1
|
Chen Z, Ren H, Li Q, Li X. Motion correction and super-resolution for multi-slice cardiac magnetic resonance imaging via an end-to-end deep learning approach. Comput Med Imaging Graph 2024; 115:102389. [PMID: 38692199 PMCID: PMC11144076 DOI: 10.1016/j.compmedimag.2024.102389] [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: 11/16/2023] [Revised: 03/08/2024] [Accepted: 04/19/2024] [Indexed: 05/03/2024]
Abstract
Accurate reconstruction of a high-resolution 3D volume of the heart is critical for comprehensive cardiac assessments. However, cardiac magnetic resonance (CMR) data is usually acquired as a stack of 2D short-axis (SAX) slices, which suffers from the inter-slice misalignment due to cardiac motion and data sparsity from large gaps between SAX slices. Therefore, we aim to propose an end-to-end deep learning (DL) model to address these two challenges simultaneously, employing specific model components for each challenge. The objective is to reconstruct a high-resolution 3D volume of the heart (VHR) from acquired CMR SAX slices (VLR). We define the transformation from VLR to VHR as a sequential process of motion correction and super-resolution. Accordingly, our DL model incorporates two distinct components. The first component conducts motion correction by predicting displacement vectors to re-position each SAX slice accurately. The second component takes the motion-corrected SAX slices from the first component and performs the super-resolution to fill the data gaps. These two components operate in a sequential way, and the entire model is trained end-to-end. Our model significantly reduced inter-slice misalignment from originally 3.33±0.74 mm to 1.36±0.63 mm and generated accurate high resolution 3D volumes with Dice of 0.974±0.010 for left ventricle (LV) and 0.938±0.017 for myocardium in a simulation dataset. When compared to the LAX contours in a real-world dataset, our model achieved Dice of 0.945±0.023 for LV and 0.786±0.060 for myocardium. In both datasets, our model with specific components for motion correction and super-resolution significantly enhance the performance compared to the model without such design considerations. The codes for our model are available at https://github.com/zhennongchen/CMR_MC_SR_End2End.
Collapse
Affiliation(s)
- Zhennong Chen
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Hui Ren
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - Xiang Li
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, USA.
| |
Collapse
|
2
|
Meng Q, Bai W, O’Regan DP, Rueckert D. DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1489-1500. [PMID: 38064325 PMCID: PMC7615801 DOI: 10.1109/tmi.2023.3340118] [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] [Indexed: 01/22/2024]
Abstract
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current state-of-the art methods focus on estimating dense pixel-/voxel-wise motion fields in image space, which ignores the fact that motion estimation is only relevant and useful within the anatomical objects of interest, e.g., the heart. In this work, we model the heart as a 3D mesh consisting of epi- and endocardial surfaces. We propose a novel learning framework, DeepMesh, which propagates a template heart mesh to a subject space and estimates the 3D motion of the heart mesh from CMR images for individual subjects. In DeepMesh, the heart mesh of the end-diastolic frame of an individual subject is first reconstructed from the template mesh. Mesh-based 3D motion fields with respect to the end-diastolic frame are then estimated from 2D short- and long-axis CMR images. By developing a differentiable mesh-to-image rasterizer, DeepMesh is able to leverage 2D shape information from multiple anatomical views for 3D mesh reconstruction and mesh motion estimation. The proposed method estimates vertex-wise displacement and thus maintains vertex correspondences between time frames, which is important for the quantitative assessment of cardiac function across different subjects and populations. We evaluate DeepMesh on CMR images acquired from the UK Biobank. We focus on 3D motion estimation of the left ventricle in this work. Experimental results show that the proposed method quantitatively and qualitatively outperforms other image-based and mesh-based cardiac motion tracking methods.
Collapse
Affiliation(s)
- Qingjie Meng
- The Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2AZ, UK
| | - Wenjia Bai
- The Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2AZ, UK; Department of Brain Sciences, Imperial College London
| | - Declan P O’Regan
- The MRC London Institute of Medical Sciences, Imperial College London, W12 0HS, UK
| | - Daniel Rueckert
- The Biomedical Image Analysis Group, Department of Computing, Imperial College London, SW7 2AZ, UK; Klinikum rechts der Isar, Technical University Munich, Germany
| |
Collapse
|
3
|
Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Bell JD, Thomas EL. Liver shape analysis using statistical parametric maps at population scale. BMC Med Imaging 2024; 24:15. [PMID: 38195400 PMCID: PMC10775563 DOI: 10.1186/s12880-023-01149-5] [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: 12/05/2022] [Accepted: 10/31/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Morphometric image analysis enables the quantification of differences in the shape and size of organs between individuals. METHODS Here we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes. RESULTS We found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease. CONCLUSIONS The results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.
Collapse
Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| |
Collapse
|
4
|
Thanaj M, Basty N, Cule M, Sorokin EP, Whitcher B, Srinivasan R, Lennon R, Bell JD, Thomas EL. Kidney shape statistical analysis: associations with disease and anthropometric factors. BMC Nephrol 2023; 24:362. [PMID: 38057740 PMCID: PMC10698953 DOI: 10.1186/s12882-023-03407-8] [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: 07/04/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Organ measurements derived from magnetic resonance imaging (MRI) have the potential to enhance our understanding of the precise phenotypic variations underlying many clinical conditions. METHODS We applied morphometric methods to study the kidneys by constructing surface meshes from kidney segmentations from abdominal MRI data in 38,868 participants in the UK Biobank. Using mesh-based analysis techniques based on statistical parametric maps (SPMs), we were able to detect variations in specific regions of the kidney and associate those with anthropometric traits as well as disease states including chronic kidney disease (CKD), type-2 diabetes (T2D), and hypertension. Statistical shape analysis (SSA) based on principal component analysis was also used within the disease population and the principal component scores were used to assess the risk of disease events. RESULTS We show that CKD, T2D and hypertension were associated with kidney shape. Age was associated with kidney shape consistently across disease groups. Body mass index (BMI) and waist-to-hip ratio (WHR) were also associated with kidney shape for the participants with T2D. Using SSA, we were able to capture kidney shape variations, relative to size, angle, straightness, width, length, and thickness of the kidneys, within disease populations. We identified significant associations between both left and right kidney length and width and incidence of CKD (hazard ratio (HR): 0.74, 95% CI: 0.61-0.90, p < 0.05, in the left kidney; HR: 0.76, 95% CI: 0.63-0.92, p < 0.05, in the right kidney) and hypertension (HR: 1.16, 95% CI: 1.03-1.29, p < 0.05, in the left kidney; HR: 0.87, 95% CI: 0.79-0.96, p < 0.05, in the right kidney). CONCLUSIONS The results suggest that shape-based analysis of the kidneys can augment studies aiming at the better categorisation of pathologies associated with chronic kidney conditions.
Collapse
Affiliation(s)
- Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | - Rachel Lennon
- Wellcome Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Department of Paediatric Nephrology, Royal Manchester Children's Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| |
Collapse
|
5
|
Beetz M, Banerjee A, Ossenberg-Engels J, Grau V. Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images. Med Image Anal 2023; 90:102975. [PMID: 37804586 DOI: 10.1016/j.media.2023.102975] [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: 09/13/2022] [Revised: 07/08/2023] [Accepted: 09/18/2023] [Indexed: 10/09/2023]
Abstract
Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions.
Collapse
Affiliation(s)
- Marcel Beetz
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK.
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK; Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK.
| | - Julius Ossenberg-Engels
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK
| |
Collapse
|
6
|
Sander J, de Vos BD, Bruns S, Planken N, Viergever MA, Leiner T, Išgum I. Reconstruction and completion of high-resolution 3D cardiac shapes using anisotropic CMRI segmentations and continuous implicit neural representations. Comput Biol Med 2023; 164:107266. [PMID: 37494823 DOI: 10.1016/j.compbiomed.2023.107266] [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: 04/10/2023] [Revised: 06/26/2023] [Accepted: 07/16/2023] [Indexed: 07/28/2023]
Abstract
Since the onset of computer-aided diagnosis in medical imaging, voxel-based segmentation has emerged as the primary methodology for automatic analysis of left ventricle (LV) function and morphology in cardiac magnetic resonance images (CMRI). In standard clinical practice, simultaneous multi-slice 2D cine short-axis MR imaging is performed under multiple breath-holds resulting in highly anisotropic 3D images. Furthermore, sparse-view CMRI often lacks whole heart coverage caused by large slice thickness and often suffers from inter-slice misalignment induced by respiratory motion. Therefore, these volumes only provide limited information about the true 3D cardiac anatomy which may hamper highly accurate assessment of functional and anatomical abnormalities. To address this, we propose a method that learns a continuous implicit function representing 3D LV shapes by training an auto-decoder. For training, high-resolution segmentations from cardiac CT angiography are used. The ability of our approach to reconstruct and complete high-resolution shapes from manually or automatically obtained sparse-view cardiac shape information is evaluated by using paired high- and low-resolution CMRI LV segmentations. The results show that the reconstructed LV shapes have an unconstrained subvoxel resolution and appear smooth and plausible in through-plane direction. Furthermore, Bland-Altman analysis reveals that reconstructed high-resolution ventricle volumes are closer to the corresponding reference volumes than reference low-resolution volumes with bias of [limits of agreement] -3.51 [-18.87, 11.85] mL, and 12.96 [-10.01, 35.92] mL respectively. Finally, the results demonstrate that the proposed approach allows recovering missing shape information and can indirectly correct for limited motion-induced artifacts.
