BACKGROUND AND PURPOSE: Parathyroid 4-dimensional CT (4DCT) involves repeated noncontrast and multiphase contrast-enhanced acquisitions, leading to increased radiation exposure. Because the examination targets the anatomically complex structures of the neck, maintaining high image quality is particularly important. Photon-counting CT (PCCT) is a novel technology reported to improve dose efficiency and image quality compared with conventional energy-integrating detector CT (EIDCT). This study provides a systematic, quantitative comparison of CT scanner radiation output (volume CT dose index [CTDIvol] and dose-length product [DLP]) and image quality, assessed by SNR and contrast-to-noise ratio (CNR), between PCCT and EIDCT for multiphase parathyroid 4DCT examinations. MATERIALS AND METHODS: In this institutional review board–approved retrospective study, 96 patients who underwent a 4DCT for the work-up of hyperparathyroidism (PCCT, n =32; EIDCT, n =64) were reviewed. ROIs were placed on the noncontrast, arterial, and venous phase images in the sternocleidomastoid muscles at the upper thyroid level, the anterior scalene muscles at the lower thyroid level, and in the extracorporeal air. Mean attenuation, SNR, and CNR were measured. Statistical analyses included unpaired t tests and 2-way ANOVA. RESULTS: PCCT had significantly lower radiation output across all 4DCT phases (total CTDIvol 30.6 ± 3.3 vs 60.8 ± 15.7 mGy; DLP 859 ± 178 vs 1523 ± 519 mGy·cm; P < .001). SNR and CNR were significantly higher on PCCT across all evaluated ROIs, with lower background noise (all P < .05). Two-way ANOVA confirmed the advantages of PCCT regardless of contrast injection side, with no significant interactions. CONCLUSIONS: PCCT substantially reduces CT scanner radiation output while improving quantitative image quality in multiphase parathyroid 4DCT, including technically challenging arterial-phase acquisitions at the lower thyroid level, supporting its technical advantage over conventional EIDCT. AP : arterial phase CNR : contrast to noise ratio CTDIvol : volume CT dose index DLP : dose-length product EIDCT : energy-integrating detector CT LT : lower thyroid NC : noncontrast phase PCCT : photon-counting CT UT : upper thyroid VP : venous phase

Project Unity’s presenter outlines claims and counterclaims surrounding the Nazca ‘tridactyl’ mummies, citing CT imaging, radiocarbon dating, metallic implants, and alleged reproductive evidence across multiple specimens. The discussion details a growing catalog—from adult and juvenile forms to purported egg-bearing females—while noting the need for independent, peer-reviewed validation.

A current study shows that patients can quickly read simplified findings generated by AI and find them helpful.

Background: Estimated pulmonary biological age (ePBA) has emerged as a more reliable indicator for disease progression and mortality than chronological age, with chest computed tomography (CT) as a promising tool for calculating ePBA. However, the lack of models trained and validated with large-scale healthy adults hinders the generalizability of the CT-based ePBA. Objective: This study aims to develop an aging biomarker (ePBA) from multicenter chest CTs of healthy adults using deep learning and investigate the association between age gap (ePBA - chronological age) and pulmonary function as well as all-cause mortality in patients with chronic obstructive pulmonary disease (COPD). Methods: We used 11,187 chest CT scans from healthy adults at 3 health management centers and used multiple deep learning models. Of these, 7726 scans from institution A were used for model development. The remaining CT scans from institutions B (n=1506) and C (n=1955) served as external test datasets. To examine whether ePBA provided information beyond chronological age in patients with the disease, we investigated the association of age gap with lung function and all-cause mortality among 138 patients with COPD hospitalized at the same time period in institution A. Results: The deep learning models demonstrated acceptable applicability for this task and exhibited a strong correlation between ePBA and chronological age. Age gap was significantly associated with forced expiratory volume in 1 second expressed as percentage of predicted values reduction (=−0.18; =.03) and an increased risk of all-cause mortality (hazard ratio: 1.16, 95% CI 1.08-1.25) in patients with COPD. Conclusions: This study developed and validated a biomarker of aging—ePBA—with deep learning models based on chest CT. Age gap could serve as a novel clinical biomarker in patients with COPD.