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Ariyurek C, Koçanaoğulları A, Afacan O, Kurugol S. Motion-compensated image reconstruction for improved kidney function assessment using dynamic contrast-enhanced MRI. NMR IN BIOMEDICINE 2024; 37:e5116. [PMID: 38359842 DOI: 10.1002/nbm.5116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 12/08/2023] [Accepted: 01/15/2024] [Indexed: 02/17/2024]
Abstract
Accurately measuring renal function is crucial for pediatric patients with kidney conditions. Traditional methods have limitations, but dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a safe and efficient approach for detailed anatomical evaluation and renal function assessment. However, motion artifacts during DCE-MRI can degrade image quality and introduce misalignments, leading to unreliable results. This study introduces a motion-compensated reconstruction technique for DCE-MRI data acquired using golden-angle radial sampling. Our proposed method achieves three key objectives: (1) identifying and removing corrupted data (outliers) using a Gaussian process model fitting with a k -space center navigator, (2) efficiently clustering the data into motion phases and performing interphase registration, and (3) utilizing a novel formulation of motion-compensated radial reconstruction. We applied the proposed motion correction (MoCo) method to DCE-MRI data affected by varying degrees of motion, including both respiratory and bulk motion. We compared the outcomes with those obtained from the conventional radial reconstruction. Our evaluation encompassed assessing the quality of images, concentration curves, and tracer kinetic model fitting, and estimating renal function. The proposed MoCo reconstruction improved the temporal signal-to-noise ratio for all subjects, with a 21.8% increase on average, while total variation values of the aorta, right, and left kidney concentration were improved for each subject, with 32.5%, 41.3%, and 42.9% increases on average, respectively. Furthermore, evaluation of tracer kinetic model fitting indicated that the median standard deviation of the estimated filtration rate (σ F T ), mean normalized root-mean-squared error (nRMSE), and chi-square goodness-of-fit of tracer kinetic model fit were decreased from 0.10 to 0.04, 0.27 to 0.24, and, 0.43 to 0.27, respectively. The proposed MoCo technique enabled more reliable renal function assessment and improved image quality for detailed anatomical evaluation in the case of bulk and respiratory motion during the acquisition of DCE-MRI.
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Affiliation(s)
- Cemre Ariyurek
- Quantitative Intelligent Imaging Lab (QUIN), Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Aziz Koçanaoğulları
- Quantitative Intelligent Imaging Lab (QUIN), Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Quantitative Intelligent Imaging Lab (QUIN), Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Sila Kurugol
- Quantitative Intelligent Imaging Lab (QUIN), Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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2
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Hu P, Tong X, Lin L, Wang LV. Data-driven system matrix manipulation enabling fast functional imaging and intra-image nonrigid motion correction in tomography. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.574504. [PMID: 38260429 PMCID: PMC10802502 DOI: 10.1101/2024.01.07.574504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Tomographic imaging modalities are described by large system matrices. Sparse sampling and tissue motion degrade system matrix and image quality. Various existing techniques improve the image quality without correcting the system matrices. Here, we compress the system matrices to improve computational efficiency (e.g., 42 times) using singular value decomposition and fast Fourier transform. Enabled by the efficiency, we propose (1) fast sparsely sampling functional imaging by incorporating a densely sampled prior image into the system matrix, which maintains the critical linearity while mitigating artifacts and (2) intra-image nonrigid motion correction by incorporating the motion as subdomain translations into the system matrix and reconstructing the translations together with the image iteratively. We demonstrate the methods in 3D photoacoustic computed tomography with significantly improved image qualities and clarify their applicability to X-ray CT and MRI or other types of imperfections due to the similarities in system matrices.
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Affiliation(s)
- Peng Hu
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Xin Tong
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Li Lin
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Present address: College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Lihong V Wang
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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Cruz G, Hua A, Munoz C, Ismail TF, Chiribiri A, Botnar RM, Prieto C. Low-rank motion correction for accelerated free-breathing first-pass myocardial perfusion imaging. Magn Reson Med 2023; 90:64-78. [PMID: 36861454 PMCID: PMC10952238 DOI: 10.1002/mrm.29626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 12/29/2022] [Accepted: 02/10/2023] [Indexed: 03/03/2023]
Abstract
PURPOSE Develop a novel approach for accelerated 2D free-breathing myocardial perfusion via low-rank motion-corrected (LRMC) reconstructions. METHODS Myocardial perfusion imaging requires high spatial and temporal resolution, despite scan time constraints. Here, we incorporate LRMC models into the reconstruction-encoding operator, together with high-dimensionality patch-based regularization, to produce high quality, motion-corrected myocardial perfusion series from free-breathing acquisitions. The proposed framework estimates beat-to-beat nonrigid respiratory (and any other incidental) motion and the dynamic contrast subspace from the actual acquired data, which are then incorporated into the proposed LRMC reconstruction. LRMC was compared with iterative SENSitivity Encoding (SENSE) (itSENSE) and low-rank plus sparse (LpS) reconstruction in 10 patients based on image-quality scoring and ranking by two clinical expert readers. RESULTS LRMC achieved significantly improved results relative to itSENSE and LpS in terms of image sharpness, temporal coefficient of variation, and expert reader evaluation. Left ventricle image sharpness was approximately 75%, 79%, and 86% for itSENSE, LpS and LRMC, respectively, indicating improved image sharpness for the proposed approach. Corresponding temporal coefficient of variation results were 23%, 11% and 7%, demonstrating improved temporal fidelity of the perfusion signal with the proposed LRMC. Corresponding clinical expert reader scores (1-5, from poor to excellent image quality) were 3.3, 3.9 and 4.9, demonstrating improved image quality with the proposed LRMC, in agreement with the automated metrics. CONCLUSION LRMC produces motion-corrected myocardial perfusion in free-breathing acquisitions with substantially improved image quality when compared with iterative SENSE and LpS reconstructions.
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Affiliation(s)
- Gastao Cruz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Alina Hua
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Camila Munoz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Tevfik Fehmi Ismail
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - René Michael Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de Ingeniería, Pontificia Universidad Católica de ChileSantiagoChile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTHSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de Ingeniería, Pontificia Universidad Católica de ChileSantiagoChile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTHSantiagoChile
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Usui K, Muro I, Shibukawa S, Goto M, Ogawa K, Sakano Y, Kyogoku S, Daida H. Evaluation of motion artefact reduction depending on the artefacts' directions in head MRI using conditional generative adversarial networks. Sci Rep 2023; 13:8526. [PMID: 37237139 DOI: 10.1038/s41598-023-35794-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 05/24/2023] [Indexed: 05/28/2023] Open
Abstract
Motion artefacts caused by the patient's body movements affect magnetic resonance imaging (MRI) accuracy. This study aimed to compare and evaluate the accuracy of motion artefacts correction using a conditional generative adversarial network (CGAN) with an autoencoder and U-net models. The training dataset consisted of motion artefacts generated through simulations. Motion artefacts occur in the phase encoding direction, which is set to either the horizontal or vertical direction of the image. To create T2-weighted axial images with simulated motion artefacts, 5500 head images were used in each direction. Of these data, 90% were used for training, while the remainder were used for the evaluation of image quality. Moreover, the validation data used in the model training consisted of 10% of the training dataset. The training data were divided into horizontal and vertical directions of motion artefact appearance, and the effect of combining this data with the training dataset was verified. The resulting corrected images were evaluated using structural image similarity (SSIM) and peak signal-to-noise ratio (PSNR), and the metrics were compared with the images without motion artefacts. The best improvements in the SSIM and PSNR were observed in the consistent condition in the direction of the occurrence of motion artefacts in the training and evaluation datasets. However, SSIM > 0.9 and PSNR > 29 dB were accomplished for the learning model with both image directions. The latter model exhibited the highest robustness for actual patient motion in head MRI images. Moreover, the image quality of the corrected image with the CGAN was the closest to that of the original image, while the improvement rates for SSIM and PSNR were approximately 26% and 7.7%, respectively. The CGAN model demonstrated a high image reproducibility, and the most significant model was the consistent condition of the learning model and the direction of the appearance of motion artefacts.
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Affiliation(s)
- Keisuke Usui
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan.
| | - Isao Muro
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan
| | - Syuhei Shibukawa
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan
| | - Masami Goto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan
| | - Koichi Ogawa
- Faculty of Science and Engineering, Hosei University, Tokyo, Japan
| | - Yasuaki Sakano
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan
| | - Shinsuke Kyogoku
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan
| | - Hiroyuki Daida
- Department of Radiological Technology, Faculty of Health Science, Juntendo University, Tokyo, Japan
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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: 0] [Impact Index Per Article: 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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Mayer J, Blaszczyk E, Cipriani A, Ferrazzi G, Schulz-Menger J, Schaeffter T, Kolbitsch C. Cardio-respiratory motion-corrected 3D cardiac water-fat MRI using model-based image reconstruction. Magn Reson Med 2022; 88:1561-1574. [PMID: 35775790 DOI: 10.1002/mrm.29284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 03/04/2022] [Accepted: 04/13/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE Myocardial fat infiltrations are associated with a range of cardiomyopathies. The purpose of this study was to perform cardio-respiratory motion-correction for model-based water-fat separation to image fatty infiltrations of the heart in a free-breathing, non-cardiac-triggered high-resolution 3D MRI acquisition. METHODS Data were acquired in nine patients using a free-breathing, non-cardiac-triggered high-resolution 3D Dixon gradient-echo sequence and radial phase encoding trajectory. Motion correction was combined with a model-based water-fat reconstruction approach. Respiratory and cardiac motion models were estimated using a dual-mode registration algorithm incorporating both motion-resolved water and fat information. Qualitative comparisons of fat structures were made between 2D clinical routine reference scans and reformatted 3D motion-corrected images. To evaluate the effect of motion correction the local sharpness of epicardial fat structures was analyzed for motion-averaged and motion-corrected fat images. RESULTS The reformatted 3D motion-corrected reconstructions yielded qualitatively comparable fat structures and fat structure sharpness in the heart as the standard 2D breath-hold. Respiratory motion correction improved the local sharpness on average by 32% ± 24% with maximum improvements of 81% and cardiac motion correction increased the sharpness further by another 15% ± 11% with maximum increases of 31%. One patient showed a fat infiltration in the myocardium and cardio-respiratory motion correction was able to improve its visualization in 3D. CONCLUSION The 3D water-fat separated cardiac images were acquired during free-breathing and in a clinically feasible and predictable scan time. Compared to a motion-averaged reconstruction an increase in sharpness of fat structures by 51% ± 27% using the presented motion correction approach was observed for nine patients.
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Affiliation(s)
- Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Berlin, Germany
| | - Edyta Blaszczyk
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Experimental and Clinical Research Center, Berlin, Germany. HELIOS Klinikum Berlin Buch, Department of Cardiology and Nephrology, Berlin, Germany
- Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany
| | - Alberto Cipriani
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Experimental and Clinical Research Center, Berlin, Germany. HELIOS Klinikum Berlin Buch, Department of Cardiology and Nephrology, Berlin, Germany
- Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany
- Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padua, Padua, Italy
| | | | - Jeanette Schulz-Menger
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Experimental and Clinical Research Center, Berlin, Germany. HELIOS Klinikum Berlin Buch, Department of Cardiology and Nephrology, Berlin, Germany
- Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- German Center for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Berlin, Germany
- Department of Medical Engineering, Technical University of Berlin, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Berlin, Germany
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Lima da Cruz GJ, Velasco C, Lavin B, Jaubert O, Botnar RM, Prieto C. Myocardial T1, T2, T2*, and fat fraction quantification via low-rank motion-corrected cardiac MR fingerprinting. Magn Reson Med 2022; 87:2757-2774. [PMID: 35081260 PMCID: PMC9306903 DOI: 10.1002/mrm.29171] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 12/06/2021] [Accepted: 01/05/2022] [Indexed: 12/11/2022]
Abstract
Purpose Develop a novel 2D cardiac MR fingerprinting (MRF) approach to enable simultaneous T1, T2, T2*, and fat fraction (FF) myocardial tissue characterization in a single breath‐hold scan. Methods Simultaneous, co‐registered, multi‐parametric mapping of T1, T2, and FF has been recently achieved with cardiac MRF. Here, we further incorporate T2* quantification within this approach, enabling simultaneous T1, T2, T2*, and FF myocardial tissue characterization in a single breath‐hold scan. T2* quantification is achieved with an eight‐echo readout that requires a long cardiac acquisition window. A novel low‐rank motion‐corrected (LRMC) reconstruction is exploited to correct for cardiac motion within the long acquisition window. The proposed T1/T2/T2*/FF cardiac MRF was evaluated in phantom and in 10 healthy subjects in comparison to conventional mapping techniques. Results The proposed approach achieved high quality parametric mapping of T1, T2, T2*, and FF with corresponding normalized RMS error (RMSE) T1 = 5.9%, T2 = 9.6% (T2 values <100 ms), T2* = 3.3% (T2* values <100 ms), and FF = 0.8% observed in phantom scans. In vivo, the proposed approach produced higher left‐ventricular myocardial T1 values than MOLLI (1148 vs 1056 ms), lower T2 values than T2‐GraSE (42.8 vs 50.6 ms), lower T2* values than eight‐echo gradient echo (GRE) (35.0 vs 39.4 ms), and higher FF values than six‐echo GRE (0.8 vs 0.3 %) reference techniques. The proposed approach achieved considerable reduction in motion artifacts compared to cardiac MRF without motion correction, improved spatial uniformity, and statistically higher apparent precision relative to conventional mapping for all parameters. Conclusion The proposed cardiac MRF approach enables simultaneous, co‐registered mapping of T1, T2, T2*, and FF in a single breath‐hold for comprehensive myocardial tissue characterization, achieving higher apparent precision than conventional methods.
