28 research outputs found
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
Cluster of microcalcifications can be an early sign of breast cancer. In this
paper we propose a novel approach based on convolutional neural networks for
the detection and segmentation of microcalcification clusters. In this work we
used 283 mammograms to train and validate our model, obtaining an accuracy of
98.22% in the detection of preliminary suspect regions and of 97.47% in the
segmentation task. Our results show how deep learning could be an effective
tool to effectively support radiologists during mammograms examination.Comment: 13 pages, 7 figure
A Conway–Maxwell–Poisson (CMP) model to address data dispersion on positron emission tomography
Positron emission tomography (PET) in medicine exploits the properties of positron-emitting unstable nuclei. The pairs of γ- rays emitted after annihilation are revealed by coincidence detectors and stored as projections in a sinogram. It is well known that radioactive decay follows a Poisson distribution; however, deviation from Poisson statistics occurs on PET projection data prior to reconstruction due to physical effects, measurement errors, correction of deadtime, scatter, and random coincidences. A model that describes the statistical behavior of measured and corrected PET data can aid in understanding the statistical nature of the data: it is a prerequisite to develop efficient reconstruction and processing methods and to reduce noise. The deviation from Poisson statistics in PET data could be described by the Conway-Maxwell-Poisson (CMP) distribution model, which is characterized by the centring parameter λ and the dispersion parameter ν, the latter quantifying the deviation from a Poisson distribution model. In particular, the parameter ν allows quantifying over-dispersion (ν<1) or under-dispersion (ν>1) of data. A simple and efficient method for λ and ν parameters estimation is introduced and assessed using Monte Carlo simulation for a wide range of activity values. The application of the method to simulated and experimental PET phantom data demonstrated that the CMP distribution parameters could detect deviation from the Poisson distribution both in raw and corrected PET data. It may be usefully implemented in image reconstruction algorithms and quantitative PET data analysis, especially in low counting emission data, as in dynamic PET data, where the method demonstrated the best accuracy
4-Dimensional Velocity Mapping Cardiac Magnetic Resonance of Extracardiac Bypass for Aortic Coarctation Repair
Abstract This report describes a case of a young lady who, following extracardiac bypass between ascending and descending aorta for severe aortic coarctation, underwent 4-dimensional flow cardiac magnetic resonance, a technique that, by 3-dimensional flow assessment over time (4-dimensional), allows not only quantification of flows but also wall shear stress. In this case, increased wall shear stress was observed in the conduit's acute angle (kinking) as well as at the distal anastomosis level. The authors postulate that increased wall shear stress could help identify and risk stratify adult congenital heart disease who could develop vascular complications in the future. ( Level of Difficulty: Intermediate.
Locus Coeruleus Magnetic Resonance Imaging in Neurological Diseases
Locus coeruleus (LC) is the main noradrenergic nucleus of the brain, and its degeneration is considered to be key in the pathogenesis of neurodegenerative diseases. In the last 15 years,MRI has been used to assess LC in vivo, both in healthy subjects and in patients suffering from neurological disorders. In this review, we summarize the main findings of LC-MRI studies, interpreting them in light of preclinical and histopathological data, and discussing its potential role as diagnostic and experimental tool
Quantitative imaging and automated fuel pin identification for passive gamma emission tomography
Compliance of member States to the Treaty on the Non-Proliferation of Nuclear
Weapons is monitored through nuclear safeguards. The Passive Gamma Emission
Tomography system is a novel instrument developed by the International Atomic
Energy Agency (IAEA) for the verification of spent nuclear fuel stored in water
pools. Advanced image reconstruction techniques are crucial for obtaining
high-quality cross-sectional images of the spent-fuel bundle to allow
inspectors of the IAEA to monitor nuclear material and promptly identify its
diversion. In this work, we have developed a software suite to accurately
reconstruct the spent-fuel cross sectional image, automatically identify
present fuel rods, and estimate their activity. Unique image reconstruction
challenges are posed by the measurement of spent fuel, due to its high activity
and the self-attenuation. We implemented a linear forward model to model the
detector responses to the fuel rods inside the PGET. The image reconstruction
is performed by solving a regularized linear inverse problem using the
fast-iterative shrinkage-thresholding algorithm. We have also implemented the
traditional filtered back projection method for comparison and applied both
methods to reconstruct images of simulated mockup fuel assemblies. Higher image
resolution and fewer reconstruction artifacts were obtained with the
inverse-problem approach, with the mean-square-error reduced by 50%, and the
structural-similarity improved by 200%. We then used a convolutional neural
network to automatically identify the bundle type and extract the pin locations
from the images; the estimated activity levels finally being compared with the
ground truth. The proposed computational methods accurately estimated the
activity levels of the present pins, with an associated uncertainty of
approximately 5%.Comment: 11 pages, 14 figures, submitted to Scientific Report
Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging
Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination