Quantification of extracellular volume fraction by cardiac computed tomography for noninvasive assessment of myocardial fibrosis in hemodialysis patients

Radiology of fibrosis. Part I: Thoracic organs

Sustained injury from factors such as hypoxia, infection, or physical damage may provoke improper tissue repair and the anomalous deposition of connective tissue that causes fibrosis. This phenomenon may take place in any organ, ultimately leading to their dysfunction and eventual failure. Tissue fibrosis has also been found to be central in both the process of carcinogenesis and cancer progression. Thus, its prompt diagnosis and regular monitoring is necessary for implementing effective disease-modifying interventions aiming to reduce mortality and improve overall quality of life. While significant research has been conducted on these subjects, a comprehensive understanding of how their relationship manifests through modern imaging techniques remains to be established. This work intends to provide a comprehensive overview of imaging technologies relevant to the detection of fibrosis affecting thoracic organs as well as to explore potential future advancements in this field.

Clinical Utility of Computed Tomography-Derived Myocardial Extracellular Volume Fraction: A Systematic Review and Meta-Analysis

BACKGROUND

Computed tomography (CT)-derived extracellular volume fraction (ECV) is a noninvasive method to quantify myocardial fibrosis. Although studies suggest CT is a suitable measure of ECV, clinical use remains limited.

OBJECTIVES

A meta-analysis was performed to determine the clinical value of CT-derived ECV in cardiovascular diseases.

METHODS

Electronic database searches of PubMed, Web of Science Core Collection, Cochrane advanced search, and EMBASE were performed. The most pivotal analysis entailed the comparison of ECV ascertained through CT-ECV among the control, aortic stenosis, and cardiac amyloidosis cohorts. The diagnostic test accuracy for detecting cardiac amyloidosis was assessed using summary receiver-operating characteristics curve.

RESULTS

Pooled CT-derived ECV values were 28.5% (95% CI: 27.3%-29.7%) in the control, 31.9% (95% CI: 30.2%-33.8%) in the aortic stenosis, and 48.9% (95% CI: 44.5%-53.3%) in the cardiac amyloidosis group. ECV was significantly elevated in aortic stenosis (P = 0.002) (vs controls) but further elevated in cardiac amyloidosis (P < 0.001) (vs aortic stenosis). CT-derived ECV had a high diagnostic accuracy for cardiac amyloidosis, with sensitivity of 92.8% (95% CI: 86.7%-96.2%), specificity of 84.8% (95% CI: 68.6%-93.4%), and area under the summary receiver-operating characteristic curve of 0.94 (95% CI: 0.88-1.00).

CONCLUSIONS

This study is the first comprehensive systematic review and meta-analysis of CT-derived ECV evaluation in cardiac disease. The high diagnostic accuracy of CT-ECV suggests the usefulness of CT-ECV in the diagnosis of cardiac amyloidosis in preoperative CT planning for transcatheter aortic valve replacement.

Spectral Photon-Counting Computed Tomography: Technical Principles and Applications in the Assessment of Cardiovascular Diseases

Spectral Photon-Counting Computed Tomography (SPCCT) represents a groundbreaking advancement in X-ray imaging technology. The core innovation of SPCCT lies in its photon-counting detectors, which can count the exact number of incoming x-ray photons and individually measure their energy. The first part of this review summarizes the key elements of SPCCT technology, such as energy binning, energy weighting, and material decomposition. Its energy-discriminating ability represents the key to the increase in the contrast between different tissues, the elimination of the electronic noise, and the correction of beam-hardening artifacts. Material decomposition provides valuable insights into specific elements' composition, concentration, and distribution. The capability of SPCCT to operate in three or more energy regimes allows for the differentiation of several contrast agents, facilitating quantitative assessments of elements with specific energy thresholds within the diagnostic energy range. The second part of this review provides a brief overview of the applications of SPCCT in the assessment of various cardiovascular disease processes. SPCCT can support the study of myocardial blood perfusion and enable enhanced tissue characterization and the identification of contrast agents, in a manner that was previously unattainable.

