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Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 43050

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Special Issue Editors

Dr. Mihaela Pop

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Guest Editor

Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
Interests: emerging techniques in imaging; modelling and visualization for cardiovascular diagnosis; therapy; X-ray guided electro-anatomical; mapping of cardiac electrical activity; MR-EP data fusion and integration into predictive 3D biophysical models for accurate diagnostic and ablation therapy guidance

Dr. Cristian A. Linte

E-Mail Website
Guest Editor

Department of Biomedical Engineering, School of Mathematical Sciences, Rochester Institute of Technology, New York, NY, USA
Interests: biomedical imaging resource; medical image analysis; image-guided intervention technology; reconstructing cardiac wave; cardiac MRI using an adversarial network

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue entitled “Emerging techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy”, to be published by the journal Applied Sciences (MDPI).

The goal of this Special Issue is to disseminate emerging techniques and innovative solutions that comprehensively address unmet needs in cardiovascular disease and can be rapidly translated into the clinical arena in order to significantly improve diagnostic accuracy and precision in treatment delivery, as well as to enhance therapy guidance and procedural success. We invite research contributions from cross-disciplinary scientists and professionals who work in the cardiovascular field at the interface of basic and translational research, clinical practice, medical (bio)physics, engineering, mathematics and computer science. Specifically, we welcome papers focused on the development of: advanced techniques in cardiovascular imaging (MRI, CT, ECGI, ultrasound, optics) to investigate structure-function interaction and identify pathology; image analysis (e.g. registration, segmentation, visualization) along with deep-learning/AI classification methods to better characterize tissue and physiological signals; novel preclinical experimental models and clinical approaches employed in electro-anatomical mapping and image-aided therapeutic procedures (e.g., cardiac ablation, electroporation, resynchronization); as well as innovative image-guided interventional procedures for vascular applications. We also invite research contributions focused on developing computational modelling platforms such as: construction and visualization of normal or pathologic anatomy, geometry and morphology of the heart and coronary vessels (including applications relevant to 3D model printing); personalized in silico predictions to aid in diagnosis and therapy delivery; integration of tissue properties in computational cardiovascular models; non-invasive modelling-based assessment of cardiovascular function as well as simulation-based planning and optimization of treatments.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except for conference proceedings papers, for which a similarity index less than 40% is imposed).

Dr. Mihaela Pop
Dr. Cristian A. Linte
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

cardiovascular imaging
image analysis (segmentation, registration, visualization)
image-guided interventions and navigation
electro-anatomical mapping
cardiovascular therapeutic procedures (ablation, electroporation, resynchronization)
computational models of the heart and vessels

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Published Papers (14 papers)

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Editorial

Jump to: Research, Review, Other

4 pages, 161 KiB

Open AccessEditorial

Applied Sciences—Special Issue on Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy

by Cristian A. Linte and Mihaela Pop

Appl. Sci. 2023, 13(2), 984; https://doi.org/10.3390/app13020984 - 11 Jan 2023

Cited by 1 | Viewed by 1283

Abstract

Ongoing developments in computing and data acquisition, along with continuous advances in medical imaging technology, computational modelling, robotics and visualization have revolutionized many medical specialties and, in particular, diagnostic and interventional cardiology [...] Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

Research

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28 pages, 7660 KiB

Open AccessArticle

Learning Deep Representations of Cardiac Structures for 4D Cine MRI Image Segmentation through Semi-Supervised Learning

by S. M. Kamrul Hasan and Cristian A. Linte

Appl. Sci. 2022, 12(23), 12163; https://doi.org/10.3390/app122312163 - 28 Nov 2022

Cited by 1 | Viewed by 2441

Abstract

Learning good data representations for medical imaging tasks ensures the preservation of relevant information and the removal of irrelevant information from the data to improve the interpretability of the learned features. In this paper, we propose a semi-supervised model—namely, combine-all in semi-supervised learning (CqSL)—to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging: segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data. A disentanglement block decomposes an input image into a domain-invariant spatial factor and a domain-specific non-spatial factor. We assume that medical images acquired using multiple scanners (different domain information) share a common spatial space but differ in non-spatial space (intensities, contrast, etc.). Hence, we utilize our spatial information to generate segmentation masks from unlabeled datasets using a generative adversarial network (GAN). Finally, to reconstruct the original image, our conditioning layer-based reconstruction block recombines spatial information with random non-spatial information sampled from the generative models. Our ablation study demonstrates the benefits of disentanglement in holding domain-invariant (spatial) as well as domain-specific (non-spatial) information with high accuracy. We further apply a structured

