1
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Coolen BF. Editorial for "Black-Blood Magnetization Prepared 2 Rapid Acquisition Gradient Echoes: A Fast and Three-dimensional MR Black-blood T 1 Mapping Technique for Quantitative Assessment of Atherosclerosis and Venous Thrombosis". J Magn Reson Imaging 2024; 60:1163-1164. [PMID: 38014867 DOI: 10.1002/jmri.29154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 11/02/2023] [Accepted: 11/03/2023] [Indexed: 11/29/2023] Open
Affiliation(s)
- Bram F Coolen
- Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
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2
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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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3
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Pace DF, Contreras HTM, Romanowicz J, Ghelani S, Rahaman I, Zhang Y, Gao P, Jubair MI, Yeh T, Golland P, Geva T, Ghelani S, Powell AJ, Moghari MH. HVSMR-2.0: A 3D cardiovascular MR dataset for whole-heart segmentation in congenital heart disease. Sci Data 2024; 11:721. [PMID: 38956063 PMCID: PMC11219801 DOI: 10.1038/s41597-024-03469-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 06/04/2024] [Indexed: 07/04/2024] Open
Abstract
Patients with congenital heart disease often have cardiac anatomy that deviates significantly from normal, frequently requiring multiple heart surgeries. Image segmentation from a preoperative cardiovascular magnetic resonance (CMR) scan would enable creation of patient-specific 3D surface models of the heart, which have potential to improve surgical planning, enable surgical simulation, and allow automatic computation of quantitative metrics of heart function. However, there is no publicly available CMR dataset for whole-heart segmentation in patients with congenital heart disease. Here, we release the HVSMR-2.0 dataset, comprising 60 CMR scans alongside manual segmentation masks of the 4 cardiac chambers and 4 great vessels. The images showcase a wide range of heart defects and prior surgical interventions. The dataset also includes masks of required and optional extents of the great vessels, enabling fairer comparisons across algorithms. Detailed diagnoses for each subject are also provided. By releasing HVSMR-2.0, we aim to encourage development of robust segmentation algorithms and clinically relevant tools for congenital heart disease.
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Affiliation(s)
- Danielle F Pace
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Hannah T M Contreras
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jennifer Romanowicz
- Department of Pediatrics, Section of Cardiology, Children's Hospital Colorado, Aurora, CO, USA
| | - Shruti Ghelani
- Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA
| | - Imon Rahaman
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yue Zhang
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Biochemistry and Molecular Genetics, Northwestern University, Chicago, IL, USA
- School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Patricia Gao
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Tom Yeh
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology, Ewha Womans University, Seoul, South Korea
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tal Geva
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Sunil Ghelani
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Andrew J Powell
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Mehdi Hedjazi Moghari
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- School of Medicine, The University of Colorado, Aurora, CO, USA
- Department of Radiology, Children's Hospital Colorado, Aurora, CO, USA
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4
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Minocha PK, Englund EK, Friesen RM, Fujiwara T, Smith SA, Meyers ML, Browne LP, Barker AJ. Reference Values for Fetal Cardiac Dimensions, Volumes, Ventricular Function and Left Ventricular Longitudinal Strain Using Doppler Ultrasound Gated Cardiac Magnetic Resonance Imaging in Healthy Third Trimester Fetuses. J Magn Reson Imaging 2024; 60:365-374. [PMID: 37855630 PMCID: PMC11026299 DOI: 10.1002/jmri.29077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/02/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Recent advances in hardware and software permit the use of cardiac MRI of late gestation fetuses, however there is a paucity of MRI-based reference values. PURPOSE To provide initial data on fetal cardiac MRI-derived cardiac dimensions, volumes, ventricular function, and left ventricular longitudinal strain in healthy developing fetuses >30 weeks gestational age. STUDY TYPE Prospective. POPULATION Twenty-five third trimester (34 ± 1 weeks, range of 32-37 weeks gestation) women with healthy developing fetuses. FIELD STRENGTH/SEQUENCE Studies were performed at 1.5 T and 3 T. Cardiac synchronization was achieved with a Doppler ultrasound device. The protocol included T2 single shot turbo spin echo stacks for fetal weight and ultrasound probe positioning, and multiplanar multi-slice cine balanced steady state free precession gradient echo sequences. ASSESSMENT Primary analyses were performed by a single observer. Weight indexed right ventricular (RV) and left ventricular (LV) volumes and function were calculated from short axis (SAX) stacks. Cardiac dimensions were calculated from the four-chamber and SAX stacks. Single plane LV longitudinal strain was calculated from the four-chamber stack. Interobserver variability was assessed in 10 participants. Cardiac MRI values were compared against available published normative fetal echocardiogram data using z-scores. STATISTICAL TESTS Mean and SDs were calculated for baseline maternal/fetal demographics, cardiac dimensions, volumes, ventricular function, and left ventricular longitudinal strain. Bland-Altman and intraclass correlation coefficient analysis was performed to test interobserver variability. RESULTS The mean gestational age was 34 ± 1.4 weeks. The mean RV and LV end diastolic volumes were 3.1 ± 0.6 mL/kg and 2.4 ± 0.5 mL/kg respectively. The mean RV cardiac output was 198 ± 49 mL/min/kg while the mean LV cardiac output was 173 ± 43 mL/min/kg. DATA CONCLUSION This paper reports initial reference values obtained by cardiac MRI in healthy developing third trimester fetuses. MRI generally resulted in slightly larger indexed values (by z-score) compared to reports in literature using fetal echocardiography. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Prashant K. Minocha
- Division of Cardiology, Heart Institute, Children’s Hospital Colorado, University of Colorado School of Medicine, USA
| | - Erin K. Englund
