division of Plastic and Reconstructive Surgery, Washington
University in St. Louis, St. Louis, Missouri 63110, USA
| | - Kamlesh B. Patel
- Division of Plastic and Reconstructive Surgery, Washington
University in St. Louis, St. Louis, Missouri 63110, USA
| | - Hongyu An
- Dept. of Biomedical Engineering, Washington University in
St. Louis, St. Louis, Missouri 63110, USA
- Dept. of Neurology, Washington University in St. Louis, St.
Louis, Missouri 63110, USA
- Mallinckrodt Institute of Radiology, Washington University
in St. Louis, St. Louis, Missouri 63110, USA
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Patel KB, Eldeniz C, Skolnick GB, Commean PK, Eshraghi Boroojeni P, Jammalamadaka U, Merrill C, Smyth MD, Goyal MS, An H. Cranial vault imaging for pediatric head trauma using a radial VIBE MRI sequence. J Neurosurg Pediatr 2022; 30:113-118. [PMID: 35453112 PMCID: PMC9587135 DOI: 10.3171/2022.2.peds2224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/28/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Head trauma is the most common indication for a CT scan. In this pilot study, the authors assess the feasibility of a 5-minute high-resolution 3D golden-angle (GA) stack-of-stars radial volumetric interpolated breath-hold examination (VIBE) MRI sequence (GA-VIBE) to obtain clinically acceptable cranial bone images and identify cranial vault fractures compared to CT. METHODS Patients younger than 18 years of age presenting after head trauma were eligible for the study. Three clinicians reviewed and assessed 1) slice-by-slice volumetric CT and inverted MR images, and 2) 3D reconstructions obtained from inverted MR images and the gold standard (CT). For each image set, reviewers noted on 5-point Likert scales whether they recommended that a repeat scan be performed and the presence or absence of cranial vault fractures. RESULTS Thirty-one patients completed MRI after a clinical head CT scan was performed. Based on CT imaging, 8 of 31 patients had cranial fractures. Two of 31 patients were sedated as part of their clinical MRI scan. In 30 (97%) of 31 MRI reviews, clinicians agreed (or strongly agreed) that the image quality was acceptable for clinical diagnosis. Overall, comparing MRI to acceptable gold-standard CT, sensitivity and specificity of fracture detection were 100%. Furthermore, there were no discrepancies between CT and MRI in classification of fracture type or location. CONCLUSIONS When compared with the gold standard (CT), the volumetric and 3D reconstructed images using the GA-VIBE sequence were able to produce clinically acceptable cranial images with excellent ability to detect cranial vault fractures.
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Affiliation(s)
| | - Cihat Eldeniz
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, Missouri; and
| | | | - Paul K. Commean
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, Missouri; and
| | | | | | | | - Matthew D. Smyth
- Department of Neurosurgery, Johns Hopkins All Children’s Hospital, St. Petersburg, Florida
| | - Manu S. Goyal
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, Missouri; and
| | - Hongyu An
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, Missouri; and
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Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI. Neuroimage 2022; 259:119411. [PMID: 35753594 DOI: 10.1016/j.neuroimage.2022.119411] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/12/2022] [Accepted: 06/22/2022] [Indexed: 11/23/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient movement due to the relatively long data acquisition time. This could cause severe degradation of image quality and therefore affect the overall diagnosis. In this paper, we develop an efficient retrospective 2D deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in 3D brain MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns the missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving the spatial image details and improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. The proposed network is optimized by minimizing the loss of structural similarity (SSIM) using the synthesized motion-corrupted images from 83 real motion-free subjects. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans. The overall image quality of the motion-corrected images via the proposed motion correction network significantly improves SSIM from 71.66% to 95.03% and declines the mean square error from 99.25 to 29.76. These results indicate the high similarity of the brain's anatomical structure in the corrected images compared to the motion-free data. The motion-corrected results of both the simulated and real motion data showed the potential of the proposed motion correction network to be feasible and applicable in clinical practices.