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Affiliation(s)
- Gastao José Lima da Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Biochemistry and Molecular Biology, School of Chemistry, Complutense University, Madrid, Spain
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Rene Michael Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Oh G, Lee JE, Ye JC. Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3125-3139. [PMID: 34133276 DOI: 10.1109/tmi.2021.3089708] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performance and lower computational complexity compared to classical methods, most of them require supervised training using paired artifact-free and artifact-corrupted images, which may prohibit its use in many important clinical applications. For example, transient severe motion (TSM) due to acute transient dyspnea in Gd-EOB-DTPA-enhanced MR is difficult to control and model for paired data generation. To address this issue, here we propose a novel unpaired deep learning scheme that does not require matched motion-free and motion artifact images. Specifically, the first step of our method is k -space random subsampling along the phase encoding direction that can remove some outliers probabilistically. In the second step, the neural network reconstructs fully sampled resolution image from a downsampled k -space data, and motion artifacts can be reduced in this step. Last, the aggregation step through averaging can further improve the results from the reconstruction network. We verify that our method can be applied for artifact correction from simulated motion as well as real motion from TSM successfully from both single and multi-coil data with and without k -space raw data, outperforming existing state-of-the-art deep learning methods.
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Cruz G, Qi H, Jaubert O, Kuestner T, Schneider T, Botnar RM, Prieto C. Generalized low-rank nonrigid motion-corrected reconstruction for MR fingerprinting. Magn Reson Med 2021; 87:746-763. [PMID: 34601737 DOI: 10.1002/mrm.29027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 08/12/2021] [Accepted: 09/09/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE Develop a novel low-rank motion-corrected (LRMC) reconstruction for nonrigid motion-corrected MR fingerprinting (MRF). METHODS Generalized motion-corrected (MC) reconstructions have been developed for steady-state imaging. Here we extend this framework to enable nonrigid MC for transient imaging applications with varying contrast, such as MRF. This is achieved by integrating low-rank dictionary-based compression into the generalized MC model to reconstruct MC singular images, reducing motion artifacts in the resulting parametric maps. The proposed LRMC reconstruction was applied for cardiac motion correction in 2D myocardial MRF (T1 and T2 ) with extended cardiac acquisition window (~450 ms) and for respiratory MC in free-breathing 3D myocardial and 3D liver MRF. Experiments were performed in phantom and 22 healthy subjects. The proposed approach was compared with reference spin echo (phantom) and with 2D electrocardiogram-triggered/breath-hold MOLLI and T2 gradient-and-spin echo conventional maps (in vivo 2D and 3D myocardial MRF). RESULTS Phantom results were in general agreement with reference spin-echo measurements, presenting relative errors of approximately 5.4% and 5.5% for T1 and short T2 (<100 ms), respectively. The proposed LRMC MRF reduced residual blurring artifacts with respect to no MC for cardiac or respiratory motion in all cases (2D and 3D myocardial, 3D abdominal). In 2D myocardial MRF, left-ventricle T1 values were 1150 ± 41 ms for LRMC MRF and 1010 ± 56 ms for MOLLI; T2 values were 43.8 ± 2.3 ms for LRMC MRF and 49.5 ± 4.5 ms for T2 gradient and spin echo. Corresponding measurements for 3D myocardial MRF were 1085 ± 30 ms and 1062 ± 29 ms for T1 , and 43.5 ± 1.9 ms and 51.7 ± 1.7 ms for T2 . For 3D liver, LRMC MRF measured liver T1 at 565 ± 44 ms and liver T2 at 35.4 ± 2.4 ms. CONCLUSION The proposed LRMC reconstruction enabled generalized (nonrigid) MC for 2D and 3D MRF, both for cardiac and respiratory motion. The proposed approach reduced motion artifacts in the MRF maps with respect to no motion compensation and achieved good agreement with reference measurements.
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Affiliation(s)
- Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Thomas Kuestner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Rene Michael Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Qi H, Hajhosseiny R, Cruz G, Kuestner T, Kunze K, Neji R, Botnar R, Prieto C. End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA. Magn Reson Med 2021; 86:1983-1996. [PMID: 34096095 DOI: 10.1002/mrm.28851] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/22/2021] [Accepted: 04/29/2021] [Indexed: 12/26/2022]
Abstract
PURPOSE To develop an end-to-end deep learning technique for nonrigid motion-corrected (MoCo) reconstruction of ninefold undersampled free-breathing whole-heart coronary MRA (CMRA). METHODS A novel deep learning framework was developed consisting of a diffeomorphic registration network and a motion-informed model-based deep learning (MoDL) reconstruction network. The registration network receives as input highly undersampled (~22×) respiratory-resolved images and outputs 3D nonrigid respiratory motion fields between the images. The motion-informed MoDL performs MoCo reconstruction from undersampled data using the predicted motion fields. The whole deep learning framework, termed as MoCo-MoDL, was trained end-to-end in a supervised manner for simultaneous 3D nonrigid motion estimation and MoCo reconstruction. MoCo-MoDL was compared with a state-of-the-art nonrigid MoCo CMRA reconstruction technique in 15 retrospectively undersampled datasets and 9 prospectively undersampled acquisitions. RESULTS The acquisition time for ninefold accelerated CMRA was ~2.5 min. The reconstruction time was ~22 s for the proposed MoCo-MoDL and ~35 min for the conventional approach. MoCo-MoDL achieved higher peak SNR (27.86 ± 3.00 vs. 26.71 ± 2.79; P < .05) and structural similarity (0.78 ± 0.06 vs. 0.75 ± 0.06; P < .05) than the conventional approach. Similar vessel length and visual image quality score were obtained with the 2 methods, whereas improved vessel sharpness was observed with MoCo-MoDL. CONCLUSION An end-to-end deep learning approach was introduced for simultaneous nonrigid motion estimation and MoCo reconstruction of highly undersampled free-breathing whole-heart CMRA. The rapid free-breathing CMRA acquisition together with the fast reconstruction of the proposed approach promises easy integration into clinical workflow.
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Affiliation(s)
- Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, People's Republic of China
| | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Thomas Kuestner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Medical Image and Data Analysis, Department of Interventional and Diagnostic Radiology, University Hospital of Tübingen, Tübingen, Germany
| | - Karl Kunze
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - René Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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11
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Li GY, Wang CY, Lv J. Current status of deep learning in abdominal image reconstruction. Artif Intell Med Imaging 2021; 2:86-94. [DOI: 10.35711/aimi.v2.i4.86] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/24/2021] [Accepted: 08/17/2021] [Indexed: 02/06/2023] Open
Abstract
Abdominal magnetic resonance imaging (MRI) and computed tomography (CT) are commonly used for disease screening, diagnosis, and treatment guidance. However, abdominal MRI has disadvantages including slow speed and vulnerability to motions, while CT suffers from problems of radiation. It has been reported that deep learning reconstruction can solve such problems while maintaining good image quality. Recently, deep learning-based image reconstruction has become a hot topic in the field of medical imaging. This study reviews the latest research on deep learning reconstruction in abdominal imaging, including the widely used convolutional neural network, generative adversarial network, and recurrent neural network.
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Affiliation(s)
- Guang-Yuan Li
- School of Computer and Control Engineering, Yantai University, Yantai 264000, Shandong Province, China
| | - Cheng-Yan Wang
- Human Phenome Institute, Fudan University, Shanghai 201203, China
| | - Jun Lv
- School of Computer and Control Engineering, Yantai University, Yantai 264000, Shandong Province, China
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12
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Brown R, Kolbitsch C, Delplancke C, Papoutsellis E, Mayer J, Ovtchinnikov E, Pasca E, Neji R, da Costa-Luis C, Gillman AG, Ehrhardt MJ, McClelland JR, Eiben B, Thielemans K. Motion estimation and correction for simultaneous PET/MR using SIRF and CIL. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200208. [PMID: 34218674 DOI: 10.1098/rsta.2020.0208] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/07/2021] [Indexed: 05/10/2023]
Abstract
SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Affiliation(s)
- Richard Brown
- Institute of Nuclear Medicine, University College London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Christoph Kolbitsch
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
| | | | - Evangelos Papoutsellis
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | - Johannes Mayer
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
| | - Evgueni Ovtchinnikov
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK
| | - Edoardo Pasca
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare, Frimley, UK
| | - Casper da Costa-Luis
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Ashley G Gillman
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Australia
| | - Matthias J Ehrhardt
- Department of Mathematical Sciences, University of Bath, Bath, UK
- Institute for Mathematical Innovation, University of Bath, UK
| | - Jamie R McClelland
- Centre for Medical Image Computing, University College London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Bjoern Eiben
- Centre for Medical Image Computing, University College London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
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13
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Mayer J, Jin Y, Wurster TH, Makowski MR, Kolbitsch C. Evaluation of synergistic image registration for motion-corrected coronary NaF-PET-MR. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200202. [PMID: 33966463 PMCID: PMC8107649 DOI: 10.1098/rsta.2020.0202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Coronary artery disease (CAD) is caused by the formation of plaques in the coronary arteries and is one of the most common cardiovascular diseases. NaF-PET can be used to assess plaque composition, which could be important for therapy planning. One of the main challenges of NaF-PET is cardiac and respiratory motion which can strongly impair diagnostic accuracy. In this study, we investigated the use of a synergistic image registration approach which combined motion-resolved MR and PET data to estimate cardiac and respiratory motion. This motion estimation could then be used to improve the NaF-PET image quality. The approach was evaluated with numerical simulations and in vivo scans of patients suffering from CAD. In numerical simulations, it was shown, that combining MR and PET information can improve the accuracy of motion estimation by more than 15%. For the in vivo scans, the synergistic image registration led to an improvement in uptake visualization. This is the first study to assess the benefit of combining MR and NaF-PET for cardiac and respiratory motion estimation. Further patient evaluation is required to fully evaluate the potential of this approach. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Yining Jin
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Thomas-Heinrich Wurster
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Marcus R. Makowski
- Department of Radiology, Charité, Universitätsmedizin Berlin, Berlin, Germany
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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14
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Freedman JN, Gurney-Champion OJ, Nill S, Shiarli AM, Bainbridge HE, Mandeville HC, Koh DM, McDonald F, Kachelrieß M, Oelfke U, Wetscherek A. Rapid 4D-MRI reconstruction using a deep radial convolutional neural network: Dracula. Radiother Oncol 2021; 159:209-217. [PMID: 33812914 PMCID: PMC8216429 DOI: 10.1016/j.radonc.2021.03.034] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/07/2021] [Accepted: 03/26/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE 4D and midposition MRI could inform plan adaptation in lung and abdominal MR-guided radiotherapy. We present deep learning-based solutions to overcome long 4D-MRI reconstruction times while maintaining high image quality and short scan times. METHODS Two 3D U-net deep convolutional neural networks were trained to accelerate the 4D joint MoCo-HDTV reconstruction. For the first network, gridded and joint MoCo-HDTV-reconstructed 4D-MRI were used as input and target data, respectively, whereas the second network was trained to directly calculate the midposition image. For both networks, input and target data had dimensions of 256 × 256 voxels (2D) and 16 respiratory phases. Deep learning-based MRI were verified against joint MoCo-HDTV-reconstructed MRI using the structural similarity index (SSIM) and the naturalness image quality evaluator (NIQE). Moreover, two experienced observers contoured the gross tumour volume and scored the images in a blinded study. RESULTS For 12 subjects, previously unseen by the networks, high-quality 4D and midposition MRI (1.25 × 1.25 × 3.3 mm3) were each reconstructed from gridded images in only 28 seconds per subject. Excellent agreement was found between deep-learning-based and joint MoCo-HDTV-reconstructed MRI (average SSIM ≥ 0.96, NIQE scores 7.94 and 5.66). Deep-learning-based 4D-MRI were clinically acceptable for target and organ-at-risk delineation. Tumour positions agreed within 0.7 mm on midposition images. CONCLUSION Our results suggest that the joint MoCo-HDTV and midposition algorithms can each be approximated by a deep convolutional neural network. This rapid reconstruction of 4D and midposition MRI facilitates online treatment adaptation in thoracic or abdominal MR-guided radiotherapy.