Collapse
Affiliation(s)
- Jörg Sander
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center location University of Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.
| | - Bob D de Vos
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center location University of Amsterdam, The Netherlands
| | - Steffen Bruns
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center location University of Amsterdam, The Netherlands
| | - Nils Planken
- Department of Radiology and Nuclear Medicine,Amsterdam University Medical Center location University of Amsterdam, Amsterdam, The Netherlands
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tim Leiner
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center location University of Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Department of Radiology and Nuclear Medicine,Amsterdam University Medical Center location University of Amsterdam, Amsterdam, The Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
7
|
Pilia N, Schuler S, Rees M, Moik G, Potyagaylo D, Dössel O, Loewe A. Non-invasive localization of the ventricular excitation origin without patient-specific geometries using deep learning. Artif Intell Med 2023; 143:102619. [PMID: 37673581 DOI: 10.1016/j.artmed.2023.102619] [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: 09/12/2022] [Revised: 06/18/2023] [Accepted: 06/24/2023] [Indexed: 09/08/2023]
Abstract
Cardiovascular diseases account for 17 million deaths per year worldwide. Of these, 25% are categorized as sudden cardiac death, which can be related to ventricular tachycardia (VT). This type of arrhythmia can be caused by focal activation sources outside the sinus node. Catheter ablation of these foci is a curative treatment in order to inactivate the abnormal triggering activity. However, the localization procedure is usually time-consuming and requires an invasive procedure in the catheter lab. To facilitate and expedite the treatment, we present two novel localization support techniques based on convolutional neural networks (CNNs) that address these clinical needs. In contrast to existing methods, our approaches were designed to be independent of the patient-specific geometry and directly applicable to surface ECG signals, while also delivering a binary transmural position. Moreover, one of the method's outputs can be interpreted as several ranked solutions. The CNNs were trained on a dataset containing only simulated data and evaluated both on simulated test data and clinical data. On a novel large and open simulated dataset, the median test error was below 3 mm. The median localization error on the unseen clinical data ranged from 32 mm to 41 mm without optimizing the pre-processing and CNN to the clinical data. Interpreting the output of one of the approaches as ranked solutions, the best median error of the top-3 solutions decreased to 20 mm on the clinical data. The transmural position was correctly detected in up to 82% of all clinical cases. These results demonstrate a proof of principle to utilize CNNs to localize the activation source without the intrinsic need for patient-specific geometrical information. Furthermore, providing multiple solutions can assist physicians in identifying the true activation source amongst more than one possible location. With further optimization to clinical data, these methods have high potential to accelerate clinical interventions, replace certain steps within these procedures and consequently reduce procedural risk and improve VT patient outcomes.
Collapse
Affiliation(s)
- Nicolas Pilia
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
| | - Steffen Schuler
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Maike Rees
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gerald Moik
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | | | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| |
Collapse
|
8
|
Sarmah M, Neelima A, Singh HR. Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images. Vis Comput Ind Biomed Art 2023; 6:15. [PMID: 37495817 PMCID: PMC10371974 DOI: 10.1186/s42492-023-00142-7] [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: 02/28/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023] Open
Abstract
Three-dimensional (3D) reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing units. In the coming years, most patient care will shift toward this new paradigm. However, development of fast and accurate 3D models from medical images or a set of medical scans remains a daunting task due to the number of pre-processing steps involved, most of which are dependent on human expertise. In this review, a survey of pre-processing steps was conducted, and reconstruction techniques for several organs in medical diagnosis were studied. Various methods and principles related to 3D reconstruction were highlighted. The usefulness of 3D reconstruction of organs in medical diagnosis was also highlighted.
Collapse
Affiliation(s)
- Mriganka Sarmah
- Department of Computer Science and Engineering, National Institute of Technology, Nagaland, 797103, India.
| | - Arambam Neelima
- Department of Computer Science and Engineering, National Institute of Technology, Nagaland, 797103, India
| | - Heisnam Rohen Singh
- Department of Information Technology, Nagaland University, Nagaland, 797112, India
| |
Collapse
|
9
|
Meiburg R, Rijks JHJ, Beela AS, Bressi E, Grieco D, Delhaas T, Luermans JGLM, Prinzen FW, Vernooy K, Lumens J. Comparison of novel ventricular pacing strategies using an electro-mechanical simulation platform. Europace 2023; 25:euad144. [PMID: 37306315 PMCID: PMC10259067 DOI: 10.1093/europace/euad144] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/06/2023] [Indexed: 06/13/2023] Open
Abstract
AIMS Focus of pacemaker therapy is shifting from right ventricular (RV) apex pacing (RVAP) and biventricular pacing (BiVP) to conduction system pacing. Direct comparison between the different pacing modalities and their consequences to cardiac pump function is difficult, due to the practical implications and confounding variables. Computational modelling and simulation provide the opportunity to compare electrical, mechanical, and haemodynamic consequences in the same virtual heart. METHODS AND RESULTS Using the same single cardiac geometry, electrical activation maps following the different pacing strategies were calculated using an Eikonal model on a three-dimensional geometry, which were then used as input for a lumped mechanical and haemodynamic model (CircAdapt). We then compared simulated strain, regional myocardial work, and haemodynamic function for each pacing strategy. Selective His-bundle pacing (HBP) best replicated physiological electrical activation and led to the most homogeneous mechanical behaviour. Selective left bundle branch (LBB) pacing led to good left ventricular (LV) function but significantly increased RV load. RV activation times were reduced in non-selective LBB pacing (nsLBBP), reducing RV load but increasing heterogeneity in LV contraction. LV septal pacing led to a slower LV and more heterogeneous LV activation than nsLBBP, while RV activation was similar. BiVP led to a synchronous LV-RV, but resulted in a heterogeneous contraction. RVAP led to the slowest and most heterogeneous contraction. Haemodynamic differences were small compared to differences in local wall behaviour. CONCLUSION Using a computational modelling framework, we investigated the mechanical and haemodynamic outcome of the prevailing pacing strategies in hearts with normal electrical and mechanical function. For this class of patients, nsLBBP was the best compromise between LV and RV function if HBP is not possible.
Collapse
Affiliation(s)
- Roel Meiburg
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 40, 6200 MD, Maastricht, The Netherlands
| | - Jesse H J Rijks
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
| | - Ahmed S Beela
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 40, 6200 MD, Maastricht, The Netherlands
- Department of Cardiovascular Diseases, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Edoardo Bressi
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
- Department of Cardiovascular Sciences, Policlinico Casilino of Rome, Rome, Italy
| | - Domenico Grieco
- Department of Cardiovascular Sciences, Policlinico Casilino of Rome, Rome, Italy
| | - Tammo Delhaas
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 40, 6200 MD, Maastricht, The Netherlands
| | - Justin G LM Luermans
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
- Department of Cardiology, Radboud University Medical Centre (Radboudumc), Nijmegen, The Netherlands
| | - Frits W Prinzen
- Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Kevin Vernooy
- Department of Cardiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center+ (MUMC+), Maastricht, The Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 40, 6200 MD, Maastricht, The Netherlands
| |
Collapse
|
10
|
Nguyen HP, Lee HJ, Kim S. Feasibility study for the automatic surgical planning method based on statistical model. J Orthop Surg Res 2023; 18:398. [PMID: 37264435 DOI: 10.1186/s13018-023-03870-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/21/2023] [Indexed: 06/03/2023] Open
Abstract
PURPOSE In this study, we proposed establishing an automatic computer-assisted surgical planning approach based on average population models. METHODS We built the average population models from humerus datasets using the Advanced Normalization Toolkits (ANTs) and Shapeworks. Experiments include (1) evaluation of the average population models before surgical planning and (2) validation of the average population models in the context of predicting clinical landmarks on the humerus from the new dataset that was not involved in the process of building the average population model. The evaluation experiment consists of explained variation and distance model. The validation experiment calculated the root-mean-square error (RMSE) between the expert-determined clinical ground truths and the landmarks transferred from the average population model to the new dataset. The evaluation results and validation results when using the templates built from ANTs were compared to when using the mean shape generated from Shapeworks. RESULTS The average population models predicted clinical locations on the new dataset with acceptable errors when compared to the ground truth determined by an expert. However, the templates built from ANTs present better accuracy in landmark prediction when compared to the mean shape built from the Shapeworks. CONCLUSION The average population model could be utilized to assist anatomical landmarks checking automatically and following surgical decisions for new patients who are not involved in the dataset used to generate the average population model.
Collapse
Affiliation(s)
- Hang Phuong Nguyen
- Department of Electrical, Electronic, and Computer Engineering, University of Ulsan, Ulsan, Korea
| | - Hyun-Joo Lee
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
| | - Sungmin Kim
- Department of Electrical, Electronic, and Computer Engineering, University of Ulsan, Ulsan, Korea.
| |
Collapse
|
11
|
Zhao D, Mauger CA, Gilbert K, Wang VY, Quill GM, Sutton TM, Lowe BS, Legget ME, Ruygrok PN, Doughty RN, Pedrosa J, D'hooge J, Young AA, Nash MP. Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping. Sci Rep 2023; 13:8118. [PMID: 37208380 PMCID: PMC10199025 DOI: 10.1038/s41598-023-33968-5] [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: 12/16/2022] [Accepted: 04/21/2023] [Indexed: 05/21/2023] Open
Abstract
Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 ± 1 to 4 ± 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.
Collapse
Affiliation(s)
- Debbie Zhao
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand.
| | - Charlène A Mauger
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Vicky Y Wang
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Gina M Quill
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Timothy M Sutton
- Counties Manukau Health Cardiology, Middlemore Hospital, Auckland, New Zealand
| | - Boris S Lowe
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
| | - Malcolm E Legget
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Peter N Ruygrok
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Robert N Doughty
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - João Pedrosa
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal
| | - Jan D'hooge
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Department of Biomedical Engineering, King's College London, London, UK
| | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| |
Collapse
|
12
|
Buoso S, Joyce T, Schulthess N, Kozerke S. MRXCAT2.0: Synthesis of realistic numerical phantoms by combining left-ventricular shape learning, biophysical simulations and tissue texture generation. J Cardiovasc Magn Reson 2023; 25:25. [PMID: 37076840 PMCID: PMC10116689 DOI: 10.1186/s12968-023-00934-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 03/15/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Standardised performance assessment of image acquisition, reconstruction and processing methods is limited by the absence of images paired with ground truth reference values. To this end, we propose MRXCAT2.0 to generate synthetic data, covering healthy and pathological function, using a biophysical model. We exemplify the approach by generating cardiovascular magnetic resonance (CMR) images of healthy, infarcted, dilated and hypertrophic left-ventricular (LV) function. METHOD In MRXCAT2.0, the XCAT torso phantom is coupled with a statistical shape model, describing population (patho)physiological variability, and a biophysical model, providing known and detailed functional ground truth of LV morphology and function. CMR balanced steady-state free precession images are generated using MRXCAT2.0 while realistic image appearance is ensured by assigning texturized tissue properties to the phantom labels. FINDING Paired CMR image and ground truth data of LV function were generated with a range of LV masses (85-140 g), ejection fractions (34-51%) and peak radial and circumferential strains (0.45 to 0.95 and - 0.18 to - 0.13, respectively). These ranges cover healthy and pathological cases, including infarction, dilated and hypertrophic cardiomyopathy. The generation of the anatomy takes a few seconds and it improves on current state-of-the-art models where the pathological representation is not explicitly addressed. For the full simulation framework, the biophysical models require approximately two hours, while image generation requires a few minutes per slice. CONCLUSION MRXCAT2.0 offers synthesis of realistic images embedding population-based anatomical and functional variability and associated ground truth parameters to facilitate a standardized assessment of CMR acquisition, reconstruction and processing methods.