L_{2}

similarity (S

L_{2}

SIM) loss along with a mutual information minimizer (MIM) to improve the adversarially trained generative models for better reconstruction. Experimental results achieved on the STACOM 2017 ACDC cine cardiac magnetic resonance (MR) dataset suggest that our proposed (CqSL) model outperforms fully supervised and semi-supervised models, achieving an 83.2% performance accuracy even when using only 1% labeled data. We hypothesize that our proposed model has the potential to become an efficient semantic segmentation tool that may be used for domain adaptation in data-limited medical imaging scenarios, where annotations are expensive. Code, and experimental configurations will be made available publicly. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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14 pages, 5003 KiB

Open AccessArticle

Cardiac Radiofrequency Ablation Simulation Using a 3D-Printed Bi-Atrial Thermochromic Model

by Shu Wang, Carlo Saija, Justin Choo, Zhanchong Ou, Maria Birsoan, Sarah Germanos, Joshua Rothwell, Behrad Vakili, Irum Kotadia, Zhouyang Xu, Adrian Rolet, Adriana Namour, Woo Suk Yang, Steven E. Williams and Kawal Rhode

Appl. Sci. 2022, 12(13), 6553; https://doi.org/10.3390/app12136553 - 28 Jun 2022

Cited by 4 | Viewed by 4716

Abstract

Radiofrequency ablation (RFA) is a treatment used in the management of various arrhythmias including atrial fibrillation. Enhanced training for electrophysiologists through the use of physical simulators has a significant role in improving patient outcomes. The requirements for a high-fidelity simulator for cardiac RFA are challenging and not fully met by any research or commercial simulator at present. In this study, we have produced and evaluated a 3D-printed, bi-atrial model contained in a custom-made enclosure for RFA simulation using a new soft tissue-mimicking polymer, Layfomm-40, combined with thermochromic pigment and barium sulphate in an acrylic paint carrier. We evaluated the conductive properties of Layfomm-40, its sensitivity to RFA, and its visibility in X-ray imaging, and carried a full simulation of RFA in the cardiac catheterization laboratory by an electrophysiologist. We demonstrated that a patient-specific 3D-printed Layfomm-40 bi-atrial model coated with a custom thermochromic/barium sulphate paint was compatible with the CARTO3 electroanatomic mapping system and could be effectively imaged using X-ray fluoroscopy. We demonstrated the effective delivery and visualization of radiofrequency ablation lesions in this model. The simulator meets nearly all the requirements for high-fidelity physical simulation of RFA. The use of such simulators is likely to have impact on the training of electrophysiologists and the evaluation of novel RFA devices. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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27 pages, 53972 KiB

Open AccessArticle

Meshless Electrophysiological Modeling of Cardiac Resynchronization Therapy—Benchmark Analysis with Finite-Element Methods in Experimental Data

by Carlos Albors, Èric Lluch, Juan Francisco Gomez, Nicolas Cedilnik, Konstantinos A. Mountris, Tommaso Mansi, Svyatoslav Khamzin, Arsenii Dokuchaev, Olga Solovyova, Esther Pueyo, Maxime Sermesant, Rafael Sebastian, Hernán G. Morales and Oscar Camara

Appl. Sci. 2022, 12(13), 6438; https://doi.org/10.3390/app12136438 - 24 Jun 2022

Cited by 2 | Viewed by 2242

Abstract

Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution). Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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18 pages, 6700 KiB

Open AccessFeature PaperArticle

MUSIC: Cardiac Imaging, Modelling and Visualisation Software for Diagnosis and Therapy

by Mathilde Merle, Florent Collot, Julien Castelneau, Pauline Migerditichan, Mehdi Juhoor, Buntheng Ly, Valery Ozenne, Bruno Quesson, Nejib Zemzemi, Yves Coudière, Pierre Jaïs, Hubert Cochet and Maxime Sermesant

