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Brandt Y, Lubrecht JM, Adriaans BP, Aben JP, Gerretsen SC, Ghossein-Doha C, Spaanderman MEA, Prinzen FW, Kooi ME. Quantification of left ventricular myocardial strain: Comparison between MRI tagging, MRI feature tracking, and ultrasound speckle tracking. NMR IN BIOMEDICINE 2024; 37:e5164. [PMID: 38664924 DOI: 10.1002/nbm.5164] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 03/25/2024] [Accepted: 03/28/2024] [Indexed: 08/07/2024]
Abstract
Ultrasound speckle tracking is frequently used to quantify myocardial strain, and magnetic resonance imaging (MRI) feature tracking is rapidly gaining interest. Our aim is to validate cardiac MRI feature tracking by comparing it with the gold standard method (i.e., MRI tagging) in healthy subjects and patients. Furthermore, we aim to perform an indirect validation by comparing ultrasound speckle tracking with MRI feature tracking. Forty-two subjects (17 formerly preeclamptic women, three healthy women, and 22 left bundle branch block patients of both sexes) received 3-T cardiac MRI and echocardiography. Cine and tagged MRI, and B-mode ultrasound images, were acquired. Intrapatient global and segmental left ventricular circumferential (MRI tagging vs. MRI feature tracking) and longitudinal (MRI feature tracking vs. ultrasound speckle tracking) peak strain and time to peak strain were compared between the three techniques. Intraclass correlation coefficient (ICC) (< 0.50 = poor, 0.50-0.75 = moderate, > 0.75-0.90 = good, > 0.90 = excellent) and Bland-Altman analysis were used to assess correlation and bias; p less than 0.05 indicates a significant ICC or bias. Global peak strain parameters showed moderate-to-good correlations between methods (ICC = 0.71-0.83, p < 0.01) with no significant biases. Global time to peak strain parameters showed moderate-to-good correlations (ICC = 0.56-0.82, p < 0.01) with no significant biases. Segmental peak strains showed significant biases in all parameters and moderate-to-good correlation (ICC = 0.62-0.77, p < 0.01), except for lateral longitudinal peak strain (ICC = 0.23, p = 0.22). Segmental time to peak strain parameters showed moderate-to-good correlation (ICC = 0.58-0.74, p < 0.01) with no significant biases. MRI feature tracking is a valid method to examine myocardial strain, but there is bias in absolute segmental strain values between imaging techniques. MRI feature tracking shows adequate comparability with ultrasound speckle tracking.
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Affiliation(s)
- Yentl Brandt
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Jolijn M Lubrecht
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Bouke P Adriaans
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Cardiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Jean-Paul Aben
- Department of Research and Development, Pie Medical Imaging B.V., Maastricht, The Netherlands
| | - Suzanne C Gerretsen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Chahinda Ghossein-Doha
- Department of Cardiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Marc E A Spaanderman
- Department of Obstetrics and Gynecology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Obstetrics and Gynecology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Frits W Prinzen
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - M Eline Kooi
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
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2
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Huff RD, Houghton F, Earl CC, Ghajar-Rahimi E, Dogra I, Yu D, Harris-Adamson C, Goergen CJ, O'Connell GD. Deep learning enables accurate soft tissue tendon deformation estimation in vivo via ultrasound imaging. Sci Rep 2024; 14:18401. [PMID: 39117664 PMCID: PMC11310354 DOI: 10.1038/s41598-024-68875-w] [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: 02/08/2024] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.
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Affiliation(s)
- Reece D Huff
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Frederick Houghton
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Conner C Earl
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Elnaz Ghajar-Rahimi
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Ishan Dogra
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Denny Yu
- School of Industrial Engineering, Purdue University, West Lafayette, IN, 47906, USA
| | - Carisa Harris-Adamson
- School of Public Health, University of California, Berkeley, Berkeley, CA, 94704, USA
- Department of Occupational and Environmental Medicine, University of California, San Francisco, San Francisco, CA, 94117, USA
| | - Craig J Goergen
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Grace D O'Connell
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA.
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, 94142, USA.