- Department of Radiology, Section of Pediatric Radiology, Children’s Hospital Colorado, University of Colorado Anschutz Medical Campus, USA
| | - Richard M. Friesen
- Division of Cardiology, Heart Institute, Children’s Hospital Colorado, University of Colorado School of Medicine, USA
| | - Takashi Fujiwara
- Department of Radiology, Section of Pediatric Radiology, Children’s Hospital Colorado, University of Colorado Anschutz Medical Campus, USA
| | - Sarah A. Smith
- Department of Radiology, Section of Pediatric Radiology, Children’s Hospital Colorado, University of Colorado Anschutz Medical Campus, USA
| | - Mariana L. Meyers
- Department of Radiology, Section of Pediatric Radiology, Children’s Hospital Colorado, University of Colorado Anschutz Medical Campus, USA
| | - Lorna P. Browne
- Department of Radiology, Section of Pediatric Radiology, Children’s Hospital Colorado, University of Colorado Anschutz Medical Campus, USA
| | - Alex J. Barker
- Department of Radiology, Section of Pediatric Radiology, Children’s Hospital Colorado, University of Colorado Anschutz Medical Campus, USA
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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5
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Petoello E, Flore AI, Nogara S, Bonafiglia E, Lenzi MB, Arnone OC, Benfari G, Ciarcià M, Corsini I, De Waal K, Gottin L, Ficial B. Global longitudinal strain is an informative index of left ventricular performance in neonates receiving intensive care. Sci Rep 2024; 14:8881. [PMID: 38632330 PMCID: PMC11024117 DOI: 10.1038/s41598-024-59441-5] [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: 01/22/2024] [Accepted: 04/10/2024] [Indexed: 04/19/2024] Open
Abstract
Echocardiographic assessment of left ventricular function is crucial in NICU. The study aimed to compare the accuracy and agreement of global longitudinal strain (GLS) with conventional measurements. Real-life echocardiograms of neonates receiving intensive care were retrospectively reviewed. Shortening fraction (SF), ejection fraction (EF) and S' measurements were retrieved from health records. GLS was calculated offline from stored images. The association with stroke volume indexed for body weight (iSV) was evaluated by regression analysis. The diagnostic ability to identify uncompensated shock was assessed by ROC curve analysis. Cohen's κ was run to assess agreement. 334 echocardiograms of 155 neonates were evaluated. Mean ± SD gestational age and birth weight were 34.5 ± 4.1 weeks and 2264 ± 914 g, respectively. SF, EF, S' and GLS were associated with iSV with R2 of 0.133, 0.332, 0.252 and 0.633, (all p < .001). Including all variables in a regression model, iSV prediction showed an adjusted R2 of 0.667, (p < .001). GLS explained 73% of the model variance. GLS showed a better ability to diagnose uncompensated shock (AUC 0.956) compared to EF, S' and SF (AUC 0.757, 0.737 and 0.606, respectively). GLS showed a moderate agreement with EF (κ = .500, p < .001) and a limited agreement with S' and SF (κ = .260, p < .001, κ = .242, p < .001). GLS was a more informative index of left ventricular performance, providing the rationale for a more extensive use of GLS at the cotside.
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Affiliation(s)
- Enrico Petoello
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, P.le A. Stefani 1, 37126, Verona, Italy
| | - Alice Iride Flore
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, P.le A. Stefani 1, 37126, Verona, Italy
| | - Silvia Nogara
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, P.le A. Stefani 1, 37126, Verona, Italy
| | - Elena Bonafiglia
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, P.le A. Stefani 1, 37126, Verona, Italy
| | - Maria Beatrice Lenzi
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, P.le A. Stefani 1, 37126, Verona, Italy
| | - Olivia C Arnone
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, P.le A. Stefani 1, 37126, Verona, Italy
| | - Giovanni Benfari
- Section of Cardiology, Department of Medicine, University of Verona, Verona, Italy
| | - Martina Ciarcià
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, P.le A. Stefani 1, 37126, Verona, Italy
| | - Iuri Corsini
- Division of Neonatology, Careggi University Hospital of Florence, Florence, Italy
| | - Koert De Waal
- Department of Neonatology, John Hunter Children's Hospital, Newcastle, NSW, Australia
- University of Newcastle, Newcastle, NSW, Australia
| | - Leonardo Gottin
- Intensive Care Unit, Department of Surgery, Dentistry, Maternity and Infant, University and Hospital Trust of Verona, Verona, Italy
| | - Benjamim Ficial
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, P.le A. Stefani 1, 37126, Verona, Italy.
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6
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Ficial B, Benfari G, Bonafiglia E, Clemente M, Cappelleri A, Flore AI, Petoello E, Ciarcià M, Nogara S, Milocchi C, Dani C, Ribichini FL, Gottin L, Corsini I. Tissue-Tracking Mitral Annular Displacement in Neonates: A Novel Index of Left Ventricular Systolic Function. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:729-739. [PMID: 38140738 DOI: 10.1002/jum.16399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/23/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
OBJECTIVES To assess the feasibility, accuracy, and reproducibility of tissue-tracking mitral annular displacement (TMAD) compared with other measures of left ventricular systolic function in healthy preterm and term neonates in the transitional period. METHODS This was a prospective observational study. Two echocardiograms were performed at 24 and 48 hours of life. TMAD, shortening fraction (SF), ejection fraction (EF), s', and global longitudinal strain (GLS) were measured offline. Accuracy to detect impaired GLS was tested by ROC curve analysis. DeLong test was used to compare AUCs. Intra and interobserver reproducibility of the off-line analysis was calculated. RESULTS Mean ± SD gestational age and weight were 34.2 ± 3.8 weeks and 2162 ± 833 g, respectively. TMAD was feasible in 168/180 scans (93%). At 24 hours the AUC (95% CI) of SF, EF, s', and TMAD (%) was 0.51 (0.36-0.67), 0.68 (0.54-0.82), 0.63 (0.49-0.77), and 0.89 (0.79-0.99) respectively. At 48 hours the AUC (95% CI) of SF, EF, s', and TMAD (%) was 0.64 (0.51-0.77), 0.59 (0.37-0.80), 0.70 (0.54-0.86), and 0.96 (0.91-1.00), respectively. The AUC of TMAD was superior to the AUC of SF, EF, s', at both timepoints (P < .02). Intraclass correlation coefficients (95% CI) of intra and interobserver reproducibility of TMAD were 0.97 (0.95-0.99) and 0.94 (0.88-0.97), respectively. CONCLUSION TMAD showed improved accuracy and optimal reproducibility in neonates in the first 48 hours of life.
