Parametric Image

About: Parametric Image is a research topic. Over the lifetime, 311 publications have been published within this topic receiving 6095 citations.

Correction of errors in magnetic resonance images due to magnetic gradient non-uniformity

Abstract: A system and method for correcting systematic errors that occur in MR images due to magnetic gradient non-uniformity is disclosed for use with parametric analysis. A GradWarp geometric correction operation is applied in reconstructing quantitative parametric analysis images in regions of gradient non-uniformity. The method includes generating an error map of magnetic gradient strength as a function of distance for an MR image scan and acquiring MR data (60) that contain such systematic errors. The method next includes either calculating a measured diffusion image, a phase difference image (62,64,66), or similar image, based on the acquired MR data, and then calculating a corrected parametric image (68,70,72) using the error map and the measured diffusion image, the phase difference image, or other similar parametric image. The method is incorporated into a system having a computer programmed to perform the aforementioned steps and functions.

Parametric image reconstruction using the discrete cosine transform for optical tomograhy

Abstract: It is well know that the inverse problem in optical tomography is highly ill-posed. The image reconstruction process is often unstable and non-unique, because the number of the boundary measurements data is far fewer than the number of the unknown parameters (optical properties) to be reconstructed. To overcome this problem one can either increase the number of measurement data (e.g. multi-spectral or multi-frequency methods), or reduce the number of unknows (e.g. using prior structural information from other imaging modalities). In this paper, we introduce a novel approach for reducing the unknown parameters in the reconstruction process. The discrete cosine transform (DCT), which has long been used in image compression, is here employed to parameterize the reconstructed image. In general, only a few DCT coefficient are needed to describe the main features in an image, and the number of unknowns in the image reconstruction process can be drastically reduced. Numerical as well as experimental examples are shown that illustrate the performance of the new code.

Noise reduction of parametric images of myocardial blood flow by filtering H/sub 2//sup 15/O dynamic PET images using wavelet transform

Abstract: Dynamic PET images are useful for the measurement of physiological functions. However, signal to noise ratio of H/sub 2//sup 15/O dynamic PET images is poor, thus filtering is necessary. It is well known that discrete wavelet transform saves detailed information in high frequency while diminishing noise. Recently, two methods to generate parametric images of myocardial blood flow using H/sub 2//sup 15/O dynamic PET images have been suggested by our group, but the signal to noise ratio of the suggested parametric images has a room for improvement by applying appropriate temporal or spatial filters to raw dynamic images. Thus, in this study, we applied wavelet transform to reduce the statistical noise in time-activity curve in each pixel prior to generating parametric images of myocardial blood flow and related parameters, and compared the image quality of parametric images with and without wavelet filtering. Wavelet denoising applied to raw dynamic images prior to generating parametric images, removed noise in time-activity curves and, as a result, increased image quality of parametric images without the degradation of spatial resolution.

New Imaging Frontiers in Cardiology: Fast and Quantitative Maps from Raw Data.

Abstract: Background: Among the novelties in the field of cardiovascular imaging, the construction of quantitative maps in a fast and efficient way is one of the most interesting aspects of the clinical research. Quantitative parametric maps are typically obtained by post processing dynamic images, that is, sets of images usually acquired in different temporal intervals, where several images with different contrasts are obtained. Magnetic resonance (MR) imaging, and emission tomography (positron emission and single photon emission) are the imaging techniques best suited for the formation of quantitative maps. Methods: In this review article we present several methods that can be used for obtaining parametric maps, in a fast way, starting from the acquired raw data. We describe both methods commonly used in clinical research, and more innovative methods that build maps directly from the raw data, without going through the image reconstruction. Results: We briefly described recently developed methods in magnetic resonance (MR) imaging that accelerate further the MR raw data generation, based on appropriate sub-sampling of k-space; then, we described recently developed methods for generating MR parametric maps. With regard to the emission tomography techniques, we gave an overview of both conventional methods, and more recently developed direct estimation algorithms for parametric image reconstruction from dynamic positron emission tomography data. Conclusion: We have provided an overview of the possible approaches that can be followed to realize useful parametric maps from imaging raw data. We moved from the conventional approaches to more recent and efficient methods for accelerating the raw data generation and the of parametric maps formation.