Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep learning (DL) methods have provided accelerated high-quality image reconstructions from undersampled data, but it is unclear if DL image reconstruction can be reliably translated to everyday clinical practice. Purpose To determine the diagnostic equivalence of prospectively accelerated DL-reconstructed knee…
IntroductionSoon after Roentgen’s discovery of the x-ray (radiography) in 1895, chest fluoroscopy and chest radiography became established clinical procedures. The standard radiograph remained the mainstay…
Purpose To update previously summarized estimates of diagnostic accuracy for acute cholecystitis and to obtain summary estimates for more recently introduced modalities. Materials and Methods…
References1. Hwang EJ, Nam JG, Lim WH, et al. Deep Learning for Chest Radiograph Diagnosis in the Emergency Department. Radiology 2019;293(3):573–580. Link, Google Scholar2. Niehues SM,…
Dr. Manisha Bahl discusses the issues surrounding implementation of clinical artificial intelligence in radiology with Dr. Dania Daye and Dr. Curtis Langlotz.
Background The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) is a recognized tool for management of thyroid nodules in adults…
Background To the knowledge of the authors, no strong evidence supports surveillance imaging in patients with head and neck cancer (HNC). Purpose To investigate the…
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Gallium 68 (68Ga)–labeled fibroblast activation protein (FAP) inhibitor (FAPI) PET is based on the molecular targeting of the FAP, which is known to be highly…
Background Breast cancer tumors can be identified as different luminal molecular subtypes depending on either immunohistochemical (IHC) staining or St Gallen criteria that includes Ki-67.…
BECAUSE of the discoveries and developments in the field of nuclear physics during the past few years, artificial radio-activity is available in nearly all of…
