Heart applications of 4D flow

Recent Advances in Cardiac Magnetic Resonance for Imaging of Acute Myocardial Infarction

Acute myocardial infarction (AMI) is one of the primary causes of death worldwide, with a high incidence and mortality rate. Assessment of the infarcted and surviving myocardium, along with microvascular obstruction, is crucial for risk stratification, treatment, and prognosis in patients with AMI. Nonionizing radiation, excellent soft tissue contrast resolution, a large field of view, and multiplane imaging make cardiac magnetic resonance (CMR) a "one-stop" method for assessing cardiac structure, function, perfusion, and metabolism. Hence, this imaging technology is considered the "gold standard" for evaluating myocardial function and viability in AMI. This review critically compares the advantages and disadvantages of CMR with other cardiac imaging technologies, and relates the imaging findings to the underlying pathophysiological processes in AMI. A more thorough understanding of CMR technology will clarify their advanced clinical diagnosis and prognostic assessment applications, and assess the future approaches and challenges of CMR in the setting of AMI.

The role of computational methods in cardiovascular medicine: a narrative review

Background and Objective

Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data with physical knowledge of the processes underlying the heart function. These models have been increasingly employed in clinical practice to understand pathological mechanisms and their progression, design medical devices, support clinicians in improving therapies. Hinging upon a long-year experience in cardiovascular modeling, we have recently constructed a computational multi-physics and multi-scale integrated model of the heart for the investigation of its physiological function, the analysis of pathological conditions, and to support clinicians in both diagnosis and treatment planning. This narrative review aims to systematically discuss the role that such model had in addressing specific clinical questions, and how further impact of computational models on clinical practice are envisaged.

Methods

We developed computational models of the physical processes encompassed by the heart function (electrophysiology, electrical activation, force generation, mechanics, blood flow dynamics, valve dynamics, myocardial perfusion) and of their inherently strong coupling. To solve the equations of such models, we devised advanced numerical methods, implemented in a flexible and highly efficient software library. We also developed computational procedures for clinical data post-processing-like the reconstruction of the heart geometry and motion from diagnostic images-and for their integration into computational models.

Key Content and Findings

Our integrated computational model of the heart function provides non-invasive measures of indicators characterizing the heart function and dysfunctions, and sheds light on its underlying processes and their coupling. Moreover, thanks to the close collaboration with several clinical partners, we addressed specific clinical questions on pathological conditions, such as arrhythmias, ventricular dyssynchrony, hypertrophic cardiomyopathy, degeneration of prosthetic valves, and the way coronavirus disease 2019 (COVID-19) infection may affect the cardiac function. In multiple cases, we were also able to provide quantitative indications for treatment.

Conclusions

Computational models provide a quantitative and detailed tool to support clinicians in patient care, which can enhance the assessment of cardiac diseases, the prediction of the development of pathological conditions, and the planning of treatments and follow-up tests.

Clinical application of four-dimensional flow cardiovascular magnetic resonance in Marfan syndrome: A systematic review and meta-analysis

This study aims to fill this gap by assessing the application of 4D flow CMR in MFS through a systematic review and meta-analysis. We conducted a comprehensive search of databases from their inception to May 1, 2023. Eligibility criteria were established based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of studies was assessed using the Newcastle-Ottawa Scale (NOS), with studies scoring above five deemed high quality. Meta-analyses were performed using Stata 15.1 software. Nine studies were analyzed. Findings indicate MFS patients had increased vortex flow in the descending aorta (DAo), larger aortic root diameter (ARD) and Z-scores, lower inner wall shear stress (WSS) in the proximal descending aorta (pDAo), reduced in-plane rotational flow (IRF) in the aortic arch and proximal descending aorta (pDAo), and increased pulse wave velocity (PWV) in the ascending aorta (AAo) and DAo compared to healthy subjects. No significant difference in systolic flow reversal ratio was observed. Sensitivity analysis showed no heterogeneity and Egger's test revealed no publication bias. This meta-analysis underscores the effectiveness of 4D flow CMR in detecting MFS, particularly through indicators such as vortex flow, WSS, IRF, ARD, and PWV. The findings provide insights into diagnosing cardiovascular diseases and predicting cardiovascular events in MFS patients. Further case-control studies are needed to establish measurement standards and explore potential indicators for improved diagnosis and treatment of MFS.

CT and MR imaging of patients with a dilated right ventricle due to congenital causes and their treatment

A variety of both acquired and congenital conditions can significantly affect the right ventricle, with a variety of potential origins that can have substantial clinical ramifications. These conditions can range from the impact of diseases like pulmonary arterial hypertension and ischaemic heart disease to valvular deficiencies resulting in heart failure. Moreover, the right ventricle response to factors like abnormal loading conditions, and its subsequent clinical effects, are influenced by factors such as age, disease progression, potential interventions, and their immediate and long-term clinical outcomes. Therefore, a readily available and reproducible non-invasive imaging assessment can aid in diagnosing the underlying condition of a dilated right ventricle, track its evolution, and help devising the most appropriate treatment strategy and optimal timing for its implementation throughout the patient's life.In this review, our primary focus will be on the non-invasive imaging with CT and MR of an enlarged right ventricle resulting from congenital causes and their treatment.

