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Gheysen L, Maes L, Caenen A, Segers P, Peirlinck M, Famaey N. Uncertainty quantification of the wall thickness and stiffness in an idealized dissected aorta. J Mech Behav Biomed Mater 2024; 151:106370. [PMID: 38224645 DOI: 10.1016/j.jmbbm.2024.106370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 12/21/2023] [Accepted: 01/01/2024] [Indexed: 01/17/2024]
Abstract
Personalized treatment informed by computational models has the potential to markedly improve the outcome for patients with a type B aortic dissection. However, existing computational models of dissected walls significantly simplify the characteristic false lumen, tears and/or material behavior. Moreover, the patient-specific wall thickness and stiffness cannot be accurately captured non-invasively in clinical practice, which inevitably leads to assumptions in these wall models. It is important to evaluate the impact of the corresponding uncertainty on the predicted wall deformations and stress, which are both key outcome indicators for treatment optimization. Therefore, a physiology-inspired finite element framework was proposed to model the wall deformation and stress of a type B aortic dissection at diastolic and systolic pressure. Based on this framework, 300 finite element analyses, sampled with a Latin hypercube, were performed to assess the global uncertainty, introduced by 4 uncertain wall thickness and stiffness input parameters, on 4 displacement and stress output parameters. The specific impact of each input parameter was estimated using Gaussian process regression, as surrogate model of the finite element framework, and a δ moment-independent analysis. The global uncertainty analysis indicated minor differences between the uncertainty at diastolic and systolic pressure. For all output parameters, the 4th quartile contained the major fraction of the uncertainty. The parameter-specific uncertainty analysis elucidated that the material stiffness and relative thickness of the dissected membrane were the respective main determinants of the wall deformation and stress. The uncertainty analysis provides insight into the effect of uncertain wall thickness and stiffness parameters on the predicted deformation and stress. Moreover, it emphasizes the need for probabilistic rather than deterministic predictions for clinical decision making in aortic dissections.
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Affiliation(s)
- Lise Gheysen
- Institute for Biomedical Engineering and Technology, Electronics and Information Systems, Ghent University, Belgium.
| | - Lauranne Maes
- Biomechanics Section, Mechanical Engineering, KU Leuven, Belgium
| | - Annette Caenen
- Institute for Biomedical Engineering and Technology, Electronics and Information Systems, Ghent University, Belgium; Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven, Belgium
| | - Patrick Segers
- Institute for Biomedical Engineering and Technology, Electronics and Information Systems, Ghent University, Belgium
| | - Mathias Peirlinck
- Department of BioMechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, the Netherlands
| | - Nele Famaey
- Biomechanics Section, Mechanical Engineering, KU Leuven, Belgium
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Zhou H, Xiao J, Ganesh S, Lerner A, Ruan D, Fan Z. VWI-APP: Vessel wall imaging-dedicated automated processing pipeline for intracranial atherosclerotic plaque quantification. Med Phys 2023; 50:1496-1506. [PMID: 36345580 PMCID: PMC10033308 DOI: 10.1002/mp.16074] [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: 07/27/2022] [Revised: 10/16/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Quantitative plaque assessment based on 3D magnetic resonance (MR) vessel wall imaging (VWI) has been shown to provide valuable numerical markers of the burden and risk of intracranial atherosclerotic disease (ICAD). However, plaque quantification is currently time-consuming and observer-dependent due to the demand for heavy manual effort. A VWI-dedicated automated processing pipeline (VWI-APP) is desirable. PURPOSE To develop and evaluate a VWI-APP for end-to-end quantitative analysis of intracranial atherosclerotic plaque. METHODS We retrospectively enrolled 91 subjects with ICAD (80 for pipeline development, 10 for an end-to-end pipeline evaluation, and 1 for demonstrating longitudinal plaque assessment) who had undergone VWI and MR angiography. In an end-to-end evaluation, diameter stenosis (DS), normalized wall index (NWI), remodeling ratio (RR), plaque wall contrast ratio (CR), and total plaque volume (TPV) were quantified at each culprit lesion using the developed VWI-APP and a computer-aided manual approach by a neuroradiologist, respectively. The time consumed in each quantification approach was recorded. Two-sided paired t-tests and intraclass correlation coefficient (ICC) were used to determine the difference and agreement in each plaque metric between VWI-APP and manual quantification approaches. RESULTS There was no significant difference between VWI-APP and manual quantification in each plaque metric. The ICC was 0.890, 0.813, 0.827, 0.891, and 0.991 for DS, NWI, RR, CR, and TPV, respectively, suggesting good to excellent accuracy of the pipeline method in plaque quantification. Quantitative analysis of each culprit lesion on average took 675.7 s using the manual approach but shortened to 238.3 s with the aid of VWI-APP. CONCLUSIONS VWI-APP is an accurate and efficient approach to intracranial atherosclerotic plaque quantification. Further clinical assessment of this automated tool is warranted to establish its utility in the risk assessment of ICAD lesions.
