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Vornehm M, Wetzl J, Giese D, Fürnrohr F, Pang J, Chow K, Gebker R, Ahmad R, Knoll F. CineVN: Variational network reconstruction for rapid functional cardiac cine MRI. Magn Reson Med 2024. [PMID: 39188085 DOI: 10.1002/mrm.30260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/09/2024] [Accepted: 08/01/2024] [Indexed: 08/28/2024]
Abstract
PURPOSE To develop a reconstruction method for highly accelerated cardiac cine MRI with high spatiotemporal resolution and low temporal blurring, and to demonstrate accurate estimation of ventricular volumes and myocardial strain in healthy subjects and in patients. METHODS The proposed method, called CineVN, employs a spatiotemporal Variational Network combined with conjugate gradient descent for optimized data consistency and improved image quality. The method is first evaluated on retrospectively undersampled cine MRI data in terms of image quality. Then, prospectively accelerated data are acquired in 18 healthy subjects both segmented over two heartbeats per slice as well as in real time with 1.6 mm isotropic resolution. Ventricular volumes and strain parameters are computed and compared to a compressed sensing reconstruction and to a conventional reference cine MRI acquisition. Lastly, the method is demonstrated in 46 patients and ventricular volumes and strain parameters are evaluated. RESULTS CineVN outperformed compressed sensing in image quality metrics on retrospectively undersampled data. Functional parameters and myocardial strain were the most accurate for CineVN compared to two state-of-the-art compressed sensing methods. CONCLUSION Deep learning-based reconstruction using our proposed method enables accurate evaluation of cardiac function in real-time cine MRI with high spatiotemporal resolution. This has the potential to improve cardiac imaging particularly for patients with arrhythmia or impaired breath-hold capability.
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Affiliation(s)
- Marc Vornehm
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
| | - Jens Wetzl
- Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
| | - Daniel Giese
- Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Florian Fürnrohr
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Jianing Pang
- Siemens Medical Solutions USA Inc, Chicago, Illinois, USA
| | - Kelvin Chow
- Siemens Medical Solutions USA Inc, Chicago, Illinois, USA
| | - Rolf Gebker
- MVZ Diagnostikum Berlin 2020 GmbH, Berlin, Germany
| | - Rizwan Ahmad
- Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Florian Knoll
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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Longère B, Abassebay N, Gkizas C, Hennicaux J, Simeone A, Rodriguez Musso A, Carpentier P, Coisne A, Pang J, Schmidt M, Toupin S, Montaigne D, Pontana F. A new compressed sensing cine cardiac MRI sequence with free-breathing real-time acquisition and fully automated motion-correction: A comprehensive evaluation. Diagn Interv Imaging 2023; 104:538-546. [PMID: 37328394 DOI: 10.1016/j.diii.2023.06.005] [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: 03/30/2023] [Revised: 05/24/2023] [Accepted: 06/06/2023] [Indexed: 06/18/2023]
Abstract
PURPOSE The purpose of this study was to compare a new free-breathing compressed sensing cine (FB-CS) cardiac magnetic resonance imaging (CMR) to the standard reference multi-breath-hold segmented cine (BH-SEG) CMR in an unselected population. MATERIALS AND METHODS From January to April 2021, 52 consecutive adult patients who underwent both conventional BH-SEG CMR and new FB-CS CMR with fully automated respiratory motion correction were retrospectively enrolled. There were 29 men and 23 women with a mean age of 57.7 ± 18.9 (standard deviation [SD]) years (age range: 19.0-90.0 years) and a mean cardiac rate of 74.6 ± 17.9 (SD) bpm. For each patient, short-axis stacks were acquired with similar parameters providing a spatial resolution of 1.8 × 1.8 × 8.0 mm3 and 25 cardiac frames. Acquisition and reconstruction times, image quality (Likert scale from 1 to 4), left and right ventricular volumes and ejection fractions, left ventricular mass, and global circumferential strain were assessed for each sequence. RESULTS FB-CS CMR acquisition time was significantly shorter (123.8 ± 28.4 [SD] s vs. 267.2 ± 39.3 [SD] s for BH-SEG CMR; P < 0.0001) at the penalty of a longer reconstruction time (271.4 ± 68.7 [SD] s vs. 9.9 ± 2.1 [SD] s for BH-SEG CMR; P < 0.0001). In patients without arrhythmia or dyspnea, FB-CS CMR provided subjective image quality that was not different from that of BH-SEG CMR (P = 0.13). FB-CS CMR improved image quality in patients with arrhythmia (n = 18; P = 0.002) or dyspnea (n = 7; P = 0.02), and the edge sharpness was improved at end-systole and end-diastole (P = 0.0001). No differences were observed between the two techniques in ventricular volumes and ejection fractions, left ventricular mass or global circumferential strain in patients in sinus rhythm or with cardiac arrhythmia. CONCLUSION This new FB-CS CMR addresses respiratory motion and arrhythmia-related artifacts without compromising the reliability of ventricular functional assessment.
