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Garzia S, Capellini K, Gasparotti E, Pizzuto D, Spinelli G, Berti S, Positano V, Celi S. Three-Dimensional Multi-Modality Registration for Orthopaedics and Cardiovascular Settings: State-of-the-Art and Clinical Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:1072. [PMID: 38400229 PMCID: PMC10891817 DOI: 10.3390/s24041072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024]
Abstract
The multimodal and multidomain registration of medical images have gained increasing recognition in clinical practice as a powerful tool for fusing and leveraging useful information from different imaging techniques and in different medical fields such as cardiology and orthopedics. Image registration could be a challenging process, and it strongly depends on the correct tuning of registration parameters. In this paper, the robustness and accuracy of a landmarks-based approach have been presented for five cardiac multimodal image datasets. The study is based on 3D Slicer software and it is focused on the registration of a computed tomography (CT) and 3D ultrasound time-series of post-operative mitral valve repair. The accuracy of the method, as a function of the number of landmarks used, was performed by analysing root mean square error (RMSE) and fiducial registration error (FRE) metrics. The validation of the number of landmarks resulted in an optimal number of 10 landmarks. The mean RMSE and FRE values were 5.26 ± 3.17 and 2.98 ± 1.68 mm, respectively, showing comparable performances with respect to the literature. The developed registration process was also tested on a CT orthopaedic dataset to assess the possibility of reconstructing the damaged jaw portion for a pre-operative planning setting. Overall, the proposed work shows how 3D Slicer and registration by landmarks can provide a useful environment for multimodal/unimodal registration.
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Affiliation(s)
- Simone Garzia
- BioCardioLab, Bioengineering Unit, Fondazione Toscana G. Monasterio, 54100 Massa, Italy; (S.G.); (K.C.); (E.G.); (V.P.)
- Department of Information Engineering, University of Pisa, 56122 Pisa, Italy;
| | - Katia Capellini
- BioCardioLab, Bioengineering Unit, Fondazione Toscana G. Monasterio, 54100 Massa, Italy; (S.G.); (K.C.); (E.G.); (V.P.)
| | - Emanuele Gasparotti
- BioCardioLab, Bioengineering Unit, Fondazione Toscana G. Monasterio, 54100 Massa, Italy; (S.G.); (K.C.); (E.G.); (V.P.)
| | - Domenico Pizzuto
- Department of Information Engineering, University of Pisa, 56122 Pisa, Italy;
| | - Giuseppe Spinelli
- Maxillofacial Surgery Department, Azienda Ospedaliero-Universitaria Careggi, 50134 Firenze, Italy;
| | - Sergio Berti
- Diagnostic and Interventional Cardiology Department, Fondazione Toscana G. Monasterio, 54100 Massa, Italy;
| | - Vincenzo Positano
- BioCardioLab, Bioengineering Unit, Fondazione Toscana G. Monasterio, 54100 Massa, Italy; (S.G.); (K.C.); (E.G.); (V.P.)
| | - Simona Celi
- BioCardioLab, Bioengineering Unit, Fondazione Toscana G. Monasterio, 54100 Massa, Italy; (S.G.); (K.C.); (E.G.); (V.P.)
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Li L, Ding W, Huang L, Zhuang X, Grau V. Multi-modality cardiac image computing: A survey. Med Image Anal 2023; 88:102869. [PMID: 37384950 DOI: 10.1016/j.media.2023.102869] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 05/01/2023] [Accepted: 06/12/2023] [Indexed: 07/01/2023]
Abstract
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.
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Affiliation(s)
- Lei Li
- Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Wangbin Ding
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Liqin Huang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
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Shoaib MA, Chuah JH, Ali R, Hasikin K, Khalil A, Hum YC, Tee YK, Dhanalakshmi S, Lai KW. An Overview of Deep Learning Methods for Left Ventricle Segmentation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:4208231. [PMID: 36756163 PMCID: PMC9902166 DOI: 10.1155/2023/4208231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/25/2022] [Accepted: 11/24/2022] [Indexed: 01/31/2023]
Abstract
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.
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Affiliation(s)
- Muhammad Ali Shoaib
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Joon Huang Chuah
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Raza Ali
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Azira Khalil
- Faculty of Science & Technology, Universiti Sains Islam Malaysia, Nilai 71800, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia
| | - Yee Kai Tee
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, India
| | - Khin Wee Lai
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
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Khalil A, Rahimi A, Luthfi A, Azizan MM, Satapathy SC, Hasikin K, Lai KW. Brain Tumour Temporal Monitoring of Interval Change Using Digital Image Subtraction Technique. Front Public Health 2021; 9:752509. [PMID: 34621723 PMCID: PMC8490781 DOI: 10.3389/fpubh.2021.752509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 08/25/2021] [Indexed: 11/13/2022] Open
Abstract
A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.