Collapse
Affiliation(s)
- Stefano Buoso
- Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland.
| | - Thomas Joyce
- Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland
| | - Nico Schulthess
- Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland
| |
Collapse
|
13
|
Kebaili A, Lapuyade-Lahorgue J, Ruan S. Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review. J Imaging 2023; 9:81. [PMID: 37103232 PMCID: PMC10144738 DOI: 10.3390/jimaging9040081] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 03/31/2023] [Accepted: 04/07/2023] [Indexed: 04/28/2023] Open
Abstract
Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing the number of training samples, but these techniques often produce limited and unconvincing results. To address this issue, a growing number of studies have proposed the use of deep generative models to generate more realistic and diverse data that conform to the true distribution of the data. In this review, we focus on three types of deep generative models for medical image augmentation: variational autoencoders, generative adversarial networks, and diffusion models. We provide an overview of the current state of the art in each of these models and discuss their potential for use in different downstream tasks in medical imaging, including classification, segmentation, and cross-modal translation. We also evaluate the strengths and limitations of each model and suggest directions for future research in this field. Our goal is to provide a comprehensive review about the use of deep generative models for medical image augmentation and to highlight the potential of these models for improving the performance of deep learning algorithms in medical image analysis.
Collapse
Affiliation(s)
| | | | - Su Ruan
- Université Rouen Normandie, INSA Rouen Normandie, Université Le Havre Normandie, Normandie Univ, LITIS UR 4108, F-76000 Rouen, France
| |
Collapse
|
14
|
Govil S, Crabb BT, Deng Y, Dal Toso L, Puyol-Antón E, Pushparajah K, Hegde S, Perry JC, Omens JH, Hsiao A, Young AA, McCulloch AD. A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot. J Cardiovasc Magn Reson 2023; 25:15. [PMID: 36849960 PMCID: PMC9969707 DOI: 10.1186/s12968-023-00924-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/25/2023] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardiac shape modeling a more robust and broadly applicable tool that has processing times consistent with clinical workflows. METHODS Cardiovascular magnetic resonance (CMR) images from a cohort of 123 patients with repaired tetralogy of Fallot (rTOF) from two internal sites were used to train and validate each step in the automated pipeline. The complete automated pipeline was tested using CMR images from a cohort of 12 rTOF patients from an internal site and 18 rTOF patients from an external site. Manually and automatically generated shape models from the test set were compared using Euclidean projection distances, global ventricular measurements, and atlas-based shape mode scores. RESULTS The mean absolute error (MAE) between manually and automatically generated shape models in the test set was similar to the voxel resolution of the original CMR images for end-diastolic models (MAE = 1.9 ± 0.5 mm) and end-systolic models (MAE = 2.1 ± 0.7 mm). Global ventricular measurements computed from automated models were in good agreement with those computed from manual models. The average mean absolute difference in shape mode Z-score between manually and automatically generated models was 0.5 standard deviations for the first 20 modes of a reference statistical shape atlas. CONCLUSIONS Using deep learning, accurate three-dimensional, biventricular shape models can be reliably created. This fully automated end-to-end approach dramatically reduces the manual input required to create shape models, thereby enabling the rapid analysis of large-scale datasets and the potential to deploy statistical atlas-based analyses in point-of-care clinical settings. Training data and networks are available from cardiacatlas.org.
Collapse
Affiliation(s)
- Sachin Govil
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| | - Brendan T. Crabb
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| | - Yu Deng
- Department of Biomedical Engineering, King’s College London, London, UK
| | - Laura Dal Toso
- Department of Biomedical Engineering, King’s College London, London, UK
| | | | | | - Sanjeet Hegde
- Department of Pediatrics, University of California San Diego, La Jolla, CA USA
- Division of Cardiology, Rady Children’s Hospital San Diego, San Diego, CA USA
| | - James C. Perry
- Department of Pediatrics, University of California San Diego, La Jolla, CA USA
- Division of Cardiology, Rady Children’s Hospital San Diego, San Diego, CA USA
| | - Jeffrey H. Omens
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| | - Albert Hsiao
- Department of Radiology, University of California San Diego, La Jolla, CA USA
| | - Alistair A. Young
- Department of Biomedical Engineering, King’s College London, London, UK
| | - Andrew D. McCulloch
- Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412 USA
| |
Collapse
|
15
|
Rinné S, Oertli A, Nagel C, Tomsits P, Jenewein T, Kääb S, Kauferstein S, Loewe A, Beckmann BM, Decher N. Functional Characterization of a Spectrum of Novel Romano-Ward Syndrome KCNQ1 Variants. Int J Mol Sci 2023; 24:ijms24021350. [PMID: 36674868 PMCID: PMC9865342 DOI: 10.3390/ijms24021350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/20/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023] Open
Abstract
The KCNQ1 gene encodes the α-subunit of the cardiac voltage-gated potassium (Kv) channel KCNQ1, also denoted as Kv7.1 or KvLQT1. The channel assembles with the ß-subunit KCNE1, also known as minK, to generate the slowly activating cardiac delayed rectifier current IKs, a key regulator of the heart rate dependent adaptation of the cardiac action potential duration (APD). Loss-of-function variants in KCNQ1 cause the congenital Long QT1 (LQT1) syndrome, characterized by delayed cardiac repolarization and a QT interval prolongation in the surface electrocardiogram (ECG). Autosomal dominant loss-of-function variants in KCNQ1 result in the LQT syndrome called Romano-Ward syndrome (RWS), while autosomal recessive variants affecting function, lead to Jervell and Lange-Nielsen syndrome (JLNS), associated with deafness. The aim of this study was the characterization of novel KCNQ1 variants identified in patients with RWS to widen the spectrum of known LQT1 variants, and improve the interpretation of the clinical relevance of variants in the KCNQ1 gene. We functionally characterized nine human KCNQ1 variants using the voltage-clamp technique in Xenopus laevis oocytes, from which we report seven novel variants. The functional data was taken as input to model surface ECGs, to subsequently compare the functional changes with the clinically observed QTc times, allowing a further interpretation of the severity of the different LQTS variants. We found that the electrophysiological properties of the variants correlate with the severity of the clinically diagnosed phenotype in most cases, however, not in all. Electrophysiological studies combined with in silico modelling approaches are valuable components for the interpretation of the pathogenicity of KCNQ1 variants, but assessing the clinical severity demands the consideration of other factors that are included, for example in the Schwartz score.
Collapse
Affiliation(s)
- Susanne Rinné
- Institute of Physiology and Pathophysiology, Vegetative Physiology, University of Marburg, 35037 Marburg, Germany
| | - Annemarie Oertli
- Institute of Physiology and Pathophysiology, Vegetative Physiology, University of Marburg, 35037 Marburg, Germany
| | - Claudia Nagel
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Philipp Tomsits
- Department of Medicine I, University Hospital, LMU Munich, 80802 Munich, Germany
- Deutsches Zentrum für Herz-Kreislauferkrankungen (DZHK), Partner Site Munich, 80636 Munich, Germany
- Member of the European Reference Network for Rare, Low Prevalance and Complex Diseases of the Heart (ERN GUARD-Heart), 81377 Munich, Germany
- Institute of Surgical Research at the Walter-Brendel-Centre of Experimental Medicine, University Hospital, LMU Munich, Marchioninistrasse 27, 81377 Munich, Germany
| | - Tina Jenewein
- Institute of Legal Medicine, Goethe University, University Hospital Frankfurt, 60590 Frankfurt, Germany
- Institute for Transfusion Medicine and Immunohematology, German Red Cross Blood Service Baden-Württemberg-Hessen, Goethe University Frankfurt, 60528 Frankfurt, Germany
| | - Stefan Kääb
- Department of Medicine I, University Hospital, LMU Munich, 80802 Munich, Germany
- Deutsches Zentrum für Herz-Kreislauferkrankungen (DZHK), Partner Site Munich, 80636 Munich, Germany
- Member of the European Reference Network for Rare, Low Prevalance and Complex Diseases of the Heart (ERN GUARD-Heart), 81377 Munich, Germany
| | - Silke Kauferstein
- Institute of Legal Medicine, Goethe University, University Hospital Frankfurt, 60590 Frankfurt, Germany
- Deutsches Zentrum für Herz-Kreislauferkrankungen (DZHK), Partner Site Frankfurt, 60596 Frankfurt, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Britt Maria Beckmann
- Department of Medicine I, University Hospital, LMU Munich, 80802 Munich, Germany
- Institute of Legal Medicine, Goethe University, University Hospital Frankfurt, 60590 Frankfurt, Germany
| | - Niels Decher
- Institute of Physiology and Pathophysiology, Vegetative Physiology, University of Marburg, 35037 Marburg, Germany
- Correspondence: ; Tel.: +49-(0)6421-28-62148
| |
Collapse
|
16
|
Asheghan MM, Javadikasgari H, Attary T, Rouhollahi A, Straughan R, Willi JN, Awal R, Sabe A, de la Cruz KI, Nezami FR. Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming. Front Cardiovasc Med 2023; 10:1130152. [PMID: 37082454 PMCID: PMC10111021 DOI: 10.3389/fcvm.2023.1130152] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/16/2023] [Indexed: 04/22/2023] Open
Abstract
Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.