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Affiliation(s)
- Benjamin Longère
- Univ. Lille, U1011-European Genomic Institute for Diabetes (EGID), 59000 Lille, France; Inserm, U1011, 59000 Lille, France; CHU Lille, Department of Cardiovascular Radiology, 59000 Lille, France; Institut Pasteur Lille, 59000 Lille, France.
| | - Neelem Abassebay
- CHU Lille, Department of Cardiovascular Radiology, 59000 Lille, France
| | - Christos Gkizas
- CHU Lille, Department of Cardiovascular Radiology, 59000 Lille, France
| | - Justin Hennicaux
- CHU Lille, Department of Cardiovascular Radiology, 59000 Lille, France
| | - Arianna Simeone
- CHU Lille, Department of Cardiovascular Radiology, 59000 Lille, France
| | | | - Paul Carpentier
- CHU Lille, Department of Cardiovascular Radiology, 59000 Lille, France
| | - Augustin Coisne
- Univ. Lille, U1011-European Genomic Institute for Diabetes (EGID), 59000 Lille, France; Inserm, U1011, 59000 Lille, France; CHU Lille, Department of Cardiovascular Radiology, 59000 Lille, France; Institut Pasteur Lille, 59000 Lille, France
| | - Jianing Pang
- MR R&D, Siemens Medical Solutions USA Inc., Chicago, IL 60611, USA
| | - Michaela Schmidt
- MR Product Innovation and Definition, Healthcare Sector, Siemens GmbH, 91052 Erlangen, Germany
| | - Solenn Toupin
- Scientific Partnerships, Siemens Healthcare France, 93200 Saint-Denis, France
| | - David Montaigne
- Univ. Lille, U1011-European Genomic Institute for Diabetes (EGID), 59000 Lille, France; Inserm, U1011, 59000 Lille, France; CHU Lille, Department of Cardiovascular Radiology, 59000 Lille, France; Institut Pasteur Lille, 59000 Lille, France
| | - François Pontana
- Univ. Lille, U1011-European Genomic Institute for Diabetes (EGID), 59000 Lille, France; Inserm, U1011, 59000 Lille, France; CHU Lille, Department of Cardiovascular Radiology, 59000 Lille, France; Institut Pasteur Lille, 59000 Lille, France
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3
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Cruz G, Hammernik K, Kuestner T, Velasco C, Hua A, Ismail TF, Rueckert D, Botnar RM, Prieto C. Single-heartbeat cardiac cine imaging via jointly regularized nonrigid motion-corrected reconstruction. NMR IN BIOMEDICINE 2023; 36:e4942. [PMID: 36999225 PMCID: PMC10909414 DOI: 10.1002/nbm.4942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 03/07/2023] [Accepted: 03/26/2023] [Indexed: 05/14/2023]
Abstract
The aim of the current study was to develop a novel approach for 2D breath-hold cardiac cine imaging from a single heartbeat, by combining cardiac motion-corrected reconstructions and nonrigidly aligned patch-based regularization. Conventional cardiac cine imaging is obtained via motion-resolved reconstructions of data acquired over multiple heartbeats. Here, we achieve single-heartbeat cine imaging by incorporating nonrigid cardiac motion correction into the reconstruction of each cardiac phase, in conjunction with a motion-aligned patch-based regularization. The proposed Motion-Corrected CINE (MC-CINE) incorporates all acquired data into the reconstruction of each (motion-corrected) cardiac phase, resulting in a better posed problem than motion-resolved approaches. MC-CINE was compared with iterative sensitivity encoding (itSENSE) and Extra-Dimensional Golden Angle Radial Sparse Parallel (XD-GRASP) in 14 healthy subjects in terms of image sharpness, reader scoring (range: 1-5) and reader ranking (range: 1-9) of image quality, and single-slice left ventricular assessment. MC-CINE was significantly superior to both itSENSE and XD-GRASP using 20 heartbeats, two heartbeats, and one heartbeat. Iterative SENSE, XD-GRASP, and MC-CINE achieved a sharpness of 74%, 74%, and 82% using 20 heartbeats, and 53%, 66%, and 82% with one heartbeat, respectively. The corresponding results for reader scoring were 4.0, 4.7, and 4.9 with 20 heartbeats, and 1.1, 3.0, and 3.9 with one heartbeat. The corresponding results for reader ranking were 5.3, 7.3, and 8.6 with 20 heartbeats, and 1.0, 3.2, and 5.4 with one heartbeat. MC-CINE using a single heartbeat presented nonsignificant differences in image quality to itSENSE with 20 heartbeats. MC-CINE and XD-GRASP at one heartbeat both presented a nonsignificant negative bias of less than 2% in ejection fraction relative to the reference itSENSE. It was concluded that the proposed MC-CINE significantly improves image quality relative to itSENSE and XD-GRASP, enabling 2D cine from a single heartbeat.