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Affiliation(s)
- Benjamim Ficial
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, Verona, Italy
| | - Giovanni Benfari
- Section of Cardiology, Department of Medicine, University of Verona, Verona, Italy
| | - Elena Bonafiglia
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, Verona, Italy
| | - Maria Clemente
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, Verona, Italy
| | - Alessia Cappelleri
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, Verona, Italy
| | - Alice Iride Flore
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, Verona, Italy
| | - Enrico Petoello
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, Verona, Italy
| | - Martina Ciarcià
- Division of Neonatology, Careggi University Hospital of Florence, Florence, Italy
| | - Silvia Nogara
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, Verona, Italy
| | - Carlotta Milocchi
- Neonatal Intensive Care Unit, University and Hospital Trust of Verona, Verona, Italy
| | - Carlo Dani
- Division of Neonatology, Careggi University Hospital of Florence, Florence, Italy
| | | | - Leonardo Gottin
- Intensive Care Unit, Department of Surgery, Dentistry, Maternity and Infant, University and Hospital Trust of Verona, Verona, Italy
| | - Iuri Corsini
- Division of Neonatology, Careggi University Hospital of Florence, Florence, Italy
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7
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Khan D, Elhadi M, Vasile VC. 59-Year-Old Woman With Episodic Chest Pain. Mayo Clin Proc 2024; 99:146-152. [PMID: 38176823 DOI: 10.1016/j.mayocp.2023.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/05/2023] [Accepted: 05/08/2023] [Indexed: 01/06/2024]
Affiliation(s)
- Daniel Khan
- Resident in Internal Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN
| | - Mohamed Elhadi
- Resident in Internal Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN
| | - Vlad C Vasile
- Advisor to Residents and Consultant in Cardiovascular Diseases, Mayo Clinic, Rochester, MN.
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8
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Di Marco A. Cardiac Magnetic Resonance and Ventricular Arrhythmias: An Indissoluble Liaison. JACC Cardiovasc Imaging 2023; 16:1550-1551. [PMID: 37452822 DOI: 10.1016/j.jcmg.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 06/02/2023] [Indexed: 07/18/2023]
Affiliation(s)
- Andrea Di Marco
- Department of Cardiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain; Bioheart-Cardiovascular Diseases Group, Cardiovascular, Respiratory and Systemic Diseases and Cellular Aging Program, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.
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9
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Fries RC. Current use of cardiac MRI in animals. J Vet Cardiol 2023; 51:13-23. [PMID: 38052149 DOI: 10.1016/j.jvc.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 12/07/2023]
Abstract
Cardiovascular magnetic resonance (CMR) imaging has evolved to become an indispensable tool in human cardiology. It is a non-invasive technique that enables objective assessment of myocardial function, size, and tissue composition. Recent innovations in magnetic resonance imaging scanner technology and parallel imaging techniques have facilitated the generation of parametric mapping to explore tissue characteristics, and the emergence of strain imaging has enabled cardiologists to evaluate cardiac function beyond conventional metrics. As veterinary cardiology continues to utilize CMR beyond the reference standard, clinical application of CMR will further expand our capabilities. This article describes the current use of CMR and adoption of more recent advances such as T1/T2 mapping in veterinary cardiology.
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Affiliation(s)
- R C Fries
- Department of Veterinary Clinical Medicine, University of Illinois at Urbana-Champaign College of Veterinary Medicine, Urbana, IL, USA.
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10
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Alkassar M, Engelhardt S, Abu-Tair T, Ojeda E, Treffer PC, Weyand M, Rompel O. Comparative Study of 2D-Cine and 3D-wh Volumetry: Revealing Systemic Error of 2D-Cine Volumetry. Diagnostics (Basel) 2023; 13:3162. [PMID: 37891983 PMCID: PMC10605840 DOI: 10.3390/diagnostics13203162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/28/2023] [Accepted: 10/06/2023] [Indexed: 10/29/2023] Open
Abstract
This study investigates the crucial factors influencing the end-systolic and end-diastolic volumes in MRI volumetry and their direct effects on the derived functional parameters. Through the simultaneous acquisition of 2D-cine and 3D whole-heart slices in end-diastole and end-systole, we present a novel direct comparison of the volumetric measurements from both methods. A prospective study was conducted with 18 healthy participants. Both 2D-cine and 3D whole-heart sequences were obtained. Despite the differences in the creation of 3D volumes and trigger points, the impact on the LV volume was minimal (134.9 mL ± 16.9 mL vs. 136.6 mL ± 16.6 mL, p < 0.01 for end-diastole; 50.6 mL ± 11.0 mL vs. 51.6 mL ± 11.2 mL, p = 0.03 for end-systole). In our healthy patient cohort, a systematic underestimation of the end-systolic volume resulted in a significant overestimation of the SV (5.6 mL ± 2.6 mL, p < 0.01). The functional calculations from the 3D whole-heart method proved to be highly accurate and correlated well with function measurements from the phase-contrast sequences. Our study is the first to demonstrate the superiority of 3D whole-heart volumetry over 2D-cine volumetry and sheds light on the systematic error inherent in 2D-cine measurements.
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Affiliation(s)
- Muhnnad Alkassar
- Department of Cardiac Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (S.E.); (M.W.)