Appl. Sci. 2022, 12(12), 6145; https://doi.org/10.3390/app12126145 - 16 Jun 2022

Cited by 3 | Viewed by 4061

Abstract

The tremendous advancement of cardiac imaging methods, the substantial progress in predictive modelling, along with the amount of new investigative multimodalities, challenge the current technologies in the cardiology field. Innovative, robust and multimodal tools need to be created in order to fuse imaging data (e.g., MR, CT) with mapped electrical activity and to integrate those into 3D biophysical models. In the past years, several cross-platform toolkits have been developed to provide image analysis tools to help build such software. The aim of this study is to introduce a novel multimodality software platform dedicated to cardiovascular diagnosis and therapy guidance: MUSIC. This platform was created to improve the image-guided cardiovascular interventional procedures and is a robust platform for AI/Deep Learning, image analysis and modelling in a newly created consortium with international hospitals. It also helps our researchers develop new techniques and have a better understanding of the cardiac tissue properties and physiological signals. Thus, this extraction of quantitative information from medical data leads to more repeatable and reliable medical diagnoses. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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10 pages, 6957 KiB

Open AccessArticle

Multi-View 3D Transesophageal Echocardiography Registration and Volume Compounding for Mitral Valve Procedure Planning

by Patrick Carnahan, John Moore, Daniel Bainbridge, Elvis C. S. Chen and Terry M. Peters

Appl. Sci. 2022, 12(9), 4562; https://doi.org/10.3390/app12094562 - 30 Apr 2022

Cited by 2 | Viewed by 1915

Abstract

Three-dimensional ultrasound mosaicing can increase image quality and expand the field of view. However, limited work has been done applying these compounded approaches for cardiac procedures focused on the mitral valve. For procedures targeting the mitral valve, transesophageal echocardiography (TEE) is the primary imaging modality used as it provides clear 3D images of the valve and surrounding tissues. However, TEE suffers from image artefacts and signal dropout, particularly for structures lying below the valve, including chordae tendineae, making it necessary to acquire alternative echo views to visualize these structures. Due to the limited field of view obtainable, the entire ventricle cannot be directly visualized in sufficient detail from a single image acquisition in 3D. We propose applying an image compounding technique to TEE volumes acquired from a mid-esophageal position and several transgastric positions in order to reconstruct a high-detail volume of the mitral valve and sub-valvular structures. This compounding technique utilizes both fully and semi-simultaneous group-wise registration to align the multiple 3D volumes, followed by a weighted intensity compounding step based on the monogenic signal. This compounding technique is validated using images acquired from two excised porcine mitral valve units and three patient data sets. We demonstrate that this compounding technique accurately captures the physical structures present, including the mitral valve, chordae tendineae and papillary muscles. The chordae length measurement error between the compounded ultrasound and ground-truth CT for two porcine valves is reported as 0.7 ± 0.6 mm and 0.6 ± 0.6 mm. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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15 pages, 3495 KiB

Open AccessArticle

Structure (Epicardial Stenosis) and Function (Microvascular Dysfunction) That Influence Coronary Fractional Flow Reserve Estimation

by Jermiah J. Joseph, Clara Sun, Ting-Yim Lee, Daniel Goldman, Sanjay R. Kharche and Christopher W. McIntyre

Appl. Sci. 2022, 12(9), 4281; https://doi.org/10.3390/app12094281 - 23 Apr 2022

Cited by 1 | Viewed by 1829

Abstract

Background. The treatment of coronary stenosis is decided by performing high risk invasive surgery to generate the fractional flow reserve diagnostics index, a ratio of distal to proximal pressures in respect of coronary atherosclerotic plaques. Non-invasive methods are a need of the times that necessitate the use of mathematical models of coronary hemodynamic physiology. This study proposes an extensible mathematical description of the coronary vasculature that provides an estimate of coronary fractional flow reserve. Methods. By adapting an existing computational model of human coronary blood flow, the effects of large vessel stenosis and microvascular disease on fractional flow reserve were quantified. Several simulations generated flow and pressure information, which was used to compute fractional flow reserve under several conditions including focal stenosis, diffuse stenosis, and microvascular disease. Sensitivity analysis was used to uncover the influence of model parameters on fractional flow reserve. The model was simulated as coupled non-linear ordinary differential equations and numerically solved using our implicit higher order method. Results. Large vessel stenosis affected fractional flow reserve. The model predicts that the presence, rather than severity, of microvascular disease affects coronary flow deleteriously. Conclusions. The model provides a computationally inexpensive instrument for future in silico coronary blood flow investigations as well as clinical-imaging decision making. A combination of focal and diffuse stenosis appears to be essential to limit coronary flow. In addition to pressure measurements in the large epicardial vessels, diagnosis of microvascular disease is essential. The independence of the index with respect to heart rate suggests that computationally inexpensive steady state simulations may provide sufficient information to reliably compute the index. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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16 pages, 5082 KiB

