Cardiac computed tomography (CT) has evolved significantly as a critical tool in diagnosing and managing cardiac diseases, greatly facilitated by technological advancements in multidetector systems, dose-reduction techniques, and sophisticated imaging algorithms. This article discusses the historical progression and technological evolution in cardiac CT (CCT), focusing on the impact of 64-multidetector row CT and dual-energy CT systems on improving spatial and temporal resolutions and reducing radiation exposure. It explores the role of these technologies in enhancing diagnostic accuracy, such as through detailed three-dimensional reconstructions and minimized imaging artifacts. Furthermore, it highlights the integration of machine learning to automate complex imaging analysis and photon-counting CT, which promises higher resolution and further dose reduction. Prospective studies and ongoing trials such as FASTTRACK coronary artery bypass grafting also underscore the potential of advanced CT technologies in refining procedural planning and execution. The continuous advancements in detector technology, computational techniques, and image reconstruction are poised to expand the applications and efficacy of CCT, cementing its role in modern cardiology.

We aimed to implement a fully automatic computed tomography (CT) image-detection programming algorithm as a pectus excavatum (PE) diagnostic tool, facilitating comprehensive chest wall deformity evaluation. We developed our algorithm using MATLAB, leveraging the Hounsfield unit threshold and region growing methods. The MATLAB graphical user interface enables the direct use of our program. We validated the model using CT images of anthropomorphic phantoms and one normal individual. The measurement values obtained by our algorithm demonstrated very small differences compared to the known anthropomorphic phantom model data and manual measurement. For algorithm testing, 17,214 chest CT scans obtained from 57 PE patients were processed by the algorithm and independently reviewed by a radiologist and a thoracic surgeon. The measurements of transverse, anteroposterior, and sternum-to-vertebral distance of the thoracic cavity, along with the calculated data of four indices, exhibited high positive correlations (0.94–0.99). The asymmetry index and maximum anteroposterior hemithorax distance exhibited moderate correlation (0.40–0.83). Our automatic PE diagnostic tool demonstrated high accuracy; four chest wall deformity indices were obtained simultaneously without any initial manual marking, correlating well with manual measurements.

Background: Computed tomography (CT) angiography is widely regarded as the gold standard for diagnosing acute aortic dissection. However, in patients with contraindications to iodinated contrast media, such as those with renal insufficiency or hemodynamic instability, non-contrast CT may offer a viable alternative for initial evaluation. Understanding the molecular mechanisms underlying aortic dissection, including extracellular matrix degradation, smooth muscle cell apoptosis, and inflammatory pathways, is crucial for developing novel diagnostic and therapeutic approaches. This report describes a single case of acute Stanford type A aortic dissection initially detected on non-contrast CT. Case Presentation: We describe a 74-year-old man who presented to the emergency department with fever and suspected infection, but without chest pain. An incidental finding on non-contrast CT revealed ascending aortic dilatation, pericardial effusion, and a suspected intimal flap. Subsequent CT angiography confirmed a Stanford type A aortic dissection. Conclusions: This case highlights the potential value of non-contrast CT in the early detection of aortic dissection, particularly when CT angiography cannot be performed. Recent advances in artificial intelligence (AI) and radiomic analysis have shown promise in augmenting the diagnostic capabilities of non-contrast CT by identifying subtle imaging features that may correlate with underlying molecular pathology and elude human observers. Emerging evidence suggests that radiomic features may reflect molecular alterations in the aortic wall, including metalloproteinase activity, collagen degradation, and inflammatory cell infiltration. Incorporating AI-assisted interpretation alongside insights into molecular mechanisms could facilitate earlier diagnosis, improve risk stratification, and guide personalized treatment strategies in critically ill patients. Although non-contrast CT has limited sensitivity for aortic dissection, it may still reveal crucial findings in selected cases and should be considered when contrast-enhanced imaging is not feasible. Ongoing progress in AI, radiomics, and molecular biomarker research may further expand the clinical applications of non-contrast CT in emergency cardiovascular care and bridge the gap between imaging phenotypes and molecular endotypes. These findings are hypothesis-generating and require validation in larger cohorts before clinical generalization.

Pediatric patients are more sensitive to ionising radiation, making radiation protection a critical concern in dental cone-beam computed tomography (CBCT). This study aimed to assess organ and effective doses and associated radiation risk in pediatric imaging, to validate the PCXMC-20Rotation Monte Carlo code against optically stimulated luminescence dosimeters (OSLDs), and to evaluate dose-area product (DAP) as an indicator for effective dose ( E ) and risk of exposure-induced cancer death (REID). Age-specific 5 and 10 year-old head-and-neck phantoms were manufactured by three-dimensional (3D) printing and embedded with seventeen OSLDs each. The phantoms were scanned on a CS 9000 system in panoramic mode at both ages and in 3D maxillary and mandibular CBCT mode at 10 years. DAP was recorded with a VacuDAP metre and used in PCXMC-20Rotation to calculate organ doses, E , and REID. In panoramic mode, differences in E between PCXMC and OSLD were −4.7% (5 year) and −10.7% (10 year), and organ dose rankings were generally similar. PCXMC overestimated oral mucosa dose in 3D mandibular imaging, where total E was approximately 3–5 times higher than in panoramic mode. DAP correlated strongly with E ( R ² = 0.971–0.987) and with REID ( R ² = 0.967–0.986); the highest REID (8.1 × 10 ⁻⁶ ) was predicted for 10 year-old girls undergoing 3D mandibular CBCT. PCXMC estimated E within ±15% of OSLD measurements for both panoramic and 3D modes. DAP may serve as a practical indicator of E and REID in pediatric CBCT. From a radiation protection perspective, the findings support the use of child-specific exposure settings and DAP monitoring as approaches to minimise unnecessary radiation exposure, particularly in female pediatric patients.