- Department of Pediatrics, Paracelsus Medical School, General Hospital of Nuremberg, 90419 Nuremberg, Germany
| | - Sophia Engelhardt
- Department of Cardiac Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (S.E.); (M.W.)
| | - Tariq Abu-Tair
- Department of Congenital Heart Disease, Centre for Diseases in Childhood and Adolescence, University Medicine Mainz, 55131 Mainz, Germany;
| | - Efren Ojeda
- Siemens Healtineers, 91052 Erlangen, Germany; (E.O.); (P.C.T.)
| | | | - Michael Weyand
- Department of Cardiac Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; (S.E.); (M.W.)
| | - Oliver Rompel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany;
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11
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Sheikhjafari A, Krishnaswamy D, Noga M, Ray N, Punithakumar K. Deep Learning Based Parameterization of Diffeomorphic Image Registration for Cardiac Image Segmentation. IEEE Trans Nanobioscience 2023; 22:800-807. [PMID: 37220045 DOI: 10.1109/tnb.2023.3276867] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Cardiac segmentation from magnetic resonance imaging (MRI) is one of the essential tasks in analyzing the anatomy and function of the heart for the assessment and diagnosis of cardiac diseases. However, cardiac MRI generates hundreds of images per scan, and manual annotation of them is challenging and time-consuming, and therefore processing these images automatically is of interest. This study proposes a novel end-to-end supervised cardiac MRI segmentation framework based on a diffeomorphic deformable registration that can segment cardiac chambers from 2D and 3D images or volumes. To represent actual cardiac deformation, the method parameterizes the transformation using radial and rotational components computed via deep learning, with a set of paired images and segmentation masks used for training. The formulation guarantees transformations that are invertible and prevents mesh folding, which is essential for preserving the topology of the segmentation results. A physically plausible transformation is achieved by employing diffeomorphism in computing the transformations and activation functions that constrain the range of the radial and rotational components. The method was evaluated over three different data sets and showed significant improvements compared to exacting learning and non-learning based methods in terms of the Dice score and Hausdorff distance metrics.
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12
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Demirel ÖB, Weingärtner S, Moeller S, Akçakaya M. Improved Simultaneous Multi-slice imaging with Composition of k-space Interpolations (SMS-COOKIE) for myocardial T1 mapping. PLoS One 2023; 18:e0283972. [PMID: 37478080 PMCID: PMC10361528 DOI: 10.1371/journal.pone.0283972] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 03/21/2023] [Indexed: 07/23/2023] Open
Abstract
The aim of this study is to develop and evaluate a regularized Simultaneous Multi-Slice (SMS) reconstruction method for improved Cardiac Magnetic Resonance Imaging (CMR). The proposed reconstruction method, SMS with COmpOsition of k-space IntErpolations (SMS-COOKIE) combines the advantages of Iterative Self-consistent Parallel Imaging Reconstruction (SPIRiT) and split slice-Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA), while allowing regularization for further noise reduction. The proposed SMS-COOKIE was implemented with and without regularization, and validated using a Saturation Pulse-Prepared Heart rate Independent inversion REcovery (SAPPHIRE) myocardial T1 mapping sequence. The performance of the proposed reconstruction method was compared to ReadOut (RO)-SENSE-GRAPPA and split slice-GRAPPA, on both retrospectively and prospectively three-fold SMS-accelerated data with an additional two-fold in-plane acceleration. All SMS reconstruction methods yielded similar T1 values compared to single band imaging. SMS-COOKIE showed lower spatial variability in myocardial T1 with significant improvement over RO-SENSE-GRAPPA and split slice-GRAPPA (P < 10-4). The proposed method with additional locally low rank (LLR) regularization reduced the spatial variability, again with significant improvement over RO-SENSE-GRAPPA and split slice-GRAPPA (P < 10-4). In conclusion, improved reconstruction quality was achieved with the proposed SMS-COOKIE, which also provided lower spatial variability with significant improvement over split slice-GRAPPA.
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Affiliation(s)
- Ömer Burak Demirel
- Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Steen Moeller
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Mehmet Akçakaya
- Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, United States of America
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, United States of America
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13
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Basit A, Inam O, Omer H. Accelerating GRAPPA reconstruction using SoC design for real-time cardiac MRI. Comput Biol Med 2023; 160:107008. [PMID: 37159960 DOI: 10.1016/j.compbiomed.2023.107008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 04/19/2023] [Accepted: 05/03/2023] [Indexed: 05/11/2023]
Abstract
Real-time cardiac MRI is a rapidly developing area of research that has the potential to improve the diagnosis and treatment of cardiovascular diseases. However, the acquisition of high-quality real-time cardiac MR (CMR) images is challenging as it requires a high frame rate and temporal resolution. To overcome this challenge, there have been recent efforts on several approaches including hardware-based improvements and image reconstruction techniques such as compressed sensing and parallel MRI. The use of parallel MRI techniques such as GRAPPA (Generalized Autocalibrating Partial Parallel Acquisition) is a promising approach for improving the temporal resolution of MRI and expanding its applications in clinical practice. However, the GRAPPA algorithm involves a significant amount of computation, particularly for high acceleration factors and large datasets. This can result in long reconstruction times, which can limit the ability to achieve real-time imaging or high frame rates. One solution to this challenge is to use specialized hardware i.e. field-programmable gate arrays (FPGAs). In this work, a novel 32-bit floating-point FPGA-based GRAPPA accelerator is proposed with an aim to reconstruct high-quality cardiac MR images at higher frame rates, making it well suited for real-time clinical applications. The proposed FPGA-based accelerator consists of custom-designed data processing units named as dedicated computational engines (DCEs) that allow for a continuous flow of data between the calibration and synthesis stages of GRAPPA reconstruction process. This greatly increases the throughput and reduces the latency of the overall proposed system. Moreover, a high-speed memory module (DDR4-SDRAM) is integrated with the proposed architecture to store the multi-coil MR data. An on-chip quad-core ARM Cortex-A53 processor is used to manage access control information required for data transfer between the DCEs and DDR4-SDRAM. The proposed accelerator is implemented on Xilinx Zynq UltraScale + MPSoC using high-level synthesis (HLS) and hardware descriptive language (HDL) with an aim to explore the trade-offs between the reconstruction time, resource utilization and design effort. Several experiments have been performed using in-vivo cardiac datasets i.e. 18-receiver coil and 30-receiver coil to evaluate the performance of the proposed accelerator. A comparison is performed with the contemporary CPU and GPU-based GRAPPA reconstruction methods in terms of reconstruction time, frames-per-second and reconstruction accuracy (RMSE and SNR). The results show that the proposed accelerator achieves speed-up factors up to 121× and 9× as compared to the contemporary CPU-based and GPU-based GRAPPA reconstruction methods, respectively. Moreover, it has been demonstrated that the proposed accelerator can achieve reconstruction rates of up to ∼27 frames-per-second while maintaining the visual quality of the reconstructed images.