Collapse
Affiliation(s)
- Mohammad Mostafa Asheghan
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Hoda Javadikasgari
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Taraneh Attary
- Bio-Intelligence Unit, Sharif Brain Center, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Amir Rouhollahi
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Ross Straughan
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - James Noel Willi
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Rabina Awal
- Mechanical Engineering Department, University of Louisiana at Lafayette, Louisiana, LA, United States
| | - Ashraf Sabe
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Kim I. de la Cruz
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Farhad R. Nezami
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Correspondence: Farhad R. Nezami
| |
Collapse
|
17
|
Beetz M, Corral Acero J, Banerjee A, Eitel I, Zacur E, Lange T, Stiermaier T, Evertz R, Backhaus SJ, Thiele H, Bueno-Orovio A, Lamata P, Schuster A, Grau V. Interpretable cardiac anatomy modeling using variational mesh autoencoders. Front Cardiovasc Med 2022; 9:983868. [PMID: 36620629 PMCID: PMC9813669 DOI: 10.3389/fcvm.2022.983868] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022] Open
Abstract
Cardiac anatomy and function vary considerably across the human population with important implications for clinical diagnosis and treatment planning. Consequently, many computer-based approaches have been developed to capture this variability for a wide range of applications, including explainable cardiac disease detection and prediction, dimensionality reduction, cardiac shape analysis, and the generation of virtual heart populations. In this work, we propose a variational mesh autoencoder (mesh VAE) as a novel geometric deep learning approach to model such population-wide variations in cardiac shapes. It embeds multi-scale graph convolutions and mesh pooling layers in a hierarchical VAE framework to enable direct processing of surface mesh representations of the cardiac anatomy in an efficient manner. The proposed mesh VAE achieves low reconstruction errors on a dataset of 3D cardiac meshes from over 1,000 patients with acute myocardial infarction, with mean surface distances between input and reconstructed meshes below the underlying image resolution. We also find that it outperforms a voxelgrid-based deep learning benchmark in terms of both mean surface distance and Hausdorff distance while requiring considerably less memory. Furthermore, we explore the quality and interpretability of the mesh VAE's latent space and showcase its ability to improve the prediction of major adverse cardiac events over a clinical benchmark. Finally, we investigate the method's ability to generate realistic virtual populations of cardiac anatomies and find good alignment between the synthesized and gold standard mesh populations in terms of multiple clinical metrics.
Collapse
Affiliation(s)
- Marcel Beetz
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Jorge Corral Acero
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Abhirup Banerjee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Ingo Eitel
- University Heart Center Lübeck, Medical Clinic II, Cardiology, Angiology, and Intensive Care Medicine, Lübeck, Germany
- University Hospital Schleswig-Holstein, Lübeck, Germany
- German Centre for Cardiovascular Research, Partner Site Lübeck, Lübeck, Germany
| | - Ernesto Zacur
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Torben Lange
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Göttingen, Germany
| | - Thomas Stiermaier
- University Heart Center Lübeck, Medical Clinic II, Cardiology, Angiology, and Intensive Care Medicine, Lübeck, Germany
- University Hospital Schleswig-Holstein, Lübeck, Germany
- German Centre for Cardiovascular Research, Partner Site Lübeck, Lübeck, Germany
| | - Ruben Evertz
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Göttingen, Germany
| | - Sören J. Backhaus
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Göttingen, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
- Leipzig Heart Institute, Leipzig, Germany
| | | | - Pablo Lamata
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Andreas Schuster
- Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University, Göttingen, Germany
- German Centre for Cardiovascular Research, Partner Site Göttingen, Göttingen, Germany
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
18
|
Goubergrits L, Vellguth K, Obermeier L, Schlief A, Tautz L, Bruening J, Lamecker H, Szengel A, Nemchyna O, Knosalla C, Kuehne T, Solowjowa N. CT-Based Analysis of Left Ventricular Hemodynamics Using Statistical Shape Modeling and Computational Fluid Dynamics. Front Cardiovasc Med 2022; 9:901902. [PMID: 35865389 PMCID: PMC9294248 DOI: 10.3389/fcvm.2022.901902] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022] Open
Abstract
Background Cardiac computed tomography (CCT) based computational fluid dynamics (CFD) allows to assess intracardiac flow features, which are hypothesized as an early predictor for heart diseases and may support treatment decisions. However, the understanding of intracardiac flow is challenging due to high variability in heart shapes and contractility. Using statistical shape modeling (SSM) in combination with CFD facilitates an intracardiac flow analysis. The aim of this study is to prove the usability of a new approach to describe various cohorts. Materials and Methods CCT data of 125 patients (mean age: 60.6 ± 10.0 years, 16.8% woman) were used to generate SSMs representing aneurysmatic and non-aneurysmatic left ventricles (LVs). Using SSMs, seven group-averaged LV shapes and contraction fields were generated: four representing patients with and without aneurysms and with mild or severe mitral regurgitation (MR), and three distinguishing aneurysmatic patients with true, intermediate aneurysms, and globally hypokinetic LVs. End-diastolic LV volumes of the groups varied between 258 and 347 ml, whereas ejection fractions varied between 21 and 26%. MR degrees varied from 1.0 to 2.5. Prescribed motion CFD was used to simulate intracardiac flow, which was analyzed regarding large-scale flow features, kinetic energy, washout, and pressure gradients. Results SSMs of aneurysmatic and non-aneurysmatic LVs were generated. Differences in shapes and contractility were found in the first three shape modes. Ninety percent of the cumulative shape variance is described with approximately 30 modes. A comparison of hemodynamics between all groups found shape-, contractility- and MR-dependent differences. Disturbed blood washout in the apex region was found in the aneurysmatic cases. With increasing MR, the diastolic jet becomes less coherent, whereas energy dissipation increases by decreasing kinetic energy. The poorest blood washout was found for the globally hypokinetic group, whereas the weakest blood washout in the apex region was found for the true aneurysm group. Conclusion The proposed CCT-based analysis of hemodynamics combining CFD with SSM seems promising to facilitate the analysis of intracardiac flow, thus increasing the value of CCT for diagnostic and treatment decisions. With further enhancement of the computational approach, the methodology has the potential to be embedded in clinical routine workflows and support clinicians.
Collapse
Affiliation(s)
- Leonid Goubergrits
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Katharina Vellguth
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lukas Obermeier
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Adriano Schlief
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lennart Tautz
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Jan Bruening
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | | | - Olena Nemchyna
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Christoph Knosalla
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Titus Kuehne
- Institute of Computer-Assisted Cardiovascular Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany
| | - Natalia Solowjowa
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- *Correspondence: Natalia Solowjowa
| |
Collapse
|
19
|
The Use of Digital Coronary Phantoms for the Validation of Arterial Geometry Reconstruction and Computation of Virtual FFR. FLUIDS 2022. [DOI: 10.3390/fluids7060201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
We present computational fluid dynamics (CFD) results of virtual fractional flow reserve (vFFR) calculations, performed on reconstructed arterial geometries derived from a digital phantom (DP). The latter provides a convenient and parsimonious description of the main vessels of the left and right coronary arterial trees, which, crucially, is CFD-compatible. Using our DP, we investigate the reconstruction error in what we deem to be the most relevant way—by evaluating the change in the computed value of vFFR, which results from varying (within representative clinical bounds) the selection of the virtual angiogram pair (defined by their viewing angles) used to segment the artery, the eccentricity and severity of the stenosis, and thereby, the CFD simulation’s luminal boundary. The DP is used to quantify reconstruction and computed haemodynamic error within the VIRTUheartTM software suite. However, our method and the associated digital phantom tool are readily transferable to equivalent, clinically oriented workflows. While we are able to conclude that error within the VIRTUheartTM workflow is suitably controlled, the principal outcomes of the work reported here are the demonstration and provision of a practical tool along with an exemplar methodology for evaluating error in a coronary segmentation process.
Collapse
|
20
|
Beetz M, Banerjee A, Grau V. Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology. Front Physiol 2022; 13:886723. [PMID: 35755443 PMCID: PMC9213788 DOI: 10.3389/fphys.2022.886723] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/02/2022] [Indexed: 11/16/2022] Open
Abstract
Human cardiac function is characterized by a complex interplay of mechanical deformation and electrophysiological conduction. Similar to the underlying cardiac anatomy, these interconnected physiological patterns vary considerably across the human population with important implications for the effectiveness of clinical decision-making and the accuracy of computerized heart models. While many previous works have investigated this variability separately for either cardiac anatomy or physiology, this work aims to combine both aspects in a single data-driven approach and capture their intricate interdependencies in a multi-domain setting. To this end, we propose a novel multi-domain Variational Autoencoder (VAE) network to capture combined Electrocardiogram (ECG) and Magnetic Resonance Imaging (MRI)-based 3D anatomy information in a single model. Each VAE branch is specifically designed to address the particular challenges of the respective input domain, enabling efficient encoding, reconstruction, and synthesis of multi-domain cardiac signals. Our method achieves high reconstruction accuracy on a United Kingdom Biobank dataset, with Chamfer Distances between reconstructed and input anatomies below the underlying image resolution and ECG reconstructions outperforming multiple single-domain benchmarks by a considerable margin. The proposed VAE is capable of generating realistic virtual populations of arbitrary size with good alignment in clinical metrics between the synthesized and gold standard anatomies and Maximum Mean Discrepancy (MMD) scores of generated ECGs below those of comparable single-domain approaches. Furthermore, we observe the latent space of our VAE to be highly interpretable with separate components encoding different aspects of anatomical and ECG variability. Finally, we demonstrate that the combined anatomy and ECG representation improves the performance in a cardiac disease classification task by 3.9% in terms of Area Under the Receiver Operating Characteristic (AUROC) curve over the best corresponding single-domain modeling approach.
