The clinical application of PET/CT: a contemporary review

The combination of positron emission tomography (PET) scanners and x-ray computed tomography (CT) scanners into a single PET/CT scanner has resulted in vast improvements in the diagnosis of disease, particularly in the field of oncology. A decade on from the publication of the details of the first PET/CT scanner, we review the technology and applications of the modality. We examine the design aspects of combining two different imaging types into a single scanner, and the artefacts produced such as attenuation correction, motion and CT truncation artefacts. The article also provides a discussion and literature review of the applications of PET/CT to date, covering detection of tumours, radiotherapy treatment planning, patient management, and applications external to the field of oncology

Real-time virtual sonography in gynecology & obstetrics. literature's analysis and case series

Fusion Imaging is a latest generation diagnostic technique, designed to combine ultrasonography with a second-tier technique such as magnetic resonance imaging and computer tomography. It has been mainly used until now in urology and hepatology. Concerning gynecology and obstetrics, the studies mostly focus on the diagnosis of prenatal disease, benign pathology and cervical cancer. We provided a systematic review of the literature with the latest publications regarding the role of Fusion technology in gynecological and obstetrics fields and we also described a case series of six emblematic patients enrolled from Gynecology Department of Sant ‘Andrea Hospital, “la Sapienza”, Rome, evaluated with Esaote Virtual Navigator equipment. We consider that Fusion Imaging could add values at the diagnosis of various gynecological and obstetrics conditions, but further studies are needed to better define and improve the role of this fascinating diagnostic tool

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201