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Voges I, Raimondi F, McMahon CJ, Ait-Ali L, Babu-Narayan SV, Botnar RM, Burkhardt B, Gabbert DD, Grosse-Wortmann L, Hasan H, Hansmann G, Helbing WA, Krupickova S, Latus H, Martini N, Martins D, Muthurangu V, Ojala T, van Ooij P, Pushparajah K, Rodriguez-Palomares J, Sarikouch S, Grotenhuis HB, Greil FG. Clinical impact of novel CMR technology on patients with congenital heart disease. A scientific statement of the Association for European Pediatric and Congenital Cardiology (AEPC) and the European Association of Cardiovascular Imaging (EACVI) of the ESC. Eur Heart J Cardiovasc Imaging 2024:jeae172. [PMID: 38985851 DOI: 10.1093/ehjci/jeae172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 07/01/2024] [Indexed: 07/12/2024] Open
Abstract
Cardiovascular magnetic resonance (CMR) imaging is recommended in patients with congenital heart disease (CHD) in clinical practice guidelines as the imaging standard for a large variety of diseases. As CMR is evolving, novel techniques are becoming available. Some of them are already used clinically, whereas others still need further evaluation. In this statement the authors give an overview of relevant new CMR techniques for the assessment of CHD. Studies with reference values for these new techniques are listed in the supplement.
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Affiliation(s)
- Inga Voges
- Department of Congenital Heart Disease and Pediatric Cardiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Lübeck/Kiel, Germany
| | | | - Colin J McMahon
- Department of Paediatric Cardiology, Children's Health Ireland at Crumlin, Dublin 12, Ireland
| | - Lamia Ait-Ali
- Institute of clinical Physiology CNR, Massa, Italy
- Heart Hospital, G. Monastery foundation, Massa, Italy
| | - Sonya V Babu-Narayan
- Royal Brompton Hospital, Part of Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, SW3 6NP, UK
- National Heart and Lung Institute, Imperial College, London, England
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
- Institute for Biological and Medical Engineering and School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Barbara Burkhardt
- Pediatric Heart Center, University Children's Hospital Zurich, Zurich, Switzerland
| | - Dominik D Gabbert
- Department of Congenital Heart Disease and Pediatric Cardiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Lübeck/Kiel, Germany
| | - Lars Grosse-Wortmann
- Division of Cardiology, Oregon Health and Science University Hospital, Portland, Oregon, United States
| | - Hosan Hasan
- Department of Pediatric Cardiology and Critical Care, Hannover Medical School, Hannover, Germany
- European Pediatric Pulmonary Vascular Disease Network, Berlin, Germany
| | - Georg Hansmann
- Department of Pediatric Cardiology and Critical Care, Hannover Medical School, Hannover, Germany
- European Pediatric Pulmonary Vascular Disease Network, Berlin, Germany
| | - Willem A Helbing
- Department of Pediatrics, division of cardiology, and department of Radiology, Erasmus MC-Sophia children's hospital, Rotterdam, the Netherlands
| | - Sylvia Krupickova
- Royal Brompton Hospital, Part of Guy's and St Thomas' NHS Foundation Trust, Sydney Street, London, SW3 6NP, UK
- National Heart and Lung Institute, Imperial College, London, England
- Department of Paediatric Cardiology, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| | - Heiner Latus
- Clinic for Pediatric Cardiology and Congenital Heart Disease Klinikum Stuttgart Germany
| | - Nicola Martini
- Department of Radiology, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
- U.O.C. Bioingegneria, Fondazione G. Monasterio CNR-Regione Toscana, Pisa, Italy
| | - Duarte Martins
- Pediatric Cardiology Department, Hospital de Santa Cruz, Centro Hospitalar Lisboa Ocidental, Lisbon, Portugal
| | - Vivek Muthurangu
- Centre for Translational Cardiovascular Imaging, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Tiina Ojala
- New Children's Hospital Pediatric Research Center, Helsinki University Hospital, Helsinki, Finland
| | - Pim van Ooij
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands
- Department of Pediatric Cardiology, Wilhelmina Children's Hospital/University Medical Center Utrecht, Utrecht, the Netherlands
| | - Kuberan Pushparajah
- School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Jose Rodriguez-Palomares
- CIBER Cardiovascular, Instituto de Salud Carlos III, Madrid, Spain
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, Amsterdam, the Netherlands
- Servicio de Cardiología, Hospital Universitario Vall Hebrón. Institut de Recerca Vall Hebrón (VHIR). Departamento de Medicina, Universitat Autònoma de Barcelona. Barcelona. Spain
| | - Samir Sarikouch
- Department for Cardiothoracic, Transplant, and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - Heynric B Grotenhuis
- Department of Pediatric Cardiology, Wilhelmina Children's Hospital/University Medical Center Utrecht, Utrecht, the Netherlands
| | - F Gerald Greil
- Department of Pediatrics, UT Southwestern/Children's Health, 1935 Medical District Drive B3.09, Dallas, TX 75235
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Baker RR, Muthurangu V, Rega M, Walsh SB, Steeden JA. Rapid 2D 23Na MRI of the calf using a denoising convolutional neural network. Magn Reson Imaging 2024; 110:184-194. [PMID: 38642779 DOI: 10.1016/j.mri.2024.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/12/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024]
