A GPU Simulation Tool for Training and Optimisation in 2D Digital X-Ray Imaging

Conventional radiology is performed by means of digital detectors, with various types of technology and different performance in terms of efficiency and image quality. Following the arrival of a new digital detector in a radiology department, all the staff involved should adapt the procedure parameters to the properties of the detector, in order to achieve an optimal result in terms of correct diagnostic information and minimum radiation risks for the patient. The aim of this study was to develop and validate a software capable of simulating a digital X-ray imaging system, using graphics processing unit computing. All radiological image components were implemented in this application: an X-ray tube with primary beam, a virtual patient, noise, scatter radiation, a grid and a digital detector. Three different digital detectors (two digital radiography and a computed radiography systems) were implemented. In order to validate the software, we carried out a quantitative comparison of geometrical and anthropomorphic phantom simulated images with those acquired. In terms of average pixel values, the maximum differences were below 15%, while the noise values were in agreement with a maximum difference of 20%. The relative trends of contrast to noise ratio versus beam energy and intensity were well simulated. Total calculation times were below 3 seconds for clinical images with pixel size of actual dimensions less than 0.2 mm. The application proved to be efficient and realistic. Short calculation times and the accuracy of the results obtained make this software a useful tool for training operators and dose optimisation studies

Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor

OBJECTIVE: To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from 68Ga-DOTA-TOC PET images in patients with neuroendocrine tumors.METHODS: Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUVmax fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COVL). The RFs' correlation with volume and SUVmax was analyzed by calculating Pearson's correlation coefficients.RESULTS: DSC mean value was 0.75 ± 0.11 (0.45-0.92) between SAEB and operators and 0.78 ± 0.09 (0.36-0.97), among the four manual segmentations. The study showed high robustness (ICC >0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUVmax threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUVmax thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUVmax.CONCLUSIONS: RFs robustness to manual segmentation resulted higher in NET 68Ga-DOTA-TOC images compared to 18F-FDG PET/CT images. Forty percent SUVmax thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated

Prognostic Value of Whole-Body PET Volumetric Parameters Extracted from 68Ga-DOTATOC PET/CT in Well-Differentiated Neuroendocrine Tumors

Our objective was to evaluate the prognostic value of somatostatin receptor tumor burden on Ga-68-DOTATOC PET/CT in patients with well-differentiated (WD) neuroendocrine tumors (NETs). Methods: We retrospectively analyzed the 68Ga-DOTATOC PET/CT scans of 84 patients with histologically confirmed WD NETs (51 grade 1, 30 grade 2, and 3 grade 3). For each PET/CT scan, all Ga-68-DOTATO-Cavid lesions were independently segmented by 2 operators using a customized threshold based on the healthy liver SUVmax (LIFEx, version 5.1). Somatostatin receptor-expressing tumor volume (SRETV) and total lesion somatostatin receptor expression (TLSRE = SRETV X SUVmean) were extracted for each lesion, and then whole-body SRETV and TLSRE (SRETVwb and TLSREwb, respectively) were defined as the sum of SRETV and TLSRE, respectively, for all segmented lesions in each patient. Time to progression (TTP) was defined as the combination of disease-free survival in patients undergoing curative surgery (n = 10) and progression-free survival for patients with unresectable or metastatic disease (n = 74). TTP and overall survival were calculated by Kaplan-Meier analysis, log-rank testing, and the Cox proportional-hazards regression model. Results: After a median follow-up of 15.5 mo, disease progression was confirmed in 35 patients (41.7%) and 14 patients died. A higher SRETVwb (>39.1 cm(3)) and TLSREwb (>306.8 g) correlated significantly with a shortermedian TTP (12 mo vs. not reached; P < 0.001). In multivariate analysis, SRETVwb (P = 0.005) was the only independent predictor of TTP regardless of histopathologic grade and TNM staging. Conclusion: According to our results, SRETVwb and TLSREwb extracted from Ga-68-DOTATOC PET/CT could predict TTP or overall survival and might have important clinical utility in the management of patients with WD NETs

Ultra-Low-Dose Whole-Body Computed Tomography Protocol Optimization for Patients With Plasma Cell Disorders: Diagnostic Accuracy and Effective Dose Analysis From a Reference Center