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3
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Bivona DJ, Ghadimi S, Wang Y, Oomen PJA, Malhotra R, Darby A, Mangrum JM, Mason PK, Mazimba S, Patel AR, Epstein FH, Bilchick KC. Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy. Comput Biol Med 2024; 178:108627. [PMID: 38850959 PMCID: PMC11265973 DOI: 10.1016/j.compbiomed.2024.108627] [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: 02/16/2024] [Revised: 04/22/2024] [Accepted: 05/18/2024] [Indexed: 06/10/2024]
Abstract
Cardiac resynchronization therapy (CRT) can lead to marked symptom reduction and improved survival in selected patients with heart failure with reduced ejection fraction (HFrEF); however, many candidates for CRT based on clinical guidelines do not have a favorable response. A better way to identify patients expected to benefit from CRT that applies machine learning to accessible and cost-effective diagnostic tools such as the 12-lead electrocardiogram (ECG) could have a major impact on clinical care in HFrEF by helping providers personalize treatment strategies and avoid delays in initiation of other potentially beneficial treatments. This study addresses this need by demonstrating that a novel approach to ECG waveform analysis using functional principal component decomposition (FPCD) performs better than measures that require manual ECG analysis with the human eye and also at least as well as a previously validated but more expensive approach based on cardiac magnetic resonance (CMR). Analyses are based on five-fold cross validation of areas under the curve (AUCs) for CRT response and survival time after the CRT implant using Cox proportional hazards regression with stratification of groups using a Gaussian mixture model approach. Furthermore, FPCD and CMR predictors are shown to be independent, which demonstrates that the FPCD electrical findings and the CMR mechanical findings together provide a synergistic model for response and survival after CRT. In summary, this study provides a highly effective approach to prognostication after CRT in HFrEF using an accessible and inexpensive diagnostic test with a major expected impact on personalization of therapies.
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Affiliation(s)
- Derek J Bivona
- Department of Medicine, University of Virginia Health System, Charlottesville, VA 22903, USA; Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Sona Ghadimi
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Yu Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Pim J A Oomen
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92697, USA
| | - Rohit Malhotra
- Department of Medicine, University of Virginia Health System, Charlottesville, VA 22903, USA
| | - Andrew Darby
- Department of Medicine, University of Virginia Health System, Charlottesville, VA 22903, USA
| | - J Michael Mangrum
- Department of Medicine, University of Virginia Health System, Charlottesville, VA 22903, USA
| | - Pamela K Mason
- Department of Medicine, University of Virginia Health System, Charlottesville, VA 22903, USA
| | - Sula Mazimba
- Advent Health Transplant Institute, AdventHealth, Orlando, FL 32804, USA
| | - Amit R Patel
- Department of Medicine, University of Virginia Health System, Charlottesville, VA 22903, USA; Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA 22903, USA
| | - Frederick H Epstein
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA; Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA 22903, USA
| | - Kenneth C Bilchick
- Department of Medicine, University of Virginia Health System, Charlottesville, VA 22903, USA.
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Zhou Z, Hu P, Qi H. Stop moving: MR motion correction as an opportunity for artificial intelligence. MAGMA (NEW YORK, N.Y.) 2024; 37:397-409. [PMID: 38386151 DOI: 10.1007/s10334-023-01144-5] [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: 09/28/2023] [Revised: 12/09/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction.
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Affiliation(s)
- Zijian Zhou
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China.
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China.
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5
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Zhang Q, Fotaki A, Ghadimi S, Wang Y, Doneva M, Wetzl J, Delfino JG, O'Regan DP, Prieto C, Epstein FH. Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation. J Cardiovasc Magn Reson 2024; 26:101051. [PMID: 38909656 PMCID: PMC11331970 DOI: 10.1016/j.jocmr.2024.101051] [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: 03/17/2024] [Revised: 06/09/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024] Open
Abstract
BACKGROUND Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR. METHODS Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis. RESULTS These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives. CONCLUSIONS Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.
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Affiliation(s)
- Qiang Zhang
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UK.
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Sona Ghadimi
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Yu Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | | | - Jens Wetzl
- Siemens Healthineers AG, Erlangen, Germany.
| | - Jana G Delfino
- US Food and Drug Administration, Center for Devices and Radiological Health (CDRH), Office of Science and Engineering Laboratories (OSEL), Silver Spring, MD, USA.
| | - Declan P O'Regan
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Frederick H Epstein
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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6
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Stamate E, Piraianu AI, Ciobotaru OR, Crassas R, Duca O, Fulga A, Grigore I, Vintila V, Fulga I, Ciobotaru OC. Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years. Diagnostics (Basel) 2024; 14:1103. [PMID: 38893630 PMCID: PMC11172021 DOI: 10.3390/diagnostics14111103] [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: 04/22/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. RESULTS We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. CONCLUSIONS The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.
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Affiliation(s)
- Elena Stamate
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Alin-Ionut Piraianu
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
| | - Oana Roxana Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
| | - Rodica Crassas
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Oana Duca
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Ana Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Ionica Grigore
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Emergency County Hospital Braila, 810325 Braila, Romania;
| | - Vlad Vintila
- Department of Cardiology, Emergency University Hospital of Bucharest, 050098 Bucharest, Romania; (E.S.); (V.V.)
- Clinical Department of Cardio-Thoracic Pathology, University of Medicine and Pharmacy “Carol Davila” Bucharest, 37 Dionisie Lupu Street, 4192910 Bucharest, Romania
| | - Iuliu Fulga
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei Street, 800578 Galati, Romania
| | - Octavian Catalin Ciobotaru
- Faculty of Medicine and Pharmacy, University “Dunarea de Jos” of Galati, 35 AI Cuza Street, 800010 Galati, Romania; (O.D.); (A.F.); (I.G.); (I.F.); (O.C.C.)
