126,220 research outputs found
Can Image Enhancement Allow Radiation Dose to Be Reduced Whilst Maintaining the Perceived Diagnostic Image Quality Required for Coronary Angiography?
Objectives: The aim of this research was to quantify the reduction in radiation dose facilitated by image processing alone for percutaneous coronary intervention (PCI) patient angiograms, without reducing the perceived image quality required to confidently make a diagnosis. Methods: Incremental amounts of image noise were added to five PCI angiograms, simulating the angiogram as having been acquired at corresponding lower dose levels (10-89% dose reduction). Sixteen observers with relevant experience scored the image quality of these angiograms in three states - with no image processing and with two different modern image processing algorithms applied. These algorithms are used on state-of-the-art and previous generation cardiac interventional X-ray systems. Ordinal regression allowing for random effects and the delta method were used to quantify the dose reduction possible by the processing algorithms, for equivalent image quality scores. Results: Observers rated the quality of the images processed with the state-of-the-art and previous generation image processing with a 24.9% and 15.6% dose reduction respectively as equivalent in quality to the unenhanced images. The dose reduction facilitated by the state-of-the-art image processing relative to previous generation processing was 10.3%. Conclusions: Results demonstrate that statistically significant dose reduction can be facilitated with no loss in perceived image quality using modern image enhancement; the most recent processing algorithm was more effective in preserving image quality at lower doses. Advances in knowledge: Image enhancement was shown to maintain perceived image quality in coronary angiography at a reduced level of radiation dose using computer software to produce synthetic images from real angiograms simulating a reduction in dose
Diversified Texture Synthesis with Feed-forward Networks
Recent progresses on deep discriminative and generative modeling have shown
promising results on texture synthesis. However, existing feed-forward based
methods trade off generality for efficiency, which suffer from many issues,
such as shortage of generality (i.e., build one network per texture), lack of
diversity (i.e., always produce visually identical output) and suboptimality
(i.e., generate less satisfying visual effects). In this work, we focus on
solving these issues for improved texture synthesis. We propose a deep
generative feed-forward network which enables efficient synthesis of multiple
textures within one single network and meaningful interpolation between them.
Meanwhile, a suite of important techniques are introduced to achieve better
convergence and diversity. With extensive experiments, we demonstrate the
effectiveness of the proposed model and techniques for synthesizing a large
number of textures and show its applications with the stylization.Comment: accepted by CVPR201
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