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Huang P, Eck B, Liu R, Li H, Yang M, Kim J, Zhang X, Li X, Ying L. Robust Highly-accelerated MR Fingerprinting Using Transformer-based Deep Learning. PROCEEDINGS OF THE INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE ... SCIENTIFIC MEETING AND EXHIBITION. INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE. SCIENTIFIC MEETING AND EXHIBITION 2024; 32:3577. [PMID: 38798756 PMCID: PMC11127716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Affiliation(s)
- Peizhou Huang
- Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
| | - Brendan Eck
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Ruiying Liu
- Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
| | - Hongyu Li
- Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
| | - Mingrui Yang
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Jeehun Kim
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Xiaoliang Zhang
- Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
| | - Xiaojuan Li
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Leslie Ying
- Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
- Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
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Li Y, Qi Y, Hu Z, Zhang K, Jia S, Zhang L, Xu W, Shen S, Wáng YXJ, Li Z, Liang D, Liu X, Zheng H, Cheng G, Zhang N. A novel automatic segmentation method directly based on magnetic resonance imaging K-space data for auxiliary diagnosis of glioma. Quant Imaging Med Surg 2024; 14:2008-2020. [PMID: 38415166 PMCID: PMC10895104 DOI: 10.21037/qims-23-946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/14/2023] [Indexed: 02/29/2024]
Abstract
Background The use of segmentation architectures in medical imaging, particularly for glioma diagnosis, marks a significant advancement in the field. Traditional methods often rely on post-processed images; however, key details can be lost during the fast Fourier transformation (FFT) process. Given the limitations of these techniques, there is a growing interest in exploring more direct approaches. The adaption of segmentation architectures originally designed for road extraction for medical imaging represents an innovative step in this direction. By employing K-space data as the modal input, this method completely eliminates the information loss inherent in FFT, thereby potentially enhancing the precision and effectiveness of glioma diagnosis. Methods In the study, a novel architecture based on a deep-residual U-net was developed to accomplish the challenging task of automatically segmenting brain tumors from K-space data. Brain tumors from K-space data with different under-sampling rates were also segmented to verify the clinical application of our method. Results Compared to the benchmarks set in the 2018 Brain Tumor Segmentation (BraTS) Challenge, our proposed architecture had superior performance, achieving Dice scores of 0.8573, 0.8789, and 0.7765 for the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions, respectively. The corresponding Hausdorff distances were 2.5649, 1.6146, and 2.7187 for the WT, TC, and ET regions, respectively. Notably, compared to traditional image-based approaches, the architecture also exhibited an improvement of approximately 10% in segmentation accuracy on the K-space data at different under-sampling rates. Conclusions These results show the superiority of our method compared to previous methods. The direct performance of lesion segmentation based on K-space data eliminates the time-consuming and tedious image reconstruction process, thus enabling the segmentation task to be accomplished more efficiently.
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Affiliation(s)
- Yikang Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Imperial College London, London, UK
| | - Yulong Qi
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Zhanli Hu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ke Zhang
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Sen Jia
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Wenjing Xu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shuai Shen
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yì Xiáng J Wáng
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Zongyang Li
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Guanxun Cheng
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Na Zhang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Ismail TF, Strugnell W, Coletti C, Božić-Iven M, Weingärtner S, Hammernik K, Correia T, Küstner T. Cardiac MR: From Theory to Practice. Front Cardiovasc Med 2022; 9:826283. [PMID: 35310962 PMCID: PMC8927633 DOI: 10.3389/fcvm.2022.826283] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/17/2022] [Indexed: 01/10/2023] Open
Abstract
Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.
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Affiliation(s)
- Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Cardiology Department, Guy's and St Thomas' Hospital, London, United Kingdom
| | - Wendy Strugnell
- Queensland X-Ray, Mater Hospital Brisbane, Brisbane, QLD, Australia
| | - Chiara Coletti
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Maša Božić-Iven
- Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
- Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany
| | | | - Kerstin Hammernik
- Lab for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre of Marine Sciences, Faro, Portugal
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
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Rioux JA, Hewlett M, Davis C, Bowen CV, Brewer K, Clarke SE, Beyea SD. Mapping of fatty acid composition with free-breathing MR spectroscopic imaging and compressed sensing. NMR IN BIOMEDICINE 2021; 34:e4241. [PMID: 31898379 PMCID: PMC8244113 DOI: 10.1002/nbm.4241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 11/13/2019] [Accepted: 11/18/2019] [Indexed: 06/10/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a growing health problem, and a major challenge in NAFLD management is identifying which patients are at risk of progression to more serious disease. Simple measurements of liver fat content are not strong predictors of clinical outcome, but biomarkers related to fatty acid composition (ie, saturated vs. unsaturated fat) may be more effective. MR spectroscopic imaging (MRSI) methods allow spatially resolved, whole-liver measurements of chemical composition but are traditionally limited by slow acquisition times. In this work we present an accelerated MRSI acquisition based on spin echo single point imaging (SE-SPI), which, using appropriate sampling and compressed sensing reconstruction, allows free-breathing acquisition in a mouse model of fatty liver disease. After validating the technique's performance in oil/water phantoms, we imaged mice that had received a normal diet or a methionine and choline deficient (MCD) diet, some of which also received supplemental injections of iron to mimic hepatic iron overload. SE-SPI was more resistant to the line-broadening effects of iron than single-voxel spectroscopy measurements, and was consistently able to measure the amplitudes of low-intensity spectral peaks that are important to characterizing fatty acid composition. In particular, in the mice receiving the MCD diet, SE-SPI showed a significant decrease in a metric associated with unsaturated fat, which is consistent with the literature. This or other related metrics may therefore offer more a specific biomarker of liver health than fat content alone. This preclinical study is an important precursor to clinical testing of the proposed method. MR-based quantification of fatty acid composition may allow for improved characterization of non-alcoholic fatty liver disease. A spectroscopic imaging method with appropriate sampling strategy allows whole-liver mapping of fat composition metrics in a free-breathing mouse model. Changes in metrics like the surrogate unsaturation index (UIs) are visible in mice receiving a diet which induces fat accumulation in the liver, as compared to a normal diet; such metrics may prove useful in future clinical studies of liver disease.