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Affiliation(s)
- Azira Khalil
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, Bandar Baru Nilai, Malaysia
| | - Aisyah Rahimi
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, Bandar Baru Nilai, Malaysia
| | - Aida Luthfi
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, Bandar Baru Nilai, Malaysia
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, Malaysia
| | - Suresh Chandra Satapathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneshwar, India
| | - Khairunnisa Hasikin
- Biomedical Engineering Department, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Biomedical Engineering Department, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
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Development and External Validation of a Nomogram for Predicting Overall Survival in Stomach Cancer: A Population-Based Study. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8605869. [PMID: 34608415 PMCID: PMC8487388 DOI: 10.1155/2021/8605869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 09/01/2021] [Indexed: 12/26/2022]
Abstract
Objective The study was to develop and externally validate a prognostic nomogram to effectively predict the overall survival of patients with stomach cancer. Methods Demographic and clinical variables of patients with stomach cancer in the Surveillance, Epidemiology, and End Results (SEER) database from 2007–2016 were retrospectively collected. Patients were then divided into the Training Group (n = 4,456) for model development and the Testing Group (n = 4,541) for external validation. Univariate and multivariate Cox regressions were used to explore prognostic factors. The concordance index (C-index) and the Kolmogorov–Smirnov (KS) value were used to measure the discrimination, and the calibration curve was used to assess the calibration of the nomogram. Results Prognostic factors including age, race, marital status, TNM stage, surgery, chemotherapy, grade, and the number of regional nodes positive were used to construct a nomogram. The C-index was 0.790 and the KS value was 0.45 for the Training Group, and the C-index was 0.789 for the Testing Group, all suggesting the good performance of the nomogram. Conclusion We have developed an effective nomogram with ten easily acquired prognostic factors. The nomogram could accurately predict the overall survival of patients with stomach cancer and performed well on external validation, which would help improve the individualized survival prediction and decision-making, thereby improving the outcome and survival of stomach cancer.
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Rahimi A, Khalil A, Faisal A, Lai KW. CT-MRI Dual Information Registration for the Diagnosis of Liver Cancer: A Pilot Study Using Point-Based Registration. Curr Med Imaging 2021; 18:61-66. [PMID: 34433403 DOI: 10.2174/1573405617666210825155659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 05/17/2021] [Accepted: 06/01/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Early diagnosis of liver cancer may increase life expectancy. Computed tomography (CT) and magnetic resonance imaging (MRI) play a vital role in diagnosing liver cancer. Together, both modalities offer significant individual and specific diagnosis data to physicians; however, they lack the integration of both types of information. To address this concern, a registration process has to be utilized for the purpose, as multimodal details are crucial in providing the physician with complete information. OBJECTIVE The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities. METHODS In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel(R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively. RESULTS The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution. CONCLUSION The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.
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Affiliation(s)
- Aisyah Rahimi
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, 71800 Nilai, Negeri Sembilan. Malaysia
| | - Azira Khalil
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, 71800 Nilai, Negeri Sembilan. Malaysia
| | - Amir Faisal
- Biomedical Engineering, Institut Teknologi Sumatera, Lampung Selatan, 35365. Indonesia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur. Malaysia
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Meng LK, Khalil A, Ahmad Nizar MH, Nisham MK, Pingguan-Murphy B, Hum YC, Mohamad Salim MI, Lai KW. Carpal Bone Segmentation Using Fully Convolutional Neural Network. Curr Med Imaging 2020; 15:983-989. [PMID: 32008525 DOI: 10.2174/1573405615666190724101600] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 05/30/2019] [Accepted: 07/08/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. METHODS In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. RESULTS AND CONCLUSION The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.
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Affiliation(s)
- Liang Kim Meng
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Azira Khalil
- Faculty of Science and Technology, Islamic Science University of Malaysia, 71800, Nilai, Negeri Sembilan, Malaysia
| | - Muhamad Hanif Ahmad Nizar
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Maryam Kamarun Nisham
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Belinda Pingguan-Murphy
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Cheras, 43000 Kajang, Selangor Darul Ehsan, Malaysia
| | - Maheza Irna Mohamad Salim
- Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, Johor, 81310, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia
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Nizar MHA, Chan CK, Khalil A, Yusof AKM, Lai KW. Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks. Curr Med Imaging 2020; 16:584-591. [PMID: 32484093 DOI: 10.2174/1573405615666190114151255] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 12/14/2018] [Accepted: 12/20/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. METHODS Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. RESULTS Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. CONCLUSION Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.
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Affiliation(s)
- Muhammad Hanif Ahmad Nizar
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia
| | - Chow Khuen Chan
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia
| | - Azira Khalil
- Department of Applied Physics, Islamic Science University of Malaysia, Nilai, Negeri Sembilan 71800, Malaysia
| | | | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia
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Peters A, Motiwala A, O'Neill B, Patil P. Novel use of fused cardiac computed tomography and transesophageal echocardiography for left atrial appendage closure. Catheter Cardiovasc Interv 2020; 97:E719-E723. [PMID: 32150324 DOI: 10.1002/ccd.28840] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/23/2020] [Accepted: 02/25/2020] [Indexed: 11/08/2022]
Abstract
The use of the Watchman left atrial appendage occlusion device (Boston Scientific Inc.) is becoming increasingly frequent in patients with atrial fibrillation. Cardiac computed tomography (CT) for device sizing pre-procedure can help facilitate more accurate device selection compared with transesophageal echo (TEE) alone. CT can also help identify minor lobes and trabeculations that may not be apparent on TEE. We report a series of three cases to highlight the utility of a novel application of CT-TEE fusion imaging to provide procedural guidance during Watchman implant and to assess for peri-device leak post-implant.
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Affiliation(s)
- Andrew Peters
- Section of Cardiology, Department of Medicine, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania
| | - Afaq Motiwala
- Section of Cardiology, Department of Medicine, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania
| | - Brian O'Neill
- Section of Cardiology, Department of Medicine, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania
| | - Pravin Patil
- Section of Cardiology, Department of Medicine, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania
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Khalil A, Ng SC, Liew YM, Lai KW. An Overview on Image Registration Techniques for Cardiac Diagnosis and Treatment. Cardiol Res Pract 2018; 2018:1437125. [PMID: 30159169 PMCID: PMC6109558 DOI: 10.1155/2018/1437125] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 07/05/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022] Open
Abstract
Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.
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Affiliation(s)
- Azira Khalil
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
- Faculty of Science and Technology, Islamic Science University of Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia
| | - Siew-Cheok Ng
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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