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Affiliation(s)
- Hanyue Zhou
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jiayu Xiao
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA
| | - Siddarth Ganesh
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Alexander Lerner
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA
| | - Dan Ruan
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, USA
| | - Zhaoyang Fan
- Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Radiation Oncology, University of Southern California, Los Angeles, CA 90033, USA
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Popa IP, Haba MȘC, Mărănducă MA, Tănase DM, Șerban DN, Șerban LI, Iliescu R, Tudorancea I. Modern Approaches for the Treatment of Heart Failure: Recent Advances and Future Perspectives. Pharmaceutics 2022; 14:pharmaceutics14091964. [PMID: 36145711 PMCID: PMC9503448 DOI: 10.3390/pharmaceutics14091964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/13/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
Heart failure (HF) is a progressively deteriorating medical condition that significantly reduces both the patients’ life expectancy and quality of life. Even though real progress was made in the past decades in the discovery of novel pharmacological treatments for HF, the prevention of premature deaths has only been marginally alleviated. Despite the availability of a plethora of pharmaceutical approaches, proper management of HF is still challenging. Thus, a myriad of experimental and clinical studies focusing on the discovery of new and provocative underlying mechanisms of HF physiopathology pave the way for the development of novel HF therapeutic approaches. Furthermore, recent technological advances made possible the development of various interventional techniques and device-based approaches for the treatment of HF. Since many of these modern approaches interfere with various well-known pathological mechanisms in HF, they have a real ability to complement and or increase the efficiency of existing medications and thus improve the prognosis and survival rate of HF patients. Their promising and encouraging results reported to date compel the extension of heart failure treatment beyond the classical view. The aim of this review was to summarize modern approaches, new perspectives, and future directions for the treatment of HF.