BACKGROUND: The whole-body low-dose CT (WBLDCT) is the first-choice imaging technique in patients with suspected plasma cell disorder to assess the presence of osteolytic lesions. We investigated the performances of an optimized protocol, evaluating diagnostic accuracy and effective patient dose reduction using a latest generation scanner. METHODS AND MATERIALS: Retrospective study on 212 patients with plasma cell disorders performed on a 256-row CT scanner. First, WBLDCT examinations were performed using a reference protocol with acquisition parameters obtained from literature. A phantom study was performed for protocol optimization for subsequent exams to minimize dose while maintaining optimal diagnostic accuracy. Images were analyzed by three readers to evaluate image quality and to detect lesions. Effective doses (E) were evaluated for each patient considering the patient dimensions and the tube current modulation. RESULTS: A similar, very good image quality was observed for both protocols by all readers with a good agreement at repeated measures ANOVA test (p>0.05). An excellent inter-rater agreement for lesion detection was achieved obtaining high values of Fleiss’ kappa for all the districts considered (p<0.001). The optimized protocol resulted in a 56% reduction of median DLP (151) mGycm, interquartile range (IQR) 128–188 mGycm vs. 345 mGycm, IQR 302–408 mGycm), of 60% of CTDIvol (2.2 mGy, IQR 1.9–2.7 mGy vs. 0.9 mGy, IQR 0.8–1.2 mGy). The median E value was about 2.6 mSv (IQR 1.7–3.5 mSv) for standard protocol and about 1.5 mSv (IQR 1.4–1.7 mSv) for the optimized one. Dose reduction was statistically significant with p<0.001. CONCLUSIONS: Protocol optimization makes ultra-low-dose WBLDCT feasible on latest generation CT scanners for patients with plasma cell disorders with effective doses inferior to conventional skeletal survey while maintaining excellent image quality and diagnostic accuracy. Dose reduction is crucial in such patients, as they are likely to undergo multiple whole-body CT scans during follow-up

18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival

Objectives: (1.1) to evaluate the association between baseline 18F-FDG PET/CT semi-quantitative parameters of the primary lesion with progression free survival (PFS), overall survival (OS) and response to immunotherapy, in advanced non-small cell lung carcinoma (NSCLC) patients eligible for immunotherapy; (1.2) to evaluate the application of radiomics analysis of the primary lesion to identify features predictive of response to immunotherapy; (1.3) to evaluate if tumor burden assessed by 18F-FDG PET/CT (N and M factors) is associated with PFS and OS. Materials and Methods: we retrospectively analyzed clinical records of advanced NCSLC patients (stage IIIb/c or stage IV) candidate to immunotherapy who performed 18F-FDG PET/CT before treatment to stage the disease. Fifty-seven (57) patients were included in the analysis (F:M 17:40; median age = 69 years old). Notably, 38/57 of patients had adenocarcinoma (AC), 10/57 squamous cell carcinoma (SCC) and 9/57 were not otherwise specified (NOS). Overall, 47.4% patients were stage IVA, 42.1% IVB and 8.8% IIIB. Immunotherapy was performed as front-line therapy in 42/57 patients and as second line therapy after chemotherapy platinum-based in 15/57. The median follow up after starting immunotherapy was 10 months (range: 1.5&ndash;68.6). Therapy response was assessed by RECIST 1.1 criteria (CT evaluation every 4 cycles of therapy) in 48/57 patients or when not feasible by clinical and laboratory data (fast disease progression or worsening of patient clinical condition in nine patients). Radiomics analysis was performed by applying regions of interest (ROIs) of the primary tumor delineated manually by two operators and semi-automatically applying a threshold at 40% of SUVmax. Results: (1.1) metabolic tumor volume (MTV) (p = 0.028) and total lesion glycolysis (TLG) (p = 0.035) were significantly associated with progressive vs. non-progressive disease status. Patients with higher values of MTV and TLG had higher probability of disease progression, compared to those patients presenting with lower values. SUVmax did not show correlation with PD status, PFS and OS. MTV (p = 0.027) and TLG (p = 0.022) also resulted in being significantly different among PR, SD and PD groups, while SUVmax was confirmed to not be associated with response to therapy (p = 0.427). (1.2) We observed the association of several radiomics features with PD status. Namely, patients with high tumor volume, TLG and heterogeneity expressed by &ldquo;skewness&rdquo; and &ldquo;kurtosis&rdquo; had a higher probability of failing immunotherapy. (1.3) M status at 18F-FDG PET/CT was significantly associated with PFS (p = 0.002) and OS (p = 0.049). No significant associations were observed for N status. Conclusions: 18F-FDG PET/CT performed before the start of immunotherapy might be an important prognostic tool able to predict the disease progression and response to immunotherapy in patients with advanced NSCLC, since MTV, TLG and radiomics features (volume and heterogeneity) are associated with disease progression

Elementi di risonanza magneticaDal protone alle sequenze per le principali applicazioni diagnostiche /

XV, 300 pagg.online resource

Qualitative comparison between measured and simulated images for Philips DigitalDiagnost: particular of two lumbar spine images with different exposure parameters.

<p>See below the acquired images and simulated images, pixel value profiles obtained from a rectangular ROI.</p

Mean pixel value and SD for several ROIs of five examinations carried out with common exposure parameters, for the three detectors analysed.

<p>Mean pixel value and SD for several ROIs of five examinations carried out with common exposure parameters, for the three detectors analysed.</p

Application display screen: virtual radiological room and simulation parameters in the lower right window; grey value histogram in the upper right window; image preview and radiography on the left hand side.

<p>Application display screen: virtual radiological room and simulation parameters in the lower right window; grey value histogram in the upper right window; image preview and radiography on the left hand side.</p