1,721,010 research outputs found
A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future
Get PDFDosimetric assessment of a novel metal artifact reduction method in CT images
No full textThe aim of this study was to assess the ability of metal artifact reduction (MAR) algorithm in restoring the CT image quality while correcting the tissue density information for the accurate estimation of the absorbed dose. A phantom filled with titanium (low-Z metal) and Cerrobend (high-Z metal) inserts was used for this purpose. The MAR algorithm was applied to phantom’s CT dataset. Static intensity-modulated radiation therapy (IMRT) plans, including five beam angles, were designed and optimized on the uncorrected images to deliver 10 Gy on the simulated target. Monte Carlo dose calculation was computed on uncorrected, corrected, and ground truth image datasets. It was firstly verified that MAR methodology was able to correct HU errors due to the metal presence. In the worst situation (high-Z phantom), the image difference, uncorrected ground truth and corrected ground truth, went from -4.4 ± 118.8 HU to 0.4 ± 10.8 HU, respectively. Secondly, it was observed that the impact of dose errors estimation depends on the atomic number of the metal: low-Z inserts do not produce significant dose inaccuracies, while high-Z implants substantially influence the computation of the absorbed dose. In this latter case, dose errors in the PTV region were up to 23.56% (9.72% mean value) when comparing the uncorrected vs. the ground truth dataset. After MAR correction, errors dropped to 0.11% (0.10% mean value). In conclusion, it was assessed that the new MAR algorithm is able to restore image quality without distorting mass density information, thus producing a more accurate dose estimation
A phantom study on a neural algorithm for patient position optical verification in breast cancer radiotherapy.
No full textAugmented Reality in Microinvasive Cardiac Surgery: Towards a Training Simulator for Mitral Valve Repair Intervention
No full textA feature matching approach for the automatic correlation of internal and external motion in lung tumors.
No full textImplementation of FEM simulation techniques for organ motion modelling in prostate cancer radiotherapy.
No full textBenefits of 6-dofs couch in automatic patient position correction: a retrospective analysis in extra-cranial radiotherapy on a 90 patient population.
No full textRepeatability of radiomic features from brain T1-W MRI after image intensity normalization: Implications for longitudinal studies on structural neurodegeneration
No full textBackground and Objective: Radiomics extracts quantitative features from magnetic resonance images (MRI) and is especially useful in the presence of subtle pathological changes within human soft tissues. This scenario, however, may not cover the effects of intrinsic, e.g., aging-related, (physiological) neurodegeneration of normal brain tissue. The aim of the work was to study the repeatability of radiomic features extracted from an apparently normal area in longitudinally acquired T1-weighted MR brain images using three different intensity normalization approaches typically used in radiomics: Z-score, WhiteStripe and Nyul. Methods: Fifty-nine images of hearing impaired, yet cognitively intact, patients were repeatedly acquired in two different time points within six months. Ninety-one radiomic features were obtained from an area within the pons region, considered to be a healthy brain tissue according to previous analyses and reports. The Intraclass Correlation Coefficient (ICC) and the Concordance Correlation Coefficient (CCC) in the repeatability study were used as metrics. Results: Features extracted from the MRI normalized with Z-score showed results comparable in both ICC (0.90 (0.82–0.98)) and CCC (0.82 (0.69–0.95)) distribution values, in terms of median and quartiles, with those extracted from the images normalized with WhiteStripe (0.89 (0.84–0.92)) and (0.80 (0.73–0.84)), respectively. Conclusion: Our findings underline the importance of, providing useful guidelines for, the intensity normalization of brain MRI prior to a longitudinal radiomic analysis
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