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Affiliation(s)
- Gastao Cruz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Kerstin Hammernik
- Department of ComputingImperial College LondonLondonUK
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Thomas Kuestner
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Medical Image and Data Analysis, Department of Diagnostic and Interventional RadiologyUniversity Hospital TübingenTübingenGermany
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Alina Hua
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Tevfik Fehmi Ismail
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Daniel Rueckert
- Department of ComputingImperial College LondonLondonUK
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Rene Michael Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile
- Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
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Yang J, Küstner T, Hu P, Liò P, Qi H. End-to-End Deep Learning of Non-rigid Groupwise Registration and Reconstruction of Dynamic MRI. Front Cardiovasc Med 2022; 9:880186. [PMID: 35571217 PMCID: PMC9095964 DOI: 10.3389/fcvm.2022.880186] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/08/2022] [Indexed: 12/03/2022] Open
Abstract
Temporal correlation has been exploited for accelerated dynamic MRI reconstruction. Some methods have modeled inter-frame motion into the reconstruction process to produce temporally aligned image series and higher reconstruction quality. However, traditional motion-compensated approaches requiring iterative optimization of registration and reconstruction are time-consuming, while most deep learning-based methods neglect motion in the reconstruction process. We propose an unrolled deep learning framework with each iteration consisting of a groupwise diffeomorphic registration network (GRN) and a motion-augmented reconstruction network. Specifically, the whole dynamic sequence is registered at once to an implicit template which is used to generate a new set of dynamic images to efficiently exploit the full temporal information of the acquired data via the GRN. The generated dynamic sequence is then incorporated into the reconstruction network to augment the reconstruction performance. The registration and reconstruction networks are optimized in an end-to-end fashion for simultaneous motion estimation and reconstruction of dynamic images. The effectiveness of the proposed method is validated in highly accelerated cardiac cine MRI by comparing with other state-of-the-art approaches.
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Affiliation(s)
- Junwei Yang
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- The School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
| | - Peng Hu
- The School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Haikun Qi
- The School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
- *Correspondence: Haikun Qi
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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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6
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Oglesby RT, Lam WW, Stanisz GJ. A strategy to prevent a temperature-induced MRI artifact in warm liquid phantoms due to convection currents. NMR IN BIOMEDICINE 2021; 34:e4494. [PMID: 33586271 DOI: 10.1002/nbm.4494] [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: 11/23/2020] [Revised: 01/16/2021] [Accepted: 02/02/2021] [Indexed: 06/12/2023]
Abstract
MRI phantom studies often fail to mimic the temperature of the human body, which can negatively impact accuracy. An artifact induced by increasing temperature in liquid phantoms was observed, presenting a significant challenge to temperature-controlled experiments. In this study we characterize and provide a solution to eliminate this temperature-induced MRI artifact. Low concentration (0.5-2.5 mM) agar phantoms were prepared. Utilizing a temperature-controlled phantom holder, T1 - and T2 -weighted structural images were acquired at 7 T along with quantitative B0 , B1 , T1 , T2 and ADC maps at both 25 and 37°C. Additionally, computer simulations were conducted to demonstrate the fluid flow and thermal flux patterns in water to provide an insight into the origins of the artifact. Evidence from computer simulation and quantitative MRI strongly suggest the artifact was caused by heat transfer in the form of natural convection leading to structured patterns of signal loss in MR images. The artifact was present up to agar concentrations of 1.5 mM (T1 = 3068 ± 16 ms, T2 = 1052 ± 20 ms, ADC = 2.29 ± 0.36 × 10-3 mm2 /s at 25°C; T1 = 3928 ± 44 ms, T2 = 1122 ± 24 ms, ADC = 2.64 ± 0.49 × 10-3 mm2 /s at 37°C), above which point increased sample viscosity no longer allows for convection currents, thereby eliminating the artifact. The methodology described in this work simplifies quantitative MR acquisition of liquid phantoms at physiological temperature by suppressing convection currents with relatively small changes to intrinsic MR parameters (T1 increased by 1.4% and T2 decreased by 17% for 1.5 mM agar at 25°C).