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Ljungberg E, Wood TC, Solana AB, Williams SCR, Barker GJ, Wiesinger F. Motion corrected silent ZTE neuroimaging. Magn Reson Med 2022; 88:195-210. [PMID: 35381110 PMCID: PMC9321117 DOI: 10.1002/mrm.29201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/16/2022] [Accepted: 01/28/2022] [Indexed: 11/11/2022]
Abstract
Purpose To develop self‐navigated motion correction for 3D silent zero echo time (ZTE) based neuroimaging and characterize its performance for different types of head motion. Methods The proposed method termed MERLIN (Motion Estimation & Retrospective correction Leveraging Interleaved Navigators) achieves self‐navigation by using interleaved 3D phyllotaxis k‐space sampling. Low resolution navigator images are reconstructed continuously throughout the ZTE acquisition using a sliding window and co‐registered in image space relative to a fixed reference position. Rigid body motion corrections are then applied retrospectively to the k‐space trajectory and raw data and reconstructed into a final, high‐resolution ZTE image. Results MERLIN demonstrated successful and consistent motion correction for magnetization prepared ZTE images for a range of different instructed motion paradigms. The acoustic noise response of the self‐navigated phyllotaxis trajectory was found to be only slightly above ambient noise levels (<4 dBA). Conclusion Silent ZTE imaging combined with MERLIN addresses two major challenges intrinsic to MRI (i.e., subject motion and acoustic noise) in a synergistic and integrated manner without increase in scan time and thereby forms a versatile and powerful framework for clinical and research MR neuroimaging applications.
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Affiliation(s)
- Emil Ljungberg
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Tobias C Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Florian Wiesinger
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,GE Healthcare, Munich, Germany
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Continuous cardiac thermometry via simultaneous catheter tracking and undersampled radial golden angle acquisition for radiofrequency ablation monitoring. Sci Rep 2022; 12:4006. [PMID: 35256627 PMCID: PMC8901729 DOI: 10.1038/s41598-022-06927-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 01/24/2022] [Indexed: 01/18/2023] Open
Abstract
The complexity of the MRI protocol is one of the factors limiting the clinical adoption of MR temperature mapping for real-time monitoring of cardiac ablation procedures and a push-button solution would ease its use. Continuous gradient echo golden angle radial acquisition combined with intra-scan motion correction and undersampled temperature determination could be a robust and more user-friendly alternative than the ultrafast GRE-EPI sequence which suffers from sensitivity to magnetic field susceptibility artifacts and requires ECG-gating. The goal of this proof-of-concept work is to establish the temperature uncertainty as well as the spatial and temporal resolutions achievable in an Agar-gel phantom and in vivo using this method. GRE radial golden angle acquisitions were used to monitor RF ablations in a phantom and in vivo in two sheep hearts with different slice orientations. In each case, 2D rigid motion correction based on catheter micro-coil signal, tracking its motion, was performed and its impact on the temperature imaging was assessed. The temperature uncertainty was determined for three spatial resolutions (1 × 1 × 3 mm3, 2 × 2 × 3 mm3, and 3 × 3 × 3 mm3) and three temporal resolutions (0.48, 0.72, and 0.97 s) with undersampling acceleration factors ranging from 2 to 17. The combination of radial golden angle GRE acquisition, simultaneous catheter tracking, intra-scan 2D motion correction, and undersampled thermometry enabled temperature monitoring in the myocardium in vivo during RF ablations with high temporal (< 1 s) and high spatial resolution. The temperature uncertainty ranged from 0.2 ± 0.1 to 1.8 ± 0.2 °C for the various temporal and spatial resolutions and, on average, remained superior to the uncertainty of an EPI acquisition while still allowing clinical monitoring of the RF ablation process. The proposed method is a robust and promising alternative to EPI acquisition to monitor in vivo RF cardiac ablations. Further studies remain required to improve the temperature uncertainty and establish its clinical applicability.