- Railway Hospital Galati, 800223 Galati, Romania
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Xing J, Wu N, Bilchick KC, Epstein FH, Zhang M. MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2024; 2024:10.1109/isbi56570.2024.10635410. [PMID: 39371471 PMCID: PMC11450657 DOI: 10.1109/isbi56570.2024.10635410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/08/2024]
Abstract
This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs. Our framework consists of two major components: (i) a DENSE-supervised strain network leveraging latent motion features learned from a registration network to predict myocardial strains; and (ii) a LMA network taking advantage of the predicted strain for effective LMA detection. Experimental results show that our proposed work substantially improves the performance of strain analysis and LMA detection from cine CMR images, aligning more closely with the achievements of DENSE.
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Affiliation(s)
- Jiarui Xing
- Department of Electrical and Computer Engineering, University of Virginia, USA
| | - Nian Wu
- Department of Electrical and Computer Engineering, University of Virginia, USA
| | | | - Frederick H Epstein
- Department of Biomedical Engineering, University of Virginia Health System, USA
| | - Miaomiao Zhang
- Department of Electrical and Computer Engineering, University of Virginia, USA
- Department of Computer Science, University of Virginia, USA
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8
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Bivona DJ, Oomen PJA, Wang Y, Morales FL, Abdi M, Gao X, Malhotra R, Darby A, Mehta N, Monfredi OJ, Mangrum JM, Mason PK, Levy WC, Mazimba S, Patel AR, Epstein FH, Bilchick KC. Cardiac Magnetic Resonance, Electromechanical Activation, Kidney Function, and Natriuretic Peptides in Cardiac Resynchronization Therapy Upgrades. J Cardiovasc Dev Dis 2023; 10:409. [PMID: 37887856 PMCID: PMC10607260 DOI: 10.3390/jcdd10100409] [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: 08/01/2023] [Revised: 09/07/2023] [Accepted: 09/18/2023] [Indexed: 10/28/2023] Open
Abstract
As the mechanism for worse prognosis after cardiac resynchronization therapy (CRT) upgrades in heart failure patients with RVP dependence (RVP-HF) has clinical implications for patient selection and CRT implementation approaches, this study's objective was to evaluate prognostic implications of cardiac magnetic resonance (CMR) findings and clinical factors in 102 HF patients (23.5% female, median age 66.5 years old, median follow-up 4.8 years) with and without RVP dependence undergoing upgrade and de novo CRT implants. Compared with other CRT groups, RVP-HF patients had decreased survival (p = 0.02), more anterior late-activated LV pacing sites (p = 0.002) by CMR, more atrial fibrillation (p = 0.0006), and higher creatinine (0.002). CMR activation timing at the LV pacing site predicted post-CRT LV functional improvement (p < 0.05), and mechanical activation onset < 34 ms by CMR at the LVP site was associated with decreased post-CRT survival in a model with higher pre-CRT creatinine and B-type natriuretic peptide (AUC 0.89; p < 0.0001); however, only the higher pre-CRT creatinine partially mediated (37%) the decreased survival in RVP-HF patients. In conclusion, RVP-HF had a distinct CMR phenotype, which has important implications for the selection of LV pacing sites in CRT upgrades, and only chronic kidney disease mediated the decreased survival after CRT in RVP-HF.
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Affiliation(s)
- Derek J. Bivona
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
| | - Pim J. A. Oomen
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA 92617, USA;
| | - Yu Wang
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, VA 22908, USA; (Y.W.); (M.A.); (F.H.E.)
| | - Frances L. Morales
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
| | - Mohamad Abdi
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, VA 22908, USA; (Y.W.); (M.A.); (F.H.E.)
| | - Xu Gao
- Department of Medicine, Northwestern University, Chicago, IL 60611, USA;
| | - Rohit Malhotra
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
| | - Andrew Darby
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
| | - Nishaki Mehta
- Department of Medicine, William Beaumont Oakland University School of Medicine, Royal Oak, MI 48309, USA;
| | - Oliver J. Monfredi
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
| | - J. Michael Mangrum
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
| | - Pamela K. Mason
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
| | - Wayne C. Levy
- Department of Medicine, University of Washington, Seattle, WA 98195, USA;
| | - Sula Mazimba
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
| | - Amit R. Patel
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
| | - Frederick H. Epstein
- Department of Biomedical Engineering, University of Virginia Health System, Charlottesville, VA 22908, USA; (Y.W.); (M.A.); (F.H.E.)
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA 22908, USA
| | - Kenneth C. Bilchick
- Department of Cardiovascular Medicine, University of Virginia Health System, Charlottesville, VA 22908, USA; (D.J.B.); (F.L.M.); (R.M.); (A.D.); (O.J.M.); (J.M.M.); (P.K.M.); (S.M.); (A.R.P.)
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Collapse
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