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Affiliation(s)
- Irene Paula Popa
- Cardiology Clinic, “St. Spiridon” County Clinical Emergency Hospital, 700111 Iași, Romania
| | - Mihai Ștefan Cristian Haba
- Cardiology Clinic, “St. Spiridon” County Clinical Emergency Hospital, 700111 Iași, Romania
- Department of Internal Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Minela Aida Mărănducă
- Department of Physiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Daniela Maria Tănase
- Department of Internal Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
- Internal Medicine Clinic, “St. Spiridon” County Clinical Emergency Hospital, 700115 Iași, Romania
| | - Dragomir N. Șerban
- Department of Physiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Lăcrămioara Ionela Șerban
- Department of Physiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Radu Iliescu
- Department of Pharmacology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
| | - Ionuț Tudorancea
- Cardiology Clinic, “St. Spiridon” County Clinical Emergency Hospital, 700111 Iași, Romania
- Department of Physiology, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iași, Romania
- Correspondence:
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Chen L, Zhao H, Jiang H, Balu N, Geleri DB, Chu B, Watase H, Zhao X, Li R, Xu J, Hatsukami TS, Xu D, Hwang JN, Yuan C. Domain adaptive and fully automated carotid artery atherosclerotic lesion detection using an artificial intelligence approach (LATTE) on 3D MRI. Magn Reson Med 2021; 86:1662-1673. [PMID: 33885165 DOI: 10.1002/mrm.28794] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 03/07/2021] [Accepted: 03/18/2021] [Indexed: 01/17/2023]
Abstract
PURPOSE To develop and evaluate a domain adaptive and fully automated review workflow (lesion assessment through tracklet evaluation, LATTE) for assessment of atherosclerotic disease in 3D carotid MR vessel wall imaging (MR VWI). METHODS VWI of 279 subjects with carotid atherosclerosis were used to develop LATTE, mainly convolutional neural network (CNN)-based domain adaptive lesion classification after image quality assessment and artery of interest localization. Heterogeneity in test sets from various sites usually causes inferior CNN performance. With our novel unsupervised domain adaptation (DA), LATTE was designed to accurately classify arteries into normal arteries and early and advanced lesions without additional annotations on new datasets. VWI of 271 subjects from four datasets (eight sites) with slightly different imaging parameters/signal patterns were collected to assess the effectiveness of DA of LATTE using the area under the receiver operating characteristic curve (AUC) on all lesions and advanced lesions before and after DA. RESULTS LATTE had good performance with advanced/all lesion classification, with the AUC of >0.88/0.83, significant improvements from >0.82/0.80 if without DA. CONCLUSIONS LATTE can locate target arteries and distinguish carotid atherosclerotic lesions with consistently improved performance with DA on new datasets. It may be useful for carotid atherosclerosis detection and assessment on various clinical sites.
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Affiliation(s)
- Li Chen
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Huilin Zhao
- Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongjian Jiang
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Niranjan Balu
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | | | - Baocheng Chu
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Hiroko Watase
- Department of Surgery, University of Washington, Seattle, Washington, USA
| | - Xihai Zhao
- Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China
| | - Rui Li
- Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China
| | - Jianrong Xu
- Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Thomas S Hatsukami
- Department of Surgery, University of Washington, Seattle, Washington, USA
| | - Dongxiang Xu
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Jenq-Neng Hwang
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Chun Yuan
- Department of Radiology, University of Washington, Seattle, Washington, USA
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Chen L, Sun J, Canton G, Balu N, Hippe DS, Zhao X, Li R, Hatsukami TS, Hwang JN, Yuan C. Automated Artery Localization and Vessel Wall Segmentation using Tracklet Refinement and Polar Conversion. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:217603-217614. [PMID: 33777593 PMCID: PMC7996631 DOI: 10.1109/access.2020.3040616] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment.