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7
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Ma S, Wang N, Xie Y, Fan Z, Li D, Christodoulou AG. Motion-robust quantitative multiparametric brain MRI with motion-resolved MR multitasking. Magn Reson Med 2022; 87:102-119. [PMID: 34398991 PMCID: PMC8616852 DOI: 10.1002/mrm.28959] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/30/2021] [Accepted: 07/20/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To address head motion in brain MRI with a novel motion-resolved imaging framework, with application to motion-robust quantitative multiparametric mapping. METHODS MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low-rank tensor. By incorporating a motion-state dimension into the parameter dimensions and introducing unsupervised motion-state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion-robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion-resolved imaging for simultaneous T1 /T2 /T1ρ mapping in the brain was investigated. RESULTS For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion-resolved approach produced T1 /T2 /T1ρ maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T1 /T2 /T1ρ measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion-resolved approach produced T1 /T2 /T1ρ maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T1 /T2 /T1ρ measurement distributions closest to the motion-free reference, with minimal measurement bias and variance. CONCLUSION Motion-resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration-based motion correction.
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Affiliation(s)
- Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA,Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA,Corresponding author: Anthony G. Christodoulou, 8700 Beverly Blvd, PACT 400, Los Angeles, CA 90048, , phone: 3104236754
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Lee J, Kim B, Park H. MC 2 -Net: motion correction network for multi-contrast brain MRI. Magn Reson Med 2021; 86:1077-1092. [PMID: 33720462 DOI: 10.1002/mrm.28719] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 12/29/2020] [Accepted: 01/15/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE A motion-correction network for multi-contrast brain MRI is proposed to correct in-plane rigid motion artifacts in brain MR images using deep learning. METHOD The proposed method consists of 2 parts: image alignment and motion correction. Alignment of multi-contrast MR images is performed in an unsupervised manner by a CNN work, yielding transformation parameters to align input images in order to minimize the normalized cross-correlation loss among multi-contrast images. Then, fine-tuning for image alignment is performed by maximizing the normalized mutual information. The motion correction network corrects motion artifacts in the aligned multi-contrast images. The correction network is trained to minimize the structural similarity loss and the VGG loss in a supervised manner. All datasets of motion-corrupted images are generated using motion simulation based on MR physics. RESULTS A motion-correction network for multi-contrast brain MRI successfully corrected artifacts of simulated motion for 4 test subjects, showing 0.96%, 7.63%, and 5.03% increases in the average structural simularity and 5.19%, 10.2%, and 7.48% increases in the average normalized mutual information for T1 -weighted, T2 -weighted, and T2 -weighted fluid-attenuated inversion recovery images, respectively. The experimental setting with image alignment and artifact-free input images for other contrasts shows better performances in correction of simulated motion artifacts. Furthermore, the proposed method quantitatively outperforms recent deep learning motion correction and synthesis methods. Real motion experiments from 5 healthy subjects demonstrate the potential of the proposed method for use in a clinical environment. CONCLUSION A deep learning-based motion correction method for multi-contrast MRI was successfully developed, and experimental results demonstrate the validity of the proposed method.
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Affiliation(s)
- Jongyeon Lee
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Byungjai Kim
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - HyunWook Park
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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Hanhela M, Gröhn O, Kettunen M, Niinimäki K, Vauhkonen M, Kolehmainen V. Data-Driven Regularization Parameter Selection in Dynamic MRI. J Imaging 2021; 7:jimaging7020038. [PMID: 34460637 PMCID: PMC8321258 DOI: 10.3390/jimaging7020038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 11/23/2022] Open
Abstract
In dynamic MRI, sufficient temporal resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based reconstructions. One problem in CS approaches is determining the regularization parameters, which control the balance between data fidelity and regularization. We propose a data-driven approach for the total variation regularization parameter selection, where reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are obtained from the measurement data for temporal regularization and from a reference image for spatial regularization. Two formulations are proposed. Simultaneous search for a parameter pair yielding expected sparsity in both domains (S-surface), and a sequential parameter selection using the S-curve method (Sequential S-curve). The approaches are evaluated using simulated and experimental DCE-MRI. In the simulated test case, both methods produce a parameter pair and reconstruction that is close to the root mean square error (RMSE) optimal pair and reconstruction. In the experimental test case, the methods produce almost equal parameter selection, and the reconstructions are of high perceived quality. Both methods lead to a highly feasible selection of the regularization parameters in both test cases while the sequential method is computationally more efficient.
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Affiliation(s)
- Matti Hanhela
- Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland; (M.V.); (V.K.)
