Aim: To compare low-contrast detectability, and qualitative and quantitative image parameters on standard and reduced radiation dose abdominal CT reconstructed with filtered back projection (FBP) and model-based iterative reconstruction (MBIR).
Materials And Methods: A custom built liver phantom containing 43 lesions was imaged at 120 kVp and four radiation dose levels (100% = 188 mAs, 50%, 25%, and 10%). Image noise and contrast-to-noise ratios (CNR) were assessed. Lesion detection and qualitative image analysis (five-point Likert scale with 1 = worst, 5 = best for confidence) was performed by three independent radiologists.
Results: CNR on MBIR images was significantly higher (mean 246%, range 151-383%) and image noise was significantly lower (69%, 59-78%) than on FBP images at the same radiation dose (both p < 0.05). On MBIR 10% images, CNR (3.3 ± 0.3) was significantly higher and noise (15 ± 1HU) significantly lower than on FBP 100% images (2.5 ± 0.1; 21 ± 1 HU). On 100% images, lesion attenuation was significantly lower with MBIR than with FBP (mean difference -2 HU). Low-contrast detectability and qualitative results were similar with MBIR 50% and FBP 100%.
Conclusion: Low-contrast detectability with MBIR 50% and FBP 100% were equal. Quantitative parameters on even lower dose MBIR images are superior to 100%-dose FBP images. Some attenuation values differ significantly with MBIR compared with FBP.
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http://dx.doi.org/10.1016/j.crad.2014.11.015 | DOI Listing |
Sensors (Basel)
January 2025
National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430000, China.
Despite rapid progress in UAV-based infrared vehicle detection, achieving reliable target recognition remains challenging due to dynamic viewpoint variations and platform instability. The inherent limitations of infrared imaging, particularly low contrast ratios and thermal crossover effects, significantly compromise detection accuracy. Moreover, the computational constraints of edge computing platforms pose a fundamental challenge in balancing real-time processing requirements with detection performance.
View Article and Find Full Text PDFPurpose: As the pancreas is a low contrast visibility organ, pancreatic ductal adenocarcinoma detection is challenging due to subtle attenuation differences between tumor and pancreatic parenchyma. Photon counting CT (PCCT) has superior iodine contrast-to-noise ratio than conventional CT and also affords the creation of low keV virtual monoenergetic images, both of which increase adenocarcinoma conspicuity. The purpose therefore was to identify the optimal virtual monoenergy for visualizing PDAC during the pancreatic parenchymal phase of enhancement at PCCT using both quantitative and qualitative analyses.
View Article and Find Full Text PDFMedicina (Kaunas)
December 2024
Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznan University of Medical Sciences, Bukowska 70, 60-812 Poznan, Poland.
Intracranial calcifications, particularly within the falx cerebri, serve as crucial diagnostic markers ranging from benign accumulations to signs of severe pathologies. The falx cerebri, a dural fold that separates the cerebral hemispheres, presents challenges in visualization due to its low contrast in standard imaging techniques. Recent advancements in artificial intelligence (AI), particularly in machine learning and deep learning, have significantly transformed radiological diagnostics.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 13120, Gyeonggi-Do, Republic of Korea.
Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional segmentation models, such as U-Net, excel in capturing spatial information but often struggle with complex tumor boundaries and subtle variations in image contrast. These limitations can lead to inconsistencies in identifying critical regions, impacting the accuracy of clinical outcomes.
View Article and Find Full Text PDFChemosphere
February 2025
State Key Laboratory of Featured Metal Materials and Life-Cycle Safety for Composite Structures, School of Resources, Environment and Materials, Guangxi University, Nanning, 530004, Guangxi, China; College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China; Department of Chemistry, Imperial College London, 82 Wood Lane, London, W12 0BZ, UK. Electronic address:
Exploiting solid powder fluorescence holds significant potential in diverse domains including medicine and forensics. Conventional fingerprint detection methods often fall short due to low contrast, sensitivity, and high toxicity. To addressing these challenges, we present a novel method for latent fingerprint detection using fluorescent carbon dots (CDs) encapsulated into conventional or mesoporous SiO colloidal spheres (CD@SiO or CDs@m-SiO) through a surface functionalization-assisted cooperative assembly process.
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