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Rock CA, Chen YI, Wang R, Philip AL, Keil B, Weiner RB, Elmariah S, Mekkaoui C, Nguyen CT, Sosnovik DE. Diffusion Tensor Phenomapping of the Healthy and Pressure-Overloaded Human Heart. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.03.24306781. [PMID: 38746173 PMCID: PMC11092740 DOI: 10.1101/2024.05.03.24306781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Current techniques to image the microstructure of the heart with diffusion tensor MRI (DTI) are highly under-resolved. We present a technique to improve the spatial resolution of cardiac DTI by almost 10-fold and leverage this to measure local gradients in cardiomyocyte alignment or helix angle (HA). We further introduce a phenomapping approach based on voxel-wise hierarchical clustering of these gradients to identify distinct microstructural microenvironments in the heart. Initial development was performed in healthy volunteers (n=8). Thereader, subjects with severe but well-compensated aortic stenosis (AS, n=10) were compared to age-matched controls (CTL, n=10). Radial HA gradient was significantly reduced in AS (8.0±0.8°/mm vs. 10.2±1.8°/mm, p=0.001) but the other HA gradients did not change significantly. Four distinct microstructural clusters could be idenJfied in both the CTL and AS subjects and did not differ significantly in their properties or distribution. Despite marked hypertrophy, our data suggest that the myocardium in well-compensated AS can maintain its microstructural coherence. The described phenomapping approach can be used to characterize microstructural plasticity and perturbation in any organ system and disease.
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Huang J, Ferreira PF, Wang L, Wu Y, Aviles-Rivero AI, Schönlieb CB, Scott AD, Khalique Z, Dwornik M, Rajakulasingam R, De Silva R, Pennell DJ, Nielles-Vallespin S, Yang G. Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study. Sci Rep 2024; 14:5658. [PMID: 38454072 PMCID: PMC10920645 DOI: 10.1038/s41598-024-55880-2] [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: 05/05/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024] Open
Abstract
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) of × 2 and × 4 , with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AF × 2 or most DT parameters at AF × 4 , and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF × 2 and AF × 4 . However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF × 8 , the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.
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Grants
- Wellcome Trust
- RG/19/1/34160 British Heart Foundation
- This study was supported in part by the UKRI Future Leaders Fellowship (MR/V023799/1), BHF (RG/19/1/34160), the ERC IMI (101005122), the H2020 (952172), the MRC (MC/PC/21013), the Royal Society (IEC/NSFC/211235), the NVIDIA Academic Hardware Grant Program, EPSRC (EP/V029428/1, EP/S026045/1, EP/T003553/1, EP/N014588/1, EP/T017961/1), and the Cambridge Mathematics of Information in Healthcare Hub (CMIH) Partnership Fund.
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Affiliation(s)
- Jiahao Huang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
- Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK.
| | - Pedro F Ferreira
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Lichao Wang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Department of Computing, Imperial College London, London, UK
| | - Yinzhe Wu
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Angelica I Aviles-Rivero
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Andrew D Scott
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Zohya Khalique
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Maria Dwornik
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Ramyah Rajakulasingam
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Ranil De Silva
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Dudley J Pennell
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Sonia Nielles-Vallespin
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
- Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK.
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Vollbrecht TM, Hart C, Zhang S, Katemann C, Sprinkart AM, Isaak A, Attenberger U, Pieper CC, Kuetting D, Geipel A, Strizek B, Luetkens JA. Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI. Front Cardiovasc Med 2024; 11:1323443. [PMID: 38410246 PMCID: PMC10894983 DOI: 10.3389/fcvm.2024.1323443] [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: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 02/28/2024] Open
Abstract
Purpose This study aims to evaluate deep learning (DL) denoising reconstructions for image quality improvement of Doppler ultrasound (DUS)-gated fetal cardiac MRI in congenital heart disease (CHD). Methods Twenty-five fetuses with CHD (mean gestational age: 35 ± 1 weeks) underwent fetal cardiac MRI at 3T. Cine imaging was acquired using a balanced steady-state free precession (bSSFP) sequence with Doppler ultrasound gating. Images were reconstructed using both compressed sensing (bSSFP CS) and a pre-trained convolutional neural network trained for DL denoising (bSSFP DL). Images were compared qualitatively based on a 5-point Likert scale (from 1 = non-diagnostic to 5 = excellent) and quantitatively by calculating the apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (aCNR). Diagnostic confidence was assessed for the atria, ventricles, foramen ovale, valves, great vessels, aortic arch, and pulmonary veins. Results Fetal cardiac cine MRI was successful in 23 fetuses (92%), with two studies excluded due to extensive fetal motion. The image quality of bSSFP DL cine reconstructions was rated superior to standard bSSFP CS cine images in terms of contrast [3 (interquartile range: 2-4) vs. 5 (4-5), P < 0.001] and endocardial edge definition [3 (2-4) vs. 4 (4-5), P < 0.001], while the extent of artifacts was found to be comparable [4 (3-4.75) vs. 4 (3-4), P = 0.40]. bSSFP DL images had higher aSNR and aCNR compared with the bSSFP CS images (aSNR: 13.4 ± 6.9 vs. 8.3 ± 3.6, P < 0.001; aCNR: 26.6 ± 15.8 vs. 14.4 ± 6.8, P < 0.001). Diagnostic confidence of the bSSFP DL images was superior for the evaluation of cardiovascular structures (e.g., atria and ventricles: P = 0.003). Conclusion DL image denoising provides superior quality for DUS-gated fetal cardiac cine imaging of CHD compared to standard CS image reconstruction.