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Affiliation(s)
- Li Chen
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Jie Sun
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Gador Canton
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Niranjan Balu
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Daniel S. Hippe
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
| | - Xihai Zhao
- Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China
| | - Rui Li
- Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China
| | | | - Jenq-Neng Hwang
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Chun Yuan
- Department of Radiology, University of Washington, Seattle, WA, 98195, USA
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6
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Chen L, Canton G, Liu W, Hippe DS, Balu N, Watase H, Hatsukami TS, Waterton JC, Hwang JN, Yuan C. Fully automated and robust analysis technique for popliteal artery vessel wall evaluation (FRAPPE) using neural network models from standardized knee MRI. Magn Reson Med 2020; 84:2147-2160. [PMID: 32162395 PMCID: PMC8320767 DOI: 10.1002/mrm.28237] [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: 11/14/2019] [Revised: 01/27/2020] [Accepted: 02/07/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a fully automated vessel wall (VW) analysis workflow (fully automated and robust analysis technique for popliteal artery evaluation, FRAPPE) on the popliteal artery in standardized knee MR images. METHODS Popliteal artery locations were detected from each MR slice by a deep neural network model and connected into a 3D artery centerline. Vessel wall regions around the centerline were then segmented using another neural network model for segmentation in polar coordinate system. Contours from vessel wall segmentations were used for vascular feature calculation, such as mean wall thickness and wall area. A transfer learning and active learning framework was applied in training the localization and segmentation neural network models to maintain accuracy while reducing manual annotations. This new popliteal artery analysis technique (FRAPPE) was validated against manual segmentation qualitatively and quantitatively in a series of 225 cases from the Osteoarthritis Initiative (OAI) dataset. RESULTS FRAPPE demonstrated high accuracy and robustness in locating popliteal arteries, segmenting artery walls, and quantifying arterial features. Qualitative evaluations showed 1.2% of slices had noticeable major errors, including segmenting the wrong target and irregular vessel wall contours. The mean Dice similarity coefficient with manual segmentation was 0.79, which is comparable to inter-rater variations. Repeatability evaluations show most of the vascular features have good to excellent repeatability from repeated scans of same subjects, with intra-class coefficient ranging from 0.80 to 0.98. CONCLUSION This technique can be used in large population-based studies, such as OAI, to efficiently assess the burden of atherosclerosis from routine MR knee scans.
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Affiliation(s)
- Li Chen
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Gador Canton
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Wenjin Liu
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Daniel S. Hippe
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Niranjan Balu
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Hiroko Watase
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | | | - John C. Waterton
- Centre for Imaging Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Jenq-Neng Hwang
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, USA
| | - Chun Yuan
- Department of Radiology, University of Washington, Seattle, Washington, USA
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7
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Mathur P, Srivastava S, Xu X, Mehta JL. Artificial Intelligence, Machine Learning, and Cardiovascular Disease. CLINICAL MEDICINE INSIGHTS-CARDIOLOGY 2020; 14:1179546820927404. [PMID: 32952403 PMCID: PMC7485162 DOI: 10.1177/1179546820927404] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 04/23/2020] [Indexed: 12/11/2022]
Abstract
Artificial intelligence (AI)-based applications have found widespread
applications in many fields of science, technology, and medicine. The use of
enhanced computing power of machines in clinical medicine and diagnostics has
been under exploration since the 1960s. More recently, with the advent of
advances in computing, algorithms enabling machine learning, especially deep
learning networks that mimic the human brain in function, there has been renewed
interest to use them in clinical medicine. In cardiovascular medicine, AI-based
systems have found new applications in cardiovascular imaging, cardiovascular
risk prediction, and newer drug targets. This article aims to describe different
AI applications including machine learning and deep learning and their
applications in cardiovascular medicine. AI-based applications have enhanced our
understanding of different phenotypes of heart failure and congenital heart
disease. These applications have led to newer treatment strategies for different
types of cardiovascular diseases, newer approach to cardiovascular drug therapy
and postmarketing survey of prescription drugs. However, there are several
challenges in the clinical use of AI-based applications and interpretation of
the results including data privacy, poorly selected/outdated data, selection
bias, and unintentional continuance of historical biases/stereotypes in the data
which can lead to erroneous conclusions. Still, AI is a transformative
technology and has immense potential in health care.