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Affiliation(s)
- James A. Rioux
- Biomedical Translational Imaging CentreHalifaxNova ScotiaCanada
- Department of Diagnostic RadiologyDalhousie UniversityHalifaxNova ScotiaCanada
- Department of Physics and Atmospheric ScienceDalhousie UniversityHalifaxNova ScotiaCanada
| | - Miriam Hewlett
- Biomedical Translational Imaging CentreHalifaxNova ScotiaCanada
- Department of Medical BiophysicsUniversity of Western OntarioLondonOntarioCanada
| | - Christa Davis
- Biomedical Translational Imaging CentreHalifaxNova ScotiaCanada
| | - Chris V. Bowen
- Biomedical Translational Imaging CentreHalifaxNova ScotiaCanada
- Department of Diagnostic RadiologyDalhousie UniversityHalifaxNova ScotiaCanada
- Department of Physics and Atmospheric ScienceDalhousie UniversityHalifaxNova ScotiaCanada
- School of Biomedical EngineeringDalhousie UniversityHalifaxNova ScotiaCanada
| | - Kimberly Brewer
- Biomedical Translational Imaging CentreHalifaxNova ScotiaCanada
- Department of Diagnostic RadiologyDalhousie UniversityHalifaxNova ScotiaCanada
- Department of Physics and Atmospheric ScienceDalhousie UniversityHalifaxNova ScotiaCanada
- School of Biomedical EngineeringDalhousie UniversityHalifaxNova ScotiaCanada
| | - Sharon E. Clarke
- Biomedical Translational Imaging CentreHalifaxNova ScotiaCanada
- Department of Diagnostic RadiologyDalhousie UniversityHalifaxNova ScotiaCanada
- Department of Physics and Atmospheric ScienceDalhousie UniversityHalifaxNova ScotiaCanada
| | - Steven D. Beyea
- Biomedical Translational Imaging CentreHalifaxNova ScotiaCanada
- Department of Diagnostic RadiologyDalhousie UniversityHalifaxNova ScotiaCanada
- Department of Physics and Atmospheric ScienceDalhousie UniversityHalifaxNova ScotiaCanada
- School of Biomedical EngineeringDalhousie UniversityHalifaxNova ScotiaCanada
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Peyvandi S, Xu D, Wang Y, Hogan W, Moon-Grady A, Barkovich AJ, Glenn O, McQuillen P, Liu J. Fetal Cerebral Oxygenation Is Impaired in Congenital Heart Disease and Shows Variable Response to Maternal Hyperoxia. J Am Heart Assoc 2020; 10:e018777. [PMID: 33345557 PMCID: PMC7955474 DOI: 10.1161/jaha.120.018777] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Impairments in fetal oxygen delivery have been implicated in brain dysmaturation seen in congenital heart disease (CHD), suggesting a role for in utero transplacental oxygen therapy. We applied a novel imaging tool to quantify fetal cerebral oxygenation by measuring T2* decay. We compared T2* in fetuses with CHD with controls with a focus on cardiovascular physiologies (transposition or left‐sided obstruction) and described the effect of brief administration of maternal hyperoxia on T2* decay. Methods and Results This is a prospective study performed on pregnant mothers with a prenatal diagnosis of CHD compared with controls in the third trimester. Participants underwent a fetal brain magnetic resonance imaging scan including a T2* sequence before and after maternal hyperoxia. Comparisons were made between control and CHD fetuses including subgroup analyses by cardiac physiology. Forty‐four mothers (CHD=24, control=20) participated. Fetuses with CHD had lower total brain volume (238.2 mm3, 95% CI, 224.6–251.9) compared with controls (262.4 mm3, 95% CI, 245.0–279.8, P=0.04). T2* decay time was faster in CHD compared with controls (beta=−14.4, 95% CI, −23.3 to −5.6, P=0.002). The magnitude of change in T2* with maternal hyperoxia was higher in fetuses with transposition compared with controls (increase of 8.4 ms, 95% CI, 0.5–14.3, P=0.01), though between‐subject variability was noted. Conclusions Cerebral tissue oxygenation is lower in fetuses with complex CHD. There was variability in the response to maternal hyperoxia by CHD subgroup that can be tested in future larger studies. Cardiovascular physiology is critical when designing neuroprotective clinical trials in the fetus with CHD.
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Affiliation(s)
- Shabnam Peyvandi
- Department of Pediatrics Division of Cardiology University of California San Francisco San Francisco CA.,Department of Epidemiology and Biostatistics University of California San Francisco San Francisco CA
| | - Duan Xu
- Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco CA
| | - Yan Wang
- Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco CA
| | - Whitnee Hogan
- Department of Pediatrics Division of Cardiology University of California San Francisco San Francisco CA
| | - Anita Moon-Grady
- Department of Pediatrics Division of Cardiology University of California San Francisco San Francisco CA
| | - A James Barkovich
- Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco CA
| | - Orit Glenn
- Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco CA
| | - Patrick McQuillen
- Department of Pediatrics, Division of Critical Care University of California San Francisco San Francisco CA
| | - Jing Liu
- Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco CA
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Murtha N, Mason A, Bowen C, Clarke S, Rioux J, Beyea S. Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study. Tomography 2020; 6:362-372. [PMID: 33364426 PMCID: PMC7744192 DOI: 10.18383/j.tom.2020.00045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enhanced (DCE) MRI data acquired using golden-angle sampling and compressed sensing (CS). To address the difficulty of obtaining ground-truth knowledge of parameters describing dynamics in real patient data, we developed a Matlab simulation framework to assess quantitative CS-DCE-MRI. We began by validating the response of each IQM to the CS-MRI reconstruction process using static data and the performance of our simulation framework with simple dynamic data. We then extended the simulations to the more realistic extended Tofts model. When assessing the Tofts model, we tested 4 different methods of selecting a reference image for the IQMs. Results from the retrospective static CS-MRI reconstructions showed that each IQM is responsive to the CS-MRI reconstruction process. Simulations of a simple contrast evolution model validated the performance of our framework. Despite the complexity of the Tofts model, both IQM scores correlated well with the recovery accuracy of a central model parameter for all reference cases studied. This finding may form the basis of algorithms for automated selection of image reconstruction aspects, such as temporal resolution, in golden-angle-sampled CS-DCE-MRI. These further suggest that objective measures of image quality may find use in general dynamic MRI applications.