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Affiliation(s)
- Thomas M Vollbrecht
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
| | - Christopher Hart
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
- Department of Pediatric Cardiology, University Hospital Bonn, Bonn, Germany
| | - Shuo Zhang
- Philips GmbH Market DACH, PD Clinical Science, Hamburg, Germany
| | | | - Alois M Sprinkart
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
| | - Alexander Isaak
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
| | - Ulrike Attenberger
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Claus C Pieper
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Daniel Kuetting
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
| | - Annegret Geipel
- Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany
| | - Brigitte Strizek
- Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany
| | - Julian A Luetkens
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
- Quantitative Imaging Lab Bonn (QILaB), Bonn, Germany
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Morales MA, Manning WJ, Nezafat R. Present and Future Innovations in AI and Cardiac MRI. Radiology 2024; 310:e231269. [PMID: 38193835 PMCID: PMC10831479 DOI: 10.1148/radiol.231269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/21/2023] [Accepted: 10/26/2023] [Indexed: 01/10/2024]
Abstract
Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.
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Affiliation(s)
- Manuel A. Morales
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
| | - Warren J. Manning
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
| | - Reza Nezafat
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
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Shusharina N, Liu X, Coll-Font J, Foster A, El Fakhri G, Woo J, Bortfeld T, Nguyen C. Feasibility study of clinical target volume definition for soft-tissue sarcoma using muscle fiber orientations derived from diffusion tensor imaging. Phys Med Biol 2022; 67. [PMID: 35817048 PMCID: PMC9344976 DOI: 10.1088/1361-6560/ac8045] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 07/11/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. Soft-tissue sarcoma spreads preferentially along muscle fibers. We explore the utility of deriving muscle fiber orientations from diffusion tensor MRI (DT-MRI) for defining the boundary of the clinical target volume (CTV) in muscle tissue. Approach. We recruited eight healthy volunteers to acquire MR images of the left and right thigh. The imaging session consisted of (a) two MRI spin-echo-based scans, T1- and T2-weighted; (b) a diffusion weighted (DW) spin-echo-based scan using an echo planar acquisition with fat suppression. The thigh muscles were auto-segmented using the convolutional neural network. DT-MRI data were used as a geometry encoding input to solve the anisotropic Eikonal equation with the Hamiltonian Fast-Marching method. The isosurfaces of the solution modeled the CTV boundary. Main results. The auto-segmented muscles of the thigh agreed with manually delineated with the Dice score ranging from 0.8 to 0.94 for different muscles. To validate our method of deriving muscle fiber orientations, we compared anisotropy of the isosurfaces across muscles with different anatomical orientations within a thigh, between muscles in the left and right thighs of each subject, and between different subjects. The fiber orientations were identified reproducibly across all comparisons. We identified two controlling parameters, the distance from the gross tumor volume to the isosurface and the eigenvalues ratio, to tailor the proposed CTV to the satisfaction of the clinician. Significance. Our feasibility study with healthy volunteers shows the promise of using muscle fiber orientations derived from DW MRI data for automated generation of anisotropic CTV boundary in soft tissue sarcoma. Our contribution is significant as it serves as a proof of principle for combining DT-MRI information with tumor spread modeling, in contrast to using moderately informative 2D CT planes for the CTV delineation. Such improvements will positively impact the cancer centers with a small volume of sarcoma patients.
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Weine J, van Gorkum RJH, Stoeck CT, Vishnevskiy V, Kozerke S. Synthetically Trained Convolutional Neural Networks for Improved Tensor Estimation from Free-Breathing Cardiac DTI. Comput Med Imaging Graph 2022; 99:102075. [DOI: 10.1016/j.compmedimag.2022.102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 03/15/2022] [Accepted: 05/05/2022] [Indexed: 10/18/2022]
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Brendlin AS, Estler A, Plajer D, Lutz A, Grözinger G, Bongers MN, Tsiflikas I, Afat S, Artzner CP. AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography. Tomography 2022; 8:933-947. [PMID: 35448709 PMCID: PMC9031402 DOI: 10.3390/tomography8020075] [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: 02/22/2022] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 11/16/2022] Open
Abstract
(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed interventions and peri-procedural cbCT were included. The unenhanced mask run and the contrast-enhanced fill run of the cbCT were reconstructed using weighted filtered back projection. Additionally, each dataset was post-processed using a novel denoising software solution. Place-consistent regions of interest measured signal-to-noise ratio (SNR) per dataset. Corrected mixed-effects analysis with BMI subgroup analyses compared objective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Body-Mass-Index”, and “Mode” to SNR. Two radiologists independently rated diagnostic confidence. Inter-rater agreement was measured using Spearman correlation (r); (3) SNR was significantly higher in the denoised datasets than in the regular datasets (p < 0.001). Furthermore, BMI subgroup analysis showed significant SNR deteriorations in the regular datasets for higher patient BMI (p < 0.001), but stable results for denoising (p > 0.999). In regression, only denoising contributed positively towards SNR (0.6191; 95%CI 0.6096 to 0.6286; p < 0.001). The denoised datasets received overall significantly higher diagnostic confidence grades (p = 0.010), with good inter-rater agreement (r ≥ 0.795, p < 0.001). In a subgroup analysis, diagnostic confidence deteriorated significantly for higher patient BMI (p < 0.001) in the regular datasets but was stable in the denoised datasets (p ≥ 0.103).; (4) AI denoising can significantly enhance image quality in interventional cone-beam CT and effectively mitigate diagnostic confidence deterioration for rising patient BMI.
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