Spectral Photon-Counting Computed Tomography: Technical Principles and Applications in the Assessment of Cardiovascular Diseases.
Antonella MeloniErica MaffeiAlberto ClementeCarmelo De GoriMariaelena OcchipintiVincenzo PositanoSergio BertiLudovico La GruttaLuca SabaRiccardo CauEduardo BossoneCesare MantiniCarlo CavaliereBruna PunzoSimona CeliFilippo CademartiriPublished in: Journal of clinical medicine (2024)
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.
Keyphrases
- cardiovascular disease
- computed tomography
- high resolution
- dual energy
- magnetic resonance
- contrast enhanced
- magnetic resonance imaging
- gene expression
- optical coherence tomography
- metabolic syndrome
- coronary artery disease
- air pollution
- left ventricular
- density functional theory
- monte carlo
- bioinformatics analysis