Purpose To present a deep learning segmentation model that can automatically and robustly
segment all major anatomic structures on body CT images. Materials and Methods In this …

Purpose To present a method that automatically segments and quantifies abnormal CT
patterns commonly present in COVID-19, namely ground-glass opacities and consolidations. …

Given the need for sophisticated in vivo detection techniques to better characterize the cellular
and subcellular processes in animals and humans, molecular imaging has become an …

Objectives To evaluate the performance of an AI-powered algorithm for the automatic detection
of pulmonary embolism (PE) on chest computed tomography pulmonary angiograms (…

The combination of PET and MR imaging forms a powerful new imaging modality, PET/MR.
The major advantages of concurrent PET/MR acquisitions range from patient comfort and …

Purpose The purpose of this retrospective study was to correlate CT patterns of fatal cases
of coronavirus disease 2019 (COVID-19) with postmortem pathology observations. Materials …

Accurate segmentations in medical images are the foundations for various clinical applications.
Advances in machine learning-based techniques show great potential for automatic …

Patients with metastatic medullary thyroid cancer (MTC) have limited systemic treatment
options. The use of radiolabeled gastrin analogs targeting the cholecystokinin-2 receptor (…

Purpose During the emerging COVID-19 pandemic, radiology departments faced a substantial
increase in chest CT admissions coupled with the novel demand for quantification of …

Objectives To assess the value of iodine concentration (IC) in computed tomography data
acquired with 80 kVp, as a surrogate for perfusion imaging in hepatocellular carcinoma (HCC) …