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Affiliation(s)
- Ryan T Oglesby
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Wilfred W Lam
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Greg J Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Neurosurgery and Paediatric Neurosurgery, Medical University of Lublin, Lublin, Poland
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Stone ML, Schäfer M, von Alvensleben JC, Browne LP, Di Maria M, Campbell DN, Jaggers J, Mitchell MB. Increased Aortic Stiffness and Left Ventricular Dysfunction Exist After Truncus Arteriosus Repair. Ann Thorac Surg 2020; 112:809-815. [PMID: 33307069 DOI: 10.1016/j.athoracsur.2020.10.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 10/22/2020] [Accepted: 10/26/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND The purpose of this study was to determine whether aortic biomechanical properties are abnormal in children with repaired truncus arteriosus (TA) and to concurrently evaluate left ventricular (LV) function post-repair utilizing a novel platform for regional ventricular function. METHODS Cardiac magnetic resonance (CMR) studies from 26 children (mean age: 15.6 ± 7.2 years) post-TA repair were compared with 20 normal controls (mean age: 14.7 ± 2.6 years). Parameters of aortic stiffness (pulse wave velocity and relative area change) were measured. Flow hemodynamic metrics (aortic regurgitant fraction, peak systolic flow, and peak systolic velocity) and LV function (volumetric data, ejection fraction, regional wall strain) were also compared. RESULTS Ascending aortic pulse wave velocity was elevated and relative area change was decreased in TA patients compared with controls. Patients post-TA repair demonstrated elevated end diastolic and end systolic volumes in addition to decreased regional wall strain and increased mechanical dyssynchrony. LV functional changes were independent of aortic biomechanical properties. CONCLUSIONS Children with repaired TA have increased ascending aortic stiffness and altered LV function as measured by CMR imaging. Longitudinal studies and advanced CMR assessments are warranted to better determine the long-term potential for late aortic complications and to optimize both the medical and surgical management of these patients after TA repair.
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Affiliation(s)
- Matthew L Stone
- Division of Pediatric Cardiothoracic Surgery, University of Colorado School of Medicine, Children's Hospital Colorado, Aurora, Colorado.
| | - Michal Schäfer
- Department of Pediatric Cardiology, Children's Hospital Colorado, Aurora, Colorado
| | | | - Lorna P Browne
- Department of Pediatric Radiology, Children's Hospital Colorado, Aurora, Colorado
| | - Michael Di Maria
- Department of Pediatric Cardiology, Children's Hospital Colorado, Aurora, Colorado
| | - David N Campbell
- Division of Pediatric Cardiothoracic Surgery, University of Colorado School of Medicine, Children's Hospital Colorado, Aurora, Colorado
| | - James Jaggers
- Division of Pediatric Cardiothoracic Surgery, University of Colorado School of Medicine, Children's Hospital Colorado, Aurora, Colorado
| | - Max B Mitchell
- Division of Pediatric Cardiothoracic Surgery, University of Colorado School of Medicine, Children's Hospital Colorado, Aurora, Colorado
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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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9
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Menchón-Lara RM, Simmross-Wattenberg F, Casaseca-de-la-Higuera P, Martín-Fernández M, Alberola-López C. Reconstruction techniques for cardiac cine MRI. Insights Imaging 2019; 10:100. [PMID: 31549235 PMCID: PMC6757088 DOI: 10.1186/s13244-019-0754-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 05/17/2019] [Indexed: 12/17/2022] Open
Abstract
The present survey describes the state-of-the-art techniques for dynamic cardiac magnetic resonance image reconstruction. Additionally, clinical relevance, main challenges, and future trends of this image modality are outlined. Thus, this paper aims to provide a general vision about cine MRI as the standard procedure in functional evaluation of the heart, focusing on technical methodologies.