- Correspondence:
| | - Olli Gröhn
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland; (O.G.); (M.K.)
| | - Mikko Kettunen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211 Kuopio, Finland; (O.G.); (M.K.)
| | - Kati Niinimäki
- Xray Division, Planmeca Oy, Asentajankatu 6, 00880 Helsinki, Finland;
| | - Marko Vauhkonen
- Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland; (M.V.); (V.K.)
| | - Ville Kolehmainen
- Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland; (M.V.); (V.K.)
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Free-Breathing Dynamic Contrast-Enhanced Imaging of the Upper Abdomen Using a Cartesian Compressed-Sensing Sequence With Hard-Gated and Motion-State-Resolved Reconstruction. Invest Radiol 2019; 54:728-736. [DOI: 10.1097/rli.0000000000000607] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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11
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Real-time control of respiratory motion: Beyond radiation therapy. Phys Med 2019; 66:104-112. [PMID: 31586767 DOI: 10.1016/j.ejmp.2019.09.241] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/23/2019] [Accepted: 09/26/2019] [Indexed: 12/16/2022] Open
Abstract
Motion management in radiation oncology is an important aspect of modern treatment planning and delivery. Special attention has been paid to control respiratory motion in recent years. However, other medical procedures related to both diagnosis and treatment are likely to benefit from the explicit control of breathing motion. Quantitative imaging - including increasingly important tools in radiology and nuclear medicine - is among the fields where a rapid development of motion control is most likely, due to the need for quantification accuracy. Emerging treatment modalities like focussed-ultrasound tumor ablation are also likely to benefit from a significant evolution of motion control in the near future. In the present article an overview of available respiratory motion systems along with ongoing research in this area is provided. Furthermore, an attempt is made to envision some of the most expected developments in this field in the near future.
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Wilke M, Baldeweg T. A multidimensional artefact-reduction approach to increase robustness of first-level fMRI analyses: Censoring vs. interpolating. J Neurosci Methods 2019; 318:56-68. [PMID: 30779930 DOI: 10.1016/j.jneumeth.2019.02.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 02/07/2019] [Accepted: 02/15/2019] [Indexed: 11/24/2022]
Abstract
BACKGROUND This manuscript describes a new, multidimensional and data-driven approach to identify outlying datapoints from a first-level fMRI dataset. NEW METHOD Using three different indicators of data corruption (the fast variance component of DVARS [Δ%D-var], scan-to-scan total displacement [STS], and each scan's overall explained variance [R2]), it identifies outlying datapoints while being balanced using Akaike'c corrected criterion (AIC C) to avoid overcorrection. We then explore the impact of censoring, interpolating, or both, to remove a bad scan's contribution to the final timeseries. RESULTS AND COMPARISON WITH EXISTING METHODS Our results (using three real-life datasets and extensive simulations) show that motion-corrupted datapoints as well as non-motion related image artefacts are detected reliably. Using several indicators is shown to be an advantage over existing single-indicator solutions in different settings. As a result of using our algorithm, stronger activation (as detected by both T-value and number of activated voxels) and an increase in the temporal signal-to-noise ratio can be seen. The effects of censoring and interpolation are distinct and complex. CONCLUSIONS The multidimensional approach described here is able to identify outlying datapoints in fMRI timeseries, with demonstrable positive effects on several outcome measures. While censoring datapoints may be preferable in many settings, the ultimate choice on which approach to choose may depend on the data at hand. Recommendations are provided for different scenarios.
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Affiliation(s)
- Marko Wilke
- Department of Pediatric Neurology and Developmental Medicine, Children's Hospital, Germany; Experimental Pediatric Neuroimaging, Children's Hospital and Department of Neuroradiology, University Hospital Tübingen, Germany.