Collapse
Affiliation(s)
- Marcel Beetz
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, United Kingdom
| | - Abhirup Banerjee
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, United Kingdom
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
| | - Vicente Grau
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, United Kingdom
| |
Collapse
|
21
|
Joyce T, Buoso S, Stoeck CT, Kozerke S. Rapid inference of personalised left-ventricular meshes by deformation-based differentiable mesh voxelization. Med Image Anal 2022; 79:102445. [DOI: 10.1016/j.media.2022.102445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 10/18/2022]
|
22
|
Javeed A, Khan SU, Ali L, Ali S, Imrana Y, Rahman A. Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9288452. [PMID: 35154361 PMCID: PMC8831075 DOI: 10.1155/2022/9288452] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/15/2022] [Indexed: 12/13/2022]
Abstract
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
Collapse
Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Sweden
| | - Shafqat Ullah Khan
- Department of Electrical Engineering, University of Science and Technology Bannu, Pakistan
| | - Liaqat Ali
- Department of Electronics, University of Buner, Buner, Pakistan
| | - Sardar Ali
- School of Engineering and Applied Sciences, Isra University Islamabad Campus, Pakistan
| | - Yakubu Imrana
- School of Engineering, University of Development Studies, Tamale, Ghana
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Atiqur Rahman
- Department of Computer Science, University of Science and Technology Bannu, Pakistan
| |
Collapse
|
23
|
Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks. Med Image Anal 2022; 78:102383. [DOI: 10.1016/j.media.2022.102383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 11/24/2021] [Accepted: 02/01/2022] [Indexed: 11/16/2022]
|
24
|
Banerjee A, Camps J, Zacur E, Andrews CM, Rudy Y, Choudhury RP, Rodriguez B, Grau V. A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200257. [PMID: 34689630 PMCID: PMC8543046 DOI: 10.1098/rsta.2020.0257] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/28/2021] [Indexed: 05/05/2023]
Abstract
Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and characterization of cardiovascular diseases, since it can identify abnormalities in structure and function of the myocardium non-invasively and without the need for ionizing radiation. However, in clinical practice, it is commonly acquired as a collection of separated and independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a completely automated pipeline for generating patient-specific 3D biventricular heart models from cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR images, segments them using a deep learning-based method to extract the heart contours, and aligns the contours in 3D space correcting possible misalignments due to breathing or subject motion first using the intensity and contours information from the cine data and next with the help of a statistical shape model. Finally, the sparse 3D representation of the contours is used to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our computational pipeline are used for simulations of electrical activation patterns, showing agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented here for patient-specific MR imaging-based 3D biventricular representations contribute to the efficient realization of precision medicine, enabling the enhanced interpretability of clinical data, the digital twin vision through patient-specific image-based modelling and simulation, and augmented reality applications. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
Collapse
Affiliation(s)
- Abhirup Banerjee
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Julià Camps
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Ernesto Zacur
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Christopher M. Andrews
- Department of Biomedical Engineering, Washington University, St Louis, Missouri, USA
- Cardiac Bioelectricity and Arrhythmia Center, Washington University, St Louis, Missouri, USA
| | - Yoram Rudy
- Department of Biomedical Engineering, Washington University, St Louis, Missouri, USA
- Cardiac Bioelectricity and Arrhythmia Center, Washington University, St Louis, Missouri, USA
| | - Robin P. Choudhury
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford Acute Vascular Imaging Centre, Oxford, UK
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| |
Collapse
|
25
|
Zakeri A, Hokmabadi A, Ravikumar N, Frangi AF, Gooya A. A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment. Med Image Anal 2021; 75:102276. [PMID: 34753021 DOI: 10.1016/j.media.2021.102276] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 10/10/2021] [Accepted: 10/15/2021] [Indexed: 11/16/2022]
Abstract
Automatic shape anomaly detection in large-scale imaging data can be useful for screening suboptimal segmentations and pathologies altering the cardiac morphology without intensive manual labour. We propose a deep probabilistic model for local anomaly detection in sequences of heart shapes, modelled as point sets, in a cardiac cycle. A deep recurrent encoder-decoder network captures the spatio-temporal dependencies to predict the next shape in the cycle and thus derive the outlier points that are attributed to excessive deviations from the network prediction. A predictive mixture distribution models the inlier and outlier classes via Gaussian and uniform distributions, respectively. A Gibbs sampling Expectation-Maximisation (EM) algorithm computes soft anomaly scores of the points via the posterior probabilities of each class in the E-step and estimates the parameters of the network and the predictive distribution in the M-step. We demonstrate the versatility of the method using two shape datasets derived from: (i) one million biventricular CMR images from 20,000 participants in the UK Biobank (UKB), and (ii) routine diagnostic imaging from Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Image (M&Ms). Experiments show that the detected shape anomalies in the UKB dataset are mostly associated with poor segmentation quality, and the predicted shape sequences show significant improvement over the input sequences. Furthermore, evaluations on U-Net based shapes from the M&Ms dataset reveals that the anomalies are attributable to the underlying pathologies that affect the ventricles. The proposed model can therefore be used as an effective mechanism to sift shape anomalies in large-scale cardiac imaging pipelines for further analysis.
Collapse
Affiliation(s)
- Arezoo Zakeri
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
| | - Alireza Hokmabadi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Alejandro F Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK
| | - Ali Gooya
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
| |
Collapse
|
26
|
Schuler S, Pilia N, Potyagaylo D, Loewe A. Cobiveco: Consistent biventricular coordinates for precise and intuitive description of position in the heart - with MATLAB implementation. Med Image Anal 2021; 74:102247. [PMID: 34592711 DOI: 10.1016/j.media.2021.102247] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 11/18/2022]
Abstract
Ventricular coordinates are widely used as a versatile tool for various applications that benefit from a description of local position within the heart. However, the practical usefulness of ventricular coordinates is determined by their ability to meet application-specific requirements. For regression-based estimation of biventricular position, for example, a symmetric definition of coordinate directions in both ventricles is important. For the transfer of data between different hearts as another use case, the consistency of coordinate values across different geometries is particularly relevant. To meet these requirements, we compare different approaches to compute coordinates and present Cobiveco, a symmetric, consistent and intuitive biventricular coordinate system that builds upon existing coordinate systems, but overcomes some of their limitations. A novel one-way transfer error is introduced to assess the consistency of the coordinates. Normalized distances along bijective trajectories between two boundaries were found to be superior to solutions of Laplace's equation for defining coordinate values, as they show better linearity in space. Evaluation of transfer and linearity errors on 36 patient geometries revealed a more than 4-fold improvement compared to a state-of-the-art method. Finally, we show two application examples underlining the relevance for cardiac data processing. Cobiveco MATLAB code is available under a permissive open-source license.
Collapse
Affiliation(s)
- Steffen Schuler
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, Karlsruhe 76131, Germany.
| | - Nicolas Pilia
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, Karlsruhe 76131, Germany
| | | | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 1, Karlsruhe 76131, Germany
| |
Collapse
|
27
|
A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations. Med Image Anal 2021; 74:102210. [PMID: 34450467 DOI: 10.1016/j.media.2021.102210] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 06/29/2021] [Accepted: 08/04/2021] [Indexed: 11/20/2022]
Abstract
Large-scale electrophysiological simulations to obtain electrocardiograms (ECG) carry the potential to produce extensive datasets for training of machine learning classifiers to, e.g., discriminate between different cardiac pathologies. The adoption of simulations for these purposes is limited due to a lack of ready-to-use models covering atrial anatomical variability. We built a bi-atrial statistical shape model (SSM) of the endocardial wall based on 47 segmented human CT and MRI datasets using Gaussian process morphable models. Generalization, specificity, and compactness metrics were evaluated. The SSM was applied to simulate atrial ECGs in 100 random volumetric instances. The first eigenmode of our SSM reflects a change of the total volume of both atria, the second the asymmetry between left vs. right atrial volume, the third a change in the prominence of the atrial appendages. The SSM is capable of generalizing well to unseen geometries and 95% of the total shape variance is covered by its first 24 eigenvectors. The P waves in the 12-lead ECG of 100 random instances showed a duration of 109.7±12.2 ms in accordance with large cohort studies. The novel bi-atrial SSM itself as well as 100 exemplary instances with rule-based augmentation of atrial wall thickness, fiber orientation, inter-atrial bridges and tags for anatomical structures have been made publicly available. This novel, openly available bi-atrial SSM can in future be employed to generate large sets of realistic atrial geometries as a basis for in silico big data approaches.
Collapse
|
28
|
Abstract
Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.
Collapse
|
29
|
Prins BP, Leitsalu L, Pärna K, Fischer K, Metspalu A, Haller T, Snieder H. Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example. J Pers Med 2021; 11:jpm11050358. [PMID: 33946982 PMCID: PMC8145318 DOI: 10.3390/jpm11050358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/19/2021] [Accepted: 04/25/2021] [Indexed: 02/07/2023] Open
Abstract
The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.
Collapse
Affiliation(s)
- Bram Peter Prins
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Correspondence: (B.P.P.); (H.S.)
| | - Liis Leitsalu
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
| | - Katri Pärna
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
- Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia
| | - Krista Fischer
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
- Institute of Mathematics and Statistics, University of Tartu, 50409 Tartu, Estonia
| | - Andres Metspalu
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
| | - Toomas Haller
- Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; (L.L.); (K.P.); (K.F.); (A.M.); (T.H.)
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
- Correspondence: (B.P.P.); (H.S.)
| |
Collapse
|
30
|
Xia Y, Ravikumar N, Greenwood JP, Neubauer S, Petersen SE, Frangi AF. Super-Resolution of Cardiac MR Cine Imaging using Conditional GANs and Unsupervised Transfer Learning. Med Image Anal 2021; 71:102037. [PMID: 33910110 DOI: 10.1016/j.media.2021.102037] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 03/02/2021] [Accepted: 03/05/2021] [Indexed: 12/22/2022]
Abstract
High-resolution (HR), isotropic cardiac Magnetic Resonance (MR) cine imaging is challenging since it requires long acquisition and patient breath-hold times. Instead, 2D balanced steady-state free precession (SSFP) sequence is widely used in clinical routine. However, it produces highly-anisotropic image stacks, with large through-plane spacing that can hinder subsequent image analysis. To resolve this, we propose a novel, robust adversarial learning super-resolution (SR) algorithm based on conditional generative adversarial nets (GANs), that incorporates a state-of-the-art optical flow component to generate an auxiliary image to guide image synthesis. The approach is designed for real-world clinical scenarios and requires neither multiple low-resolution (LR) scans with multiple views, nor the corresponding HR scans, and is trained in an end-to-end unsupervised transfer learning fashion. The designed framework effectively incorporates visual properties and relevant structures of input images and can synthesise 3D isotropic, anatomically plausible cardiac MR images, consistent with the acquired slices. Experimental results show that the proposed SR method outperforms several state-of-the-art methods both qualitatively and quantitatively. We show that subsequent image analyses including ventricle segmentation, cardiac quantification, and non-rigid registration can benefit from the super-resolved, isotropic cardiac MR images, to produce more accurate quantitative results, without increasing the acquisition time. The average Dice similarity coefficient (DSC) for the left ventricular (LV) cavity and myocardium are 0.95 and 0.81, respectively, between real and synthesised slice segmentation. For non-rigid registration and motion tracking through the cardiac cycle, the proposed method improves the average DSC from 0.75 to 0.86, compared to the original resolution images.