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Affiliation(s)
- Nathan Murtha
- Department of Physics, Carleton University, Ottawa, ON, Canada
| | - Allister Mason
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada
| | - Chris Bowen
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada; and
- BIOmedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada
| | - Sharon Clarke
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada; and
- BIOmedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada
| | - James Rioux
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada; and
- BIOmedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada
| | - Steven Beyea
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada; and
- BIOmedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada
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Kato Y, Ambale-Venkatesh B, Kassai Y, Kasuboski L, Schuijf J, Kapoor K, Caruthers S, Lima JAC. Non-contrast coronary magnetic resonance angiography: current frontiers and future horizons. MAGMA (NEW YORK, N.Y.) 2020; 33:591-612. [PMID: 32242282 PMCID: PMC7502041 DOI: 10.1007/s10334-020-00834-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 01/22/2020] [Accepted: 01/29/2020] [Indexed: 02/07/2023]
Abstract
Coronary magnetic resonance angiography (coronary MRA) is advantageous in its ability to assess coronary artery morphology and function without ionizing radiation or contrast media. However, technical limitations including reduced spatial resolution, long acquisition times, and low signal-to-noise ratios prevent it from clinical routine utilization. Nonetheless, each of these limitations can be specifically addressed by a combination of novel technologies including super-resolution imaging, compressed sensing, and deep-learning reconstruction. In this paper, we first review the current clinical use and motivations for non-contrast coronary MRA, discuss currently available coronary MRA techniques, and highlight current technical developments that hold unique potential to optimize coronary MRA image acquisition and post-processing. In the final section, we examine the various research-based coronary MRA methods and metrics that can be leveraged to assess coronary stenosis severity, physiological function, and atherosclerotic plaque characterization. We specifically discuss how such technologies may contribute to the clinical translation of coronary MRA into a robust modality for routine clinical use.
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Affiliation(s)
- Yoko Kato
- Division of Cardiology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Blalock 524, Baltimore, MD, 21287-0409, USA
| | | | | | | | | | - Karan Kapoor
- Division of Cardiology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Blalock 524, Baltimore, MD, 21287-0409, USA
| | | | - Joao A C Lima
- Division of Cardiology, Johns Hopkins University School of Medicine, 600 N Wolfe St, Blalock 524, Baltimore, MD, 21287-0409, USA.
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8
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Malavé MO, Baron CA, Koundinyan SP, Sandino CM, Ong F, Cheng JY, Nishimura DG. Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model. Magn Reson Med 2020; 84:800-812. [PMID: 32011021 DOI: 10.1002/mrm.28177] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 12/04/2019] [Accepted: 12/27/2019] [Indexed: 12/28/2022]
Abstract
PURPOSE To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA). METHODS An end-to-end unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data-consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with l 1 -ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the l 1 -ESPIRiT and DL model-based 3D iNAVs are assessed for differences. RESULTS 3D iNAVs reconstructed using the DL model-based approach and conventional l 1 -ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of l 1 -ESPIRiT (20× and 3× speed increases, respectively). CONCLUSIONS We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of nonrigid motion information offered by them for correction.
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Affiliation(s)
- Mario O Malavé
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Corey A Baron
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Srivathsan P Koundinyan
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Christopher M Sandino
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Frank Ong
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Joseph Y Cheng
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.,Department of Radiology, Stanford University, Stanford, CA
| | - Dwight G Nishimura
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
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9
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Wu Y, Ma Y, Capaldi DP, Liu J, Zhao W, Du J, Xing L. Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI. Magn Reson Imaging 2020; 66:93-103. [PMID: 30880112 PMCID: PMC6745016 DOI: 10.1016/j.mri.2019.03.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 03/01/2019] [Accepted: 03/13/2019] [Indexed: 11/28/2022]
Abstract
For sparse sampling that accelerates magnetic resonance (MR) image acquisition, non-linear reconstruction algorithms have been developed, which incorporated patient specific a prior information. More generic a prior information could be acquired via deep learning and utilized for image reconstruction. In this study, we developed a volumetric hierarchical deep residual convolutional neural network, referred to as T-Net, to provide a data-driven end-to-end mapping from sparsely sampled MR images to fully sampled MR images, where cartilage MR images were acquired using an Ultra-short TE sequence and retrospectively undersampled using pseudo-random Cartesian and radial acquisition schemes. The network had a hierarchical architecture that promoted the sparsity of feature maps and increased the receptive field, which were valuable for signal synthesis and artifact suppression. Relatively dense local connections and global shortcuts were established to facilitate residual learning and compensate for details lost in hierarchical processing. Additionally, volumetric processing was adopted to fully exploit spatial continuity in three-dimensional space. Data consistency was further enforced. The network was trained with 336 three-dimensional images (each consisting of 32 slices) and tested by 24 images. The incorporation of a priori information acquired via deep learning facilitated high acceleration factors (as high as 8) while maintaining high image fidelity (quantitatively evaluated using the structural similarity index measurement). The proposed T-Net had an improved performance as compared to several state-of-the-art networks.
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Affiliation(s)
- Yan Wu
- Radiation Oncology Department, Stanford University, Stanford 94305, CA, USA.
| | - Yajun Ma
- Radiology Department, University of California San Diego, La Jolla 92093, CA, USA.
| | | | - Jing Liu
- Radiology Department, University of California San Francisco, San Francisco 94107, CA, USA.
| | - Wei Zhao
- Radiation Oncology Department, Stanford University, Stanford 94305, CA, USA.
| | - Jiang Du
- Radiology Department, University of California San Diego, La Jolla 92093, CA, USA.
| | - Lei Xing
- Radiation Oncology Department, Stanford University, Stanford 94305, CA, USA.
