Iterative metal artifact reduction: Evaluation and optimization of technique
Naveen Subhas(Cleveland Clinic), Joseph P. Iannotti(Cleveland Clinic), Amit Gupta(University of Washington), Nancy A. Obuchowski(Cleveland Clinic), A. Krauß(Siemens (Germany)), Joshua M. Polster(Cleveland Clinic), Andrew N. Primak(Siemens (United States))
Cited by 79
Related Papers
Assessing the Performance of Prediction Models
|Epidemiology|2009|4.9k
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
|Nature Reviews Clinical Oncology|2021|810
Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non–Small Cell Lung Cancer
|Cancer Immunology Research|2019|313
Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study
|The Lancet Oncology|2024|305
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
|Insights into Imaging|2024|286