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Affiliation(s)
- Abdul Basit
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan.
| | - Omair Inam
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan
| | - Hammad Omer
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan
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14
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Counseller Q, Aboelkassem Y. Recent technologies in cardiac imaging. FRONTIERS IN MEDICAL TECHNOLOGY 2023; 4:984492. [PMID: 36704232 PMCID: PMC9872125 DOI: 10.3389/fmedt.2022.984492] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 11/30/2022] [Indexed: 01/11/2023] Open
Abstract
Cardiac imaging allows physicians to view the structure and function of the heart to detect various heart abnormalities, ranging from inefficiencies in contraction, regulation of volumetric input and output of blood, deficits in valve function and structure, accumulation of plaque in arteries, and more. Commonly used cardiovascular imaging techniques include x-ray, computed tomography (CT), magnetic resonance imaging (MRI), echocardiogram, and positron emission tomography (PET)/single-photon emission computed tomography (SPECT). More recently, even more tools are at our disposal for investigating the heart's physiology, performance, structure, and function due to technological advancements. This review study summarizes cardiac imaging techniques with a particular interest in MRI and CT, noting each tool's origin, benefits, downfalls, clinical application, and advancement of cardiac imaging in the near future.
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Affiliation(s)
- Quinn Counseller
- College of Health Sciences, University of Michigan, Flint, MI, United States
| | - Yasser Aboelkassem
- College of Innovation and Technology, University of Michigan, Flint, MI, United States,Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, United States,Correspondence: Yasser Aboelkassem
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15
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Bazmpani MA, Nikolaidou C, Papanastasiou CA, Ziakas A, Karamitsos TD. Cardiovascular Magnetic Resonance Parametric Mapping Techniques for the Assessment of Chronic Coronary Syndromes. J Cardiovasc Dev Dis 2022; 9:jcdd9120443. [PMID: 36547440 PMCID: PMC9782163 DOI: 10.3390/jcdd9120443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 11/29/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
The term chronic coronary syndromes encompasses a variety of clinical presentations of coronary artery disease (CAD), ranging from stable angina due to epicardial coronary artery disease to microvascular coronary dysfunction. Cardiac magnetic resonance (CMR) imaging has an established role in the diagnosis, prognostication and treatment planning of patients with CAD. Recent advances in parametric mapping CMR techniques have added value in the assessment of patients with chronic coronary syndromes, even without the need for gadolinium contrast administration. Furthermore, quantitative perfusion CMR techniques have enabled the non-invasive assessment of myocardial blood flow and myocardial perfusion reserve and can reliably identify multivessel coronary artery disease and microvascular dysfunction. This review summarizes the clinical applications and the prognostic value of the novel CMR parametric mapping techniques in the setting of chronic coronary syndromes and discusses their strengths, pitfalls and future directions.
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Affiliation(s)
- Maria Anna Bazmpani
- Department of First Cardiology, Aristotle University of Thessaloniki School of Medicine, AHEPA University Hospital, 54636 Thessaloniki, Greece
| | | | - Christos A. Papanastasiou
- Department of First Cardiology, Aristotle University of Thessaloniki School of Medicine, AHEPA University Hospital, 54636 Thessaloniki, Greece
| | - Antonios Ziakas
- Department of First Cardiology, Aristotle University of Thessaloniki School of Medicine, AHEPA University Hospital, 54636 Thessaloniki, Greece
| | - Theodoros D. Karamitsos
- Department of First Cardiology, Aristotle University of Thessaloniki School of Medicine, AHEPA University Hospital, 54636 Thessaloniki, Greece
- Correspondence: ; Tel.: +30-2310994832; Fax: +30-2310994673
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16
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The Merits, Limitations, and Future Directions of Cost-Effectiveness Analysis in Cardiac MRI with a Focus on Coronary Artery Disease: A Literature Review. J Cardiovasc Dev Dis 2022; 9:jcdd9100357. [PMID: 36286309 PMCID: PMC9604922 DOI: 10.3390/jcdd9100357] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/17/2022] Open
Abstract
Cardiac magnetic resonance (CMR) imaging has a wide range of clinical applications with a high degree of accuracy for many myocardial pathologies. Recent literature has shown great utility of CMR in diagnosing many diseases, often changing the course of treatment. Despite this, it is often underutilized possibly due to perceived costs, limiting patient factors and comfort, and longer examination periods compared to other imaging modalities. In this regard, we conducted a literature review using keywords “Cost-Effectiveness” and “Cardiac MRI” and selected articles from the PubMed MEDLINE database that met our inclusion and exclusion criteria to examine the cost-effectiveness of CMR. Our search result yielded 17 articles included in our review. We found that CMR can be cost-effective in quality-adjusted life years (QALYs) in select patient populations with various cardiac pathologies. Specifically, the use of CMR in coronary artery disease (CAD) patients with a pretest probability below a certain threshold may be more cost-effective compared to patients with a higher pretest probability, although its use can be limited based on geographic location, professional society guidelines, and differing reimbursement patterns. In addition, a stepwise combination of different imaging modalities, with conjunction of AHA/ACC guidelines can further enhance the cost-effectiveness of CMR.