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10
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Peper ES, Gottwald LM, Zhang Q, Coolen BF, van Ooij P, Nederveen AJ, Strijkers GJ. Highly accelerated 4D flow cardiovascular magnetic resonance using a pseudo-spiral Cartesian acquisition and compressed sensing reconstruction for carotid flow and wall shear stress. J Cardiovasc Magn Reson 2020; 22:7. [PMID: 31959203 PMCID: PMC6971939 DOI: 10.1186/s12968-019-0582-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 10/18/2019] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND 4D flow cardiovascular magnetic resonance (CMR) enables visualization of complex blood flow and quantification of biomarkers for vessel wall disease, such as wall shear stress (WSS). Because of the inherently long acquisition times, many efforts have been made to accelerate 4D flow acquisitions, however, no detailed analysis has been made on the effect of Cartesian compressed sensing accelerated 4D flow CMR at different undersampling rates on quantitative flow parameters and WSS. METHODS We implemented a retrospectively triggered 4D flow CMR acquisition with pseudo-spiral Cartesian k-space filling, which results in incoherent undersampling of k-t space. Additionally, this strategy leads to small jumps in k-space thereby minimizing eddy current related artifacts. The pseudo-spirals were rotated in a tiny golden-angle fashion, which provides optimal incoherence and a variable density sampling pattern with a fully sampled center. We evaluated this 4D flow protocol in a carotid flow phantom with accelerations of R = 2-20, as well as in carotids of 7 healthy subjects (27 ± 2 years, 4 male) for R = 10-30. Fully sampled 2D flow CMR served as a flow reference. Arteries were manually segmented and registered to enable voxel-wise comparisons of both velocity and WSS using a Bland-Altman analysis. RESULTS Magnitude images, velocity images, and pathline reconstructions from phantom and in vivo scans were similar for all accelerations. For the phantom data, mean differences at peak systole for the entire vessel volume in comparison to R = 2 ranged from - 2.3 to - 5.3% (WSS) and - 2.4 to - 2.2% (velocity) for acceleration factors R = 4-20. For the in vivo data, mean differences for the entire vessel volume at peak systole in comparison to R = 10 were - 9.9, - 13.4, and - 16.9% (WSS) and - 8.4, - 10.8, and - 14.0% (velocity), for R = 20, 25, and 30, respectively. Compared to single slice 2D flow CMR acquisitions, peak systolic flow rates of the phantom showed no differences, whereas peak systolic flow rates in the carotid artery in vivo became increasingly underestimated with increasing acceleration. CONCLUSION Acquisition of 4D flow CMR of the carotid arteries can be highly accelerated by pseudo-spiral k-space sampling and compressed sensing reconstruction, with consistent data quality facilitating velocity pathline reconstructions, as well as quantitative flow rate and WSS estimations. At an acceleration factor of R = 20 the underestimation of peak velocity and peak WSS was acceptable (< 10%) in comparison to an R = 10 accelerated 4D flow CMR reference scan. Peak flow rates were underestimated in comparison with 2D flow CMR and decreased systematically with higher acceleration factors.
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Affiliation(s)
- Eva S Peper
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Lukas M Gottwald
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Qinwei Zhang
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Bram F Coolen
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Pim van Ooij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Aart J Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Gustav J Strijkers
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
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11
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Cao P, liu J, Tang S, Leynes AP, Lupo JM, Xu D, Larson PEZ. Technical Note: Simultaneous segmentation and relaxometry for MRI through multitask learning. Med Phys 2019; 46:4610-4621. [PMID: 31396973 PMCID: PMC6800607 DOI: 10.1002/mp.13756] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 07/26/2019] [Accepted: 07/30/2019] [Indexed: 11/09/2022] Open
Abstract
PURPOSE This study demonstrated a magnetic resonance (MR) signal multitask learning method for three-dimensional (3D) simultaneous segmentation and relaxometry of human brain tissues. MATERIALS AND METHODS A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multicontrast brain images. The deep neural network contained three residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online-synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter, and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on three healthy volunteers. The mean and standard deviation for the T1 and T2 values in vivo were reported and compared to literature values. Additional animal (N = 6) and prostate patient (N = 1) experiments were performed to compare the estimated T1 and T2 values with those from gold standard methods and to demonstrate clinical applications of the proposed method. RESULTS In animal validation experiment, the differences/errors (mean difference ± standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 ± 486 and 154 ± 512 ms for T1, and 5 ± 33 and 7 ± 41 ms for T2, respectively. In healthy volunteer experiments (N = 3), whole brain segmentation and relaxometry were finished within ~ 5 s. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1- and T2-weighted images. CONCLUSION The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1- and T2-weighted images, in conjunction with segmentation of gray matter, white matter, and CSF.
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Affiliation(s)
- Peng Cao
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Jing liu
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Shuyu Tang
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Andrew P. Leynes
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Janine M. Lupo
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Peder E. Z. Larson
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158, USA
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12
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Liu J, Wang Y, Wen Z, Feng L, Lima APS, Mahadevan VS, Bolger A, Saloner D, Ordovas K. Extending Cardiac Functional Assessment with Respiratory-Resolved 3D Cine MRI. Sci Rep 2019; 9:11563. [PMID: 31399608 PMCID: PMC6689015 DOI: 10.1038/s41598-019-47869-z] [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: 03/11/2018] [Accepted: 07/25/2019] [Indexed: 01/23/2023] Open
Abstract
This study aimed to develop a cardiorespiratory-resolved 3D magnetic resonance imaging (5D MRI: x-y-z-cardiac-respiratory) approach based on 3D motion tracking for investigating the influence of respiration on cardiac ventricular function. A highly-accelerated 2.5-minute sparse MR protocol was developed for a continuous acquisition of cardiac images through multiple cardiac and respiratory cycles. The heart displacement along respiration was extracted using a 3D image deformation algorithm, and this information was used to cluster the acquired data into multiple respiratory phases. The proposed approach was tested in 15 healthy volunteers (7 females). Cardiac function parameters, including the end-systolic volume (ESV), end-diastolic volume (EDV), stroke volume (SV), and ejection fraction (EF), were measured for the left and right ventricle in both end-expiration and end-inspiration. Although with the proposed 5D cardiac MRI, there were no significant differences (p > 0.05, t-test) between end-expiration and end-inspiration measurements of the cardiac function in volunteers, incremental respiratory motion parameters that were derived from 3D motion tracking, such as the depth, expiration and inspiration distribution, correlated (p < 0.05, correlation coefficient, Mann-Whitney) with those volume-based parameters of cardiac function and varied between genders. The obtained initial results suggested that this new approach allows evaluation of cardiac function during specific respiratory phases. Thus, it can enable investigation of effects related to respiratory variability and better assessment of cardiac function for studying respiratory and/or cardiac dysfunction.
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Affiliation(s)
- Jing Liu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States.
| | - Yan Wang
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
| | - Zhaoying Wen
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States.