Abstract
PURPOSE 23Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the inherently low 23Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction algorithms such as compressed sensing (CS) have been proposed to mitigate low signal-to-noise ratio (SNR); although, these can result in unnatural images, suboptimal denoising and long processing times. Recently, machine learning has been increasingly used to denoise 1H MRI acquisitions; however, this approach typically requires large volumes of high-quality training data, which is not readily available for 23Na MRI. Here, we propose using 1H data to train a denoising convolutional neural network (CNN), which we subsequently demonstrate on prospective 23Na images of the calf. METHODS 1893 1H fat-saturated transverse slices of the knee from the open-source fastMRI dataset were used to train denoising CNNs for different levels of noise. Synthetic low SNR images were generated by adding gaussian noise to the high-quality 1H k-space data before reconstruction to create paired training data. For prospective testing, 23Na images of the calf were acquired in 10 healthy volunteers with a total of 150 averages over ten minutes, which were used as a reference throughout the study. From this data, images with fewer averages were retrospectively reconstructed using a non-uniform fast Fourier transform (NUFFT) as well as CS, with the NUFFT images subsequently denoised using the trained CNN. RESULTS CNNs were successfully applied to 23Na images reconstructed with 50, 40 and 30 averages. Muscle and skin apparent TSC quantification from CNN-denoised images were equivalent to those from CS images, with <0.9 mM bias compared to reference values. Estimated SNR was significantly higher in CNN-denoised images compared to NUFFT, CS and reference images. Quantitative edge sharpness was equivalent for all images. For subjective image quality ranking, CNN-denoised images ranked equally best with reference images and significantly better than NUFFT and CS images. CONCLUSION Denoising CNNs trained on 1H data can be successfully applied to 23Na images of the calf; thus, allowing scan time to be reduced from ten minutes to two minutes with little impact on image quality or apparent TSC quantification accuracy.
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Affiliation(s)
- Rebecca R Baker
- UCL Centre for Medical Imaging, University College London, London, UK; UCL Centre for Translational Cardiovascular Imaging, University College London, London, UK.
| | - Vivek Muthurangu
- UCL Centre for Translational Cardiovascular Imaging, University College London, London, UK.
| | - Marilena Rega
- Institute of Nuclear Medicine, University College Hospital, London, UK.
| | - Stephen B Walsh
- Department of Renal Medicine, University College London, London, UK.
| | - Jennifer A Steeden
- UCL Centre for Translational Cardiovascular Imaging, University College London, London, UK.
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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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Wei L, Aryal MP, Cuneo K, Matuszak M, Lawrence TS, Ten Haken RK, Cao Y, Naqa IE. Deep learning prediction of post-SBRT liver function changes and NTCP modeling in hepatocellular carcinoma based on DGAE-MRI. Med Phys 2023; 50:5597-5608. [PMID: 36988423 DOI: 10.1002/mp.16386] [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: 08/11/2022] [Revised: 03/07/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Stereotactic body radiation therapy (SBRT) produces excellent local control for patients with hepatocellular carcinoma (HCC). However, the risk of toxicity for normal liver tissue is still a limiting factor. Normal tissue complication probability (NTCP) models have been proposed to estimate the toxicity with the assumption of uniform liver function distribution, which is not optimal. With more accurate regional liver functional imaging available for individual patient, we can improve the estimation and be more patient-specific. PURPOSE To develop normal tissue complication probability (NTCP) models using pre-/during-treatment (RT) dynamic Gadoxetic Acid-enhanced (DGAE) MRI for adaptation of RT in a patient-specific manner in hepatocellular cancer (HCC) patients who receive SBRT. METHODS 24 of 146 HCC patients who received SBRT underwent DGAE MRI. Physical doses were converted into EQD2 for analysis. Voxel-by-voxel quantification of the contrast uptake rate (k1) from DGAE-MRI was used to quantify liver function. A logistic dose-response model was used to estimate the fraction of liver functional loss, and NTCP was estimated using the cumulative functional reserve model for changes in Child-Pugh (C-P) scores. Model parameters were calculated using maximum-likelihood estimations. During-RT liver functional maps were predicted from dose distributions and pre-RT k1 maps with a conditional Wasserstein generative adversarial network (cWGAN). Imaging prediction quality was assessed using root-mean-square error (RMSE) and structural similarity (SSIM) metrics. The dose-response and NTCP were fit on both original and cWGAN predicted images and compared using a Wilcoxon signed-rank test. RESULTS Logistic dose response models for changes in k1 yielded D50 of 35.2 (95% CI: 26.7-47.5) Gy and k of 0.62 (0.49-0.75) for the whole population. The high baseline ALBI (poor liver function) subgroup showed a significantly smaller D50 of 11.7 (CI: 9.06-15.4) Gy and larger k of 0.96 (CI: 