FDG-PET/CT and MR-DWI showed similar high accuracy for diagnosing peritoneal carcinomatosis. • In the supramesocolic area, MR-DWI could be more sensitive than PET/CT. • Both techniques showed lower sensitivity for subcentimetre lesions. • Interobserver agreement was very good for PET/CT and good for MR-DWI.
Nuclear medicine imaging is a powerful diagnostic tool for the management of patients with gastro-entero-pancreatic neuroendocrine tumors, mainly developed considering some cellular characteristics that are specific to the neuroendocrine phenotype. Hence, overexpression of specific trans membrane receptors as well as the cellular ability to take up, accumulate, and decarboxylate amine precursors have been considered for diagnostic radiotracer development. Moreover, the glycolytic metabolism, which is not a specific energetic pathway of neuroendocrine tumors, has been proposed for radionuclide imaging of neuroendocrine tumors. The results of scintigraphic examinations reflect the pathologic features and tumor metabolic properties, allowing the in vivo characterization of the disease. In this article, the influence of both cellular differentiation and tumor grade in the scintigraphic pattern is reviewed according to the literature data. The relationship between nuclear imaging results and prognosis is also discussed. Despite the existence of a relationship between the results of scintigraphic imaging and cellular differentiation, tumor grade and patient outcome, the mechanism explaining the variability of the results needs further investigation.
(18)F-FDOPA PET appears to be a sensitive functional imaging tool for the detection of primary NETs occult on SRS, especially tumors with a well-differentiated pattern and serotonin secretion.
ObjectiveWe aimed to describe a pattern of rim uptake observed in lung infarction on FDG-PET/CT, called the “rim sign.” It was defined as a continuous slight FDG uptake along the border of a subpleural consolidation without uptake within the consolidation.MethodsWe retrospectively reviewed the FDG-PET/CT studies of 400 patients referred for thoracic oncological workup from November 2010 to July 2011. The rim sign was observed in six patients who had confirmed pulmonary infarction (PI) on MDCT showing acute pulmonary embolism (n = 4) or tumoral arterial obstruction (n = 2).ResultsEight PIs in the six patients exhibited the rim sign with slight uptake (median SUVmax: 3.6, 2.2–6.8) and median size of 48.5 mm (30–74). On MDCT, central lucencies, triangular shape and vessel sign were observed in 5/8, 4/8 and 1/8 cases, respectively. Two out of the eight PIs exhibited only the rim sign and none the suggestive MDCT sign.ConclusionThe rim sign is easily recognisable at FDG-PET/CT and is strongly suggestive of PI. This pattern can be observed even in the absence of suggestive findings on MDCT. Recognition of this sign should prompt investigations for pulmonary embolism.Main Messages• The rim sign is a slight FDG uptake around an area of subpleural consolidation• The rim sign is strongly suggestive of pulmonary infarction• Recognition of the rim sign should prompt investigations for pulmonary embolism
Knowledge of vertebra location, shape, and orientation is crucial in many medical applications such as orthopedics or interventional procedures. Computed tomography (CT) offers a high contrast between bone and soft tissues, but automatic vertebra segmentation remains difficult. Hence, the wide range of shapes, aging, and degenerative joint disease alterations as well as the variety of pathological cases encountered in an aging population make automatic segmentation sometimes challenging. Besides, daily practice implies a need for affordable computation time.This paper aims to present a new automated vertebra segmentation method (using a first bounding box for initialization) for CT 3D data which tackles these problems. This method is based on two consecutive steps. The first one is a new coarse-to-fine method efficiently reducing the data amount to obtain a coarse shape of the vertebra. The second step consists in a hidden Markov chain (HMC) segmentation using a specific volume transformation within a Bayesian framework. Our method does not introduce any prior on the expected shape of the vertebra within the bounding box and thus deals with the most frequent pathological cases encountered in daily practice.We experiment this method on a set of standard lumbar, thoracic, and cervical vertebrae and on a public dataset, on pathological cases, and in a simple integration example. Quantitative and qualitative results show that our method is robust to changes in shapes and luminance and provides correct segmentation with respect to pathological cases.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.