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Affiliation(s)
- Joshua N Freedman
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Oliver J Gurney-Champion
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, The Netherlands.
| | - Simeon Nill
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Anna-Maria Shiarli
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Hannah E Bainbridge
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; Department of Radiotherapy, Portsmouth Hospitals University NHS Trust, Queen Alexandra Hospital, United Kingdom.
| | - Henry C Mandeville
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Dow-Mu Koh
- Department of Radiology, The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Fiona McDonald
- Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Marc Kachelrieß
- Division of X-Ray Imaging and CT, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
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15
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Godino-Moya A, Menchón-Lara RM, Martín-Fernández M, Prieto C, Alberola-López C. Elastic AlignedSENSE for Dynamic MR Reconstruction: A Proof of Concept in Cardiac Cine. ENTROPY 2021; 23:e23050555. [PMID: 33947089 PMCID: PMC8145958 DOI: 10.3390/e23050555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 11/25/2022]
Abstract
Numerous methods in the extensive literature on magnetic resonance imaging (MRI) reconstruction exploit temporal redundancy to accelerate cardiac cine. Some of them include motion compensation, which involves high computational costs and long runtimes. In this work, we proposed a method—elastic alignedSENSE (EAS)—for the direct reconstruction of a motion-free image plus a set of nonrigid deformations to reconstruct a 2D cardiac sequence. The feasibility of the proposed approach was tested in 2D Cartesian and golden radial multi-coil breath-hold cardiac cine acquisitions. The proposed approach was compared against parallel imaging compressed sense (sPICS) and group-wise motion corrected compressed sense (GWCS) reconstructions. EAS provides better results on objective measures with considerable less runtime when an acceleration factor is higher than 10×. Subjective assessment of an expert, however, invited proposing the combination of EAS and GWCS as a preferable alternative to GWCS or EAS in isolation.
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Affiliation(s)
- Alejandro Godino-Moya
- Laboratorio de Procesado de Imagen, E.T.S.I. Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain; (R.-M.M.-L.); (M.M.-F.); (C.A.-L.)
- Correspondence:
| | - Rosa-María Menchón-Lara
- Laboratorio de Procesado de Imagen, E.T.S.I. Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain; (R.-M.M.-L.); (M.M.-F.); (C.A.-L.)
| | - Marcos Martín-Fernández
- Laboratorio de Procesado de Imagen, E.T.S.I. Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain; (R.-M.M.-L.); (M.M.-F.); (C.A.-L.)
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK;
- School of Engineering, Pontificia Universidad Catolica de Chile, Santiago 4860, Chile
| | - Carlos Alberola-López
- Laboratorio de Procesado de Imagen, E.T.S.I. Telecomunicación, Universidad de Valladolid, Paseo Belén 15, 47011 Valladolid, Spain; (R.-M.M.-L.); (M.M.-F.); (C.A.-L.)
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16
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Ippoliti M, Lukas M, Brenner W, Schatka I, Furth C, Schaeffter T, Makowski MR, Kolbitsch C. Respiratory motion correction for enhanced quantification of hepatic lesions in simultaneous PET and DCE-MR imaging. Phys Med Biol 2021; 66. [PMID: 33823503 DOI: 10.1088/1361-6560/abf51e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 04/06/2021] [Indexed: 11/11/2022]
Abstract
Simultaneous positron-emission tomography (PET)-magnetic resonance (MR) imaging is a hybrid technique in oncological hepatic imaging combining soft-tissue and functional contrast of dynamic contrast enhanced MR (DCE-MR) with metabolic information from PET. In this context, respiratory motion represents a major challenge by introducing blurring, artifacts and misregistration in the liver. In this work, we propose a free-breathing 3D non-rigid respiratory motion correction framework for simultaneously acquired DCE-MR and PET data, which makes use of higher spatial resolution MR data to derive motion information used directly during image reconstruction to minimize image blurring and motion artifacts. The main aim was to increase contrast of hepatic metastases to improve their detection and characterization. DCE-MR data were acquired at 3T through a golden radial phase encoding scheme, enabling derivation of motion fields. These were used in the motion compensated image reconstruction of DCE-MR time-series (48 time-points, 6 s temporal resolution, 1.5 mm isotropic spatial resolution) and 3D PET activity map, which was subsequently interpolated to the DCE-MR resolution. The extended Tofts model was fitted to DCE-MR data, obtaining functional parametric maps related to perfusion such as the endothelial permeability (Kt). Fifty-seven hepatic metastases were identified and analyzed. Quantitative evaluations of motion correction in PET images demonstrated average percentage increases of 16% ± 5% (mean ± SD) in Contrast (C), 18% ± 6% in SUVmeanand 14% ± 2% in SUVmax, while DCE-MR andKtscored contrast-to-noise-ratio increases of 64% ± 3% and 90% ± 6%, respectively. Motion-corrected data visually showed improved image contrast of hepatic metastases and effectively reduced blurring and motion artefacts. Scatter plots of SUVmeanversusKtsuggested that the proposed framework improved differentiation ofKtmeasurements. The presented motion correction framework for simultaneously acquired PET-DCE-MR data provides accurately aligned images with increased contrast of hepatic lesions allowing for improved detection and characterization.
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Affiliation(s)
- Matteo Ippoliti
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Mathias Lukas
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany.,Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany.,Siemens Healthcare GmbH, Berlin, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Imke Schatka
- Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Furth
- Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,Technische Universität Berlin, Berlin, Germany.,King's College London, London, United Kingdom
| | - Marcus R Makowski
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany.,Klinikum rechts der Isar der TU München, Munich, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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17
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Kim JR, Yoon HM, Cho YA, Lee JS, Jung AY. Free-breathing contrast-enhanced upper abdominal MRI in children: comparison between Cartesian acquisition and stack-of-stars acquisition with two different fat-suppression techniques. Acta Radiol 2021; 62:541-550. [PMID: 32498544 DOI: 10.1177/0284185120928931] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
BACKGROUND Respiratory artifacts impair image quality of magnetic resonance imaging (MRI) in children who cannot hold breath during MRI examination. PURPOSE To compare the quality of free-breathing contrast-enhanced 3D T1-weighted (T1W) images of the upper abdomen in children using Cartesian acquisition (Cartesian eTHRIVE), stack-of-stars acquisition with spectral fat suppression (3D VANE eTHRIVE), and stack-of-stars acquisition with fat suppression using modified Dixon (3D VANE mDixon). MATERIAL AND METHODS Pediatric patients (aged <19 years) who underwent whole-body MRI with free-breathing contrast-enhanced T1W axial scans of upper abdomen using Cartesian eTHRIVE, 3D VANE eTHRIVE, and 3D VANE mDixon were enrolled. Image quality parameters were assessed including overall image quality, hepatic edge sharpness, hepatic vessel clarity, respiratory artifacts, radial artifacts, lesion conspicuity, and lesion edge sharpness using the Likert scale, where a lower score indicated poorer image quality. The coefficients of variation of signal intensity of liver and spleen were analyzed. RESULTS In 41 patients, 3D VANE eTHRIVE showed the highest scores for all image quality parameters (P ≤ 0.001). 3D VANE eTHRIVE also showed higher scores for respiratory (P ≤ 0.001) and radial artefacts than 3D VANE mDixon (P = 0.001). There were no significant differences in coefficients of variation of signal intensity of the liver and spleen between 3D VANE eTHRIVE and 3D VANE mDixon. Acquisition time was longer for 3D VANE eTHRIVE (81.26 ± 16 s) than for Cartesian eTHRIVE (7.87 ± 0.95 s) and 3D VANE mDixon (76.66 ± 12.4 s, P < 0.001). CONCLUSION The application of stack-of-stars acquisition to 3D T1W abdominal MRI resulted in better image quality than Cartesian acquisition in free-breathing children. In stack-of-stars acquisition, spectral fat suppression resulted in better image quality and fewer artifacts than mDixon.
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Affiliation(s)
- Jeong Rye Kim
- Department of Radiology, Dankook University Hospital, Chungcheongnam-do, Republic of Korea
| | - Hee Mang Yoon
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Ah Cho
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jin Seong Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ah Young Jung
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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18
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Ghodrati V, Bydder M, Ali F, Gao C, Prosper A, Nguyen KL, Hu P. Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning. NMR IN BIOMEDICINE 2021; 34:e4433. [PMID: 33258197 PMCID: PMC10193526 DOI: 10.1002/nbm.4433] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 09/18/2020] [Accepted: 10/02/2020] [Indexed: 05/20/2023]
Abstract
The aim of this study was to develop a deep neural network for respiratory motion compensation in free-breathing cine MRI and evaluate its performance. An adversarial autoencoder network was trained using unpaired training data from healthy volunteers and patients who underwent clinically indicated cardiac MRI examinations. A U-net structure was used for the encoder and decoder parts of the network and the code space was regularized by an adversarial objective. The autoencoder learns the identity map for the free-breathing motion-corrupted images and preserves the structural content of the images, while the discriminator, which interacts with the output of the encoder, forces the encoder to remove motion artifacts. The network was first evaluated based on data that were artificially corrupted with simulated rigid motion with regard to motion-correction accuracy and the presence of any artificially created structures. Subsequently, to demonstrate the feasibility of the proposed approach in vivo, our network was trained on respiratory motion-corrupted images in an unpaired manner and was tested on volunteer and patient data. In the simulation study, mean structural similarity index scores for the synthesized motion-corrupted images and motion-corrected images were 0.76 and 0.93 (out of 1), respectively. The proposed method increased the Tenengrad focus measure of the motion-corrupted images by 12% in the simulation study and by 7% in the in vivo study. The average overall subjective image quality scores for the motion-corrupted images, motion-corrected images and breath-held images were 2.5, 3.5 and 4.1 (out of 5.0), respectively. Nonparametric-paired comparisons showed that there was significant difference between the image quality scores of the motion-corrupted and breath-held images (P < .05); however, after correction there was no significant difference between the image quality scores of the motion-corrected and breath-held images. This feasibility study demonstrates the potential of an adversarial autoencoder network for correcting respiratory motion-related image artifacts without requiring paired data.
