2,371 research outputs found

    Monitoring Progression of Ductal Carcinoma In Situ Using Photoacoustics and Contrast-Enhanced Ultrasound.

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    Breast cancer is the leading form of cancer in women, accounting for approximately 41,400 deaths in 2018. While a variety of risk factors have been identified, physical exercise has been linked to reducing both the risk and aggressiveness of breast cancer. Within breast cancer, ductal carcinoma in situ (DCIS) is a common finding. However, less than 25% of DCIS tumors actually progress into invasive breast cancer, resulting in overtreatment. This overtreatment is due to a lack of predictive precursors to assess aggressiveness and development of DCIS. We hypothesize that tissue oxygenation and perfusion measured by photoacoustic and contrast-enhanced ultrasound imaging, respectively, can predict DCIS aggressiveness. To test this, 20 FVB/NJ and 20 SV40Tag mice that genetically develop DCIS-like breast cancers were divided evenly into exercise and control groups and imaged over the course of 6 weeks. Tissue oxygenation was a predictive precursor to invasive breast cancer for FVB/NJ mice (P = 0.015) in the early stages of tumor development. Meanwhile, perfusion results were inconclusive (P \u3e 0.2) as a marker for disease progression. Moreover, voluntary physical exercise resulted in lower weekly tumor growth and significantly improved median survival (P = 0.014)

    Abnormality Detection in Mammography using Deep Convolutional Neural Networks

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    Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

    Contrast Enhancement in Mammography Imaging Including K Edge Filtering

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    Focal Spot, Winter 2006/2007

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    https://digitalcommons.wustl.edu/focal_spot_archives/1104/thumbnail.jp

    Towards breast tomography with synchrotron radiation at Elettra: First images

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    The aim of the SYRMA-CT collaboration is to set-up the first clinical trial of phase-contrast breast CT with synchrotron radiation (SR). In order to combine high image quality and low delivered dose a number of innovative elements are merged: a CdTe single photon counting detector, state-of-the-art CT reconstruction and phase retrieval algorithms. To facilitate an accurate exam optimization, a Monte Carlo model was developed for dose calculation using GEANT4. In this study, high isotropic spatial resolution (120 μm)3 CT scans of objects with dimensions and attenuation similar to a human breast were acquired, delivering mean glandular doses in the range of those delivered in clinical breast CT (5-25 mGy). Due to the spatial coherence of the SR beam and the long distance between sample and detector, the images contain, not only absorption, but also phase information from the samples. The application of a phase-retrieval procedure increases the contrast-to-noise ratio of the tomographic images, while the contrast remains almost constant. After applying the simultaneous algebraic reconstruction technique to low-dose phase-retrieved data sets (about 5 mGy) with a reduced number of projections, the spatial resolution was found to be equal to filtered back projection utilizing a four fold higher dose, while the contrast-to-noise ratio was reduced by 30%. These first results indicate the feasibility of clinical breast CT with SR

    Mammographic image restoration using maximum entropy deconvolution

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    An image restoration approach based on a Bayesian maximum entropy method (MEM) has been applied to a radiological image deconvolution problem, that of reduction of geometric blurring in magnification mammography. The aim of the work is to demonstrate an improvement in image spatial resolution in realistic noisy radiological images with no associated penalty in terms of reduction in the signal-to-noise ratio perceived by the observer. Images of the TORMAM mammographic image quality phantom were recorded using the standard magnification settings of 1.8 magnification/fine focus and also at 1.8 magnification/broad focus and 3.0 magnification/fine focus; the latter two arrangements would normally give rise to unacceptable geometric blurring. Measured point-spread functions were used in conjunction with the MEM image processing to de-blur these images. The results are presented as comparative images of phantom test features and as observer scores for the raw and processed images. Visualization of high resolution features and the total image scores for the test phantom were improved by the application of the MEM processing. It is argued that this successful demonstration of image de-blurring in noisy radiological images offers the possibility of weakening the link between focal spot size and geometric blurring in radiology, thus opening up new approaches to system optimization.Comment: 18 pages, 10 figure
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