- Department of Radiology, Anzhen Hospital, Capital Medical University, Beijing, China.
| | - Li Feng
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Ana Paula Santos Lima
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
| | - Vaikom S Mahadevan
- Department of Cardiology, University of California San Francisco, San Francisco, California, United States
| | - Ann Bolger
- Department of Cardiology, University of California San Francisco, San Francisco, California, United States
| | - David Saloner
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
- Radiology Service, VA Medical Center, San Francisco, California, United States
| | - Karen Ordovas
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
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13
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Wu PH, Gibbons M, Foreman SC, Carballido-Gamio J, Han M, Krug R, Liu J, Link TM, Kazakia GJ. Cortical bone vessel identification and quantification on contrast-enhanced MR images. Quant Imaging Med Surg 2019; 9:928-941. [PMID: 31367547 DOI: 10.21037/qims.2019.05.23] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Cortical bone porosity is a major determinant of bone strength. Despite the biomechanical importance of cortical bone porosity, the biological drivers of cortical porosity are unknown. The content of cortical pore space can indicate pore expansion mechanisms; both of the primary components of pore space, vessels and adipocytes, have been implicated in pore expansion. Dynamic contrast-enhanced MRI (DCE-MRI) is widely used in vessel detection in cardiovascular studies, but has not been applied to visualize vessels within cortical bone. In this study, we have developed a multimodal DCE-MRI and high resolution peripheral QCT (HR-pQCT) acquisition and image processing pipeline to detect vessel-filled cortical bone pores. Methods For this in vivo human study, 19 volunteers (10 males and 9 females; mean age =63±5) were recruited. Both distal and ultra-distal regions of the non-dominant tibia were imaged by HR-pQCT (82 µm nominal resolution) for bone structure segmentation and by 3T DCE-MRI (Gadavist; 9 min scan time; temporal resolution =30 sec; voxel size 230×230×500 µm3) for vessel visualization. The DCE-MRI was registered to the HR-pQCT volume and the voxels within the MRI cortical bone region were extracted. Features of the DCE data were calculated and voxels were categorized by a 2-stage hierarchical kmeans clustering algorithm to determine which voxels represent vessels. Vessel volume fraction (volume ratio of vessels to cortical bone), vessel density (average vessel count per cortical bone volume), and average vessel volume (mean volume of vessels) were calculated to quantify the status of vessel-filled pores in cortical bone. To examine spatial resolution and perform validation, a virtual phantom with 5 channel sizes and an applied pseudo enhancement curve was processed through the proposed image processing pipeline. Overlap volume ratio and Dice coefficient was calculated to measure the similarity between the detected vessel map and ground truth. Results In the human study, mean vessel volume fraction was 2.2%±1.0%, mean vessel density was 0.68±0.27 vessel/mm3, and mean average vessel volume was 0.032±0.012 mm3/vessel. Signal intensity for detected vessel voxels increased during the scan, while signal for non-vessel voxels within pores did not enhance. In the validation phantom, channels with diameter 250 µm or greater were detected successfully, with volume ratio equal to 1 and Dice coefficient above 0.6. Both statistics decreased dramatically for channel sizes less than 250 µm. Conclusions We have a developed a multi-modal image acquisition and processing pipeline that successfully detects vessels within cortical bone pores. The performance of this technique degrades for vessel diameters below the in-plane spatial resolution of the DCE-MRI acquisition. This approach can be applied to investigate the biological systems associated with cortical pore expansion.
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Affiliation(s)
- Po-Hung Wu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Matthew Gibbons
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Sarah C Foreman
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | | | - Misung Han
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Roland Krug
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Jing Liu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Galateia J Kazakia
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
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14
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Wu Y, Ma Y, Liu J, Du J, Xing L. Self-Attention Convolutional Neural Network for Improved MR Image Reconstruction. Inf Sci (N Y) 2019; 490:317-328. [PMID: 32817993 PMCID: PMC7430761 DOI: 10.1016/j.ins.2019.03.080] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
MRI is an advanced imaging modality with the unfortunate disadvantage of long data acquisition time. To accelerate MR image acquisition while maintaining high image quality, extensive investigations have been conducted on image reconstruction of sparsely sampled MRI. Recently, deep convolutional neural networks have achieved promising results, yet the local receptive field in convolution neural network raises concerns regarding signal synthesis and artifact compensation. In this study, we proposed a deep learning-based reconstruction framework to provide improved image fidelity for accelerated MRI. We integrated the self-attention mechanism, which captured long-range dependencies across image regions, into a volumetric hierarchical deep residual convolutional neural network. Basically, a self-attention module was integrated to every convolutional layer, where signal at a position was calculated as a weighted sum of the features at all positions. Furthermore, relatively dense shortcut connections were employed, and data consistency was enforced. The proposed network, referred to as SAT-Net, was applied on cartilage MRI acquired using an ultrashort TE sequence and retrospectively undersampled in a pseudo-random Cartesian pattern. The network was trained using 336 three dimensional images (each containing 32 slices) and tested with 24 images that yielded improved outcome. The framework is generic and can be extended to various applications.
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Affiliation(s)
- Yan Wu
- Radiation Oncology Department, Stanford University. 875 Blake Wilbur Drive G204, Stanford, California 94305
| | - Yajun Ma
- Radiology Department, University of California San Diego. 9500 Gilman Driven #0888, La Jolla, California 92093
| | - Jing Liu
- Radiology Department, University of San Francisco. 185 Berry St, San Francisco, California 94107
| | - Jiang Du
- Radiology Department, University of California San Diego. 9500 Gilman Driven #0888, La Jolla, California 92093
| | - Lei Xing
- Radiation Oncology Department, Stanford University. 875 Blake Wilbur Drive G204, Stanford, California 94305
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15
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Gullberg GT, Veress AI, Shrestha UM, Liu J, Ordovas K, Segars WP, Seo Y. Multiresolution spatiotemporal mechanical model of the heart as a prior to constrain the solution for 4D models of the heart. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 11072. [PMID: 31413426 DOI: 10.1117/12.2534906] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
In several nuclear cardiac imaging applications (SPECT and PET), images are formed by reconstructing tomographic data using an iterative reconstruction algorithm with corrections for physical factors involved in the imaging detection process and with corrections for cardiac and respiratory motion. The physical factors are modeled as coefficients in the matrix of a system of linear equations and include attenuation, scatter, and spatially varying geometric response. The solution to the tomographic problem involves solving the inverse of this system matrix. This requires the design of an iterative reconstruction algorithm with a statistical model that best fits the data acquisition. The most appropriate model is based on a Poisson distribution. Using Bayes Theorem, an iterative reconstruction algorithm is designed to determine the maximum a posteriori estimate of the reconstructed image with constraints that maximizes the Bayesian likelihood function for the Poisson statistical model. The a priori distribution is formulated as the joint entropy (JE) to measure the similarity between the gated cardiac PET image and the cardiac MRI cine image modeled as a FE mechanical model. The developed algorithm shows the potential of using a FE mechanical model of the heart derived from a cardiac MRI cine scan to constrain solutions of gated cardiac PET images.
