Search results for "030218 nuclear medicine & medical imaging"

A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases

2020

Accurate three-dimensional (3D) cardiac segmentation from late gadolinium enhancement (LGE)-MRI plays a critical role in designing a structure of reference for diagnosing many cardiac pathologies such as ischemia, myocarditis and myocardial infarction. This segmentation is however still a non-trivial task, due to the motion artifacts during acquisition, and heterogeneous intensity distributions. In this study, we develop a fully 3D automated model based on deep neural networks (DNN) for LGE-MRI myocardial pathologies (scar and No-reflow tissues) segmentation in a new expert annotated dataset. Considering that damaged tissue constitutes a small area of the whole LGE-MRI, we concentrated on m…

Status of HIGISOL a new version equipped with SPIG and electric field guidance

2001

A new HIGISOL chamber devoted to the study of short-lived products from heavy-ion-induced fusion-evaporation reactions is proposed. It enables, via the extraction of ions by means of a SPIG (SextuPole rf Ion Guide), to improve the mass resolving power by a factor 2.5 compared to the previous system using a skimmer-ring assembly. The gas cell was also equiped with an electric field for faster transportation of recoiling ions to the nozzle where they are ejected with the gas jet. The first results obtained both with a radioactive α-source and cyclotron beam will be reported.

Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network

2020

Accurate registration of lung computed tomography (CT) image is a significant task in thorax image analysis. Recently deep learning-based medical image registration methods develop fast and achieve promising performance on accuracy and speed. However, most of them learned the deformation field through intensity similarity but ignored the importance of aligning anatomical landmarks (e.g., the branch points of airway and vessels). Accurate alignment of anatomical landmarks is essential for obtaining anatomically correct registration. In this work, we propose landmark constrained learning with a convolutional neural network (CNN) for lung CT registration. Experimental results of 40 lung 3D CT …

Computerised tomography and magnetic resonance imaging of laryngeal squamous cell carcinoma: A practical approach

2017

Squamous cell carcinoma is the most common head and neck cancer. This review describes the state-of-the-art computerised tomography and magnetic resonance imaging protocols of the neck and the normal larynx anatomy, and provides a practical approach for the diagnosis and staging of laryngeal squamous cell carcinoma.

Algorithms as legal norms: About extending traditional legal safeguards for regulations enacted by public administrations to the algorithms used by p…

2020

En este trabajo se argumenta que los algoritmos empleados por parte de las Administraciones públicas para la adopción efectiva de decisiones han de ser considerados reglamentos por cumplir una función material estrictamente equivalente a la de las normas jurídicas, al reglar y predeterminar la actuación de los poderes públicos. Adicionalmente se estudia cómo, una vez asumida esta naturaleza jurídica reglamentaria de estas herramientas de programación, se deducen consecuencias jurídicas respecto de cómo han de realizarse los procedimientos de elaboración y aprobación de estos algoritmos, la necesidad de que los mismos estén debidamente publicados como normas jurídicas que son o la exigencia …

Review on Machine Learning Based Lesion Segmentation Methods from Brain MR Images

2016

Brain lesions are life threatening diseases. Traditional diagnosis of brain lesions is performed visually by neuro-radiologists. Nowadays, advanced technologies and the progress in magnetic resonance imaging provide computer aided diagnosis using automated methods that can detect and segment abnormal regions from different medical images. Among several techniques, machine learning based methods are flexible and efficient. Therefore, in this paper, we present a review on techniques applied for detection and segmentation of brain lesions from magnetic resonance images with supervised and unsupervised machine learning techniques.

In Reply to the Letter to the Editor: “Comparing the Volume of Brain Metastases in F-18-FET-PET and MRI”

2016

Maternal Motivation: Exploring the Roles of Prolactin and Pup Stimuli

2020

Motherhood entails increased motivation for pups, which become strong reinforcers and guide maternal behaviours. This depends on steroids and lactogens acting on the brain of females during pregnancy and postpartum. Since virgin female mice exposed to pups are nearly spontaneously maternal, the specific roles of endocrine and pup-derived signals in the induction of maternal motivation remain unclear. This work investigates maternal motivation in dams and virgin female mice, using a novel variant of the pup retrieval paradigm, the motivated pup retrieval test. We also analyse the role of prolactin (PRL) and of stimuli derived from a litter of pups and its mother, in the acquisition of matern…

Hepatobiliary phase in cirrhotic patients with different Model for End-stage Liver Disease score: comparison of the performance of gadoxetic acid to …

2018

The purpose of this study was to compare the performance of gadobenate dimeglumine–enhanced MRI and gadoxetic acid–enhanced MRI in the hepatobiliary phase (HBP) in cirrhotic patients with different degrees of liver dysfunction. In this retrospective cross-sectional study, we analyzed the unenhanced phase and the HBP of 131 gadobenate dimeglumine–enhanced MRI examinations (gadobenate dimeglumine group) and 127 gadoxetic acid–enhanced MRI examinations (gadoxetic acid group) performed in 249 cirrhotic patients (181 men and 68 women; mean age, 64.8 years) from August 2011 to April 2017. For each MRI, the contrast enhancement index of the liver parenchyma was calculated and correlated to the Mod…

Contrast ultrasound LI-RADS LR-5 identifies hepatocellular carcinoma in cirrhosis in a multicenter restropective study of 1,006 nodules

2018

Background & Aims The use of contrast enhanced ultrasound (CEUS) for the diagnosis of hepatocellular carcinoma (HCC) in cirrhosis was questioned because of the risk of a false positive diagnosis in cases of cholangiocarcinoma. The American College of Radiology has recently released a scheme (CEUS Liver Imaging Reporting and Data System [LI-RADS®]) to classify lesions at risk of HCC investigated by CEUS. The aim of the present study was to validate this LI-RADS scheme for the diagnosis of HCC. Methods A total of 1,006 nodules from 848 patients with chronic liver disease at risk of HCC were collected in five Italian centers and retrospectively analyzed. Nodules were classified as LR-5, (HCC) …