Open AccessArticle

Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data

by Tanjib Rahman, Kévin Moulin and Luigi E. Perotti

Appl. Sci. 2022, 12(7), 3512; https://doi.org/10.3390/app12073512 - 30 Mar 2022

Cited by 1 | Viewed by 2559

Abstract

In vivo cardiac diffusion tensor imaging (cDTI) data were acquired in swine subjects six to ten weeks post-myocardial infarction (MI) to identify microstructural-based biomarkers of MI. Diffusion tensor invariants, diffusion tensor eigenvalues, and radial diffusivity (RD) are evaluated in the infarct, border, and remote myocardium, and compared with extracellular volume fraction (ECV) and native T1 values. Additionally, to aid the interpretation of the experimental results, the diffusion of water molecules was numerically simulated as a function of ECV. Finally, findings based on in vivo measures were confirmed using higher-resolution and higher signal-to-noise data acquired ex vivo in the same subjects. Mean diffusivity, diffusion tensor eigenvalues, and RD increased in the infarct and border regions compared to remote myocardium, while fractional anisotropy decreased. Secondary (e

_{2}

) and tertiary (e

_{3}

) eigenvalues increased more significantly than the primary eigenvalue in the infarct and border regions. These findings were confirmed by the diffusion simulations. Although ECV presented the largest increase in infarct and border regions, e

_{2}

, e

_{3}

, and RD increased the most among non-contrast-based biomarkers. RD is of special interest as it summarizes the changes occurring in the radial direction and may be more robust than e

_{2}

or e

_{3}

alone. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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16 pages, 6394 KiB

Open AccessFeature PaperArticle

Cardiac Magnetic Resonance Left Ventricle Segmentation and Function Evaluation Using a Trained Deep-Learning Model

by Fumin Guo, Matthew Ng, Idan Roifman and Graham Wright

Appl. Sci. 2022, 12(5), 2627; https://doi.org/10.3390/app12052627 - 3 Mar 2022

Cited by 4 | Viewed by 3481

Abstract

Cardiac MRI is the gold standard for evaluating left ventricular myocardial mass (LVMM), end-systolic volume (LVESV), end-diastolic volume (LVEDV), stroke volume (LVSV), and ejection fraction (LVEF). Deep convolutional neural networks (CNNs) can provide automatic segmentation of LV myocardium (LVF) and blood cavity (LVC) and quantification of LV function; however, the performance is typically degraded when applied to new datasets. A 2D U-net with Monte-Carlo dropout was trained on 45 cine MR images and the model was used to segment 10 subjects from the ACDC dataset. The initial segmentations were post-processed using a continuous kernel-cut method. The refined segmentations were employed to update the trained model. This procedure was iterated several times and the final updated U-net model was used to segment the remaining 90 ACDC subjects. Algorithm and manual segmentations were compared using Dice coefficient (DSC) and average surface distance in a symmetric manner (ASSD). The relationships between algorithm and manual LV indices were evaluated using Pearson correlation coefficient (r), Bland-Altman analyses, and paired t-tests. Direct application of the pre-trained model yielded DSC of 0.74 ± 0.12 for LVM and 0.87 ± 0.12 for LVC. After fine-tuning, DSC was 0.81 ± 0.09 for LVM and 0.90 ± 0.09 for LVC. Algorithm LV function measurements were strongly correlated with manual analyses (r = 0.86–0.99, p < 0.0001) with minimal biases of −8.8 g for LVMM, −0.9 mL for LVEDV, −0.2 mL for LVESV, −0.7 mL for LVSV, and −0.6% for LVEF. The procedure required ∼12 min for fine-tuning and approximately 1 s to contour a new image on a Linux (Ubuntu 14.02) desktop (Inter(R) CPU i7-7770, 4.2 GHz, 16 GB RAM) with a GPU (GeForce, GTX TITAN X, 12 GB Memory). This approach provides a way to incorporate a trained CNN to segment and quantify previously unseen cardiac MR datasets without needing manual annotation of the unseen datasets. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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14 pages, 3259 KiB