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Affiliation(s)
- Pankaj Mathur
- Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Shweta Srivastava
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Xiaowei Xu
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR USA
| | - Jawahar L Mehta
- Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
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Jodas DS, da Costa MFM, Parreira TAA, Pereira AS, Tavares JMRS. Using a distance map and an active contour model to segment the carotid artery boundary from the lumen contour in proton density weighted magnetic resonance images. Comput Biol Med 2020; 123:103901. [PMID: 32658794 DOI: 10.1016/j.compbiomed.2020.103901] [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: 03/27/2020] [Revised: 06/20/2020] [Accepted: 06/28/2020] [Indexed: 10/23/2022]
Abstract
Segmentation methods have assumed an important role in image-based diagnosis of several cardiovascular diseases. Particularly, the segmentation of the boundary of the carotid artery is demanded in the detection and characterization of atherosclerosis and assessment of the disease progression. In this article, a fully automatic approach for the segmentation of the carotid artery boundary in Proton Density Weighted Magnetic Resonance Images is presented. The approach relies on the expansion of the lumen contour based on a distance map built using the gray-weighted distance relative to the center of the identified lumen region in the image under analysis. Then, a Snake model with a modified weighted external energy based on the combination of a balloon force along with a Gradient Vector Flow-based external energy is applied to the expanded contour towards the correct boundary of the carotid artery. The average values of the Dice coefficient, Polyline distance, mean contour distance and centroid distance found in the segmentation of 139 carotid arteries were 0.83 ± 0.11, 2.70 ± 1.69 pixels, 2.79 ± 1.89 pixels and 3.44 ± 2.82 pixels, respectively. The segmentation results of the proposed approach were also compared against the ones obtained by related approaches found in the literature, which confirmed the outstanding performance of the new approach. Additionally, the proposed weighted external energy for the Snake model was shown to be also robust to carotid arteries with large thickness and weak boundary image edges.
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Affiliation(s)
- Danilo Samuel Jodas
- CAPES Foundation, Ministry of Education of Brazil, Brasília - DF, 70040-020, Brazil; Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
| | - Maria Francisca Monteiro da Costa
- IFE Neurorradiologia, Serviço de Neurorradiologia, Centro Hospitalar São João, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal.
| | - Tiago A A Parreira
- AH Neurorradiologia, Serviço de Neurorradiologia, Centro Hospitalar São João, Alameda Professor Hernâni Monteiro, 4200-319 Porto, Portugal.
| | - Aledir Silveira Pereira
- Universidade Estadual Paulista Júlio de Mesquita Filho, Rua Cristóvão Colombo, 2265, 15054-000, S. J. do Rio Preto, Brazil.
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
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9
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Biasiolli L, Hann E, Lukaschuk E, Carapella V, Paiva JM, Aung N, Rayner JJ, Werys K, Fung K, Puchta H, Sanghvi MM, Moon NO, Thomson RJ, Thomas KE, Robson MD, Grau V, Petersen SE, Neubauer S, Piechnik SK. Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data. PLoS One 2019; 14:e0212272. [PMID: 30763349 PMCID: PMC6375606 DOI: 10.1371/journal.pone.0212272] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 01/30/2019] [Indexed: 11/18/2022] Open
Abstract
Introduction Aortic distensibility can be calculated using semi-automated methods to segment the aortic lumen on cine CMR (Cardiovascular Magnetic Resonance) images. However, these methods require visual quality control and manual localization of the region of interest (ROI) of ascending (AA) and proximal descending (PDA) aorta, which limit the analysis in large-scale population-based studies. Using 5100 scans from UK Biobank, this study sought to develop and validate a fully automated method to 1) detect and locate the ROIs of AA and PDA, and 2) provide a quality control mechanism. Methods The automated AA and PDA detection-localization algorithm followed these steps: 1) foreground segmentation; 2) detection of candidate ROIs by Circular Hough Transform (CHT); 3) spatial, histogram and shape feature extraction for candidate ROIs; 4) AA and PDA detection using Random Forest (RF); 5) quality control based on RF detection probability. To provide the ground truth, overall image quality (IQ = 0–3 from poor to good) and aortic locations were visually assessed by 13 observers. The automated algorithm was trained on 1200 scans and Dice Similarity Coefficient (DSC) was used to calculate the agreement between ground truth and automatically detected ROIs. Results The automated algorithm was tested on 3900 scans. Detection accuracy was 99.4% for AA and 99.8% for PDA. Aorta localization showed excellent agreement with the ground truth, with DSC ≥ 0.9 in 94.8% of AA (DSC = 0.97 ± 0.04) and 99.5% of PDA cases (DSC = 0.98 ± 0.03). AA×PDA detection probabilities could discriminate scans with IQ ≥ 1 from those severely corrupted by artefacts (AUC = 90.6%). If scans with detection probability < 0.75 were excluded (350 scans), the algorithm was able to correctly detect and localize AA and PDA in all the remaining 3550 scans (100% accuracy). Conclusion The proposed method for automated AA and PDA localization was extremely accurate and the automatically derived detection probabilities provided a robust mechanism to detect low quality scans for further human review. Applying the proposed localization and quality control techniques promises at least a ten-fold reduction in human involvement without sacrificing any accuracy.