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Affiliation(s)
- Vahid Ghodrati
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
| | - Mark Bydder
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Fadil Ali
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
| | - Chang Gao
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
| | - Ashley Prosper
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kim-Lien Nguyen
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
- Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Peng Hu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, CA, USA
- Correspondence to: Peng Hu, PhD, Department of Radiological Sciences, 300 UCLA Medical Plaza Suite B119, Los Angeles, CA 90095,
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19
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Dietrich S, Aigner CS, Kolbitsch C, Mayer J, Ludwig J, Schmidt S, Schaeffter T, Schmitter S. 3D Free-breathing multichannel absolute B 1 + Mapping in the human body at 7T. Magn Reson Med 2020; 85:2552-2567. [PMID: 33283915 DOI: 10.1002/mrm.28602] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 10/23/2020] [Accepted: 10/25/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To introduce and investigate a method for free-breathing three-dimensional (3D) B 1 + mapping of the human body at ultrahigh field (UHF), which can be used to generate homogenous flip angle (FA) distributions in the human body at UHF. METHODS A 3D relative B 1 + mapping sequence with a radial phase-encoding (RPE) k-space trajectory was developed and applied in 11 healthy subjects at 7T. An RPE-based actual flip angle mapping method was applied with a dedicated B 1 + shim setting to calibrate the relative B 1 + maps yielding absolute B 1 + maps of the individual transmit channels. The method was evaluated in a motion phantom and by multidimensional in vivo measurements. Additionally, 3D gradient echo scans with and without static phase-only B 1 + shims were used to qualitatively validate B 1 + shim predictions. RESULTS The phantom validation revealed good agreement for B 1 + maps between dynamic measurement and static reference acquisition. The proposed 3D method was successfully validated in vivo by comparing magnitude and phase distributions with a 2D Cartesian reference. 3D B 1 + maps free from visible motion artifacts were successfully acquired for 11 subjects with body mass indexes ranging from 19 kg/m2 to 34 kg/m2 . 3D respiration-resolved absolute B 1 + maps indicated FA differences between inhalation and exhalation up to 15% for one channel and up to 24% for combined channels for shallow breathing. CONCLUSION The proposed method provides respiration-resolved absolute 3D B 1 + maps of the human body at UHF, which enables the investigation and development of 3D B 1 + shimming and parallel transmission methods to further enhance body imaging at UHF.
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Affiliation(s)
- Sebastian Dietrich
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Christoph S Aigner
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Juliane Ludwig
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Simon Schmidt
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Department of Medical Engineering, Technische Universität Berlin, Berlin, Germany
| | - Sebastian Schmitter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
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20
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Ludwig J, Speier P, Seifert F, Schaeffter T, Kolbitsch C. Pilot tone-based motion correction for prospective respiratory compensated cardiac cine MRI. Magn Reson Med 2020; 85:2403-2416. [PMID: 33226699 DOI: 10.1002/mrm.28580] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/24/2020] [Accepted: 10/12/2020] [Indexed: 12/30/2022]
Abstract
PURPOSE To evaluate prospective motion correction using the pilot tone (PT) as a quantitative respiratory motion signal with high temporal resolution for cardiac cine images during free breathing. METHODS Before cine data acquisition, a short prescan was performed, calibrating the PT to the respiratory-induced heart motion using respiratory-resolved real-time images. The calibrated PT was then applied for nearly real-time prospective motion correction of cine MRI through slice tracking (ie, updating the slice position before every readout). Additionally, in-plane motion correction was performed retrospectively also based on the calibrated PT data. The proposed method was evaluated in a moving phantom and 10 healthy volunteers. RESULTS The PT showed very good correlation to the phantom motion. In volunteer studies using a long-term scan over 7.96 ± 1.40 min, the mean absolute error between registered and predicted motion from the PT was 1.44 ± 0.46 mm in head-feet and 0.46 ± 0.07 mm in anterior-posterior direction. Irregular breathing could also be corrected well with the PT. The PT motion correction leads to a significant improvement of contrast-to-noise ratio by 68% (P ≤ .01) between blood pool and myocardium and sharpness of endocardium by 24% (P = .04) in comparison to uncorrected data. The image score, which refers to the cine image quality, has improved with the utilization of the proposed PT motion correction. CONCLUSION The proposed approach provides respiratory motion-corrected cine images of the heart with improved image quality and a high scan efficiency using the PT. The PT is independent of the MR acquisition, making this a very flexible motion-correction approach.
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Affiliation(s)
- Juliane Ludwig
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | | | - Frank Seifert
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,Technische Universität Berlin, Biomedical Engineering, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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21
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Huang Q, Xian Y, Yang D, Qu H, Yi J, Wu P, Metaxas DN. Dynamic MRI reconstruction with end-to-end motion-guided network. Med Image Anal 2020; 68:101901. [PMID: 33285480 DOI: 10.1016/j.media.2020.101901] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 10/23/2020] [Accepted: 11/09/2020] [Indexed: 10/23/2022]
Abstract
Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of body regions. Modeling such information into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. However, existing deep learning based approaches neglect motion information during the reconstruction procedure, while traditional motion-guided methods are hindered by heuristic parameter tuning and long inference time. We propose a novel dynamic MRI reconstruction approach called MODRN and an end-to-end improved version called MODRN(e2e), both of which enhance the reconstruction quality by infusing motion information into the modeling process with deep neural networks. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components: Dynamic Reconstruction Network, Motion Estimation and Motion Compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches.
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Affiliation(s)
- Qiaoying Huang
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.
| | - Yikun Xian
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.
| | | | - Hui Qu
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.
| | - Jingru Yi
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.
| | - Pengxiang Wu
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.
| | - Dimitris N Metaxas
- Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA.
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22
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Roccia E, Neji R, Benkert T, Kiefer B, Goh V, Dregely I. Distortion-free 3D diffusion imaging of the prostate using a multishot diffusion-prepared phase-cycled acquisition and dictionary matching. Magn Reson Med 2020; 85:1441-1454. [PMID: 32989765 DOI: 10.1002/mrm.28527] [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: 05/27/2020] [Revised: 07/27/2020] [Accepted: 08/31/2020] [Indexed: 11/06/2022]
Abstract
PURPOSE To achieve three-dimensional (3D) distortion-free apparent diffusion coefficient (ADC) maps for prostate imaging using a multishot diffusion prepared-gradient echo (msDP-GRE) sequence and ADC dictionary matching. METHODS The msDP-GRE sequence is combined with a 3D Cartesian, centric k-space trajectory with center oversampling. Oversampled k-space center averaging and phase cycling are used to address motion- and eddy current-induced magnitude corruption. Extended-phase-graph (EPG) simulations and ADC dictionary matching are used to compensate for T1 effects. To shorten the acquisition time, each volume is undersampled by a factor of two and reconstructed using iterative sensitivity encoding. The proposed approach is characterized using simulations and validated in a kiwifruit phantom, comparing the msDP-GRE ADC maps obtained using both standard monoexponential fitting and dictionary matching with the clinical standard single-shot diffusion weighted-echo planar imaging (ssDW-EPI) ADC. Initial in vivo feasibility is tested in three healthy subjects, and geometric distortion is compared with anatomical T2 -weighted-turbo spin echo. RESULTS In the kiwifruit phantom experiment, the signal magnitude could be recovered using k-space center averaging and phase cycling. No statistically significant difference was observed in the ADC values estimated using msDP-GRE with dictionary matching and clinical standard DW-EPI (P < .05). The in vivo prostate msDP-GRE scans were free of geometric distortion caused by off-resonance susceptibility, and the ADC values in the prostate were in agreement with values found in the published literature. CONCLUSION Nondistorted 3D ADC maps of the prostate can be achieved using a msDP sequence and dictionary matching.
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Affiliation(s)
- Elisa Roccia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,MR Research Collaboration, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Thomas Benkert
- Oncology Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Berthold Kiefer
- Oncology Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Vicky Goh
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Radiology, Guy's & St Thomas' NHS Foundation Trust, London, United Kingdom
| | - Isabel Dregely
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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23
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Bustin A, Rashid I, Cruz G, Hajhosseiny R, Correia T, Neji R, Rajani R, Ismail TF, Botnar RM, Prieto C. 3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST. J Cardiovasc Magn Reson 2020; 22:24. [PMID: 32299445 PMCID: PMC7161114 DOI: 10.1186/s12968-020-00611-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 02/19/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND To enable free-breathing whole-heart sub-millimeter resolution coronary magnetic resonance angiography (CMRA) in a clinically feasible scan time by combining low-rank patch-based undersampled reconstruction (3D-PROST) with a highly accelerated non-rigid motion correction framework. METHODS Non-rigid motion corrected CMRA combined with 2D image-based navigators has been previously proposed to enable 100% respiratory scan efficiency in modestly undersampled acquisitions. Achieving sub-millimeter isotropic resolution with such techniques still requires prohibitively long acquisition times. We propose to combine 3D-PROST reconstruction with a highly accelerated non-rigid motion correction framework to achieve sub-millimeter resolution CMRA in less than 10 min. Ten healthy subjects and eight patients with suspected coronary artery disease underwent 4-5-fold accelerated free-breathing whole-heart CMRA with 0.9 mm3 isotropic resolution. Vessel sharpness, vessel length and image quality obtained with the proposed non-rigid (NR) PROST approach were compared against translational correction only (TC-PROST) and a previously proposed NR motion-compensated technique (non-rigid SENSE) in healthy subjects. For the patient study, image quality scoring and visual comparison with coronary computed tomography angiography (CCTA) were performed. RESULTS Average scan times [min:s] were 6:01 ± 0:59 (healthy subjects) and 8:29 ± 1:41 (patients). In healthy subjects, vessel sharpness of the left anterior descending (LAD) and right (RCA) coronary arteries were improved with the proposed non-rigid PROST (LAD: 51.2 ± 8.8%, RCA: 61.2 ± 9.1%) in comparison to TC-PROST (LAD: 43.8 ± 5.1%, P = 0.051, RCA: 54.3 ± 8.3%, P = 0.218) and non-rigid SENSE (LAD: 46.1 ± 5.8%, P = 0.223, RCA: 56.7 ± 9.6%, P = 0.50), although differences were not statistically significant. The average visual image quality score was significantly higher for NR-PROST (LAD: 3.2 ± 0.6, RCA: 3.3 ± 0.7) compared with TC-PROST (LAD: 2.1 ± 0.6, P = 0.018, RCA: 2.0 ± 0.7, P = 0.014) and non-rigid SENSE (LAD: 2.3 ± 0.5, P = 0.008, RCA: 2.5 ± 0.7, P = 0.016). In patients, the proposed approach showed good delineation of the coronaries, in agreement with CCTA, with image quality scores and vessel sharpness similar to that of healthy subjects. CONCLUSIONS We demonstrate the feasibility of combining high undersampling factors with non-rigid motion-compensated reconstruction to obtain high-quality sub-millimeter isotropic CMRA images in ~ 8 min. Validation in a larger cohort of patients with coronary artery disease is now warranted.
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Affiliation(s)
- Aurélien Bustin
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Imran Rashid
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Gastao Cruz
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Reza Hajhosseiny
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Teresa Correia
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Radhouene Neji
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, UK
| | - Ronak Rajani
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
- Department of Cardiology, Guy's & St Thomas' Hospitals, London, UK
| | - Tevfik F Ismail
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
| | - René M Botnar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK.
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, 3rd Floor, Lambeth Wing, London, SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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24
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Liu F, Li D, Jin X, Qiu W, Xia Q, Sun B. Dynamic cardiac MRI reconstruction using motion aligned locally low rank tensor (MALLRT). Magn Reson Imaging 2020; 66:104-115. [DOI: 10.1016/j.mri.2019.07.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 07/01/2019] [Accepted: 07/01/2019] [Indexed: 01/10/2023]
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25
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Navest RJM, Mandija S, Bruijnen T, Stemkens B, Tijssen RHN, Andreychenko A, Lagendijk JJW, van den Berg CAT. The noise navigator: a surrogate for respiratory-correlated 4D-MRI for motion characterization in radiotherapy. Phys Med Biol 2020; 65:01NT02. [PMID: 31775130 DOI: 10.1088/1361-6560/ab5c62] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Respiratory-correlated 4D-MRI can characterize respiratory-induced motion of tumors and organs-at-risk for radiotherapy treatment planning and is a necessity for image guidance of moving tumors treated on an MRI-linac. Essential for 4D-MRI generation is a robust respiratory surrogate signal. We investigated the feasibility of the noise navigator as respiratory surrogate signal for 4D-MRI generation. The noise navigator is based on the respiratory-induced modulation of the thermal noise variance measured by the receive coils during MR acquisition and thus is inherently present and synchronized with MRI data acquisition. Additionally, the noise navigator can be combined with any rectilinear readout strategy (e.g. radial and cartesian) and is independent of MR image contrast and imaging orientation. For radiotherapy applications, the noise navigator provides a robust respiratory signal for patients scanned with an elevated coil setup. This is particularly attractive for widely used cartesian sequences where currently a non-interfering self-navigation means is lacking for MRI-based simulation and MRI-guided radiotherapy. The feasibility of 4D-MRI generation with the noise navigator as respiratory surrogate signal was demonstrated for both cartesian and radial readout strategies in radiotherapy setup on four healthy volunteers and two radiotherapy patients on a dedicated 1.5 T MRI scanner and two radiotherapy patients on a 1.5 T MRI-linac system. Moreover, the respiratory-correlated 4D-MR images showed liver motion comparable to a reference 2D cine MRI series for the volunteers. For 2D cartesian cine MRI acquisitions, both the noise navigator and respiratory bellows were benchmarked against an image navigator. Respiratory phase detection based on the noise navigator agreed 1.4 times better with the image navigator than the respiratory bellows did. For a 3D Stack-of-Stars acquisitions, the noise navigator was compared to radial self-navigation and a 1.7 times higher respiratory phase detection agreement was observed than for the respiratory bellows compared to radial self-navigation.
