ISCT 2015 Event Page (bottom article - obsolete)

May 26, 2015

This is the bottom article on the ISCT Event Page.

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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
By Kate Madden Yee
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?
By Amerigo Allegretto
High technologist vacancy rates among imaging practices could be addressed through outreach, education, and philanthropy, experts say.
August 8, 2025
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Ultrasound screening for vasa previa tied to better pregnancy outcomes
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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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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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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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