Rationale and clinical applications of 4D flow cardiovascular magnetic resonance in assessment of valvular heart disease: a comprehensive review

BACKGROUND

Accurate evaluation of valvular pathology is crucial in the timing of surgical intervention. Whilst transthoracic echocardiography is widely available and routinely used in the assessment of valvular heart disease, it is bound by several limitations. Although cardiovascular magnetic resonance (CMR) imaging can overcome many of the challenges encountered by echocardiography, it also has a number of limitations.

MAIN TEXT

4D Flow CMR is a novel technique, which allows time-resolved, 3-dimensional imaging. It enables visualisation and direct quantification of flow and peak velocities of all valves simultaneously in one simple acquisition, without any geometric assumptions. It also has the unique ability to measure advanced haemodynamic parameters such as turbulent kinetic energy, viscous energy loss rate and wall shear stress, which may add further diagnostic and prognostic information. Although 4D Flow CMR acquisition can take 5-10 min, emerging acceleration techniques can significantly reduce scan times, making 4D Flow CMR applicable in contemporary clinical practice.

CONCLUSION

4D Flow CMR is an emerging CMR technique, which has the potential to become the new reference-standard method for the evaluation of valvular lesions. In this review, we describe the clinical applications, advantages and disadvantages of 4D Flow CMR in the assessment of valvular heart disease.

The Role of Cardiac Magnetic Resonance in Aortic Stenosis and Regurgitation

Cardiac magnetic resonance (CMR) imaging is a well-set diagnostic technique for assessment of valvular heart diseases and is gaining ground in current clinical practice. It provides high-quality images without the administration of ionizing radiation and occasionally without the need of contrast agents. It offers the unique possibility of a comprehensive stand-alone assessment of the heart including biventricular function, left ventricle remodeling, myocardial fibrosis, and associated valvulopathies. CMR is the recognized reference for the quantification of ventricular volumes, mass, and function. A particular strength is the ability to quantify flow, especially with new techniques which allow accurate measurement of stenosis and regurgitation. Furthermore, tissue mapping enables the visualization and quantification of structural changes in the myocardium. In this way, CMR has the potential to yield important prognostic information predicting those patients who will progress to surgery and impact outcomes. In this review, the fundamentals of CMR in assessment of aortic valve diseases (AVD) are described, together with its strengths and weaknesses. This state-of-the-art review provides an updated overview of CMR potentials in all AVD issues, including valve anatomy, flow quantification, ventricular volumes and function, and tissue characterization.

Study protocol: MyoFit46-the cardiac sub-study of the MRC National Survey of Health and Development

BACKGROUND

The life course accumulation of overt and subclinical myocardial dysfunction contributes to older age mortality, frailty, disability and loss of independence. The Medical Research Council National Survey of Health and Development (NSHD) is the world's longest running continued surveillance birth cohort providing a unique opportunity to understand life course determinants of myocardial dysfunction as part of MyoFit46-the cardiac sub-study of the NSHD.

METHODS

We aim to recruit 550 NSHD participants of approximately 75 years+ to undertake high-density surface electrocardiographic imaging (ECGI) and stress perfusion cardiovascular magnetic resonance (CMR). Through comprehensive myocardial tissue characterization and 4-dimensional flow we hope to better understand the burden of clinical and subclinical cardiovascular disease. Supercomputers will be used to combine the multi-scale ECGI and CMR datasets per participant. Rarely available, prospectively collected whole-of-life data on exposures, traditional risk factors and multimorbidity will be studied to identify risk trajectories, critical change periods, mediators and cumulative impacts on the myocardium.

DISCUSSION

By combining well curated, prospectively acquired longitudinal data of the NSHD with novel CMR-ECGI data and sharing these results and associated pipelines with the CMR community, MyoFit46 seeks to transform our understanding of how early, mid and later-life risk factor trajectories interact to determine the state of cardiovascular health in older age.

TRIAL REGISTRATION

Prospectively registered on ClinicalTrials.gov with trial ID: 19/LO/1774 Multimorbidity Life-Course Approach to Myocardial Health- A Cardiac Sub-Study of the MCRC National Survey of Health and Development (NSHD).

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint^TM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Unseen Artificial Intelligence-Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

Background: The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the “same” ethnic group (“Seen AI”). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the “Unseen AI” paradigm where training and testing are from “different” ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between “Unseen AI” and “Seen AI”. Methodology: Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. “Unseen AI” (training: Japanese, testing: HK or vice versa) and “Seen AI” experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability. Results: When using the UNet DL architecture, the “Unseen AI” pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for “Unseen AI” pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using “Seen AI”, the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively. Conclusion: We demonstrated that “Unseen AI” was in close proximity (<10%) to “Seen AI”, validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.