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Nov 30, 2014
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PET scans tie fast food to altered blood flow in the heart
By Will Morton
Ultraprocessed foods may acutely affect vascular and brain function, researchers report.
August 8, 2025
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AI boosts rads' identification of incidental PE on CT imaging
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Study results suggest that AI could serve as an effective "second look" tool for incidental pulmonary emboli (PE) on CT imaging.
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How are technologists dealing with higher vacancy rates?
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High technologist vacancy rates among imaging practices could be addressed through outreach, education, and philanthropy, experts say.
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Screening for vasa previa with ultrasound leads to higher survival rates among prenatally diagnosed cases in pregnancy.
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AI could be cost-effective for osteoporosis screening
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A study highlights the public health potential of AI-driven osteoporosis screening to improve early detection.
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Generative AI improves clinical decision-making in the ED
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Study results suggest that AI could reduce variability among emergency department (ED) physicians when it comes to ordering imaging exams.
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Watching a movie during MR imaging reduces kids' need for sedation
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The findings are good news for pediatric patients, who often struggle to remain still during an MRI exam.
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White paper endorsed by ACR, SIIM stresses cybersecurity
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A new white paper endorsed by the ACR and SIIM outlines strategies for practices to better protect themselves from cyber attacks.
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Is imaging being used to its full potential to diagnose dementia?
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As disease-modifying therapies for various types of dementia become more available, MR and CT imaging predominate for diagnosis.
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MRI shows promise profiling hepatocellular carcinoma tumors
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A study suggests that imaging-based screening with targeted pathological workup could help refine treatment strategies.
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MRI-based DL model improves ovarian lesion classification
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A deep-learning (DL) model accurately differentiated malignant from benign ovarian lesions on MRI scans and reduced segmentation time.
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Open-source DL model does well for identifying lung cancer risk
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A deep-learning (DL) model called Sybil performs well when it comes to predicting lung cancer risk among heavy-smoking populations.
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