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17
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Eyre K, Lindsay K, Razzaq S, Chetrit M, Friedrich M. Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development. Front Cardiovasc Med 2022; 9:953823. [PMID: 36277755 PMCID: PMC9582154 DOI: 10.3389/fcvm.2022.953823] [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: 05/26/2022] [Accepted: 09/22/2022] [Indexed: 11/13/2022] Open
Abstract
Simultaneous multi-parametric acquisition and reconstruction techniques (SMART) are gaining attention for their potential to overcome some of cardiovascular magnetic resonance imaging's (CMR) clinical limitations. The major advantages of SMART lie within their ability to simultaneously capture multiple "features" such as cardiac motion, respiratory motion, T1/T2 relaxation. This review aims to summarize the overarching theory of SMART, describing key concepts that many of these techniques share to produce co-registered, high quality CMR images in less time and with less requirements for specialized personnel. Further, this review provides an overview of the recent developments in the field of SMART by describing how they work, the parameters they can acquire, their status of clinical testing and validation, and by providing examples for how their use can improve the current state of clinical CMR workflows. Many of the SMART are in early phases of development and testing, thus larger scale, controlled trials are needed to evaluate their use in clinical setting and with different cardiac pathologies.
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Affiliation(s)
- Katerina Eyre
- McGill University Health Centre, Montreal, QC, Canada,Department of Experimental Medicine, McGill University, Montreal, QC, Canada,*Correspondence: Katerina Eyre,
| | - Katherine Lindsay
- McGill University Health Centre, Montreal, QC, Canada,Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Saad Razzaq
- Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Michael Chetrit
- McGill University Health Centre, Montreal, QC, Canada,Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Matthias Friedrich
- McGill University Health Centre, Montreal, QC, Canada,Department of Experimental Medicine, McGill University, Montreal, QC, Canada
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18
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Xie E, Sung E, Saad E, Trayanova N, Wu KC, Chrispin J. Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death. Front Cardiovasc Med 2022; 9:884767. [PMID: 36072882 PMCID: PMC9441865 DOI: 10.3389/fcvm.2022.884767] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.
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Affiliation(s)
- Eric Xie
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Eric Sung
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elie Saad
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Natalia Trayanova
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Katherine C. Wu
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jonathan Chrispin
- Division of Cardiology, Department of Medicine, Section of Cardiac Electrophysiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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19
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Pace DF, Dalca AV, Brosch T, Geva T, Powell AJ, Weese J, Moghari MH, Golland P. Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease. Med Image Anal 2022; 80:102469. [PMID: 35640385 PMCID: PMC9617683 DOI: 10.1016/j.media.2022.102469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 04/26/2022] [Accepted: 04/29/2022] [Indexed: 02/08/2023]
Abstract
Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but training data is limited, as when segmenting cardiac structures in patients with congenital heart disease (CHD). In this paper, we propose an iterative segmentation model and show that it can be accurately learned from a small dataset. Implemented as a recurrent neural network, the model evolves a segmentation over multiple steps, from a single user click until reaching an automatically determined stopping point. We develop a novel loss function that evaluates the entire sequence of output segmentations, and use it to learn model parameters. Segmentations evolve predictably according to growth dynamics encapsulated by training data, which consists of images, partially completed segmentations, and the recommended next step. The user can easily refine the final segmentation by examining those that are earlier or later in the output sequence. Using a dataset of 3D cardiac MR scans from patients with a wide range of CHD types, we show that our iterative model offers better generalization to patients with the most severe heart malformations.
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Affiliation(s)
- Danielle F Pace
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tom Brosch
- Philips Research Laboratories, Hamburg, Germany
| | - Tal Geva
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Andrew J Powell
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | | | - Mehdi H Moghari
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
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20
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Bradley C, Berry C. Definition and epidemiology of coronary microvascular disease. J Nucl Cardiol 2022; 29:1763-1775. [PMID: 35534718 PMCID: PMC9345825 DOI: 10.1007/s12350-022-02974-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/17/2022] [Indexed: 11/18/2022]
Abstract
Ischemic heart disease remains one of the leading causes of death and disability worldwide. However, most patients referred for a noninvasive computed tomography coronary angiogram (CTA) or invasive coronary angiogram for the investigation of angina do not have obstructive coronary artery disease (CAD). Approximately two in five referred patients have coronary microvascular disease (CMD) as a primary diagnosis and, in addition, CMD also associates with CAD and myocardial disease (dual pathology). CMD underpins excess morbidity, impaired quality of life, significant health resource utilization, and adverse cardiovascular events. However, CMD often passes undiagnosed and the onward management of these patients is uncertain and heterogeneous. International standardized diagnostic criteria allow for the accurate diagnosis of CMD, ensuring an often overlooked patient population can be diagnosed and stratified for targeted medical therapy. Key to this is assessing coronary microvascular function-including coronary flow reserve, coronary microvascular resistance, and coronary microvascular spasm. This can be done by invasive methods (intracoronary temperature-pressure wire, intracoronary Doppler flow-pressure wire, intracoronary provocation testing) and non-invasive methods [positron emission tomography (PET), cardiac magnetic resonance imaging (CMR), transthoracic Doppler echocardiography (TTDE), cardiac computed tomography (CT)]. Coronary CTA is insensitive for CMD. Functional coronary angiography represents the combination of CAD imaging and invasive diagnostic procedures.
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Affiliation(s)
- Conor Bradley
- British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom
- NHS Golden Jubilee Hospital, Clydebank, United Kingdom
| | - Colin Berry
- British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom.
- NHS Golden Jubilee Hospital, Clydebank, United Kingdom.
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, 126 University Place, Glasgow, G12 8TA, Scotland, United Kingdom.
