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DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
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Cedars-Sinai Medical Center
DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
Iterative and deep learning image reconstruction (IR and DLIR) are valuable for reducing dose in modern CT but have the effect of reducing lesion edge sharpness. If sharpness loss becomes contrast dependent at some threshold, the decrease in sharpness may exc...
To systematically evaluate CT number accuracy on virtual monoenergetic images (VMIs) generated by a clinical photon-counting CT (PCCT) system using projection-domain multi-material modeling with a water/iodine basis.
Opportunistic coronary artery calcium (CAC) assessment from lung cancer screening (LCS) CT can broaden cardiovascular risk stratification, but cardiac motion and acquisition settings may impact the calcium score. This study quantifies the impact of helical pi...
To utilize data from a dose tracking software to retrospectively track repeat rates for frequently used CT exams
To evaluate the relationship between fat volume fraction (FVF) and Hounsfield units (HU) in unenhanced fatty lesions and to identify virtual monochromatic imaging (VMI) settings that minimize FVF quantification errors, by comparing photon-counting CT (PCCT) w...
To create user-friendly, size and heart condition-specific protocols to improve coronary CT angiography (CCTA) image quality, reduce dose, and expand eligibility to patients with contraindications due to heart activity.