Open AccessArticle

Atrial Fibrillation and Anterior Cerebral Artery Absence Reduce Cerebral Perfusion: A De Novo Hemodynamic Model

by Timothy J. Hunter, Jermiah J. Joseph, Udunna Anazodo, Sanjay R. Kharche, Christopher W. McIntyre and Daniel Goldman

Appl. Sci. 2022, 12(3), 1750; https://doi.org/10.3390/app12031750 - 8 Feb 2022

Cited by 3 | Viewed by 2510

Abstract

Background: Atrial fibrillation is a prevalent cardiac arrhythmia and may reduce cerebral blood perfusion augmenting the risk of dementia. We hypothesize that geometric variations in the cerebral arterial structure called the Circle of Willis (CoW) play an important role in influencing cerebral perfusion. The objective of this work was to develop a novel cardio-cerebral lumped parameter hemodynamic model to investigate the role of CoW variants on cerebral blood flow dynamics under atrial fibrillation conditions. Methods: A computational blood flow model was developed by coupling whole-body and detailed cerebral circulation descriptions, modified to represent six common variations of the CoW. Cerebral blood flow dynamics were simulated in common CoW variants, under control and imposed atrial fibrillation conditions. Risk was assessed based on the frequency of beat-wise hypoperfusion events, and sensitivity analysis was performed with respect to this model output. Results: It was found that the geometry of the CoW influenced the frequency of hypoperfusion events at different heart rates, with the variant missing a P1 segment having the highest risk. Sensitivity analysis revealed that intrinsic heart rate is most associated with the considered outcome. Conclusions: Our results suggest that CoW geometry plays an important role in influencing cerebral hemodynamics during atrial fibrillation. The presented study may assist in guiding our future clinical-imaging research. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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26 pages, 4111 KiB

Open AccessArticle

Subclinical Atherosclerosis Progression in Obese Children with Relevant Cardiometabolic Risk Factors Can Be Assessed through Carotid Intima Media Thickness

by Monica-Simina Mihuta, Corina Paul, Adrian Ciulpan, Farah Dacca, Iulian Puiu Velea, Ioana Mozos and Dana Stoian

Appl. Sci. 2021, 11(22), 10721; https://doi.org/10.3390/app112210721 - 13 Nov 2021

Cited by 9 | Viewed by 2850

Abstract

Given the growing obesity rates among children, a more complete evaluation of their potential cardiometabolic risk is needed. Carotid intima-media thickness (CIMT), a marker of endothelial distress and a predictor of atherosclerotic progression in adulthood, may complete the day-to-day evaluation of children at risk. Multiple risk factors act as additional precipitant causes of atherosclerosis. We analyzed 60 patients aged 6–17 years old by measuring their CIMT using the Aixplorer MACH 30 echography machine automatic measurement software. All subjects were clinically and anamnestically assessed to identify risk factors. CIMT values are significantly higher in older children and boys. Over 20 kg weight gain during pregnancy and other at-risk disorders (p = 0.047), family history of cardiovascular risk (p = 0.049), hypertension (p = 0.012), and smoking (p = 0.015) are linked to increased CIMT. Our study also supports international data on artificial postnatal nutrition, high/low birth weight, and sedentary lifestyle being linked to increased CIMT. Significant correlations were detected between CIMT and the entire lipid panel. Weight excess and abdominal adiposity in children is clearly linked to increased CIMT. Moreover, waist circumference and TG/HDL-c are significant predictors of CIMT. Although each parameter of the lipid panel is correlated to CIMT, fasting glucose is not. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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Review

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71 pages, 13509 KiB

Open AccessReview

Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications

by Johane H. Bracamonte, Sarah K. Saunders, John S. Wilson, Uyen T. Truong and Joao S. Soares

Appl. Sci. 2022, 12(8), 3954; https://doi.org/10.3390/app12083954 - 14 Apr 2022

Cited by 8 | Viewed by 5718

Abstract

Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs. These methods have become available for medical applications mainly due to the continuing development of image-based kinematic techniques, the maturity of the associated theories describing cardiovascular function, and recent progress in computer science, modeling, and simulation engineering. Inverse method applications are multidisciplinary, requiring tailored solutions to the available clinical data, pathology of interest, and available computational resources. Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis. In the final section, the major advances in inverse modeling of human cardiovascular mechanics since its early development in the early 2000s are reviewed with emphasis on method-specific descriptions, results, and conclusions. We draw selected studies on healthy and diseased hearts, aortas, and pulmonary arteries achieved through the incorporation of tissue mechanics, hemodynamics, and fluid–structure interaction methods paired with patient-specific data acquired with medical imaging in inverse modeling approaches. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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19 pages, 461 KiB