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21
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Zens TJ, Casar Berazaluce AM, Jenkins TM, Hardie W, Alsaied T, Tretter JT, Moore R, Foster K, Fleck RJ, Hanke RE, Colvin BE, Garrison AP, Kraemer A, Crotty E, Taylor M, Garcia VF, Brown RL. The Severity of Pectus Excavatum Defect Is Associated With Impaired Cardiopulmonary Function. Ann Thorac Surg 2021; 114:1015-1021. [PMID: 34419435 DOI: 10.1016/j.athoracsur.2021.07.051] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 05/13/2021] [Accepted: 07/14/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND Repair of pectus excavatum has cosmetic benefits, but the physiologic impact remains controversial. The aim of this study was to characterize the relationship between the degree of pectus excavatum and cardiopulmonary dysfunction seen on cardiac magnetic resonance (CMR) imaging, cardiopulmonary exercise testing (CPET), and pulmonary function testing (PFT). METHODS A single-center analysis of CMR, CPET, and PFT was conducted. Regression models evaluated relationships between pectus indices and the clinical end points of cardiopulmonary function. RESULTS Data from 345 CMRs, 261 CPETs, and 281 PFTs were analyzed. Patients were a mean age of 15.2 ± 4 years, and 81% were aged <18 years. The right ventricular ejection fraction (RVEF) was <0.50 in 16% of patients, left ventricular ejection fraction (LVEF) was <0.55 in 22%, RVEF Z-score was < -2 in 32%, and the LVEF Z-score was < -2 in 18%. CPET revealed 33% of patients had reduced aerobic fitness. PFT results were abnormal in 23.1% of patients. Adjusted analyses revealed the Haller index had significant (P < .05) inverse associations with RVEF and LVEF. CONCLUSIONS The severity of pectus excavatum is associated with ventricular systolic dysfunction. Pectus excavatum impacts right and left ventricular systolic function and can also impact exercise tolerance. The Haller index and correction index may be the most useful predictors of impairment.
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Affiliation(s)
- Tiffany J Zens
- Department of Pediatric Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | | | - Todd M Jenkins
- Department of Pediatric Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - William Hardie
- Department of Pulmonology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Tarek Alsaied
- Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Justin T Tretter
- Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Ryan Moore
- Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Karla Foster
- Department of Pulmonology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Robert J Fleck
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Rachel E Hanke
- Department of Pediatric Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Brandon E Colvin
- Department of Pediatric Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Aaron P Garrison
- Department of Pediatric Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Aimee Kraemer
- Department of Pediatric Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Eric Crotty
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Michael Taylor
- Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Victor F Garcia
- Department of Pediatric Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Rebeccah L Brown
- Department of Pediatric Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
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22
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Cardiac Imaging in Athlete's Heart: The Role of the Radiologist. ACTA ACUST UNITED AC 2021; 57:medicina57050455. [PMID: 34066957 PMCID: PMC8148528 DOI: 10.3390/medicina57050455] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/28/2021] [Accepted: 04/30/2021] [Indexed: 02/07/2023]
Abstract
Athlete’s heart (AH) is the result of morphological and functional cardiac modifications due to long-lasting athletic training. Athletes can develop very marked structural myocardial changes, which may simulate or cover unknown cardiomyopathies. The differential diagnosis between AH and cardiomyopathy is necessary to prevent the risk of catastrophic events, such as sudden cardiac death, but it can be a challenging task. The improvement of the imaging modalities and the introduction of the new technologies in cardiac magnetic resonance (CMR) and cardiac computed tomography (CCT) can allow overcoming this challenge. Therefore, the radiologist, specialized in cardiac imaging, could have a pivotal role in the differential diagnosis between structural adaptative changes observed in the AH and pathological anomalies of cardiomyopathies. In this review, we summarize the main CMR and CCT techniques to evaluate the cardiac morphology, function, and tissue characterization, and we analyze the imaging features of the AH and the key differences with the main cardiomyopathies.
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23
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Potential Role of Artificial Intelligence in Cardiac Magnetic Resonance Imaging: Can It Help Clinicians in Making a Diagnosis? J Thorac Imaging 2021; 36:142-148. [PMID: 33769416 DOI: 10.1097/rti.0000000000000584] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In the era of modern medicine, artificial intelligence (AI) is a growing field of interest which is experiencing a steady development. Several applications of AI have been applied to various aspects of cardiac magnetic resonance to assist clinicians and engineers in reducing the costs of exams and, at the same time, to improve image acquisition and reconstruction, thus simplifying their analysis, interpretation, and decision-making process as well. In fact, the role of AI and machine learning in cardiovascular imaging relies on evaluating images more quickly, improving their quality, nulling intraobserver and interobserver variability in their interpretation, upgrading the understanding of the stage of the disease, and providing with a personalized approach to cardiovascular care. In addition, AI algorithm could be directed toward workflow management. This article presents an overview of the existing AI literature in cardiac magnetic resonance, with its strengths and limitations, recent applications, and promising developments. We conclude that AI is very likely be used in all the various process of diagnosis routine mode for cardiac care of patients.
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24
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Cashmore MT, McCann AJ, Wastling SJ, McGrath C, Thornton J, Hall MG. Clinical quantitative MRI and the need for metrology. Br J Radiol 2021; 94:20201215. [PMID: 33710907 PMCID: PMC8010554 DOI: 10.1259/bjr.20201215] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
MRI has been an essential diagnostic tool in healthcare for several decades. It offers unique insights into most tissues without the need for ionising radiation. Historically, MRI has been predominantly used qualitatively, images are formed to allow visual discrimination of tissues types and pathologies, rather than providing quantitative measurements. Increasingly, quantitative MRI (qMRI) is also finding clinical application, where images provide the basis for physical measurements of, e.g. tissue volume measures and represent aspects of tissue composition and microstructure. This article reviews some common current research and clinical applications of qMRI from the perspective of measurement science. qMRI not only offers additional information for radiologists, but also the opportunity for improved harmonisation and calibration between scanners and as such it is well-suited to large-scale investigations such as clinical trials and longitudinal studies. Realising these benefits, however, presents a new kind of technical challenge to MRI practioners. When measuring a parameter quantitatively, it is crucial that the reliability and reproducibility of the technique are well understood. Strictly speaking, a numerical result of a measurement is meaningless unless it is accompanied by a description of the associated measurement uncertainty. It is therefore necessary to produce not just estimates of physical properties in a quantitative image, but also their associated uncertainties. As the process of determining a physical property from the raw MR signal is complicated and multistep, estimation of uncertainty is challenging and there are many aspects of the MRI process that require validation. With the clinical implementation of qMRI techniques and its continued expansion, there is a clear and urgent need for metrology in this field.
