Aim: To determine the image quality and diagnostic performance of an optimized pulmonary computed tomography angiography (CTA) protocol in terms of radiation and contrast volume saving.
Materials And Methods: Seventy consecutive patients weighting ≤80 kg with clinical suspicion of pulmonary embolism (PE) were prospectively enrolled. Two pulmonary CTA protocols (group A: n = 35, 80 kV/60 ml; group B: n = 35, 100 kV/80 ml) were compared. The presence of PE, image quality parameters [contrast attenuation, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR)] and effective radiation dose (mSv) were assessed.
Results: PE was found in 11 patients (five of group A, six of group B). The total mean attenuation of the pulmonary arteries was significantly higher in group A (362.4 ± 100.2 HU) than in group B (262.4 ± 134.3 HU), whereas the CNR and SNR did not differ statistically (14.8 ± 7.4 and 16.3 ± 7.5 for group A and 12.5 ± 8.6 and 13.8 ± 9.1 for group B, respectively). The estimated effective radiation dose was significantly lower in group A (1.1 ± 0.7 mSv) than in group B (2.7 ± 1.2 mSv).
Conclusion: In individuals weighting ≤80 kg, the evaluated pulmonary CTA protocol allows similar image quality to be achieved as compared with the conventional pulmonary CTA protocol while reducing radiation exposure by 60% and contrast media volume by 25%.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.crad.2011.11.016 | DOI Listing |
Jpn J Radiol
January 2025
Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
Magnetic Resonance Imaging (MRI) safety is a critical concern in the Asia-Oceania region, as it is elsewhere in the world, due to the unique and complex MRI environment that demands attention. This call-for-action outlines ten critical steps to enhance MRI safety and promote a culture of responsibility and accountability in the Asia-Oceania region. Key focus areas include strengthening education and expertise, improving quality assurance, fostering collaboration, increasing public awareness, and establishing national safety boards.
View Article and Find Full Text PDFBiol Trace Elem Res
January 2025
Hebei Key Laboratory of Reproductive Medicine, Hebei Reproductive Health Hospital, Shijiazhuang 050071, Hebei, China.
Male infertility is a common complication of diabetes. Diabetes leads to the decrease of zinc (Zn) content, which is a necessary trace element to maintain the normal structure and function of reproductive organs and spermatogenesis. The purpose of this study was to investigate the effect of metformin combined with zinc on testis and sperm in diabetic mice.
View Article and Find Full Text PDFAnal Chem
January 2025
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, PR China.
The elemental imaging of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) provides spatial information on elements and therefore can further investigate the growth or evolution processes of an analyte. However, the accurate determination of spatial information is limited by the decoupling between the elemental distribution and mass spectrometry signals. This phenomenon, which is more distinct when high-diffusion ablation cells are used, arises from the overlap of ablation and the transport dispersion of aerosols.
View Article and Find Full Text PDFJ Neurochem
January 2025
Core Facility Small Animal MRI, Ulm University, Ulm, Germany.
Proton magnetic resonance spectroscopy (MRS) offers a non-invasive, repeatable, and reproducible method for in vivo metabolite profiling of the brain and other tissues. However, metabolite fingerprinting by MRS requires high signal-to-noise ratios for accurate metabolite quantification, which has traditionally been limited to large volumes of interest, compromising spatial fidelity. In this study, we introduce a new optimized pipeline that combines LASER MRS acquisition at 11.
View Article and Find Full Text PDFMicrosc Res Tech
January 2025
AIDA Lab. College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Microscopy magnetic resonance imaging (MMRI) scans extract detailed features, providing multi-modal insights.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!