1,721,182 research outputs found
A Novel Adaptive Lossless Compression Algorithm for Efficient Medical Image Archiving and Transmission
No full textMost medical diagnostic tools generate images that are digitally collected, stored
and transmitted. In order to perform these operations efficiently, the images are
mathematically compressed before storage or transmission and decompressed after
retrieval. Methods which, after decompression, result in a reconstruction that is
identical to the original image are referred to as lossless; those whose
reconstruction is an approximation of the original image are referred to as lossy.
Though lossy methods produce compression factors 20 times greater than lossless
methods, in medical applications the need to conserve the diagnostic validity of the
image requires the use of lossless compression methods. A medical image is always
composed of two summed terms: the first term is the image with its useful structures
and information; the second term is useless noise due to the actual experimental
measurements. A compression method which is able to compress and reconstruct a
medical image by rejecting the information due exclusively to noise has to be
considered a lossless method because it conserves all useful information.
In this context, a novel lossless compression algorithm, which almost eliminates
both redundant information and noise from a medical image while retaining all
relevant structures, is proposed. The algorithm is based on the evaluation of the
information content of a given image through the calculation of a function that is
formally identical to the entropy defined in thermodynamics. The entropy is not
calculated directly on the original image but on its projections. Reconstruction from
projections is one of the most important acquisition/reconstruction methods used in
tomography and projections are the signals that a diagnostic tool actually
measures; the image is always reconstructed mathematically, not directly measured.
The advantages of this method with respect to other compression algorithms are
threefold: first, it compresses the image while eliminating noise, i.e. useless
information; second, the resulting compressed image can be elaborated with
another standard lossless compression algorithm, further improving the
compression ratio; third, in medical tomography it can be applied directly to the
measured signals, the projections. Application of the presented algorithm to
experimental nuclear magnetic resonance imaging (NMRI) data demonstrates its
good performance; the achieved compression factor was about 21
MRI: essentials for innovative technologies
No full textMRI: Essentials for Innovative Technologies describes novel methods to improve magnetic resonance imaging (MRI) beyond its current limitations. It proposes smart encoding methods and acquisition sequences to deal with frequency displacement due to residual static magnetic field inhomogeneity, motion, and undersampling. Requiring few or no hardware modifications, these speculative methods offer building blocks that can be combined and refined to overcome barriers to more advanced MRI applications, such as real-time imaging and open systems.
After a concise review of basic mathematical tools and the physics of MRI, the book describes the severe artifacts produced by conventional MRI techniques. It first tackles magnetic field inhomogeneities, outlining conventional solutions as well as a completely different approach based on time-varying gradients and temporal frequency variation coding (acceleration). The book then proposes two innovative acquisition methods for reducing acquisition time, motion, and undersampling artifacts: adaptive acquisition and compressed sensing. The concluding chapter lays out the author's predictions for the future of MRI
Recent Advances in Acquisition/Reconstruction Algorithms for Undersampled Magnetic Resonance Imaging
Full text linkSeveral applications of Magnetic Resonance Imaging (MRI), in particular dynamic MRI and functional MRI (fMRI), require rapid acquisition to measure dynamic processes changes. Experimental data are collected in the k-space by following different trajectories to cover the whole space. Complete data acquisition necessitates waiting for a fixed time interval: a reduced number of collected trajectories allows acquisition time reduction but undersampling occurs, often producing artifacts. In what follows, a review of methods for sparse sampling acquisition and reconstruction is presented.
In particular, a differentiation is done between sparse acquisition methods which do not use any restoration algorithm (artifacts are tolerated) and those methods for which a restoration algorithm is essential. The first class contains also methods where spatial information is shared between temporal images to reduce the collected data. In the second class of methods, a differentiation is done between those reconstruction/restoration methods that reduce artifacts independently of the sample shape, and those restoration methods that adapt their action by modifying the acquisition trajectories during the acquisition, i.e. the chosen trajectories (both in number and directions) are dependent on the sample shape. A third emerging class of methods, those including hybrid forms of the second class, are also reporte
Ethical issues deriving from the delayed adoption of artificial intelligence in medical imaging
Full text linkAdaptive compression algorithm from projections: Application on medical greyscale images
No full text
Metodi di codifica e decodifica dei segnali e ricostruzione di immagini nel dominio delle accelerazioni spaziali per imaging e spettroscopia di risonanza magnetica e relativo apparato
No full textA novel lossless image compression algorithm for medical image archiving and tele-transportation
No full textConstrained Reconstruction for Sparse Magnetic Resonance Imaging
No full textMagnetic Resonance Imaging (MRI) often requires acquisition time reduction to measure dynamic processes changes. To this aim is necessary to reduce the number of measured data. This results in an undersampling problem and aliasing. In what follows, a simple constrained reconstruction algorithm for sparse k-space sampling is described, having the scope of reducing the undersampling artefacts. The proposed method can be applied to different k-space trajectories. Its performance has been demonstrated on MRI data sampled numerically using different trajectories. The presented method has been also compared with other interpolation techniques and results are reported
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