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Affiliation(s)
- Grant T Gullberg
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Alexander I Veress
- Department of Mechanical Engineering, University of Washington, Seattle WA, USA.,Numeric Design Engineering, LLC. Seattle, WA, USA
| | - Uttam M Shrestha
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Jing Liu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Karen Ordovas
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - W Paul Segars
- Department of Radiology, Duke University, Raleigh-Durham, North Carolina, USA
| | - Youngho Seo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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16
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Stemkens B, Paulson ES, Tijssen RHN. Nuts and bolts of 4D-MRI for radiotherapy. ACTA ACUST UNITED AC 2018; 63:21TR01. [DOI: 10.1088/1361-6560/aae56d] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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17
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Yang J, Liu J, Wiesinger F, Menini A, Zhu X, Hope TA, Seo Y, Larson PEZ. Developing an efficient phase-matched attenuation correction method for quiescent period PET in abdominal PET/MRI. Phys Med Biol 2018; 63:185002. [PMID: 30106008 DOI: 10.1088/1361-6560/aada26] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Respiratory motion causes misalignments between positron emission tomography (PET) and magnetic resonance (MR)-derived attenuation maps (µ-maps) in addition to artifacts on both PET and MR images in simultaneous PET/MRI for organs such as liver that can experience motion of several centimeters. To address this problem, we developed an efficient MR-based attenuation correction (MRAC) method to generate phase-matched µ-maps for quiescent period PET (PETQ) in abdominal PET/MRI. MRAC data was acquired with CIRcular Cartesian UnderSampling (CIRCUS) sampling during 100 s in free-breathing as an accelerated data acquisition strategy for phase-matched MRAC (MRACPM-CIRCUS). For comparison, MRAC data with raster (Default) k-space sampling was also acquired during 100 s in free-breathing (MRACPM-Default), and used to evaluate MRACPM-CIRCUS as well as un-matched MRAC (MRACUM) that was un-gated. We purposefully oversampled the MRACPM data to ensure we had enough information to capture all respiratory phases to make this comparison as robust as possible. The proposed MRACPM-CIRCUS was evaluated in 17 patients with 68Ga-DOTA-TOC PET/MRI exams, suspected of having neuroendocrine tumors or liver metastases. Effects of CIRCUS sampling for accelerating a data acquisition were evaluated by simulating the data acquisition time retrospectively in increments of 5 s. Effects of MRACPM-CIRCUS on PETQ were evaluated using uptake differences in the liver lesions (n = 35), compared to PETQ with MRACPM-Default and MRACUM. A Wilcoxon signed-rank test was performed to compare lesion uptakes between the MRAC methods. MRACPM-CIRCUS showed higher image quality compared to MRACPM-Default for the same acquisition times, demonstrating that a data acquisition time of 30 s was reasonable to achieve phase-matched µ-maps. Lesion update differences between MRACPM-CIRCUS (30 s) versus MRACPM-Default (reference, 100 s) were 0.1% ± 1.4% (range of -2.7% to 3.2%) and not significant (P > .05); while, the differences between MRACUM versus MRACPM-Default were 0.6% ± 11.4% with a large variation (range of -37% to 20%) and significant (P < .05). In conclusion, we demonstrated that a data acquisition of 30 s achieved phase-matched µ-maps when using specialized CIRCUS data sampling and phase-matched µ-maps improved PETQ quantification significantly.
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Affiliation(s)
- Jaewon Yang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America. UCSF Physics Research Laboratory, 185 Berry Street, Suite 350, San Francisco, CA 94143-0946, United States of America. Author to whom any correspondence should be addressed
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18
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Liu J, Koskas L, Faraji F, Kao E, Wang Y, Haraldsson H, Kefayati S, Zhu C, Ahn S, Laub G, Saloner D. Highly accelerated intracranial 4D flow MRI: evaluation of healthy volunteers and patients with intracranial aneurysms. MAGMA (NEW YORK, N.Y.) 2018; 31:295-307. [PMID: 28785850 PMCID: PMC5803461 DOI: 10.1007/s10334-017-0646-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 07/27/2017] [Accepted: 07/28/2017] [Indexed: 02/01/2023]
Abstract
OBJECTIVES To evaluate an accelerated 4D flow MRI method that provides high temporal resolution in a clinically feasible acquisition time for intracranial velocity imaging. MATERIALS AND METHODS Accelerated 4D flow MRI was developed by using a pseudo-random variable-density Cartesian undersampling strategy (CIRCUS) with the combination of k-t, parallel imaging and compressed sensing image reconstruction techniques (k-t SPARSE-SENSE). Four-dimensional flow data were acquired on five healthy volunteers and eight patients with intracranial aneurysms using CIRCUS (acceleration factor of R = 4, termed CIRCUS4) and GRAPPA (R = 2, termed GRAPPA2) as the reference method. Images with three times higher temporal resolution (R = 12, CIRCUS12) were also reconstructed from the same acquisition as CIRCUS4. Qualitative and quantitative image assessment was performed on the images acquired with different methods, and complex flow patterns in the aneurysms were identified and compared. RESULTS Four-dimensional flow MRI with CIRCUS was achieved in 5 min and allowed further improved temporal resolution of <30 ms. Volunteer studies showed similar qualitative and quantitative evaluation obtained with the proposed approach compared to the reference (overall image scores: GRAPPA2 3.2 ± 0.6; CIRCUS4 3.1 ± 0.7; CIRCUS12 3.3 ± 0.4; difference of the peak velocities: -3.83 ± 7.72 cm/s between CIRCUS4 and GRAPPA2, -1.72 ± 8.41 cm/s between CIRCUS12 and GRAPPA2). In patients with intracranial aneurysms, the higher temporal resolution improved capturing of the flow features in intracranial aneurysms (pathline visualization scores: GRAPPA2 2.2 ± 0.2; CIRCUS4 2.5 ± 0.5; CIRCUS12 2.7 ± 0.6). CONCLUSION The proposed rapid 4D flow MRI with a high temporal resolution is a promising tool for evaluating intracranial aneurysms in a clinically feasible acquisition time.