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Affiliation(s)
- R J M Navest
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands. Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands. Author to whom any correspondence should be addressed
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26
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Balza R, Jaimes C, Risacher S, Gale HI, Mahoney J, Heberlein K, Kirsch JE, Shank ES, Gee MS. Impact of a fast free-breathing 3-T abdominal MRI protocol on improving scan time and image quality for pediatric patients with tuberous sclerosis complex. Pediatr Radiol 2019; 49:1788-1797. [PMID: 31485688 DOI: 10.1007/s00247-019-04496-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 05/23/2019] [Accepted: 08/01/2019] [Indexed: 01/13/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) of the abdomen can be especially challenging in pediatric patients because of image quality degradation from respiratory motion. Abdominal MR protocols tailored for free-breathing children can potentially improve diagnostic image quality and reduce scan time. OBJECTIVE To evaluate the performance of a free-breathing 3-T MRI protocol for renal evaluation in pediatric patients with tuberous sclerosis complex (TSC). MATERIALS AND METHODS A single institution, Institutional Review Board-approved, retrospective database query identified pediatric TSC patients who underwent a free-breathing 3-T MR abdominal protocol including radial and respiratory-triggered pulse sequences and who also had a prior abdominal MRI on the same scanner using a traditional MR protocol utilizing signal averaging and Cartesian k-space sampling. Scan times and use of sedation were recorded. MR image quality was compared between the two protocols using a semiquantitative score for overall image quality and sharpness. RESULTS Forty abdominal MRI studies in 20 patients were evaluated. The mean scan time of the fast free-breathing protocol was significantly lower (mean: 42.5±9.8 min) compared with the traditional protocol (58.7±11.7 min; P=<0.001). Image sharpness was significantly improved for radial T2-weighted and T1-weighted triggered Dixon and radial T1-weighted fat-suppressed post-contrast images in the free-breathing protocol, while image quality was significantly higher on radial and Dixon T1-weighted sequences. CONCLUSION A free-breathing abdominal MR protocol in pediatric TSC patients decreases scan time and improves image quality and should be considered more widely for abdominal MRI in children.
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Affiliation(s)
- Rene Balza
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA. .,Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Camilo Jaimes
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Seretha Risacher
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA
| | - Heather I Gale
- Department of Radiology, Billings Clinic, North Billings, MT, USA
| | - Jessica Mahoney
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA
| | | | - John E Kirsch
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA
| | - Erik S Shank
- Department of Anesthesiology, Massachusetts General Hospital, Boston, MA, USA.,Department of Anesthesiology, Harvard Medical School, Boston, MA, USA
| | - Michael S Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA, 02114, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
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27
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Zhu X, Chan M, Lustig M, Johnson KM, Larson PEZ. Iterative motion-compensation reconstruction ultra-short TE (iMoCo UTE) for high-resolution free-breathing pulmonary MRI. Magn Reson Med 2019; 83:1208-1221. [PMID: 31565817 DOI: 10.1002/mrm.27998] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 08/19/2019] [Accepted: 08/26/2019] [Indexed: 12/24/2022]
Abstract
PURPOSE To develop a high-scanning efficiency, motion-corrected imaging strategy for free-breathing pulmonary MRI by combining an iterative motion-compensation reconstruction with a ultrashort echo time (UTE) acquisition called iMoCo UTE. METHODS An optimized golden-angle ordering radial UTE sequence was used to continuously acquire data for 5 minutes. All readouts were grouped to different respiratory motion states based on self-navigator signals, and then motion-resolved data was reconstructed by XD golden-angle radial sparse parallel reconstruction. One state from the motion-resolved images was selected as a reference, and then motion fields from the other states to the reference were derived via nonrigid registration. Finally, all motion-resolved data and motion fields were reconstructed by using an iterative motion-compensation (MoCo) reconstruction with a total generalized variation sparse constraint. RESULTS The iMoCo UTE strategy was evaluated in volunteers and nonsedated pediatric patient (4-6 years old) studies. Images reconstructed with iMoCo UTE provided sharper anatomical lung structures and higher apparent SNR and contrast-to-noise ratio compared to using other motion-correction strategies, such as soft-gating, motion-resolved reconstruction, and nonrigid MoCo. iMoCo UTE also showed promising results in an infant study. CONCLUSION The proposed iMoCo UTE combines self-navigation, motion modeling, and a compressed sensing reconstruction to increase scan efficiency and SNR and to reduce respiratory motion in lung MRI. This proposed strategy shows improvements in free-breathing lung MRI scans, especially in very challenging application situations such as pediatric MRI studies.
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Affiliation(s)
- Xucheng Zhu
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco, California.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Marilynn Chan
- Department of Pediatrics, Division of Pediatric Pulmonology, University of California, San Francisco, California
| | - Michael Lustig
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco, California.,Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin.,Department of Radiology, University of Wisconsin, Madison, Wisconsin
| | - Peder E Z Larson
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California, San Francisco, California.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
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28
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Kolbitsch C, Bastkowski R, Schäffter T, Prieto Vasquez C, Weiss K, Maintz D, Giese D. Respiratory motion corrected 4D flow using golden radial phase encoding. Magn Reson Med 2019; 83:635-644. [DOI: 10.1002/mrm.27918] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 06/26/2019] [Accepted: 07/04/2019] [Indexed: 01/14/2023]
Affiliation(s)
- Christoph Kolbitsch
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
- King's College London School of Biomedical Engineering and Imaging Sciences London United Kingdom
| | - Rene Bastkowski
- Department of Radiology University Hospital of Cologne Cologne Germany
| | - Tobias Schäffter
- Physikalisch‐Technische Bundesanstalt (PTB) Braunschweig and Berlin Germany
- King's College London School of Biomedical Engineering and Imaging Sciences London United Kingdom
| | - Claudia Prieto Vasquez
- King's College London School of Biomedical Engineering and Imaging Sciences London United Kingdom
| | - Kilian Weiss
- Department of Radiology University Hospital of Cologne Cologne Germany
- Philips GmbH Healthcare Hamburg Germany
| | - David Maintz
- Department of Radiology University Hospital of Cologne Cologne Germany
| | - Daniel Giese
- Department of Radiology University Hospital of Cologne Cologne Germany
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29
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Vidya Shankar R, Roccia E, Cruz G, Neji R, Botnar R, Prezzi D, Goh V, Prieto C, Dregely I. Accelerated 3D T 2 w-imaging of the prostate with 1-millimeter isotropic resolution in less than 3 minutes. Magn Reson Med 2019; 82:721-731. [PMID: 31006906 PMCID: PMC6563534 DOI: 10.1002/mrm.27764] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 03/15/2019] [Accepted: 03/16/2019] [Indexed: 12/29/2022]
Abstract
PURPOSE To achieve 3D T2 w imaging of the prostate with 1-mm isotropic resolution in less than 3 min. METHODS We devised and implemented a 3D T2 -prepared multishot balanced steady state free precession (T2 prep-bSSFP) acquisition sequence with a variable density undersampled trajectory combined with a total variation regularized iterative SENSE (TV-SENSE) reconstruction. Prospectively undersampled images of the prostate (acceleration factor R = 3) were acquired in 11 healthy subjects in an institutional review board-approved study. Image quality metrics (subjective signal-to-noise ratio, contrast, sharpness, and overall prostate image quality) were evaluated by 2 radiologists. Scores of the proposed accelerated sequence were compared using the Wilcoxon signed-rank and Kruskal-Wallis non-parametric tests to prostate images acquired using a fully sampled 3D T2 prep-bSSFP acquisition, and with clinical standard 2D and 3D turbo spin echo (TSE) T2 w acquisitions. A P-value < 0.05 was considered significant. RESULTS The 3× accelerated 3D T2 prep-bSSFP images required a scan time (min:s) of 2:45, while the fully sampled 3D T2 prep-bSSFP and clinical standard 3D TSE images were acquired in 8:23 and 7:29, respectively. Image quality scores (contrast, sharpness, and overall prostate image quality) of the accelerated 3D T2 prep-bSSFP, fully sampled T2 prep-bSSFP, and clinical standard 3D TSE acquisitions along all 3 spatial dimensions were not significantly different (P > 0.05). CONCLUSION 3D T2 w images of the prostate with 1-mm isotropic resolution can be acquired in less than 3 min, with image quality that is comparable to a clinical standard 3D TSE sequence but only takes a third of the acquisition time.
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Affiliation(s)
- Rohini Vidya Shankar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Elisa Roccia
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
- MR Research Collaborations, Siemens Healthcare LimitedFrimleyUnited Kingdom
| | - René Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Davide Prezzi
- Department of RadiologyGuy's and St Thomas' Hospitals NHS Foundation TrustLondonUnited Kingdom
| | - Vicky Goh
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
- Department of RadiologyGuy's and St Thomas' Hospitals NHS Foundation TrustLondonUnited Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Isabel Dregely
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUnited Kingdom
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30
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Oksuz I, Ruijsink B, Puyol-Antón E, Clough JR, Cruz G, Bustin A, Prieto C, Botnar R, Rueckert D, Schnabel JA, King AP. Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning. Med Image Anal 2019; 55:136-147. [PMID: 31055126 PMCID: PMC6688894 DOI: 10.1016/j.media.2019.04.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 02/13/2019] [Accepted: 04/17/2019] [Indexed: 11/17/2022]
Abstract
Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.
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Affiliation(s)
- Ilkay Oksuz
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK.
| | - Bram Ruijsink
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK; Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Esther Puyol-Antón
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - James R Clough
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Gastao Cruz
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Aurelien Bustin
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Claudia Prieto
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Rene Botnar
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College, London, UK
| | - Julia A Schnabel
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences, King's College, London, UK
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31
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Ippoliti M, Lukas M, Brenner W, Schaeffter T, Makowski MR, Kolbitsch C. 3D nonrigid motion correction for quantitative assessment of hepatic lesions in DCE-MRI. Magn Reson Med 2019; 82:1753-1766. [PMID: 31228296 PMCID: PMC6771884 DOI: 10.1002/mrm.27867] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/03/2019] [Accepted: 05/24/2019] [Indexed: 12/27/2022]
Abstract
Purpose To provide nonrigid respiratory motion‐corrected DCE‐MRI images with isotropic resolution of 1.5 mm, full coverage of abdomen, and covering the entire uptake curve with a temporal resolution of 6 seconds, for the quantitative assessment of hepatic lesions. Methods 3D DCE‐MRI data were acquired at 3 T during free breathing for 5 minutes using a 3D T1‐weighted golden‐angle radial phase‐encoding sequence. Nonrigid respiratory motion information was extracted and used in motion‐corrected image reconstruction to obtain high‐quality DCE‐MRI images with temporal resolution of 6 seconds and isotropic resolution of 1.5 mm. An extended Tofts model was fitted to the dynamic data sets, yielding quantitative parametric maps of endothelial permeability using the hepatic artery as input function. The proposed approach was evaluated in 11 patients (52 ± 17 years, 5 men) with and without known hepatic lesions, undergoing DCE‐MRI. Results Respiratory motion produced artifacts and misalignment between dynamic volumes (lesion average motion amplitude of 3.82 ± 1.11 mm). Motion correction minimized artifacts and improved average contrast‐to‐noise ratio of hepatic lesions in late phase by 47% (p < .01). Quantitative endothelial permeability maps of motion‐corrected data demonstrated enhanced visibility of different pathologies (e.g., metastases, hemangiomas, cysts, necrotic tumor substructure) and showed improved contrast‐to‐noise ratio by 62% (p < .01) compared with uncorrected data. Conclusion 3D nonrigid motion correction in DCE‐MRI improves both visual and quantitative assessment of hepatic lesions by ensuring accurate alignment between 3D DCE images and reducing motion blurring. This approach does not require breath‐holds and minimizes scan planning by using a large FOV with isotropic resolution.