0.74-1.22) compared to a low baseline ALBI (good liver function) subgroup of 54.8 (CI: 38.3-79.1) Gy and 0.59 (CI: 0.48-0.74), with p-values of < 0.001 and = 0.008, respectively, which indicates higher radiosensitivity for the worse baseline liver function cohort. Subset analyses were also performed for high/low baseline CP subgroups. The corresponding NTCP models showed good agreement for the fit parameters between cWGAN predicted and the ground-truth during-RT images with no statistical differences for low ALBI subgroup. CONCLUSIONS NTCP models which incorporate voxel-wise functional information from DGAE-MRI k1 maps were successfully developed and feasibility was demonstrated in a small patient cohort. cWGAN predicted functional maps show promise for estimating localized patient-specific response to RT and warrant further validation in a larger patient cohort.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Madhava P Aryal
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kyle Cuneo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
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Jaubert O, Montalt‐Tordera J, Brown J, Knight D, Arridge S, Steeden J, Muthurangu V. FReSCO: Flow Reconstruction and Segmentation for low-latency Cardiac Output monitoring using deep artifact suppression and segmentation. Magn Reson Med 2022; 88:2179-2189. [PMID: 35781891 PMCID: PMC9545927 DOI: 10.1002/mrm.29374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/19/2022] [Accepted: 06/09/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE Real-time monitoring of cardiac output (CO) requires low-latency reconstruction and segmentation of real-time phase-contrast MR, which has previously been difficult to perform. Here we propose a deep learning framework for "FReSCO" (Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring). METHODS Deep artifact suppression and segmentation U-Nets were independently trained. Breath-hold spiral phase-contrast MR data (N = 516) were synthetically undersampled using a variable-density spiral sampling pattern and gridded to create aliased data for training of the artifact suppression U-net. A subset of the data (N = 96) was segmented and used to train the segmentation U-net. Real-time spiral phase-contrast MR was prospectively acquired and then reconstructed and segmented using the trained models (FReSCO) at low latency at the scanner in 10 healthy subjects during rest, exercise, and recovery periods. Cardiac output obtained via FReSCO was compared with a reference rest CO and rest and exercise compressed-sensing CO. RESULTS The FReSCO framework was demonstrated prospectively at the scanner. Beat-to-beat heartrate, stroke volume, and CO could be visualized with a mean latency of 622 ms. No significant differences were noted when compared with reference at rest (bias = -0.21 ± 0.50 L/min, p = 0.246) or compressed sensing at peak exercise (bias = 0.12 ± 0.48 L/min, p = 0.458). CONCLUSIONS The FReSCO framework was successfully demonstrated for real-time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.
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Affiliation(s)
- Olivier Jaubert
- UCL Center for Translational Cardiovascular ImagingUniversity College London
LondonUK
- Department of Computer ScienceUniversity College LondonLondonUK
| | | | - James Brown
- Department of CardiologyRoyal Free London NHS Foundation TrustLondonUK
| | - Daniel Knight
- Department of CardiologyRoyal Free London NHS Foundation TrustLondonUK
| | - Simon Arridge
- Department of Computer ScienceUniversity College LondonLondonUK
| | - Jennifer Steeden
- UCL Center for Translational Cardiovascular ImagingUniversity College London
LondonUK
| | - Vivek Muthurangu
- UCL Center for Translational Cardiovascular ImagingUniversity College London
LondonUK
- Department of CardiologyRoyal Free London NHS Foundation TrustLondonUK
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Prospects of Structural Similarity Index for Medical Image Analysis. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083754] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
An image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. During the past two decades, a novel universal image quality assessment has been developed with the ability of adaptation with human visual perception for measuring the difference of a degraded image from the reference image, namely a structural similarity index. Structural similarity has since been widely used in various sectors, including medical image evaluation. Although numerous studies have reported the use of structural similarity as an evaluation strategy for computer-based medical images, reviews on the prospects of using structural similarity for medical imaging applications have been rare. This paper presents previous studies implementing structural similarity in analyzing medical images from various imaging modalities. In addition, this review describes structural similarity from the perspective of a family’s historical background, as well as progress made from the original to the recent structural similarity, and its strengths and drawbacks. Additionally, potential research directions in applying such similarities related to medical image analyses are described. This review will be beneficial in guiding researchers toward the discovery of potential medical image examination methods that can be improved through structural similarity index.
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