607 research outputs found
Visual measurement system for wheel–rail lateral position evaluation
Railway infrastructure must meet safety requirements concerning its construction and operation. Track geometry monitoring is one of the most important activities in maintaining the steady technical conditions of rail infrastructure. Commonly, it is performed using complex measurement equipment installed on track-recording coaches. Existing low-cost inertial sensor-based measurement systems provide reliable measurements of track geometry in vertical directions. However, solutions are needed for track geometry parameter measurement in the lateral direction. In this research, the authors developed a visual measurement system for track gauge evaluation. It involves the detection of measurement points and the visual measurement of the distance between them. The accuracy of the visual measurement system was evaluated in the laboratory and showed promising results. The initial field test was performed in the Vilnius railway station yard, driving at low velocity on the straight track section. The results show that the image point selection method developed for selecting the wheel and rail points to measure distance is stable enough for TG measurement. Recommendations for the further improvement of the developed system are presented
Highly sensitive alpha-fetoprotein, Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein and des-gamma-carboxy prothrombin for hepatocellular carcinoma detection
Antiglycan antibodies as serological markers in the differential diagnosis of inflammatory bowel disease
CLINICAL EVALUATION OF CIRCULATING MICRORNAS AS POTENTIAL BIOMARKERS OF HEPATOCELLULAR CARCINOMA IN PATIENTS WITH CHRONIC HBV INFECTION
CLINIC EVALUATION OF CIRCULATING MICRORNAS AS POTENTIAL BIOMARKERS OF HEPATOCELLULAR CARCINOMA IN PATIENTS WITH HBV CHRONIC INFECTION
Insulin resistance in non-diabetic patients with non-alcoholic fatty liver disease: sites and mechanisms
n/
Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and Rural Africa
peer reviewe
Performance of three model-based iterative reconstruction algorithms using a CT task-based image quality metric
In this study we evaluated the task-based image quality of a low contrast
clinical task for the abdomen protocol (e.g., pancreatic tumour) of three
different CT vendors, exploiting three model-based iterative reconstruction
(MBIR) levels. We used three CT systems equipped with a full, partial, advanced
MBIR algorithms. Acquisitions were performed on a phantom at three dose levels.
Acquisitions were reconstructed with a standard kernel, using filtered back
projection algorithm (FBP) and three levels of the MBIR. The noise power
spectrum (NPS), the normalized one (nNPS) and the task-based transfer function
(TTF) were computed following the method proposed by the American Association
of Physicists in Medicine task group report-233 (AAPM TG-233). Detectability
index (d') of a small lesion (small feature; 100 HU and 5-mm diameter) was
calculated using non-prewhitening with eye-filter model observer (NPWE).The
nNPS, NPS and TTF changed differently depending on CT system. Higher values of
d' were obtained with advanced-MBIR, followed by full-MBIR and
partial-MBIR.Task-based image quality was assessed for three CT scanners of
different vendors, considering a clinical question. Detectability can be a tool
for protocol optimisation and dose reduction since the same dose levels on
different scanners correspond to different d' values.Comment: 7 pages, 5 figures, 3 table
Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases
The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R−) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R− lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies
- …