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Affiliation(s)
- Luca Biasiolli
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Evan Hann
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Elena Lukaschuk
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Valentina Carapella
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Jose M. Paiva
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Jennifer J. Rayner
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Konrad Werys
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Kenneth Fung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Henrike Puchta
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Mihir M. Sanghvi
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Niall O. Moon
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Ross J. Thomson
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Katharine E. Thomas
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Matthew D. Robson
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Stefan Neubauer
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stefan K. Piechnik
- Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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Harteveld AA, Denswil NP, Van Hecke W, Kuijf HJ, Vink A, Spliet WGM, Daemen MJ, Luijten PR, Zwanenburg JJM, Hendrikse J, van der Kolk AG. Ex vivo vessel wall thickness measurements of the human circle of Willis using 7T MRI. Atherosclerosis 2018; 273:106-114. [PMID: 29715587 DOI: 10.1016/j.atherosclerosis.2018.04.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 03/30/2018] [Accepted: 04/18/2018] [Indexed: 10/17/2022]
Abstract
BACKGROUND AND AIMS MRI can detect intracranial vessel wall thickening before any luminal stenosis is present. Apart from representing a vessel wall lesion, wall thickening could also reflect normal (age-related) variations in vessel wall thickness present throughout the intracranial arterial vasculature. The aim of this study was to perform vessel wall thickness measurements of the major intracranial arteries in ex vivo circle of Willis (CoW) specimens using 7T MRI, to obtain more detailed information about wall thickness variations of the intracranial arteries. METHODS Fifteen human CoW specimens were scanned at 7T MRI with an ultrahigh-resolution T1-weighted sequence. Five specimens were used for validation of MRI measurements with histology and evaluation of inter-rater reliability and agreement. The other 10 specimens from patients with (n = 5) and without (n = 5) cerebrovascular disease were used for vessel wall thickness measurements over the entire length of the major arterial segments of the CoW using MRI only. RESULTS MRI measurements showed excellent agreement with histology. Mean wall thickness varied from 0.45 to 0.66 mm, minimum wall thickness from 0.31 to 0.42 mm, maximum wall thickness from 0.52 to 0.86 mm, and normalized wall index from 0.64 to 0.75. On average, vessel walls were thicker for symptomatic patients compared to asymptomatic patients. CONCLUSIONS High-resolution MRI enables accurate measurement of vessel wall thickness in ex vivo CoW specimens. Vessel wall thickness measurements over the entire length of segments showed considerable variation both within and between arterial segments of patients.
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Affiliation(s)
- Anita A Harteveld
- Department of Radiology, University Medical Center Utrecht, Postbox 85500, 3508 GA, Utrecht, The Netherlands.