Collapse
Affiliation(s)
- Yan Xia
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK; Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK.
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK; Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK
| | - John P Greenwood
- Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK
| | - Stefan Neubauer
- Oxford Center for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK; Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK
| | - Alejandro F Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK; Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK; Medical Imaging Research Center (MIRC), Cardiovascular Science and Electronic Engineering Departments, KU Leuven, Leuven, Belgium
| |
Collapse
|
31
|
Fedele M, Quarteroni A. Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3435. [PMID: 33415829 PMCID: PMC8244076 DOI: 10.1002/cnm.3435] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/01/2021] [Accepted: 01/02/2021] [Indexed: 06/05/2023]
Abstract
In order to simulate the cardiac function for a patient-specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad-hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh-size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms-implemented in the open-source software vmtk-can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed-up the mesh generation process for a large range of cardiac applications, from single-chamber single-physics simulations to multi-chambers multi-physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.
Collapse
Affiliation(s)
- Marco Fedele
- MOX, Department of MathematicsPolitecnico di MilanoMilanItaly
| | - Alfio Quarteroni
- MOX, Department of MathematicsPolitecnico di MilanoMilanItaly
- Institute of MathematicsÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| |
Collapse
|
32
|
Young DM, Fazel Darbandi S, Schwartz G, Bonzell Z, Yuruk D, Nojima M, Gole LC, Rubenstein JL, Yu W, Sanders SJ. Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain. eLife 2021; 10:61408. [PMID: 33570495 PMCID: PMC7994002 DOI: 10.7554/elife.61408] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 02/10/2021] [Indexed: 12/17/2022] Open
Abstract
3D imaging data necessitate 3D reference atlases for accurate quantitative interpretation. Existing computational methods to generate 3D atlases from 2D-derived atlases result in extensive artifacts, while manual curation approaches are labor-intensive. We present a computational approach for 3D atlas construction that substantially reduces artifacts by identifying anatomical boundaries in the underlying imaging data and using these to guide 3D transformation. Anatomical boundaries also allow extension of atlases to complete edge regions. Applying these methods to the eight developmental stages in the Allen Developing Mouse Brain Atlas (ADMBA) led to more comprehensive and accurate atlases. We generated imaging data from 15 whole mouse brains to validate atlas performance and observed qualitative and quantitative improvement (37% greater alignment between atlas and anatomical boundaries). We provide the pipeline as the MagellanMapper software and the eight 3D reconstructed ADMBA atlases. These resources facilitate whole-organ quantitative analysis between samples and across development. The research community needs precise, reliable 3D atlases of organs to pinpoint where biological structures and processes are located. For instance, these maps are essential to understand where specific genes are turned on or off, or the spatial organization of various groups of cells over time. For centuries, atlases have been built by thinly ‘slicing up’ an organ, and then precisely representing each 2D layer. Yet this approach is imperfect: each layer may be accurate on its own, but inevitable mismatches appear between the slices when viewed in 3D or from another angle. Advances in microscopy now allow entire organs to be imaged in 3D. Comparing these images with atlases could help to detect subtle differences that indicate or underlie disease. However, this is only possible if 3D maps are accurate and do not feature mismatches between layers. To create an atlas without such artifacts, one approach consists in starting from scratch and manually redrawing the maps in 3D, a labor-intensive method that discards a large body of well-established atlases. Instead, Young et al. set out to create an automated method which could help to refine existing ‘layer-based’ atlases, releasing software that anyone can use to improve current maps. The package was created by harnessing eight atlases in the Allen Developing Mouse Brain Atlas, and then using the underlying anatomical images to resolve discrepancies between layers or fill out any missing areas. Known as MagellanMapper, the software was extensively tested to demonstrate the accuracy of the maps it creates, including comparison to whole-brain imaging data from 15 mouse brains. Armed with this new software, researchers can improve the accuracy of their atlases, helping them to understand the structure of organs at the level of the cell and giving them insight into a broad range of human disorders.
Collapse
Affiliation(s)
- David M Young
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, United States.,Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
| | - Siavash Fazel Darbandi
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, United States
| | - Grace Schwartz
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, United States
| | - Zachary Bonzell
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, United States
| | - Deniz Yuruk
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, United States
| | - Mai Nojima
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, United States
| | - Laurent C Gole
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
| | - John Lr Rubenstein
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, United States
| | - Weimiao Yu
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
| | - Stephan J Sanders
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, United States
| |
Collapse
|
33
|
Xanthis CG, Filos D, Haris K, Aletras AH. Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 198:105817. [PMID: 33160692 DOI: 10.1016/j.cmpb.2020.105817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 10/21/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may be available but the size is small, 3) may be available but not representative and 4) may be available but with weak labels. The aim of this study was to overcome these limitations through advanced MR simulations on a realistic computer model of human anatomy without using a real MRI scanner, without scanning patients and without having personnel and the associated expenses. METHODS The 4D-XCAT model was used with the coreMRI simulation platform for generating artificial short-axis MR-images for training a neural-network to automatic delineate the LV endocardium and epicardium. Its performance was assessed on real MR-images acquired from eight healthy volunteers. The neural-network was also trained on real MR-images from a publicly available dataset and its performance was assessed on the same volunteers' data. RESULTS The proposed solution demonstrated a performance of 94% (endocardium) and 90% DICE (epicardium) in real mid-ventricular slices, whereas a 10% addition of real MR-images in the artificial training dataset increased the performance to 97% DICE. The use of artificial MR-images that cover the entire LV yielded 85% (endocardium) and 88% DICE (epicardium) when combined with real MR data with an 80%-20% mix respectively. CONCLUSIONS This study suggests a low-cost solution for constructing artificial training datasets for supervised learning techniques in the field of MR by using advanced MR simulations without the use of a real MRI scanner, without scanning patients and without having to use specialized personnel, such as technologists and radiologists.
Collapse
Affiliation(s)
- Christos G Xanthis
- Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece; Department of Clinical Physiology, Clinical Sciences, Lund University and Lund University Hospital, Lund, Sweden.
| | - Dimitrios Filos
- Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece.
| | - Kostas Haris
- Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece.
| | - Anthony H Aletras
- Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece; Department of Clinical Physiology, Clinical Sciences, Lund University and Lund University Hospital, Lund, Sweden.
| |
Collapse
|
34
|
Soltanmohammadi P, Elwell J, Veeraraghavan V, Athwal GS, Willing R. Investigating the Effects of Demographics on Shoulder Morphology and Density Using Statistical Shape and Density Modeling. J Biomech Eng 2020; 142:121005. [PMID: 32601709 PMCID: PMC7580668 DOI: 10.1115/1.4047664] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 06/17/2020] [Indexed: 11/08/2022]
Abstract
A better understanding of how the shape and density of the shoulder vary among members of a population can help design more effective population-based orthopedic implants. The main objective of this study was to develop statistical shape models (SSMs) and statistical density models (SDMs) of the shoulder to describe the main modes of variability in the shape and density distributions of shoulder bones within a population in terms of principal components (PCs). These PC scores were analyzed, and significant correlations were observed between the shape and density distributions of the shoulder and demographics of the population, such as sex and age. Our results demonstrated that when the overall body sizes of male and female donors were matched, males still had, on average, larger scapulae and thicker humeral cortical bones. Moreover, we concluded that age has a weak but significant inverse effect on the density within the entire shoulder. Weak and moderate, but significant, correlations were also found between many modes of shape and density variations in the shoulder. Our results suggested that donors with bigger humeri have bigger scapulae and higher bone density of humeri corresponds with higher bone density in the scapulae. Finally, asymmetry, to some extent, was noted in the shape and density distributions of the contralateral bones of the shoulder. These results can be used to help guide the designs of population-based prosthesis components and pre-operative surgical planning.
Collapse
Affiliation(s)
- Pendar Soltanmohammadi
- School of Biomedical Engineering, Western University, 1151 Richmond Street, London, ON N6A 3K7, Canada
| | - Josie Elwell
- Department of Mechanical Engineering, State University of New York at Binghamton, P.O. Box 6000, Binghamton, NY 13902-6000
| | - Vishnu Veeraraghavan
- Department of Mechanical Engineering, State University of New York at Binghamton, P.O. Box 6000, Binghamton, NY 13902-6000
| | - George S. Athwal
- Roth | McFarlane Hand & Upper Limb Centre, St. Joseph's Health Care London, STN B, P.O. Box 5777, London, ON N6A 4V2, Canada
| | - Ryan Willing
- Department of Mechanical Engineering, Western University, 1151 Richmond Street, London, ON N6A 3K7, Canada
| |
Collapse
|
35
|
Gopalan D, Gibbs JSR. From Early Morphometrics to Machine Learning-What Future for Cardiovascular Imaging of the Pulmonary Circulation? Diagnostics (Basel) 2020; 10:diagnostics10121004. [PMID: 33255668 PMCID: PMC7760106 DOI: 10.3390/diagnostics10121004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 11/19/2020] [Accepted: 11/24/2020] [Indexed: 02/07/2023] Open
Abstract
Imaging plays a cardinal role in the diagnosis and management of diseases of the pulmonary circulation. Behind the picture itself, every digital image contains a wealth of quantitative data, which are hardly analysed in current routine clinical practice and this is now being transformed by radiomics. Mathematical analyses of these data using novel techniques, such as vascular morphometry (including vascular tortuosity and vascular volumes), blood flow imaging (including quantitative lung perfusion and computational flow dynamics), and artificial intelligence, are opening a window on the complex pathophysiology and structure-function relationships of pulmonary vascular diseases. They have the potential to make dramatic alterations to how clinicians investigate the pulmonary circulation, with the consequences of more rapid diagnosis and a reduction in the need for invasive procedures in the future. Applied to multimodality imaging, they can provide new information to improve disease characterization and increase diagnostic accuracy. These new technologies may be used as sophisticated biomarkers for risk prediction modelling of prognosis and for optimising the long-term management of pulmonary circulatory diseases. These innovative techniques will require evaluation in clinical trials and may in themselves serve as successful surrogate end points in trials in the years to come.