| | - Torsten Baldeweg
- Developmental Neurosciences Programme, UCL Great Ormond Street Institute of Child Health, United Kingdom; Great Ormond Street Hospital NHS Trust, London, United Kingdom
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A Feasibility Study of Geometric-Decomposition Coil Compression in MRI Radial Acquisitions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:7685208. [PMID: 28659993 PMCID: PMC5474278 DOI: 10.1155/2017/7685208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 05/10/2017] [Indexed: 12/03/2022]
Abstract
Receiver arrays with a large number of coil elements are becoming progressively available because of their increased signal-to-noise ratio (SNR) and enhanced parallel imaging performance. However, longer reconstruction time and intensive computational cost have become significant concerns as the number of channels increases, especially in some iterative reconstructions. Coil compression can effectively solve this problem by linearly combining the raw data from multiple coils into fewer virtual coils. In this work, geometric-decomposition coil compression (GCC) is applied to radial sampling (both linear-angle and golden-angle patterns are discussed) for better compression. GCC, which is different from directly compressing in k-space, is performed separately in each spatial location along the fully sampled directions, then followed by an additional alignment step to guarantee the smoothness of the virtual coil sensitivities. Both numerical simulation data and in vivo data were tested. Experimental results demonstrated that the GCC algorithm can achieve higher SNR and lower normalized root mean squared error values than the conventional principal component analysis approach in radial acquisitions.
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Zhang Y, Zhang L, Sun Y. Rigid motion artifact reduction in CT using frequency domain analysis. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2017; 25:721-736. [PMID: 28506020 DOI: 10.3233/xst-16193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
BACKGROUND It is often unrealistic to assume that the subject remains stationary during a computed tomography (CT) imaging scan. A patient rigid motion can be decomposed into a translation and a rotation around an origin. How to minimize the motion impact on image quality is important. OBJECTIVE To eliminate artifacts caused by patient rigid motion during a CT scan, this study investigated a new method based on frequency domain analysis to estimate and compensate motion impact. METHODS Motion parameters was first determined by the magnitude correlation of projections in frequency domain. Then, the estimated parameters were applied to compensate for the motion effects in the reconstruction process. Finally, this method was extended to helical CT. RESULTS In fan-beam CT experiments, the simulation results showed that the proposed method was more accurate and faster on the performance of motion estimation than using Helgason-Ludwig consistency condition method (HLCC). Furthermore, the reconstructed images on both simulated and human head experiments indicated that the proposed method yielded superior results in artifact reduction. CONCLUSIONS The proposed method is a new tool for patient motion compensation, with a potential for practical application. It is not only applicable to motion correction in fan-beam CT imaging, but also to helical CT.
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Affiliation(s)
- Yuan Zhang
- School of Electronic Information Engineering, Tianjin University, Tianjin, China
| | - Liyi Zhang
- School of Electronic Information Engineering, Tianjin University, Tianjin, China
- School of Information Engineering, Tianjin University of Commerce, Tianjin, China
| | - Yunshan Sun
- School of Information Engineering, Tianjin University of Commerce, Tianjin, China
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15
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Graedel NN, McNab JA, Chiew M, Miller KL. Motion correction for functional MRI with three-dimensional hybrid radial-Cartesian EPI. Magn Reson Med 2016; 78:527-540. [PMID: 27604503 PMCID: PMC5516130 DOI: 10.1002/mrm.26390] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 07/26/2016] [Accepted: 07/27/2016] [Indexed: 11/13/2022]
Abstract
Purpose Subject motion is a major source of image degradation for functional MRI (fMRI), especially when using multishot sequences like three‐dimensional (3D EPI). We present a hybrid radial‐Cartesian 3D EPI trajectory enabling motion correction in k‐space for functional MRI. Methods The EPI “blades” of the 3D hybrid radial‐Cartesian EPI sequence, called TURBINE, are rotated about the phase‐encoding axis to fill out a cylinder in 3D k‐space. Angular blades are acquired over time using a golden‐angle rotation increment, allowing reconstruction at flexible temporal resolution. The self‐navigating properties of the sequence are used to determine motion parameters from a high temporal‐resolution navigator time series. The motion is corrected in k‐space as part of the image reconstruction, and evaluated for experiments with both cued and natural motion. Results We demonstrate that the motion correction works robustly and that we can achieve substantial artifact reduction as well as improvement in temporal signal‐to‐noise ratio and fMRI activation in the presence of both severe and subtle motion. Conclusion We show the potential for hybrid radial‐Cartesian 3D EPI to substantially reduce artifacts for application in fMRI, especially for subject groups with significant head motion. The motion correction approach does not prolong the scan, and no extra hardware is required. Magn Reson Med 78:527–540, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Nadine N Graedel
- FMRIB Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Mark Chiew
- FMRIB Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Karla L Miller
- FMRIB Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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16
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Godenschweger F, Kägebein U, Stucht D, Yarach U, Sciarra A, Yakupov R, Lüsebrink F, Schulze P, Speck O. Motion correction in MRI of the brain. Phys Med Biol 2016; 61:R32-56. [PMID: 26864183 DOI: 10.1088/0031-9155/61/5/r32] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Subject motion in MRI is a relevant problem in the daily clinical routine as well as in scientific studies. Since the beginning of clinical use of MRI, many research groups have developed methods to suppress or correct motion artefacts. This review focuses on rigid body motion correction of head and brain MRI and its application in diagnosis and research. It explains the sources and types of motion and related artefacts, classifies and describes existing techniques for motion detection, compensation and correction and lists established and experimental approaches. Retrospective motion correction modifies the MR image data during the reconstruction, while prospective motion correction performs an adaptive update of the data acquisition. Differences, benefits and drawbacks of different motion correction methods are discussed.
