Siemens unveils CT dose reduction
New software shown by Siemens at the RSNA meeting promises to cut patient exposure to x-rays by as much as 60% for a wide range of CT applications.
New software shown by Siemens at the RSNA meeting promises to cut patient exposure to x-rays by as much as 60% for a wide range of CT applications. The iterative reconstruction package, dubbed iterative reconstruction in image space (IRIS), is expected to begin shipping next year on new Somatom Definition CT scanners, including the dual-energy Definition Flash CT. An upgrade is being developed for installed Definition systems.
The software minimizes computing time, according to the company, by accelerating the convergence of the reconstruction. It does so by applying the raw data reconstruction only once. During this newly developed initial raw data reconstruction, a “master image” is generated that contains all the raw data information. The following iterative corrections are then performed consecutively in the image space. The resulting images are reduced in artifacts and noise, show increased image sharpness, and are created at a dose savings up to 60%, according to Siemens.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring
December 5th 2020Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.