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Affiliation(s)
- Jing Liu
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA.
| | - Louise Koskas
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Farshid Faraji
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Evan Kao
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Yan Wang
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Henrik Haraldsson
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Sarah Kefayati
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Chengcheng Zhu
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | | | | | - David Saloner
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
- Radiology Service, VA Medical Center, San Francisco, CA, USA
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19
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Feng L, Coppo S, Piccini D, Yerly J, Lim RP, Masci PG, Stuber M, Sodickson DK, Otazo R. 5D whole-heart sparse MRI. Magn Reson Med 2017; 79:826-838. [PMID: 28497486 DOI: 10.1002/mrm.26745] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Revised: 04/09/2017] [Accepted: 04/11/2017] [Indexed: 01/18/2023]
Abstract
PURPOSE A 5D whole-heart sparse imaging framework is proposed for simultaneous assessment of myocardial function and high-resolution cardiac and respiratory motion-resolved whole-heart anatomy in a single continuous noncontrast MR scan. METHODS A non-electrocardiograph (ECG)-triggered 3D golden-angle radial balanced steady-state free precession sequence was used for data acquisition. The acquired 3D k-space data were sorted into a 5D dataset containing separated cardiac and respiratory dimensions using a self-extracted respiratory motion signal and a recorded ECG signal. Images were then reconstructed using XD-GRASP, a multidimensional compressed sensing technique exploiting correlations/sparsity along cardiac and respiratory dimensions. 5D whole-heart imaging was compared with respiratory motion-corrected 3D and 4D whole-heart imaging in nine volunteers for evaluation of the myocardium, great vessels, and coronary arteries. It was also compared with breath-held, ECG-gated 2D cardiac cine imaging for validation of cardiac function quantification. RESULTS 5D whole-heart images received systematic higher quality scores in the myocardium, great vessels and coronary arteries. Quantitative coronary sharpness and length were always better for the 5D images. Good agreement was obtained for quantification of cardiac function compared with 2D cine imaging. CONCLUSION 5D whole-heart sparse imaging represents a robust and promising framework for simplified comprehensive cardiac MRI without the need for breath-hold and motion correction. Magn Reson Med 79:826-838, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Simone Coppo
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Davide Piccini
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland
| | - Jerome Yerly
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Ruth P Lim
- Department of Radiology, Austin Health and The University of Melbourne, Melbourne, Victoria, Australia
| | - Pier Giorgio Masci
- Division of Cardiology and Cardiac MR Center, University Hospital (CHUV), Lausanne, Switzerland
| | - Matthias Stuber
- Department of Radiology, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Liu J, Feng L, Shen HW, Zhu C, Wang Y, Mukai K, Brooks GC, Ordovas K, Saloner D. Highly-accelerated self-gated free-breathing 3D cardiac cine MRI: validation in assessment of left ventricular function. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2017; 30:337-346. [PMID: 28120280 DOI: 10.1007/s10334-017-0607-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Revised: 12/21/2016] [Accepted: 01/03/2017] [Indexed: 11/28/2022]
Abstract
OBJECTIVE This work presents a highly-accelerated, self-gated, free-breathing 3D cardiac cine MRI method for cardiac function assessment. MATERIALS AND METHODS A golden-ratio profile based variable-density, pseudo-random, Cartesian undersampling scheme was implemented for continuous 3D data acquisition. Respiratory self-gating was achieved by deriving motion signal from the acquired MRI data. A multi-coil compressed sensing technique was employed to reconstruct 4D images (3D+time). 3D cardiac cine imaging with self-gating was compared to bellows gating and the clinical standard breath-held 2D cine imaging for evaluation of self-gating accuracy, image quality, and cardiac function in eight volunteers. Reproducibility of 3D imaging was assessed. RESULTS Self-gated 3D imaging provided an image quality score of 3.4 ± 0.7 vs 4.0 ± 0 with the 2D method (p = 0.06). It determined left ventricular end-systolic volume as 42.4 ± 11.5 mL, end-diastolic volume as 111.1 ± 24.7 mL, and ejection fraction as 62.0 ± 3.1%, which were comparable to the 2D method, with bias ± 1.96 × SD of -0.8 ± 7.5 mL (p = 0.90), 2.6 ± 3.3 mL (p = 0.84) and 1.4 ± 6.4% (p = 0.45), respectively. CONCLUSION The proposed 3D cardiac cine imaging method enables reliable respiratory self-gating performance with good reproducibility, and provides comparable image quality and functional measurements to 2D imaging, suggesting that self-gated, free-breathing 3D cardiac cine MRI framework is promising for improved patient comfort and cardiac MRI scan efficiency.
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Affiliation(s)
- Jing Liu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA.
| | - Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Hsin-Wei Shen
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Chengcheng Zhu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Yan Wang
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Kanae Mukai
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Gabriel C Brooks
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Karen Ordovas
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - David Saloner
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA.,Radiology Service, VA Medical Center, San Francisco, CA, USA
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21
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Usman M, Ruijsink B, Nazir MS, Cruz G, Prieto C. Free breathing whole-heart 3D CINE MRI with self-gated Cartesian trajectory. Magn Reson Imaging 2016; 38:129-137. [PMID: 28034638 PMCID: PMC5375620 DOI: 10.1016/j.mri.2016.12.021] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 12/22/2016] [Accepted: 12/22/2016] [Indexed: 01/22/2023]
Abstract
Purpose To present a method that uses a novel free-running self-gated acquisition to achieve isotropic resolution in whole heart 3D Cartesian cardiac CINE MRI. Material and methods 3D cardiac CINE MRI using navigator gating results in long acquisition times. Recently, several frameworks based on self-gated non-Cartesian trajectories have been proposed to accelerate this acquisition. However, non-Cartesian reconstructions are computationally expensive due to gridding, particularly in 3D. In this work, we propose a novel highly efficient self-gated Cartesian approach for 3D cardiac CINE MRI. Acquisition is performed using CArtesian trajectory with Spiral PRofile ordering and Tiny golden angle step for eddy current reduction (so called here CASPR-Tiger). Data is acquired continuously under free breathing (retrospective ECG gating, no preparation pulses interruption) for 4–5 min and 4D whole-heart volumes (3D + cardiac phases) with isotropic spatial resolution are reconstructed from all available data using a soft gating technique combined with temporal total variation (TV) constrained iterative SENSE reconstruction. Results For data acquired on eight healthy subjects and three patients, the reconstructed images using the proposed method had good contrast and spatio-temporal variations, correctly recovering diastolic and systolic cardiac phases. Non-significant differences (P > 0.05) were observed in cardiac functional measurements obtained with proposed 3D approach and gold standard 2D multi-slice breath-hold acquisition. Conclusion The proposed approach enables isotropic 3D whole heart Cartesian cardiac CINE MRI in 4 to 5 min free breathing acquisition. A novel self-gated 3D Cartesian acquisition is proposed for free breathing whole-heart cardiac MRI The proposed framework has efficient k-space sampling, better eddy current performance and high computational efficiency The Proposed method is able to achieve high spatio-temporal resolution 3D cardiac CINE The proposed method only requires four to five minute free breathing scan
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Affiliation(s)
- M Usman
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom; Department of Computer Science, University College London, London, UK.
| | - B Ruijsink
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom
| | - M S Nazir
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom
| | - G Cruz
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom
| | - C Prieto
- King's College London, Division of Imaging Sciences and Biomedical Engineering, London, United Kingdom; Pontificia Universidad Católica de Chile, Escuela de Ingeniería, Santiago, Chile
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22
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Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H. Compressed sensing for body MRI. J Magn Reson Imaging 2016; 45:966-987. [PMID: 27981664 DOI: 10.1002/jmri.25547] [Citation(s) in RCA: 190] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 10/25/2016] [Indexed: 12/18/2022] Open
Abstract
The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities. LEVEL OF EVIDENCE 5 J. Magn. Reson. Imaging 2017;45:966-987.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Thomas Benkert
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Yang ACY, Kretzler M, Sudarski S, Gulani V, Seiberlich N. Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 2016; 51:349-64. [PMID: 27003227 PMCID: PMC4948115 DOI: 10.1097/rli.0000000000000274] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
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Affiliation(s)
- Alice Chieh-Yu Yang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
| | - Madison Kretzler
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, USA
| | - Sonja Sudarski
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim - Heidelberg University, Heidelberg, Germany
| | - Vikas Gulani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
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Liu J, Pedoia V, Heilmeier U, Ku E, Su F, Khanna S, Imboden J, Graf J, Link T, Li X. High-temporospatial-resolution dynamic contrast-enhanced (DCE) wrist MRI with variable-density pseudo-random circular Cartesian undersampling (CIRCUS) acquisition: evaluation of perfusion in rheumatoid arthritis patients. NMR IN BIOMEDICINE 2016; 29:15-23. [PMID: 26608949 PMCID: PMC4724417 DOI: 10.1002/nbm.3443] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 09/04/2015] [Accepted: 10/10/2015] [Indexed: 06/05/2023]
Abstract
This study is to evaluate highly accelerated three-dimensional (3D) dynamic contrast-enhanced (DCE) wrist MRI for assessment of perfusion in rheumatoid arthritis (RA) patients. A pseudo-random variable-density undersampling strategy, circular Cartesian undersampling (CIRCUS), was combined with k-t SPARSE-SENSE reconstruction to achieve a highly accelerated 3D DCE wrist MRI. Two healthy volunteers and 10 RA patients were studied. Two patients were on methotrexate (MTX) only (Group I) and the other eight were treated with a combination therapy of MTX and anti-tumor necrosis factor (TNF) therapy (Group II). Patients were scanned at baseline and 3 month follow-up. DCE MR images were used to evaluate perfusion in synovitis and bone marrow edema pattern in the RA wrist joints. A series of perfusion parameters was derived and compared with clinical disease activity scores of 28 joints (DAS28). 3D DCE wrist MR images were obtained with a spatial resolution of 0.3 × 0.3 × 1.5 mm(3) and temporal resolution of 5 s (with an acceleration factor of 20). The derived perfusion parameters, most notably transition time (dT) of synovitis, showed significant negative correlations with DAS28-ESR (r = -0.80, p < 0.05) and DAS28-CRP (r = -0.87, p < 0.05) at baseline and also correlated significantly with treatment responses evaluated by clinical score changes between baseline and 3 month follow-up (with DAS28-ESR r = -0.79, p < 0.05, and DAS28-CRP r = -0.82, p < 0.05). Highly accelerated 3D DCE wrist MRI with improved temporospatial resolution has been achieved in RA patients and provides accurate assessment of neovascularization and perfusion in RA joints, showing promise as a potential tool for evaluating treatment responses.
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Affiliation(s)
- Jing Liu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
| | - Ursula Heilmeier
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
| | - Eric Ku
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
| | - Favian Su
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
| | - Sameer Khanna
- University of California Berkeley, Berkeley, California, United States
| | - John Imboden
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Jonathan Graf
- Department of Medicine, University of California San Francisco, San Francisco, California, United States
| | - Thomas Link
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
| | - Xiaojuan Li
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States
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Zhang X, Ji JX. Parallel and sparse MR imaging: methods and instruments-Part 1. Quant Imaging Med Surg 2014; 4:1-3. [PMID: 24649428 DOI: 10.3978/j.issn.2223-4292.2014.03.01] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Accepted: 02/28/2014] [Indexed: 11/14/2022]
Affiliation(s)
- Xiaoliang Zhang
- 1 Department of Radiology and Biomedical Imaging, University of California San Francisco; UC Berkeley/UCSF Joint Bioengineering Program; California Institute for Quantitative Biosciences (QB3), San Francisco, CA 94158, USA ; 2 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Jim X Ji
- 1 Department of Radiology and Biomedical Imaging, University of California San Francisco; UC Berkeley/UCSF Joint Bioengineering Program; California Institute for Quantitative Biosciences (QB3), San Francisco, CA 94158, USA ; 2 Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
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