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Affiliation(s)
- Matteo Ippoliti
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Mathias Lukas
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Marcus R Makowski
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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Küstner T, Armanious K, Yang J, Yang B, Schick F, Gatidis S. Retrospective correction of motion-affected MR images using deep learning frameworks. Magn Reson Med 2019; 82:1527-1540. [PMID: 31081955 DOI: 10.1002/mrm.27783] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 04/02/2019] [Accepted: 04/03/2019] [Indexed: 01/08/2023]
Abstract
PURPOSE Motion is 1 extrinsic source for imaging artifacts in MRI that can strongly deteriorate image quality and, thus, impair diagnostic accuracy. In addition to involuntary physiological motion such as respiration and cardiac motion, intended and accidental patient movements can occur. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion-free reacquisition can become time- and cost-intensive. Numerous motion correction strategies have been proposed to reduce or prevent motion artifacts. These methods have in common that they need to be applied during the actual measurement procedure with a-priori knowledge about the expected motion type and appearance. For retrospective motion correction and without the existence of any a-priori knowledge, this problem is still challenging. METHODS We propose the use of deep learning frameworks to perform retrospective motion correction in a reference-free setting by learning from pairs of motion-free and motion-affected images. For this image-to-image translation problem, we propose and compare a variational auto encoder and generative adversarial network. Feasibility and influences of motion type and optimal architecture are investigated by blinded subjective image quality assessment and by quantitative image similarity metrics. RESULTS We observed that generative adversarial network-based motion correction is feasible producing near-realistic motion-free images as confirmed by blinded subjective image quality assessment. Generative adversarial network-based motion correction accordingly resulted in images with high evaluation metrics (normalized root mean squared error <0.08, structural similarity index >0.8, normalized mutual information >0.9). CONCLUSION Deep learning-based retrospective restoration of motion artifacts is feasible resulting in near-realistic motion-free images. However, the image translation task can alter or hide anatomical features and, therefore, the clinical applicability of this technique has to be evaluated in future studies.
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Affiliation(s)
- Thomas Küstner
- Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany.,Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany.,School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, London, United Kingdom
| | - Karim Armanious
- Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany.,Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
| | - Jiahuan Yang
- Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
| | - Bin Yang
- Department for Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany
| | - Fritz Schick
- Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
| | - Sergios Gatidis
- Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
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Nomura T, Niwa T, Ozawa S, Oguma J, Shibukawa S, Imai Y. The Visibility of the Terminal Thoracic Duct Into the Venous System Using MR Thoracic Ductography with Balanced Turbo Field Echo Sequence. Acad Radiol 2019; 26:550-554. [PMID: 29748046 DOI: 10.1016/j.acra.2018.04.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 04/17/2018] [Accepted: 04/17/2018] [Indexed: 12/30/2022]
Abstract
RATIONALE AND OBJECTIVES Magnetic resonance thoracic ductography (MRTD) with balanced turbo field echo (bTFE) can visualize both the thoracic duct and its surrounding vessels. This study aimed to investigate the visibility of the terminal thoracic duct into the venous system in the subclavian region using MRTD with bTFE. MATERIALS AND METHODS MRTD was performed with bTFE as a preoperative workup comprising respiratory gating on a 1.5-T magnetic resonance system for patients with esophageal cancer. The portion and the number of terminal thoracic ducts into the venous system and preterminal branching in the left subclavian region were assessed using MRTD in 132 patients. The confidence level of the visibility using MRTD was also evaluated. RESULTS The most frequent terminal portion of the thoracic duct was the jugulovenous angle (92 patients, 69.7%), followed by the subclavian vein (27 patients, 20.5%) and the internal jugular vein (8 patients, 6.1%). Four patients also exhibited double entry of the thoracic duct into the venous system. The preterminal branching was single in 96 patients (72.7%) and multiple in 36 patients (27.3%). The confidence level of the visibility of the thoracic duct using MRTD was absolutely certain in 112 patients (84.8%) and was somewhat certain in 20 patients (15.2%). CONCLUSIONS MRTD with bTFE is a robust imaging modality to visualize the terminal portion of the thoracic duct into the venous system in the subclavian region.
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Affiliation(s)
- Takakiyo Nomura
- Department of Diagnostic Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara, 259-1193, Japan
| | - Tetsu Niwa
- Department of Diagnostic Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara, 259-1193, Japan.
| | - Soji Ozawa
- Department of Gastroenterological Surgery, Tokai University School of Medicine, Isehara, Japan
| | - Junya Oguma
- Department of Gastroenterological Surgery, Tokai University School of Medicine, Isehara, Japan
| | - Shuhei Shibukawa
- Department of Radiology, Tokai University Hospital, Isehara, Japan
| | - Yutaka Imai
- Department of Diagnostic Radiology, Tokai University School of Medicine, 143 Shimokasuya, Isehara, 259-1193, Japan
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Roccia E, Vidya Shankar R, Neji R, Cruz G, Munoz C, Botnar R, Goh V, Prieto C, Dregely I. Accelerated 3D T 2 mapping with dictionary-based matching for prostate imaging. Magn Reson Med 2019; 81:1795-1805. [PMID: 30368900 DOI: 10.1002/mrm.27540] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 08/28/2018] [Accepted: 08/28/2018] [Indexed: 01/17/2023]
Abstract
PURPOSE To develop a fast and accurate method for 3D T2 mapping of prostate cancer using undersampled acquisition and dictionary-based fitting. METHODS 3D high-resolution T2 -weighted images (0.9 × 0.9 × 3 mm3 ) were obtained with a multishot T2 -prepared balanced steady-state free precession (T2 -prep-bSSFP) acquisition sequence using a 3D variable density undersampled Cartesian trajectory. Each T2 -weighted image was reconstructed using total variation regularized sensitivity encoding. A flexible simulation framework based on extended phase graphs generated a dictionary of magnetization signals, which was customized to the proposed sequence. The dictionary was matched to the acquired T2 -weighted images to retrieve quantitative T2 values, which were then compared to gold-standard spin echo acquisition values using monoexponential fitting. The proposed approach was validated in simulations and a T1 /T2 phantom, and feasibility was tested in 8 healthy subjects. RESULTS The simulation analysis showed that the proposed T2 mapping approach is robust to noise and typically observed T1 variations. T2 values obtained in the phantom with T2 prep-bSSFP and the acquisition-specific, dictionary-based matching were highly correlated with the gold-standard spin echo method (r = 0.99). Furthermore, no differences were observed with the accelerated acquisition compared to the fully sampled acquisition (r = 0.99). T2 values obtained in prostate peripheral zone, central gland, and muscle in healthy subjects (age, 26 ± 6 years) were 97 ± 14, 76 ± 7, and 36 ± 3 ms, respectively. CONCLUSION 3D quantitative T2 mapping of the whole prostate can be achieved in 3 minutes.
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Affiliation(s)
- Elisa Roccia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Rohini Vidya Shankar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Siemens Healthcare Limited, Frimley, United Kingdom
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Vicky Goh
- Cancer Imaging, King's College London, London, United Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Isabel Dregely
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Godino-Moya A, Royuela-Del-Val J, Usman M, Menchón-Lara RM, Martín-Fernández M, Prieto C, Alberola-López C. Space-time variant weighted regularization in compressed sensing cardiac cine MRI. Magn Reson Imaging 2019; 58:44-55. [PMID: 30654163 DOI: 10.1016/j.mri.2019.01.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 12/02/2018] [Accepted: 01/05/2019] [Indexed: 11/16/2022]
Abstract
PURPOSE To analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI. METHODS k-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain. RESULTS The proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach. CONCLUSIONS Usage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison.
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Affiliation(s)
- Alejandro Godino-Moya
- Laboratorio de Procesado de Imagen, Department of Teoría de la Señal y Comunicaciones e Ingeniería Telemática, ETSIT, Universidad de Valladolid, Campus Miguel Delibes s.n., Valladolid 47011, Spain.
| | - Javier Royuela-Del-Val
- Laboratorio de Procesado de Imagen, Department of Teoría de la Señal y Comunicaciones e Ingeniería Telemática, ETSIT, Universidad de Valladolid, Campus Miguel Delibes s.n., Valladolid 47011, Spain
| | - Muhammad Usman
- Department of Computer Science, University College London, London, United Kingdom
| | - Rosa-María Menchón-Lara
- Laboratorio de Procesado de Imagen, Department of Teoría de la Señal y Comunicaciones e Ingeniería Telemática, ETSIT, Universidad de Valladolid, Campus Miguel Delibes s.n., Valladolid 47011, Spain
| | - Marcos Martín-Fernández
- Laboratorio de Procesado de Imagen, Department of Teoría de la Señal y Comunicaciones e Ingeniería Telemática, ETSIT, Universidad de Valladolid, Campus Miguel Delibes s.n., Valladolid 47011, Spain
| | - Claudia Prieto
- King's College London, School of Biomedical Engineering and Imaging Sciences, London, United Kingdom
| | - Carlos Alberola-López
- Laboratorio de Procesado de Imagen, Department of Teoría de la Señal y Comunicaciones e Ingeniería Telemática, ETSIT, Universidad de Valladolid, Campus Miguel Delibes s.n., Valladolid 47011, Spain
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36
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Kolbitsch C, Neji R, Fenchel M, Schuh A, Mallia A, Marsden P, Schaeffter T. Joint cardiac and respiratory motion estimation for motion-corrected cardiac PET-MR. ACTA ACUST UNITED AC 2018; 64:015007. [DOI: 10.1088/1361-6560/aaf246] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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37
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Ma J, März M, Funk S, Schulz-Menger J, Kutyniok G, Schaeffter T, Kolbitsch C. Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting. Phys Med Biol 2018; 63:235004. [PMID: 30465546 DOI: 10.1088/1361-6560/aaea04] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times. Here we present a novel compressed sensing (CS) reconstruction approach using shearlets as a sparsifying transform allowing for fast 3D CMR (3DShearCS) using 3D radial phase encoding (RPE). An iterative reweighting scheme was applied during image reconstruction to ensure fast convergence and high image quality. Shearlets are mathematically optimal for a simplified model of natural images and have been proven to be more efficient than classical systems such as wavelets. 3DShearCS was compared to three other commonly used reconstruction approaches. Image quality was assessed quantitatively using general image quality metrics and using clinical diagnostic scores from expert reviewers. The proposed technique had lower relative errors, higher structural similarity and higher diagnostic scores compared to the other reconstruction techniques especially for high undersampling factors, i.e. short scan times. 3DShearCS provided ensured accurate depiction of cardiac anatomy for fast imaging and could help to promote 3D high-resolution CMR in clinical practice.
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Affiliation(s)
- Jackie Ma
- Image and Video Coding Group, Fraunhofer Institute for Telecommunications-Heinrich Hertz Institute, Berlin, Germany. Author to whom any correspondence should be addressed
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Kolbitsch C, Neji R, Fenchel M, Mallia A, Marsden P, Schaeffter T. Respiratory-resolved MR-based attenuation correction for motion-compensated cardiac PET-MR. ACTA ACUST UNITED AC 2018; 63:135008. [DOI: 10.1088/1361-6560/aaca15] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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39
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Haskell MW, Cauley SF, Wald LL. TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1253-1265. [PMID: 29727288 PMCID: PMC6633918 DOI: 10.1109/tmi.2018.2791482] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
We introduce a data consistency based retrospective motion correction method, TArgeted Motion Estimation and Reduction (TAMER), to correct for patient motion in Magnetic Resonance Imaging (MRI). Specifically, a motion free image and motion trajectory are jointly estimated by minimizing the data consistency error of a SENSE forward model including rigid-body subject motion. In order to efficiently solve this large non-linear optimization problem, we employ reduced modeling in the parallel imaging formulation by assessing only a subset of target voxels at each step of the motion search. With this strategy we are able to effectively capture the tight coupling between the image voxel values and motion parameters. We demonstrate in simulations TAMER's ability to find similar search directions compared to a full model, with an average error of 22%, vs. 73% error when using previously proposed alternating methods. The reduced model decreased the computation time fold compared to a full image volume evaluation. In phantom experiments, our method successfully mitigates both translation and rotation artifacts, reducing image RMSE compared to a motion-free gold standard from 21% to 14% in a translating phantom, and from 17% to 10% in a rotating phantom. Qualitative image improvements are seen in human imaging of moving subjects compared to conventional reconstruction. Finally, we compare in vivo image results of our method to the state-of-the-art.