Collapse
Affiliation(s)
- Deepa Gopalan
- Imperial College Healthcare NHS Trust, London W12 0HS, UK
- Imperial College London, London SW7 2AZ, UK;
- Cambridge University Hospital, Cambridge CB2 0QQ, UK
- Correspondence: ; Tel.: +44-77-3000-7780
| | - J. Simon R. Gibbs
- Imperial College London, London SW7 2AZ, UK;
- National Heart & Lung Institute, Imperial College London, London SW3 6LY, UK
| |
Collapse
|
36
|
Cetin I, Raisi-Estabragh Z, Petersen SE, Napel S, Piechnik SK, Neubauer S, Gonzalez Ballester MA, Camara O, Lekadir K. Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank. Front Cardiovasc Med 2020; 7:591368. [PMID: 33240940 PMCID: PMC7667130 DOI: 10.3389/fcvm.2020.591368] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 10/06/2020] [Indexed: 12/25/2022] Open
Abstract
Cardiovascular magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping by analyzing multiple quantifiers of shape and tissue texture. In this paper, we assess, in the largest sample published to date, the performance of CMR radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors. We evaluated five risk factor groups from the first 5,065 UK Biobank participants: hypertension (n = 1,394), diabetes (n = 243), high cholesterol (n = 779), current smoker (n = 320), and previous smoker (n = 1,394). Each group was randomly matched with an equal number of healthy comparators (without known cardiovascular disease or risk factors). Radiomics analysis was applied to short axis images of the left and right ventricles at end-diastole and end-systole, yielding a total of 684 features per study. Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. We evaluated the degree of separation achieved by the identified radiomics signatures using area under curve (AUC), receiver operating characteristic (ROC), and statistical testing. Logistic regression with L1-regularization was the optimal ML model. Compared to conventional imaging indices, radiomics signatures improved the discrimination of risk factor vs. healthy subgroups as assessed by AUC [diabetes: 0.80 vs. 0.70, hypertension: 0.72 vs. 0.69, high cholesterol: 0.71 vs. 0.65, current smoker: 0.68 vs. 0.65, previous smoker: 0.63 vs. 0.60]. Furthermore, we considered clinical interpretation of risk-specific radiomics signatures. For hypertensive individuals and previous smokers, the surface area to volume ratio was smaller in the risk factor vs. healthy subjects; perhaps reflecting a pattern of global concentric hypertrophy in these conditions. In the diabetes subgroup, the most discriminatory radiomics feature was the median intensity of the myocardium at end-systole, which suggests a global alteration at the myocardial tissue level. This study confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease. We demonstrate such analysis may have utility beyond conventional CMR metrics for improved detection and understanding of the early effects of cardiovascular risk factors on cardiac structure and tissue.
Collapse
Affiliation(s)
- Irem Cetin
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St. Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Sandy Napel
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Stefan K. Piechnik
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Miguel A. Gonzalez Ballester
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Oscar Camara
- BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Karim Lekadir
- Departament de Matematiques i Informatica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
| |
Collapse
|
37
|
Attard MI, Dawes TJW, de Marvao A, Biffi C, Shi W, Wharton J, Rhodes CJ, Ghataorhe P, Gibbs JSR, Howard LSGE, Rueckert D, Wilkins MR, O'Regan DP. Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D analysis of cardiac magnetic resonance imaging. Eur Heart J Cardiovasc Imaging 2020; 20:668-676. [PMID: 30535300 PMCID: PMC6529902 DOI: 10.1093/ehjci/jey175] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 10/27/2018] [Indexed: 12/14/2022] Open
Abstract
Aims We sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress. Methods and results In 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function—including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = −0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28–0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = −0.40) of sulfated androgen. Conclusion Using computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.
Collapse
Affiliation(s)
- Mark I Attard
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK.,Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - Timothy J W Dawes
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK.,Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK.,Royal Brompton Cardiovascular Research Centre, National Heart & Lung Institute, Imperial College London, Dovehouse Street, London, UK
| | | | - Carlo Biffi
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK.,Department of Computing, Imperial College London, South Kensington Campus, Queen's Gate, London, UK
| | - Wenzhe Shi
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK.,Department of Computing, Imperial College London, South Kensington Campus, Queen's Gate, London, UK
| | - John Wharton
- Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - Christopher J Rhodes
- Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - Pavandeep Ghataorhe
- Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - J Simon R Gibbs
- Royal Brompton Cardiovascular Research Centre, National Heart & Lung Institute, Imperial College London, Dovehouse Street, London, UK
| | | | - Daniel Rueckert
- Department of Computing, Imperial College London, South Kensington Campus, Queen's Gate, London, UK
| | - Martin R Wilkins
- Division of Experimental Medicine, Department of Medicine, Imperial College London, Du Cane Road, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Du Cane Road, London, UK
| |
Collapse
|
38
|
Genetic and functional insights into the fractal structure of the heart. Nature 2020; 584:589-594. [PMID: 32814899 DOI: 10.1038/s41586-020-2635-8] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 05/21/2020] [Indexed: 01/06/2023]
Abstract
The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a remnant of embryonic development1,2. The function of these trabeculae in adults and their genetic architecture are unknown. Here we performed a genome-wide association study to investigate image-derived phenotypes of trabeculae using the fractal analysis of trabecular morphology in 18,096 participants of the UK Biobank. We identified 16 significant loci that contain genes associated with haemodynamic phenotypes and regulation of cytoskeletal arborization3,4. Using biomechanical simulations and observational data from human participants, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Through genetic association studies with cardiac disease phenotypes and Mendelian randomization, we find a causal relationship between trabecular morphology and risk of cardiovascular disease. These findings suggest a previously unknown role for myocardial trabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity and reveal the influence of the myocardial trabeculae on susceptibility to cardiovascular disease.
Collapse
|
39
|
Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric MRI. Comput Biol Med 2020; 123:103884. [DOI: 10.1016/j.compbiomed.2020.103884] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 06/23/2020] [Accepted: 06/25/2020] [Indexed: 02/03/2023]
|
40
|
Gilbert K, Mauger C, Young AA, Suinesiaputra A. Artificial Intelligence in Cardiac Imaging With Statistical Atlases of Cardiac Anatomy. Front Cardiovasc Med 2020; 7:102. [PMID: 32695795 PMCID: PMC7338378 DOI: 10.3389/fcvm.2020.00102] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/14/2020] [Indexed: 12/14/2022] Open
Abstract
In many cardiovascular pathologies, the shape and motion of the heart provide important clues to understanding the mechanisms of the disease and how it progresses over time. With the advent of large-scale cardiac data, statistical modeling of cardiac anatomy has become a powerful tool to provide automated, precise quantification of the status of patient-specific heart geometry with respect to reference populations. Powered by supervised or unsupervised machine learning algorithms, statistical cardiac shape analysis can be used to automatically identify and quantify the severity of heart diseases, to provide morphometric indices that are optimally associated with clinical factors, and to evaluate the likelihood of adverse outcomes. Recently, statistical cardiac atlases have been integrated with deep neural networks to enable anatomical consistency of cardiac segmentation, registration, and automated quality control. These combinations have already shown significant improvements in performance and avoid gross anatomical errors that could make the results unusable. This current trend is expected to grow in the near future. Here, we aim to provide a mini review highlighting recent advances in statistical atlasing of cardiac function in the context of artificial intelligence in cardiac imaging.
Collapse
Affiliation(s)
- Kathleen Gilbert
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Charlène Mauger
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.,Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.,Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Avan Suinesiaputra
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.,Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, United Kingdom.,School of Medicine, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| |
Collapse
|
41
|
Campos JO, Sundnes J, dos Santos RW, Rocha BM. Uncertainty quantification and sensitivity analysis of left ventricular function during the full cardiac cycle. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190381. [PMID: 32448074 PMCID: PMC7287338 DOI: 10.1098/rsta.2019.0381] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/10/2020] [Indexed: 05/21/2023]
Abstract
Patient-specific computer simulations can be a powerful tool in clinical applications, helping in diagnostics and the development of new treatments. However, its practical use depends on the reliability of the models. The construction of cardiac simulations involves several steps with inherent uncertainties, including model parameters, the generation of personalized geometry and fibre orientation assignment, which are semi-manual processes subject to errors. Thus, it is important to quantify how these uncertainties impact model predictions. The present work performs uncertainty quantification and sensitivity analyses to assess the variability in important quantities of interest (QoI). Clinical quantities are analysed in terms of overall variability and to identify which parameters are the major contributors. The analyses are performed for simulations of the left ventricle function during the entire cardiac cycle. Uncertainties are incorporated in several model parameters, including regional wall thickness, fibre orientation, passive material parameters, active stress and the circulatory model. The results show that the QoI are very sensitive to active stress, wall thickness and fibre direction, where ejection fraction and ventricular torsion are the most impacted outputs. Thus, to improve the precision of models of cardiac mechanics, new methods should be considered to decrease uncertainties associated with geometrical reconstruction, estimation of active stress and of fibre orientation. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
Collapse
Affiliation(s)
- J. O. Campos
- Centro Federal de Educação Tecnológica de Minas Gerais, Leopoldina, Brazil
- Graduate Program in Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | - J. Sundnes
- Simula Research Laboratory, PO Box 134 1325 Lysaker, Norway
| | - R. W. dos Santos
- Graduate Program in Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | - B. M. Rocha
- Graduate Program in Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
- e-mail:
| |
Collapse
|
42
|
Biffi C, Cerrolaza JJ, Tarroni G, Bai W, de Marvao A, Oktay O, Ledig C, Le Folgoc L, Kamnitsas K, Doumou G, Duan J, Prasad SK, Cook SA, O'Regan DP, Rueckert D. Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2088-2099. [PMID: 31944949 PMCID: PMC7269693 DOI: 10.1109/tmi.2020.2964499] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging.