Fibre crosslinking drives the emergence of order in a three-dimensional dynamical network model

The extracellular-matrix (ECM) is a complex interconnected three-dimensional network that provides structural support for the cells and tissues and defines organ architecture as key for their healthy functioning. However, the intimate mechanisms by which ECM acquire their three-dimensional architecture are still largely unknown. In this paper, we study this question by means of a simple three-dimensional individual based model of interacting fibres able to spontaneously crosslink or unlink to each other and align at the crosslinks. We show that such systems are able to spontaneously generate different types of architectures. We provide a thorough analysis of the emerging structures by an exhaustive parametric analysis and the use of appropriate visualization tools and quantifiers in three dimensions. The most striking result is that the emergence of ordered structures can be fully explained by a single emerging variable: the number of links per fibre in the network. If validated on real tissues, this simple variable could become an important putative target to control and predict the structuring of biological tissues, to suggest possible new therapeutic strategies to restore tissue functions after disruption, and to help in the development of collagen-based scaffolds for tissue engineering. Moreover, the model reveals that the emergence of architecture is a spatially homogeneous process following a unique evolutionary path, and highlights the essential role of dynamical crosslinking in tissue structuring.

Cardiovascular Applications of Photon-Counting CT Technology: A Revolutionary New Diagnostic Step

Photon-counting computed tomography (PCCT) is an emerging technology that can potentially transform clinical CT imaging. After a brief description of the PCCT technology, this review summarizes its main advantages over conventional CT: improved spatial resolution, improved signal and contrast behavior, reduced electronic noise and artifacts, decreased radiation dose, and multi-energy capability with improved material discrimination. Moreover, by providing an overview of the existing literature, this review highlights how the PCCT benefits have been harnessed to enhance and broaden the diagnostic capabilities of CT for cardiovascular applications, including the detection of coronary artery calcifications, evaluation of coronary plaque extent and composition, evaluation of coronary stents, and assessment of myocardial tissue characteristics and perfusion.

Quantitative analysis of late iodine enhancement using dual-layer spectral detector computed tomography: comparison with magnetic resonance imaging

BACKGROUND

To evaluate the segmental myocardial extracellular volume (ECV) fraction and to define a threshold ECV value that can be used to distinguish positive late gadolinium enhancement (LGE) segments from negative myocardial segments using dual-layer spectral detector computed tomography (SDCT), with magnetic resonance imaging (MRI) as a reference.

METHODS

Fifty-six subjects with cardiac disease or suspected cardiac disease, underwent both late iodine enhancement on CT (CT-LIE) scanning and late gadolinium enhancement on MRI (MRI-LGE) scanning. Each procedure occurred within a week of the other. Global and segmental ECVs of the left ventricle were measured by CT and MRI images. According to the location and pattern of delayed enhancement on MRI image, myocardial segments were classified into 3 groups: ischemic LGE segments (group 1), nonischemic LGE segments (group 2) and negative LGE segments (group 3). The correlation and agreement between CT-ECV and MRI-ECV were compared on a per-segment basis. Receiver operating characteristic (ROC) curve analysis was performed to establish a threshold for LIE detection.

RESULTS

Among the 56 patients, 896 segments were analyzed, and of these, 73 segments were in group 1, 229 segments were in group 2, and 594 segments were in group 3. In segmental analysis, CT-ECV in group 3 (27.0%; 24.9-28.9%) was significantly lower than that in group 1 (33.2%; 30.7-36.3%) and group 2 (34.9%; 32.3-39.8%; all P<0.001). Good correlations were seen between CT-ECV and MRI-ECV for all groups (group 1: r=0.920; group 2: r=0.936; group 3: r=0.799; all P<0.001). Bland-Altman analysis between CT-ECV and MRI-ECV showed a small bias in all 3 groups (group 1: -2.1%, 95% limits of agreement -11.3-7.1%; group 2: -0.6%, 95% limits of agreement -13.1-11.9%; group 3: 1.0%, 95% limits of agreement -12.7-14.7%). CT-ECV could differentiate between LGE-positive and LGE-negative segments with 83.1% sensitivity and 93.3% specificity at a cutoff of 31%.

CONCLUSIONS

ECV values derived from CT imaging showed good correlation and agreement with MR imaging findings, and CT-ECV provided high diagnostic accuracy for discriminating between LGE-positive and LGE-negative segments. Thus, cardiac CT imaging might be a suitable noninvasive imaging technique for myocardial ECV quantification.