Open AccessReview

From Accuracy to Reliability and Robustness in Cardiac Magnetic Resonance Image Segmentation: A Review

by Francesco Galati, Sébastien Ourselin and Maria A. Zuluaga

Appl. Sci. 2022, 12(8), 3936; https://doi.org/10.3390/app12083936 - 13 Apr 2022

Cited by 10 | Viewed by 3385

Abstract

Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR) image segmentation has achieved state-of-the-art performance. Despite achieving inter-observer variability in terms of different accuracy performance measures, visual inspections reveal errors in most segmentation results, indicating a lack of reliability and robustness of DL segmentation models, which can be critical if a model was to be deployed into clinical practice. In this work, we aim to bring attention to reliability and robustness, two unmet needs of cardiac image segmentation methods, which are hampering their translation into practice. To this end, we first study the performance accuracy evolution of CMR segmentation, illustrate the improvements brought by DL algorithms and highlight the symptoms of performance stagnation. Afterwards, we provide formal definitions of reliability and robustness. Based on the two definitions, we identify the factors that limit the reliability and robustness of state-of-the-art deep learning CMR segmentation techniques. Finally, we give an overview of the current set of works that focus on improving the reliability and robustness of CMR segmentation, and we categorize them into two families of methods: quality control methods and model improvement techniques. The first category corresponds to simpler strategies that only aim to flag situations where a model may be incurring poor reliability or robustness. The second one, instead, directly tackles the problem by bringing improvements into different aspects of the CMR segmentation model development process. We aim to bring the attention of more researchers towards these emerging trends regarding the development of reliable and robust CMR segmentation frameworks, which can guarantee the safe use of DL in clinical routines and studies. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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Other

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16 pages, 5737 KiB

Open AccessTechnical Note

MR Imaging and Electrophysiological Features of Doxorubicin-Induced Fibrosis: Protocol Development in a Small Preclinical Pig Study with Histological Validation

by Peter Lin, Terenz Escartin, Melissa Larsen, Matthew Ng, Mengyuan Li, Jennifer Barry, Idan Roifman and Mihaela Pop

Appl. Sci. 2022, 12(22), 11620; https://doi.org/10.3390/app122211620 - 16 Nov 2022

Cited by 1 | Viewed by 1562

Abstract

A critical chemotherapeutic complication is cardiotoxicity, often leading, in time, to heart failure. In this work, we developed a novel animal protocol using magnetic resonance (MR) imaging and electrophysiology (EP) tests, designed to detect subtle structural and functional changes associated with myocardial damage in sub-chronic phases post-chemotherapy. A weekly dose of doxorubicin (DOX) was injected in four juvenile swine throughout a four-week plan, using an intravenous approach that mimics the treatment in cancer patients. We performed cardiac MR imaging as follows: in all four pigs pre-DOX; at 1 and 5 weeks post-DOX in a group of two pigs; and, at 1 and 9 weeks post-DOX in the other two pigs, using Cine imaging to assess ejection fraction (EF) and late gadolinium enhancement to quantify collagen density in the left ventricle. Additionally, X-ray-guided voltage mapping and arrhythmia tests were conducted in the group at 9 weeks post-DOX and in a healthy pig. Tissue samples were collected for histology. The results showed that EF decreased from ~46% pre-DOX to ~34% within the first 9 weeks post-DOX. This decline in LV function was explained by a gradual increase in collagen density, especially noticeable at week 9 post-DOX as derived from MRI analysis. Furthermore, ventricular fibrillation was induced via rapid pacing at 9 weeks post-DOX, most likely caused by fibrotic patches identified in voltage maps, as confirmed by MRI and collagen-sensitive histological stains. Overall, our novel preclinical protocol was able to reveal key signs of potentially-irreversible tissue changes, along with electrical remodeling and arrhythmia risk in the early months following DOX therapy. Future work will include more datasets to statistically power the study, and will use the protocol to test cardioprotective strategies. Full article

(This article belongs to the Special Issue Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy)

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