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Affiliation(s)
- F Godenschweger
- Biomedical Magnetic Resonance, Otto-von-Guericke University, Magdeburg, Germany
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17
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Nofiele J, Yuan Q, Kazem M, Tatebe K, Torres Q, Sawant A, Pedrosa I, Chopra R. An MRI-compatible platform for one-dimensional motion management studies in MRI. Magn Reson Med 2015; 76:702-12. [PMID: 26493684 DOI: 10.1002/mrm.25903] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Revised: 07/17/2015] [Accepted: 07/30/2015] [Indexed: 01/17/2023]
Abstract
PURPOSE Abdominal MRI remains challenging because of respiratory motion. Motion compensation strategies are difficult to compare clinically because of the variability across human subjects. The goal of this study was to evaluate a programmable system for one-dimensional motion management MRI research. METHODS A system comprised of a programmable motorized linear stage and computer was assembled and tested in the MRI environment. Tests of the mutual interference between the platform and a whole-body MRI were performed. Organ trajectories generated from a high-temporal resolution scan of a healthy volunteer were used in phantom tests to evaluate the effects of motion on image quality and quantitative MRI measurements. RESULTS No interference between the motion platform and the MRI was observed, and reliable motion could be produced across a wide range of imaging conditions. Motion-related artifacts commensurate with motion amplitude, frequency, and waveform were observed. T2 measurement of a kidney lesion in an abdominal phantom showed that its value decreased by 67% with physiologic motion, but could be partially recovered with navigator-based motion-compensation. CONCLUSION The motion platform can produce reliable linear motion within a whole-body MRI. The system can serve as a foundation for a research platform to investigate and develop motion management approaches for MRI. Magn Reson Med 76:702-712, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Joris Nofiele
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Qing Yuan
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | - Ken Tatebe
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Quinn Torres
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Amit Sawant
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Rajiv Chopra
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Sunnybrook Research Institute, Toronto, Ontario, Canada.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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18
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Optical tracking with two markers for robust prospective motion correction for brain imaging. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2015; 28:523-34. [PMID: 26121941 DOI: 10.1007/s10334-015-0493-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Revised: 06/10/2015] [Accepted: 06/11/2015] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Prospective motion correction (PMC) during brain imaging using camera-based tracking of a skin-attached marker may suffer from problems including loss of marker visibility due to the coil and false correction due to non-rigid-body facial motion, such as frowning or squinting. A modified PMC system is introduced to mitigate these problems and increase the robustness of motion correction. MATERIALS AND METHODS The method relies on simultaneously tracking two markers, each providing six degrees of freedom, that are placed on the forehead. This allows us to track head motion when one marker is obscured and detect skin movements to prevent false corrections. Experiments were performed to compare the performance of the two-marker motion correction technique to the previous single-marker approach. RESULTS Experiments validate the theory developed for adaptive marker tracking and skin movement detection, and demonstrate improved image quality during obstruction of the line-of-sight of one marker when subjects squint or when subjects squint and move simultaneously. CONCLUSION The proposed methods eliminate two common failure modes of PMC and substantially improve the robustness of PMC, and they can be applied to other optical tracking systems capable of tracking multiple markers. The methods presented can be adapted to the use of more than two markers.
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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: 348] [Impact Index Per Article: 38.7] [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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