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Affiliation(s)
- Rosa-María Menchón-Lara
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain.
| | - Federico Simmross-Wattenberg
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
| | - Pablo Casaseca-de-la-Higuera
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
| | - Marcos Martín-Fernández
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
| | - Carlos Alberola-López
- Laboratorio de Procesado de Imagen. Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad de Valladolid, Campus Miguel Delibes, Valladolid, 47011, Spain
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10
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Wang L, Clarysse P, Liu Z, Gao B, Liu W, Croisille P, Delachartre P. A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images. Med Image Anal 2019; 57:136-148. [PMID: 31302510 DOI: 10.1016/j.media.2019.06.016] [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] [Received: 12/29/2017] [Revised: 06/27/2019] [Accepted: 06/27/2019] [Indexed: 11/25/2022]
Abstract
A new method is proposed to quantify the myocardial motion from both 2D C(ine)-MRI and T(agged)-MRI sequences. The tag pattern offers natural landmarks within the image that makes it possible to accurately quantify the motion within the myocardial wall. Therefore, several methods have been proposed for T-MRI. However, the lack of salient features within the cardiac wall in C-MRI hampers local motion estimation. Our method aims to ensure the local intensity and shape features invariance during motion through the iterative minimization of a cost function via a random walk scheme. The proposed approach is evaluated on realistic simulated C-MRI and T-MRI sequences. The results show more than 53% improvements on displacement estimation, and more than 24% on strain estimation for both C-MRI and T-MRI sequences, as compared to state-of-the-art cardiac motion estimators. Preliminary experiments on clinical data have shown a good ability of the proposed method to detect abnormal motion patterns related to pathology. If those results are confirmed on large databases, this would open up the possibility for more accurate diagnosis of cardiac function from standard C-MRI examinations and also the retrospective study of prior studies.
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Affiliation(s)
- Liang Wang
- Univ Lyon, INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France.
| | - Patrick Clarysse
- Univ Lyon, INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France
| | - Zhengjun Liu
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin 150001, People's Republic of China
| | - Bin Gao
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin 150001, People's Republic of China; College of data science and technology, Heilongjiang University, Harbin 150080, People's Republic of China
| | - Wanyu Liu
- Metislab, LIA CNRS, Harbin Institute of Technology, Harbin 150001, People's Republic of China
| | - Pierre Croisille
- Univ Lyon, INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France; Department of Radiology, University Hospital of Saint-Etienne, Université Jean-Monnet, Saint-Etienne, France
| | - Philippe Delachartre
- Univ Lyon, INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, LYON, France
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11
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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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Sliding motion compensated low-rank plus sparse (SMC-LS) reconstruction for high spatiotemporal free-breathing liver 4D DCE-MRI. Magn Reson Imaging 2019; 58:56-66. [PMID: 30658071 DOI: 10.1016/j.mri.2019.01.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 12/06/2018] [Accepted: 01/12/2019] [Indexed: 02/03/2023]
Abstract
Liver dynamic contrast-enhanced MRI (DCE-MRI) requires high spatiotemporal resolution and large field of view to clearly visualize all relevant enhancement phases and detect early-stage liver lesions. The low-rank plus sparse (L + S) reconstruction outperforms standard sparsity-only-based reconstruction through separation of low-rank background component (L) and sparse dynamic components (S). However, the L + S decomposition is sensitive to respiratory motion so that image quality is compromised when breathing occurs during long time data acquisition. To enable high quality reconstruction for free-breathing liver 4D DCE-MRI, this paper presents a novel method called SMC-LS, which incorporates Sliding Motion Compensation into the standard L + S reconstruction. The global superior-inferior displacement of the internal abdominal organs is inferred directly from the undersampled raw data and then used to correct the breathing induced sliding motion which is the dominant component of respiratory motion. With sliding motion compensation, the reconstructed temporal frames are roughly registered before applying the standard L + S decomposition. The proposed method has been validated using free-breathing liver 4D MRI phantom data, free-breathing liver 4D DCE-MRI phantom data, and in vivo free breathing liver 4D MRI dataset. Results demonstrated that SMC-LS reconstruction can effectively reduce motion blurring artefacts and preserve both spatial structures and temporal variations at a sub-second temporal frame rate for free-breathing whole-liver 4D DCE-MRI.
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