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Affiliation(s)
| | | | - Stephen J Wastling
- Neuroradiological Academic Unit, UCL Institute of Neurology, University College London, London, UK
| | | | - John Thornton
- Neuroradiological Academic Unit, UCL Institute of Neurology, University College London, London, UK
| | - Matt G Hall
- National Physical Laboratory, Teddington, UK
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Kwan AC, Pourmorteza A, Stutman D, Bluemke DA, Lima JAC. Next-Generation Hardware Advances in CT: Cardiac Applications. Radiology 2020; 298:3-17. [PMID: 33201793 DOI: 10.1148/radiol.2020192791] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Impending major hardware advances in cardiac CT include three areas: ultra-high-resolution (UHR) CT, photon-counting CT, and phase-contrast CT. Cardiac CT is a particularly demanding CT application that requires a high degree of temporal resolution, spatial resolution, and soft-tissue contrast in a moving structure. In this review, cardiac CT is used to highlight the strengths of these technical advances. UHR CT improves visualization of calcified and stented vessels but may result in increased noise and radiation exposure. Photon-counting CT uses multiple photon energies to reduce artifacts, improve contrast resolution, and perform material decomposition. Finally, phase-contrast CT uses x-ray refraction properties to improve spatial and soft-tissue contrast. This review describes these hardware advances in CT and their relevance to cardiovascular imaging.
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Affiliation(s)
- Alan C Kwan
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
| | - Amir Pourmorteza
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
| | - Dan Stutman
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
| | - David A Bluemke
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
| | - João A C Lima
- From the Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP, Suite A3600, Los Angeles, CA 90048-0750 (A.C.K.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (A.P.); Winship Cancer Institute, Emory University, Atlanta, Ga (A.P.); Department of Biomedical Engineering, Georgia Institute of Technology-Emory University, Atlanta, Ga (A.P.); Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Md (D.S.); Extreme Light Infrastructure-Nuclear Physics, Bucharest-Magurele, Romania (D.S.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (D.A.B.); and Department of Cardiology, The Johns Hopkins Hospital, Baltimore, Md (J.A.C.L.)
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Nolte T, Scholten H, Gross-Weege N, Amthor T, Koken P, Doneva M, Schulz V. Confounding factors in breast magnetic resonance fingerprinting: B 1 + , slice profile, and diffusion effects. Magn Reson Med 2020; 85:1865-1880. [PMID: 33118649 DOI: 10.1002/mrm.28545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 09/03/2020] [Accepted: 09/14/2020] [Indexed: 11/09/2022]
Abstract
PURPOSE Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B 1 + , slice profile and diffusion effects on T1 and T2 estimation in the female breast at 1.5T. METHODS Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE. RESULTS Theoretic analysis predicts T1 under-/T2 overestimation if the nominal flip angles are underestimated and inversely, T1 under-/T2 overestimation if omitting slice profile correction, and T1 under-/T2 underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B 1 + effects occurred in vivo, causing T2 left-right inhomogeneity between both breasts. Their correction decreased the T2 difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T2 most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions. CONCLUSION Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.
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Affiliation(s)
- Teresa Nolte
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Hannah Scholten
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany
| | - Nicolas Gross-Weege
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Thomas Amthor
- Tomographic Imaging Systems, Philips Research Europe, Hamburg, Germany
| | - Peter Koken
- Tomographic Imaging Systems, Philips Research Europe, Hamburg, Germany
| | - Mariya Doneva
- Tomographic Imaging Systems, Philips Research Europe, Hamburg, Germany
| | - Volkmar Schulz
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.,Physics Institute III B, RWTH Aachen University, Aachen, Germany
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Campbell-Washburn AE. 2019 American Thoracic Society BEAR Cage Winning Proposal: Lung Imaging Using High-Performance Low-Field Magnetic Resonance Imaging. Am J Respir Crit Care Med 2020; 201:1333-1336. [PMID: 32298594 PMCID: PMC7258650 DOI: 10.1164/rccm.201912-2505ed] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
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Fahlenkamp UL, Ziegeler K, Adams LC, Böker SM, Engel G, Makowski MR. Native T1 mapping for assessment of the perilesional zone in metastases and benign lesions of the liver. Sci Rep 2020; 10:12889. [PMID: 32733016 PMCID: PMC7393097 DOI: 10.1038/s41598-020-69819-w] [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] [Received: 05/11/2020] [Accepted: 07/20/2020] [Indexed: 11/21/2022] Open
Abstract
Adjacent to hepatic metastases, liver parenchyma is often histopathologically altered even if its visual appearance on native magnetic resonance (MR) images is blunt. Yet, relaxation properties in MR imaging may show structural changes prior to visual alteration, and therefore, the aim of this study was to investigate whether T1 relaxation times in the perilesional zone differ between metastases and benign lesions. A total of 113 patients referred for MRI were included prospectively. Images were assessed for metastases, solid benign lesions and cysts, and regions-of-interest were drawn on T1 maps including the focal lesion and a close (inner perilesional zone = IPZ) and a larger perilesional zone (outer perilesional zone = OPZ). Simple ratios between these zones, as well as a gradient ratio between the IPZ and the entire perilesional zone (EPZ) were calculated. Within the collective, 44 patients had lesions of one or two entities. For metastases, the simple ratio between IPZ and OPZ as well as the mean EPZ gradient was significantly higher than for both solid benign lesions and cysts. Lesion size was not a significant covariate. We conclude, that native T1 properties of the perilesional zones differ significantly between malignant and both solid and cystic benign lesions.
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Affiliation(s)
- Ute Lina Fahlenkamp
- Department of Radiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
| | - Katharina Ziegeler
- Department of Radiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Lisa Christine Adams
- Department of Radiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Sarah Maria Böker
- Department of Radiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Günther Engel
- Department of Radiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Marcus Richard Makowski
- Department of Radiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Department of Radiology, Klinikum rechts der Isar der TU München, Ismaninger Straße 22, 81675, Munich, Germany
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Abstract
OBJECTIVE. The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardiac MRI, and discuss its limitations and challenges. CONCLUSION. Deep learning has shown state-of-the-art performance on quantitative analysis of multiple cardiac MRI sequences and holds great promise for future use in clinical practice and scientific research.
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