| | - Nerissa P Denswil
- Department of Pathology, Academic Medical Center, Postbox 22660, 1100 DD, Amsterdam, The Netherlands
| | - Wim Van Hecke
- Department of Pathology, University Medical Center Utrecht, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Aryan Vink
- Department of Pathology, University Medical Center Utrecht, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Wim G M Spliet
- Department of Pathology, University Medical Center Utrecht, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Mat J Daemen
- Department of Pathology, Academic Medical Center, Postbox 22660, 1100 DD, Amsterdam, The Netherlands
| | - Peter R Luijten
- Department of Radiology, University Medical Center Utrecht, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Jaco J M Zwanenburg
- Department of Radiology, University Medical Center Utrecht, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht, Postbox 85500, 3508 GA, Utrecht, The Netherlands
| | - Anja G van der Kolk
- Department of Radiology, University Medical Center Utrecht, Postbox 85500, 3508 GA, Utrecht, The Netherlands
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Gao S, van 't Klooster R, Kitslaar PH, Coolen BF, van den Berg AM, Smits LP, Shahzad R, Shamonin DP, de Koning PJH, Nederveen AJ, van der Geest RJ. Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting. Med Phys 2017; 44:5244-5259. [PMID: 28715090 DOI: 10.1002/mp.12476] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 07/10/2017] [Accepted: 07/10/2017] [Indexed: 01/24/2023] Open
Abstract
PURPOSE The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI. METHODS The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data. In particular, a hybrid segmentation which combines deformable model fitting with boundary classification was applied to extract the lumen surface. The 3D model ensures the correct shape and topology of the carotid artery, while the boundary classification uses combined image information of 3D TOF-MRA and 3D BB-MRI to promote accurate delineation of the lumen boundaries. The proposed algorithm was validated on 25 subjects (48 arteries) including both healthy volunteers and atherosclerotic patients with 30% to 70% carotid stenosis. RESULTS For both lumen and outer wall border detection, our result shows good agreement between manually and automatically determined contours, with contour-to-contour distance less than 1 pixel as well as Dice overlap greater than 0.87 at all different carotid artery sections. CONCLUSIONS The presented 3D segmentation technique has demonstrated the capability of providing vessel wall delineation for 3D carotid MRI data with high accuracy and limited user interaction. This brings benefits to large-scale patient studies for assessing the effect of pharmacological treatment of atherosclerosis by reducing image analysis time and bias between human observers.
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Affiliation(s)
- Shan Gao
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Ronald van 't Klooster
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Pieter H Kitslaar
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Bram F Coolen
- Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
| | | | - Loek P Smits
- Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
| | - Rahil Shahzad
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Denis P Shamonin
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Patrick J H de Koning
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Aart J Nederveen
- Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
| | - Rob J van der Geest
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
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Amir-Khalili A, Hamarneh G, Abugharbieh R. Modelling and extraction of pulsatile radial distension and compression motion for automatic vessel segmentation from video. Med Image Anal 2017; 40:184-198. [PMID: 28692857 DOI: 10.1016/j.media.2017.06.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 04/13/2017] [Accepted: 06/19/2017] [Indexed: 11/24/2022]
Abstract
Identification of vascular structures from medical images is integral to many clinical procedures. Most vessel segmentation techniques ignore the characteristic pulsatile motion of vessels in their formulation. In a recent effort to automatically segment vessels that are hidden under fat, we motivated the use of the magnitude of local pulsatile motion extracted from surgical endoscopic video. In this article we propose a new approach that leverages the local orientation, in addition to magnitude of motion, and demonstrate that the extended computation and utilization of motion vectors can improve the segmentation of vascular structures. We implement our approach using four alternatives to magnitude-only motion estimation by using traditional optical flow and by exploiting the monogenic signal for fast flow estimation. Our evaluations are conducted on both synthetic phantoms as well as two real ultrasound datasets showing improved segmentation results with negligible change in computational performance compared to the previous magnitude only approach.
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Affiliation(s)
- Alborz Amir-Khalili
- Biomedical Signal and Image Computing Lab, University of British Columbia, Vancouver, BC, Canada.
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC, Canada
| | - Rafeef Abugharbieh
- Biomedical Signal and Image Computing Lab, University of British Columbia, Vancouver, BC, Canada
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Automated Cardiovascular Segmentation in Patients with Congenital Heart Disease from 3D CMR Scans: Combining Multi-atlases and Level-Sets. ACTA ACUST UNITED AC 2017. [DOI: 10.1007/978-3-319-52280-7_15] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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