IEEE transactions on medical imaging
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IEEE Trans Med Imaging · Jul 2000
An inverse problem approach to the correction of distortion in EPI images.
Magnetic resonance imaging using the echo planar imaging (EPI) technique is particularly sensitive to main (B0) field inhomogeneities. The primary effect is geometrical distortion in the phase encoding direction. ⋯ Two versions are presented: one that attempts to solve the full four-dimensional (4-D) imaging equation, and one that independently solves for each profile along the blip encoding direction. Results are presented for both phantom and in vivo brain EPI images and compared with other proposed correction methods.
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IEEE Trans Med Imaging · Dec 1999
ReviewConfidence maps and confidence intervals for near infrared images in breast cancer.
This paper extends basic concepts of statistical hypothesis testing and confidence intervals to images generated by a new procedure for near infrared spectroscopic tomography being developed for use in breast cancer diagnosis. By estimating the covariance matrix of the pixels of an image from data used in the image reconstruction process, confidence maps for statistical tests on individual pixels and confidence intervals for entire images are displayed as an aid to research and clinical personnel interpreting possibly noisy images. The methods are applied to simulated and phantom-based images.
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IEEE Trans Med Imaging · Sep 1999
Comparative StudyAdaptive fuzzy segmentation of magnetic resonance images.
An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. ⋯ Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.
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IEEE Trans Med Imaging · Sep 1999
Thoracic electrical impedance tomographic measurements during volume controlled ventilation-effects of tidal volume and positive end-expiratory pressure.
The aim of the study was to analyze thoracic electrical impedance tomographic (EIT) measurements accomplished under conditions comparable with clinical situations during artificial ventilation. Multiple EIT measurements were performed in pigs in three transverse thoracic planes during the volume controlled mode of mechanical ventilation at various tidal volumes (V(T)) and positive end-expiratory pressures (PEEP). The protocol comprised following ventilatory patterns: 1) V(T)(400, 500, 600, 700 ml) was varied in a random order at various constant PEEP levels and 2) PEEP (2, 5, 8, 11, 14 cm H2O) was randomly modified during ventilation with a constant V(T). ⋯ The quantitative analysis was performed in terms of the tidal amplitude of the impedance change, reflecting the volume of delivered gas at various preset V(T) and the end-expiratory impedance change, revealing the variation of the lung volume at various PEEP levels. The results showed: 1) an increase in the tidal amplitude of the impedance change, proportional to the delivered V(T) at all constant PEEP levels, 2) a rising end-expiratory impedance change, with PEEP reflecting an increase in gas volume, and 3) a PEEP-dependent redistribution of the ventilated gas between the planes. The generated images and the quantitative results indicate the ability of EIT to identify regional changes in V(T) and lung volume during mechanical ventilation.
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IEEE Trans Med Imaging · Sep 1999
Comparative StudyA novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing.
This paper presents a novel speckle suppression method for medical B-scan ultrasonic images. An original image is first separated into two parts with an adaptive filter. These two parts are then transformed into a multiscale wavelet domain and the wavelet coefficients are processed by a soft thresholding method, which is a variation of Donoho's soft thresholding method. ⋯ A computer-simulated image and an in vitro B-scan image of a pig heart have been used to test the performance of this new method. This technique effectively reduces the speckle noise, while preserving the resolvable details. It performs well in comparison to the multiscale thresholding technique without adaptive preprocessing and two other speckle-suppression methods.