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Correia T, Ginami G, Cruz G, Neji R, Rashid I, Botnar RM, Prieto C. Optimized respiratory-resolved motion-compensated 3D Cartesian coronary MR angiography. Magn Reson Med 2018; 80:2618-2629. [PMID: 29682783 PMCID: PMC6220806 DOI: 10.1002/mrm.27208] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 03/13/2018] [Accepted: 03/14/2018] [Indexed: 11/29/2022]
Abstract
Purpose To develop a robust and efficient reconstruction framework that provides high‐quality motion‐compensated respiratory‐resolved images from free‐breathing 3D whole‐heart Cartesian coronary magnetic resonance angiography (CMRA) acquisitions. Methods Recently, XD‐GRASP (eXtra‐Dimensional Golden‐angle RAdial Sparse Parallel MRI) was proposed to achieve 100% scan efficiency and provide respiratory‐resolved 3D radial CMRA images by exploiting sparsity in the respiratory dimension. Here, a reconstruction framework for Cartesian CMRA imaging is proposed, which provides respiratory‐resolved motion‐compensated images by incorporating 2D beat‐to‐beat translational motion information to increase sparsity in the respiratory dimension. The motion information is extracted from interleaved image navigators and is also used to compensate for 2D translational motion within each respiratory phase. The proposed Optimized Respiratory‐resolved Cartesian Coronary MR Angiography (XD‐ORCCA) method was tested on 10 healthy subjects and 2 patients with cardiovascular disease, and compared against XD‐GRASP. Results The proposed XD‐ORCCA provides high‐quality respiratory‐resolved images, allowing clear visualization of the right and left coronary arteries, even for irregular breathing patterns. Compared with XD‐GRASP, the proposed method improves the visibility and sharpness of both coronaries. Significant differences (p < .05) in visible vessel length and proximal vessel sharpness were found between the 2 methods. The XD‐GRASP method provides good‐quality images in the absence of intraphase motion. However, motion blurring is observed in XD‐GRASP images for respiratory phases with larger motion amplitudes and subjects with irregular breathing patterns. Conclusion A robust respiratory‐resolved motion‐compensated framework for Cartesian CMRA has been proposed and tested in healthy subjects and patients. The proposed XD‐ORCCA provides high‐quality images for all respiratory phases, independently of the regularity of the breathing pattern.
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Affiliation(s)
- Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Giulia Ginami
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Imran Rashid
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Lv J, Chen K, Yang M, Zhang J, Wang X. Reconstruction of undersampled radial free-breathing 3D abdominal MRI using stacked convolutional auto-encoders. Med Phys 2018; 45:2023-2032. [PMID: 29574939 DOI: 10.1002/mp.12870] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 02/21/2018] [Accepted: 03/06/2018] [Indexed: 01/22/2023] Open
Abstract
PURPOSE Free-breathing three-dimensional (3D) abdominal imaging is a challenging task for MRI, as respiratory motion severely degrades image quality. One of the most promising self-navigation techniques is the 3D golden-angle radial stack-of-stars (SOS) sequence, which has advantages in terms of speed, resolution, and allowing free breathing. However, streaking artifacts are still clearly observed in reconstructed images when undersampling is applied. This work presents a novel reconstruction approach based on a stacked convolutional auto-encoder (SCAE) network to solve this problem. METHODS Thirty healthy volunteers participated in our experiment. To build the dataset, reference and artifact-affected images were reconstructed using 451 golden-angle spokes and the first 20, 40, or 90 golden-angle spokes corresponding to acceleration rates of 31.4, 15.7, and 6.98, respectively. In the training step, we trained the SCAE by feeding it with patches from artifact-affected images. The SCAE outputs patches in the corresponding reference images. In the testing step, we applied the trained SCAE to map each input artifact-affected patch to the corresponding reference image patch. RESULT The SCAE-based reconstruction images with acceleration rates of 6.98 and 15.7 show nearly similar quality as the reference images. Additionally, the calculation time is below 1 s. Moreover, the proposed approach preserves important features, such as lesions not presented in the training set. CONCLUSION The preliminary results demonstrate the feasibility of the proposed SCAE-based strategy for correcting the streaking artifacts of undersampled free-breathing 3D abdominal MRI with a negligible reconstruction time.
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Affiliation(s)
- Jun Lv
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Kun Chen
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Ming Yang
- Vusion Tech Ltd. Co, Hefei, 230031, China
| | - Jue Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.,College of Engineering, Peking University, Beijing, 100871, China
| | - Xiaoying Wang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.,Department of Radiology, Peking University First Hospital, Beijing, 100034, China
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Lv J, Huang W, Zhang J, Wang X. Performance of U-net based pyramidal lucas-kanade registration on free-breathing multi-b-value diffusion MRI of the kidney. Br J Radiol 2018. [PMID: 29528241 DOI: 10.1259/bjr.20170813] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE In free-breathing multi-b-value diffusion-weighted imaging (DWI), a series of images typically requires several minutes to collect. During respiration the kidney is routinely displaced and may also undergo deformation. These respiratory motion effects generate artifacts and these are the main sources of error in the quantification of intravoxel incoherent motion (IVIM) derived parameters. This work proposes a fully automated framework that combines a kidney segmentation to improve the registration accuracy. METHODS 10 healthy subjects were recruited to participate in this experiment. For the segmentation, U-net was adopted to acquire the kidney's contour. The segmented kidney then served as a region of interest (ROI) for the registration method, known as pyramidal Lucas-Kanade. Our proposed framework confines the kidney's solution range, thus increasing the pyramidal Lucas-Kanade's accuracy. To demonstrate the feasibility of our presented framework, eight regions of interest were selected in the cortex and medulla, and data stability was estimated by comparing the normalized root-mean-square error (NRMSE) values of the fitted data from the bi-exponential intravoxel incoherent motion model pre- and post- registration. RESULTS The results show that the NRMSE was significantly lower after registration both in the cortex (p < 0.05) and medulla (p < 0.01) during free-breathing measurements. In addition, expert visual scoring of the derived apparent diffusion coefficient (ADC), f, D and D* maps indicated there were significant improvements in the alignment of the kidney in the post-registered image. CONCLUSION The proposed framework can effectively reduce the motion artifacts of misaligned multi-b-value DWIs and the inaccuracies of the ADC, f, D and D* estimations. Advances in knowledge: This study demonstrates the feasibility of our proposed fully automated framework combining U-net based segmentation and pyramidal Lucas-Kanade registration method for improving the alignment of multi-b-value diffusion-weighted MRIs and reducing the inaccuracy of parameter estimation during free-breathing.
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Affiliation(s)
- Jun Lv
- 1 Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China
| | - Wenjian Huang
- 1 Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China
| | - Jue Zhang
- 1 Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China.,2 College of Engineering, Peking University , Beijing , China
| | - Xiaoying Wang
- 1 Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China.,3 Department of Radiology, Peking University First Hospital , Beijing , China
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Polycarpou I, Soultanidis G, Tsoumpas C. Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:703-711. [PMID: 29533892 DOI: 10.1109/tmi.2017.2768130] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, 18F-FDG and 68Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions.
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Coristine AJ, Chaptinel J, Ginami G, Bonanno G, Coppo S, van Heeswijk RB, Piccini D, Stuber M. Improved respiratory self-navigation for 3D radial acquisitions through the use of a pencil-beam 2D-T 2 -prep for free-breathing, whole-heart coronary MRA. Magn Reson Med 2018; 79:1293-1303. [PMID: 28568961 PMCID: PMC5931377 DOI: 10.1002/mrm.26764] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 05/01/2017] [Accepted: 05/03/2017] [Indexed: 12/26/2022]
Abstract
PURPOSE In respiratory self-navigation (SN), signal from static structures, such as the chest wall, may complicate motion detection or introduce post-correction artefacts. Suppressing signal from superfluous tissues may therefore improve image quality. We thus test the hypothesis that SN whole-heart coronary magnetic resonance angiography (MRA) will benefit from an outer-volume suppressing 2D-T2 -Prep and present both phantom and in vivo results. METHODS A 2D-T2 -Prep and a conventional T2 -Prep were used prior to a free-breathing 3D-radial SN sequence. Both techniques were compared by imaging a home-built moving cardiac phantom and by performing coronary MRA in nine healthy volunteers. Reconstructions were performed using both a reference-based and a reference-independent approach to motion tracking, along with several coil combinations. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared, along with vessel sharpness (VS). RESULTS In phantoms, using the 2D-T2 -Prep increased SNR by 16% to 53% and mean VS by 8%; improved motion tracking precision was also achieved. In volunteers, SNR increased by an average of 29% to 33% in the blood pool and by 15% to 25% in the myocardium, depending on the choice of reconstruction coils and algorithm, and VS increased by 34%. CONCLUSION A 2D-T2 -Prep significantly improves image quality in both phantoms and volunteers when performing SN coronary MRA. Magn Reson Med 79:1293-1303, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- A. J. Coristine
- Department of BioMedical Engineering, Case Western Reserve University (CWRU), Cleveland, Ohio, USA
- Department of Radiology, University Hospital (CHUV) / University of Lausanne (UNIL), Lausanne, VD, Switzerland
| | - J. Chaptinel
- Department of Radiology, University Hospital (CHUV) / University of Lausanne (UNIL), Lausanne, VD, Switzerland
| | - G. Ginami
- Department of Radiology, University Hospital (CHUV) / University of Lausanne (UNIL), Lausanne, VD, Switzerland
| | - G. Bonanno
- Department of Radiology, University Hospital (CHUV) / University of Lausanne (UNIL), Lausanne, VD, Switzerland
| | - S. Coppo
- Department of Radiology, University Hospital (CHUV) / University of Lausanne (UNIL), Lausanne, VD, Switzerland
| | - R. B. van Heeswijk
- Department of Radiology, University Hospital (CHUV) / University of Lausanne (UNIL), Lausanne, VD, Switzerland
| | - D. Piccini
- Department of Radiology, University Hospital (CHUV) / University of Lausanne (UNIL), Lausanne, VD, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland
| | - M. Stuber
- Department of Radiology, University Hospital (CHUV) / University of Lausanne (UNIL), Lausanne, VD, Switzerland
- CardioVascular Magnetic Resonance (CVMR) research centre, Centre for BioMedical Imaging (CIBM), Lausanne, VD, Switzerland
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Han Y, Yoo J, Kim HH, Shin HJ, Sung K, Ye JC. Deep learning with domain adaptation for accelerated projection-reconstruction MR. Magn Reson Med 2018; 80:1189-1205. [PMID: 29399869 DOI: 10.1002/mrm.27106] [Citation(s) in RCA: 146] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 12/20/2017] [Accepted: 01/04/2018] [Indexed: 12/24/2022]
Affiliation(s)
- Yoseob Han
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejeon, Republic of Korea
| | - Jaejun Yoo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejeon, Republic of Korea
| | - Hak Hee Kim
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea
| | - Hee Jung Shin
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea
| | - Kyunghyun Sung
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California
| | - Jong Chul Ye
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejeon, Republic of Korea
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Lv J, Yang M, Zhang J, Wang X. Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study. Br J Radiol 2018; 91:20170788. [PMID: 29261334 DOI: 10.1259/bjr.20170788] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE Free-breathing abdomen imaging requires non-rigid motion registration of unavoidable respiratory motion in three-dimensional undersampled data sets. In this work, we introduce an image registration method based on the convolutional neural network (CNN) to obtain motion-free abdominal images throughout the respiratory cycle. METHODS Abdominal data were acquired from 10 volunteers using a 1.5 T MRI system. The respiratory signal was extracted from the central-space spokes, and the acquired data were reordered in three bins according to the corresponding breathing signal. Retrospective image reconstruction of the three near-motion free respiratory phases was performed using non-Cartesian iterative SENSE reconstruction. Then, we trained a CNN to analyse the spatial transform among the different bins. This network could generate the displacement vector field and be applied to perform registration on unseen image pairs. To demonstrate the feasibility of this registration method, we compared the performance of three different registration approaches for accurate image fusion of three bins: non-motion corrected (NMC), local affine registration method (LREG) and CNN. RESULTS Visualization of coronal images indicated that LREG had caused broken blood vessels, while the vessels of the CNN were sharper and more consecutive. As shown in the sagittal view, compared to NMC and CNN, distorted and blurred liver contours were caused by LREG. At the same time, zoom-in axial images presented that the vessels were delineated more clearly by CNN than LREG. The statistical results of the signal-to-noise ratio, visual score, vessel sharpness and registration time over all volunteers were compared among the NMC, LREG and CNN approaches. The SNR indicated that the CNN acquired the best image quality (207.42 ± 96.73), which was better than NMC (116.67 ± 44.70) and LREG (187.93 ± 96.68). The image visual score agreed with SNR, marking CNN (3.85 ± 0.12) as the best, followed by LREG (3.43 ± 0.13) and NMC (2.55 ± 0.09). A vessel sharpness assessment yielded similar values between the CNN (0.81 ± 0.03) and LREG (0.80 ± 0.04), differentiating them from the NMC (0.78 ± 0.06). When compared with the LREG-based reconstruction, the CNN-based reconstruction reduces the registration time from 1 h to 1 min. CONCLUSION Our preliminary results demonstrate the feasibility of the CNN-based approach, and this scheme outperforms the NMC- and LREG-based methods. Advances in knowledge: This method reduces the registration time from ~1 h to ~1 min, which has promising prospects for clinical use. To the best of our knowledge, this study shows the first convolutional neural network-based registration method to be applied in abdominal images.
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Affiliation(s)
- Jun Lv
- 1 Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China
| | - Ming Yang
- 2 Vusion Tech Ltd. Co , Suzhou , China
| | - Jue Zhang
- 1 Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China.,3 College of Engineering, Peking University , Beijing , China
| | - Xiaoying Wang
- 1 Academy for Advanced Interdisciplinary Studies, Peking University , Beijing , China.,4 Department of Radiology, Peking University First Hospital , Beijing , China
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Engel LC, Landmesser U, Gigengack K, Wurster T, Manes C, Girke G, Jaguszewski M, Skurk C, Leistner DM, Lauten A, Schuster A, Hamm B, Botnar RM, Makowski MR, Bigalke B. Novel Approach for In Vivo Detection of Vulnerable Coronary Plaques Using Molecular 3-T CMR Imaging With an Albumin-Binding Probe. JACC Cardiovasc Imaging 2018; 12:297-306. [PMID: 29361487 DOI: 10.1016/j.jcmg.2017.10.026] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 10/18/2017] [Accepted: 10/18/2017] [Indexed: 11/15/2022]
Abstract
OBJECTIVES This study sought to investigate the potential of the noninvasive albumin-binding probe gadofosveset-enhanced cardiac magnetic resonance (GE-CMR) for detection of coronary plaques that can cause acute coronary syndromes (ACS). BACKGROUND ACS are frequently caused by rupture or erosion of coronary plaques that initially do not cause hemodynamically significant stenosis and are therefore not detected by invasive x-ray coronary angiography (XCA). METHODS A total of 25 patients with ACS or symptoms of stable coronary artery disease underwent GE-CMR, clinically indicated XCA, and optical coherence tomography (OCT) within 24 h. GE-CMR was performed approximately 24 h following a 1-time application of gadofosveset-trisodium. Contrast-to-noise ratio (CNR) was quantified within coronary segments in comparison with blood signal. RESULTS A total of 207 coronary segments were analyzed on GE-CMR. Segments containing a culprit lesion in ACS patients (n = 11) showed significant higher signal enhancement (CNR) following gadofosveset-trisodium application than segments without culprit lesions (n = 196; 6.1 [3.9 to 16.5] vs. 2.1 [0.5 to 3.5]; p < 0.001). GE-CMR was able to correctly identify culprit coronary lesions in 9 of 11 segments (sensitivity 82%) and correctly excluded culprit coronary lesions in 162 of 195 segments (specificity 83%). Additionally, segmented areas of thin-cap fibroatheroma (n = 22) as seen on OCT demonstrated significantly higher CNR than segments without coronary plaque or segments containing early atherosclerotic lesions (n = 185; 9.2 [3.3 to 13.7] vs. 2.1 [0.5 to 3.4]; p = 0.001). CONCLUSIONS In this study, we demonstrated for the first time the noninvasive detection of culprit coronary lesions and thin-cap fibroatheroma of the coronary arteries in vivo by using GE-CMR. This method may represent a novel approach for noninvasive cardiovascular risk prediction.
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Affiliation(s)
- Leif-Christopher Engel
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany
| | - Ulf Landmesser
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany; DZHK (German Centre for Cardiovascular Research), Partner Site, Berlin, Germany
| | - Kevin Gigengack
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Wurster
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Constantina Manes
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Girke
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Milosz Jaguszewski
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Skurk
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - David M Leistner
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Alexander Lauten
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Schuster
- Department of Cardiology, Royal North Shore Hospital, The Kolling Institute, Northern Clinical School, University of Sydney, Sydney, Australia; Department of Cardiology and Pulmonology, German Centre for Cardiovascular Research Deutsches Zentrum für Herz-Kreislauf-Forschung e.V. (DZHK) Partner Site, Göttingen, Germany
| | - Bernd Hamm
- Klinik für Radiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Rene M Botnar
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom; Pontificia Universidad Católica de Chile Escuela de Ingeniería, Santiago, Chile
| | - Marcus R Makowski
- Klinik für Radiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany.
| | - Boris Bigalke
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany.
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Correia T, Cruz G, Schneider T, Botnar RM, Prieto C. Technical note: Accelerated nonrigid motion-compensated isotropic 3D coronary MR angiography. Med Phys 2017; 45:214-222. [PMID: 29131353 PMCID: PMC5814733 DOI: 10.1002/mp.12663] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 10/09/2017] [Accepted: 11/01/2017] [Indexed: 12/15/2022] Open
Abstract
PURPOSE To develop an accelerated and nonrigid motion-compensated technique for efficient isotropic 3D whole-heart coronary magnetic resonance angiography (CMRA) with Cartesian acquisition. METHODS Highly efficient whole-heart 3D CMRA was achieved by combining image reconstruction from undersampled data using compressed sensing (CS) with a nonrigid motion compensation framework. Undersampled acquisition was performed using a variable-density Cartesian trajectory with radial order (VD-CAPR). Motion correction was performed in two steps: beat-to-beat 2D translational correction with motion estimated from interleaved image navigators, and bin-to-bin 3D nonrigid correction with motion estimated from respiratory-resolved images reconstructed from undersampled 3D CMRA data using CS. Nonrigid motion fields were incorporated into an undersampled motion-compensated reconstruction, which combines CS with the general matrix description formalism. The proposed approach was tested on 10 healthy subjects and compared against a conventional twofold accelerated 5-mm navigator-gated and tracked acquisition. RESULTS The proposed method achieves isotropic 1.2-mm Cartesian whole-heart CMRA in 5 min ± 1 min (~8× acceleration). The proposed approach provides good-quality images of the left and right coronary arteries, comparable to those of a twofold accelerated navigator-gated and tracked acquisition, but scan time was up to about four times faster. For both coronaries, no significant differences (P > 0.05) in vessel sharpness and length were found between the proposed method and reference scan. CONCLUSION The feasibility of a highly efficient motion-compensated reconstruction framework for accelerated 3D CMRA has been demonstrated in healthy subjects. Further investigation is required to assess the clinical value of the method.
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Affiliation(s)
- Teresa Correia
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Gastão Cruz
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | - René M Botnar
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Pontificia Universidad Católica de Chile, Escuela de Ingeniería, Santiago, Chile
| | - Claudia Prieto
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Pontificia Universidad Católica de Chile, Escuela de Ingeniería, Santiago, Chile
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Chen F, Zhang T, Cheng JY, Shi X, Pauly JM, Vasanawala SS. Autocalibrating motion-corrected wave-encoding for highly accelerated free-breathing abdominal MRI. Magn Reson Med 2017; 78:1757-1766. [PMID: 27943402 PMCID: PMC5466545 DOI: 10.1002/mrm.26567] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2016] [Revised: 10/26/2016] [Accepted: 11/10/2016] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop a motion-robust wave-encoding technique for highly accelerated free-breathing abdominal MRI. METHODS A comprehensive 3D wave-encoding-based method was developed to enable fast free-breathing abdominal imaging: (a) auto-calibration for wave-encoding was designed to avoid extra scan for coil sensitivity measurement; (b) intrinsic butterfly navigators were used to track respiratory motion; (c) variable-density sampling was included to enable compressed sensing; (d) golden-angle radial-Cartesian hybrid view-ordering was incorporated to improve motion robustness; and (e) localized rigid motion correction was combined with parallel imaging compressed sensing reconstruction to reconstruct the highly accelerated wave-encoded datasets. The proposed method was tested on six subjects and image quality was compared with standard accelerated Cartesian acquisition both with and without respiratory triggering. Inverse gradient entropy and normalized gradient squared metrics were calculated, testing whether image quality was improved using paired t-tests. RESULTS For respiratory-triggered scans, wave-encoding significantly reduced residual aliasing and blurring compared with standard Cartesian acquisition (metrics suggesting P < 0.05). For non-respiratory-triggered scans, the proposed method yielded significantly better motion correction compared with standard motion-corrected Cartesian acquisition (metrics suggesting P < 0.01). CONCLUSION The proposed methods can reduce motion artifacts and improve overall image quality of highly accelerated free-breathing abdominal MRI. Magn Reson Med 78:1757-1766, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Feiyu Chen
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Tao Zhang
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Joseph Y. Cheng
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Xinwei Shi
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - John M. Pauly
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Jiang W, Ong F, Johnson KM, Nagle SK, Hope TA, Lustig M, Larson PEZ. Motion robust high resolution 3D free-breathing pulmonary MRI using dynamic 3D image self-navigator. Magn Reson Med 2017; 79:2954-2967. [PMID: 29023975 DOI: 10.1002/mrm.26958] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 08/16/2017] [Accepted: 09/14/2017] [Indexed: 01/01/2023]
Abstract
PURPOSE To achieve motion robust high resolution 3D free-breathing pulmonary MRI utilizing a novel dynamic 3D image navigator derived directly from imaging data. METHODS Five-minute free-breathing scans were acquired with a 3D ultrashort echo time (UTE) sequence with 1.25 mm isotropic resolution. From this data, dynamic 3D self-navigating images were reconstructed under locally low rank (LLR) constraints and used for motion compensation with one of two methods: a soft-gating technique to penalize the respiratory motion induced data inconsistency, and a respiratory motion-resolved technique to provide images of all respiratory motion states. RESULTS Respiratory motion estimation derived from the proposed dynamic 3D self-navigator of 7.5 mm isotropic reconstruction resolution and a temporal resolution of 300 ms was successful for estimating complex respiratory motion patterns. This estimation improved image quality compared to respiratory belt and DC-based navigators. Respiratory motion compensation with soft-gating and respiratory motion-resolved techniques provided good image quality from highly undersampled data in volunteers and clinical patients. CONCLUSION An optimized 3D UTE sequence combined with the proposed reconstruction methods can provide high-resolution motion robust pulmonary MRI. Feasibility was shown in patients who had irregular breathing patterns in which our approach could depict clinically relevant pulmonary pathologies. Magn Reson Med 79:2954-2967, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Wenwen Jiang
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California, USA
| | - Frank Ong
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin, Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin, Madison, Madison, Wisconsin, USA
| | - Scott K Nagle
- Department of Medical Physics, University of Wisconsin, Madison, Madison, Wisconsin, USA.,Department of Radiology, University of Wisconsin, Madison, Madison, Wisconsin, USA
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Michael Lustig
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California, USA.,Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Peder E Z Larson
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and University of California, San Francisco, California, USA.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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