Collapse
|
43
|
Piazzese C, Carminati MC, Krause R, Auricchio A, Weinert L, Gripari P, Tamborini G, Pontone G, Andreini D, Lang RM, Pepi M, Caiani EG. 3D right ventricular endocardium segmentation in cardiac magnetic resonance images by using a new inter-modality statistical shape modelling method. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
44
|
Bhuva AN, Treibel TA, De Marvao A, Biffi C, Dawes TJW, Doumou G, Bai W, Patel K, Boubertakh R, Rueckert D, O'Regan DP, Hughes AD, Moon JC, Manisty CH. Sex and regional differences in myocardial plasticity in aortic stenosis are revealed by 3D model machine learning. Eur Heart J Cardiovasc Imaging 2020; 21:417-427. [PMID: 31280289 PMCID: PMC7100908 DOI: 10.1093/ehjci/jez166] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 06/22/2019] [Indexed: 12/12/2022] Open
Abstract
AIMS Left ventricular hypertrophy (LVH) in aortic stenosis (AS) varies widely before and after aortic valve replacement (AVR), and deeper phenotyping beyond traditional global measures may improve risk stratification. We hypothesized that machine learning derived 3D LV models may provide a more sensitive assessment of remodelling and sex-related differences in AS than conventional measurements. METHODS AND RESULTS One hundred and sixteen patients with severe, symptomatic AS (54% male, 70 ± 10 years) underwent cardiovascular magnetic resonance pre-AVR and 1 year post-AVR. Computational analysis produced co-registered 3D models of wall thickness, which were compared with 40 propensity-matched healthy controls. Preoperative regional wall thickness and post-operative percentage wall thickness regression were analysed, stratified by sex. AS hypertrophy and regression post-AVR was non-uniform-greatest in the septum with more pronounced changes in males than females (wall thickness regression: -13 ± 3.6 vs. -6 ± 1.9%, respectively, P < 0.05). Even patients without LVH (16% with normal indexed LV mass, 79% female) had greater septal and inferior wall thickness compared with controls (8.8 ± 1.6 vs. 6.6 ± 1.2 mm, P < 0.05), which regressed post-AVR. These differences were not detectable by global measures of remodelling. Changes to clinical parameters post-AVR were also greater in males: N-terminal pro-brain natriuretic peptide (NT-proBNP) [-37 (interquartile range -88 to -2) vs. -1 (-24 to 11) ng/L, P = 0.008], and systolic blood pressure (12.9 ± 23 vs. 2.1 ± 17 mmHg, P = 0.009), with changes in NT-proBNP correlating with percentage LV mass regression in males only (ß 0.32, P = 0.02). CONCLUSION In patients with severe AS, including those without overt LVH, LV remodelling is most plastic in the septum, and greater in males, both pre-AVR and post-AVR. Three-dimensional machine learning is more sensitive than conventional analysis to these changes, potentially enhancing risk stratification. CLINICAL TRIAL REGISTRATION Regression of myocardial fibrosis after aortic valve replacement (RELIEF-AS); NCT02174471. https://clinicaltrials.gov/ct2/show/NCT02174471.
Collapse
Affiliation(s)
- Anish N Bhuva
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Thomas A Treibel
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Antonio De Marvao
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
| | - Carlo Biffi
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
- Department of Computing, Imperial College London, South Kensington Campus, 180 Queen's Gate, London SW72RH, UK
| | - Timothy J W Dawes
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
- National Heart and Lung Institute, Imperial College London, Du Cane Road, London W120NN, UK
| | - Georgia Doumou
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
| | - Wenjia Bai
- Department of Computing, Imperial College London, South Kensington Campus, 180 Queen's Gate, London SW72RH, UK
| | - Kush Patel
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Redha Boubertakh
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, South Kensington Campus, 180 Queen's Gate, London SW72RH, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK
| | - Alun D Hughes
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
| | - James C Moon
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| | - Charlotte H Manisty
- Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK
- Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK
| |
Collapse
|
45
|
de Marvao A, Dawes TJW, O'Regan DP. Artificial Intelligence for Cardiac Imaging-Genetics Research. Front Cardiovasc Med 2020; 6:195. [PMID: 32039240 PMCID: PMC6985036 DOI: 10.3389/fcvm.2019.00195] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 12/27/2019] [Indexed: 12/18/2022] Open
Abstract
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
Collapse
Affiliation(s)
| | | | - Declan P. O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| |
Collapse
|
46
|
Duchateau N, King AP, De Craene M. Machine Learning Approaches for Myocardial Motion and Deformation Analysis. Front Cardiovasc Med 2020; 6:190. [PMID: 31998756 PMCID: PMC6962100 DOI: 10.3389/fcvm.2019.00190] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 12/12/2019] [Indexed: 12/21/2022] Open
Abstract
Information about myocardial motion and deformation is key to differentiate normal and abnormal conditions. With the advent of approaches relying on data rather than pre-conceived models, machine learning could either improve the robustness of motion quantification or reveal patterns of motion and deformation (rather than single parameters) that differentiate pathologies. We review machine learning strategies for extracting motion-related descriptors and analyzing such features among populations, keeping in mind constraints specific to the cardiac application.
Collapse
Affiliation(s)
| | - Andrew P. King
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | |
Collapse
|
47
|
Rood JE, Stuart T, Ghazanfar S, Biancalani T, Fisher E, Butler A, Hupalowska A, Gaffney L, Mauck W, Eraslan G, Marioni JC, Regev A, Satija R. Toward a Common Coordinate Framework for the Human Body. Cell 2019; 179:1455-1467. [PMID: 31835027 PMCID: PMC6934046 DOI: 10.1016/j.cell.2019.11.019] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 10/30/2019] [Accepted: 11/13/2019] [Indexed: 01/21/2023]
Abstract
Understanding the genetic and molecular drivers of phenotypic heterogeneity across individuals is central to biology. As new technologies enable fine-grained and spatially resolved molecular profiling, we need new computational approaches to integrate data from the same organ across different individuals into a consistent reference and to construct maps of molecular and cellular organization at histological and anatomical scales. Here, we review previous efforts and discuss challenges involved in establishing such a common coordinate framework, the underlying map of tissues and organs. We focus on strategies to handle anatomical variation across individuals and highlight the need for new technologies and analytical methods spanning multiple hierarchical scales of spatial resolution.
Collapse
Affiliation(s)
- Jennifer E Rood
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA
| | - Tim Stuart
- New York Genome Center, New York, NY 10013, USA
| | - Shila Ghazanfar
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | | | - Eyal Fisher
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Andrew Butler
- New York Genome Center, New York, NY 10013, USA; New York University, Center for Genomics and Systems Biology, New York, NY 10012, USA
| | - Anna Hupalowska
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA
| | - Leslie Gaffney
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA
| | - William Mauck
- New York Genome Center, New York, NY 10013, USA; New York University, Center for Genomics and Systems Biology, New York, NY 10012, USA
| | - Gökçen Eraslan
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA
| | - John C Marioni
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute, Cambridge, MA 02142, USA; Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA 02142, USA.
| | - Rahul Satija
- New York Genome Center, New York, NY 10013, USA; New York University, Center for Genomics and Systems Biology, New York, NY 10012, USA.
| |
Collapse
|
48
|
Abstract
OBJECTIVE. The purpose of this article is to review the nascent field of radiomics in cardiac MRI. CONCLUSION. Cardiac MRI produces a large number of images in a fairly inefficient manner with sometimes limited clinical application. In the era of precision medicine, there is increasing need for imaging to account for a broader array of diseases in an efficient and objective manner. Radiomics, the extraction and analysis of quantitative imaging features from medical imaging, may offer potential solutions to this need.
Collapse
|
49
|
Krebs J, Delingette H, Mailhe B, Ayache N, Mansi T. Learning a Probabilistic Model for Diffeomorphic Registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2165-2176. [PMID: 30716033 DOI: 10.1109/tmi.2019.2897112] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We propose to learn a low-dimensional probabilistic deformation model from data which can be used for the registration and the analysis of deformations. The latent variable model maps similar deformations close to each other in an encoding space. It enables to compare deformations, to generate normal or pathological deformations for any new image, or to transport deformations from one image pair to any other image. Our unsupervised method is based on the variational inference. In particular, we use a conditional variational autoencoder network and constrain transformations to be symmetric and diffeomorphic by applying a differentiable exponentiation layer with a symmetric loss function. We also present a formulation that includes spatial regularization such as the diffusion-based filters. In addition, our framework provides multi-scale velocity field estimations. We evaluated our method on 3-D intra-subject registration using 334 cardiac cine-MRIs. On this dataset, our method showed the state-of-the-art performance with a mean DICE score of 81.2% and a mean Hausdorff distance of 7.3 mm using 32 latent dimensions compared to three state-of-the-art methods while also demonstrating more regular deformation fields. The average time per registration was 0.32 s. Besides, we visualized the learned latent space and showed that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.
Collapse
|
50
|
Lee AWC, O'Regan DP, Gould J, Sidhu B, Sieniewicz B, Plank G, Warriner DR, Lamata P, Rinaldi CA, Niederer SA. Sex-Dependent QRS Guidelines for Cardiac Resynchronization Therapy Using Computer Model Predictions. Biophys J 2019; 117:2375-2381. [PMID: 31547974 PMCID: PMC6990372 DOI: 10.1016/j.bpj.2019.08.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/16/2019] [Accepted: 08/12/2019] [Indexed: 01/30/2023] Open
Abstract
Cardiac resynchronization therapy (CRT) is an important treatment for heart failure. Low female enrollment in clinical trials means that current CRT guidelines may be biased toward males. However, females have higher response rates at lower QRS duration (QRSd) thresholds. Sex differences in the left ventricle (LV) size could provide an explanation for the improved female response at lower QRSd. We aimed to test if sex differences in CRT response at lower QRSd thresholds are explained by differences in LV size and hence predict sex-specific guidelines for CRT. We investigated the effect that LV size sex difference has on QRSd between male and females in 1093 healthy individuals and 50 CRT patients using electrophysiological computer models of the heart. Simulations on the healthy mean shape models show that LV size sex difference can account for 50–100% of the sex difference in baseline QRSd in healthy individuals. In the CRT patient cohort, model simulations predicted female-specific guidelines for CRT, which were 9–13 ms lower than current guidelines. Sex differences in the LV size are able to account for a significant proportion of the sex difference in QRSd and provide a mechanistic explanation for the sex difference in CRT response. Simulations accounting for the smaller LV size in female CRT patients predict 9–13 ms lower QRSd thresholds for female CRT guidelines.
Collapse
Affiliation(s)
- Angela W C Lee
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Baldeep Sidhu
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Benjamin Sieniewicz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Gernot Plank
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - David R Warriner
- Department of Cardiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Pablo Lamata
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Christopher A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| |
Collapse
|