Showing papers on "Parametric Image published in 2014"

A Precise Lower Bound on Image Subpixel Registration Accuracy

Abstract: A new performance bound is proposed for analyzing parametric image registration methods objectively. This original bound is derived from the Cramer-Rao lower bound on the estimation error of parameters involved in a geometric transformation assumed between reference and template images (pure translation in this work) and parameters describing the texture of these images. For describing local fragments of both the reference and the template images, the parametric fractional Brownian motion (fBm) model has been chosen. Experimental results, obtained first on pure fBm data with full matching of the data to the texture model assumption, give evidence that the proposed bound describes more adequately the performance of conventional estimators than two other bounds previously proposed in the literature. This holds with respect to the signal-to-noise ratio value of both images, the roughness of their texture, their correlation, and the actual value of translation parameters between their grids. Then, one real Hyperion hyperspectral data set is considered to test the proposed bound behavior on real data. The proposed bound is demonstrated to describe more adequately the estimation accuracy of the translation parameters between different bands of this data set.

Total Variation Regularization of Displacements in Parametric Image Registration

Abstract: Spatial regularization is indispensable in image registration to avoid both physically implausible displacement fields and potential local minima in optimization methods. Typical \(\ell _2\)-regularization is incapable of correctly recovering non-smooth displacement fields, such as at sliding organ boundaries during time-series of breathing motion. In this paper, Total Variation (TV) regularization is used to allow for accurate registration near such boundaries. We propose a novel formulation of TV-regularization for parametric displacement fields and introduce an efficient and general numerical solution scheme using the Alternating Directions Method of Multipliers (ADMM). Our method has been evaluated on two public datasets of 4D CT lung images as well as a dataset of 4D MR liver images, demonstrating accurate registrations both inside and outside moving organs. The target registration error of our method is 2.56 mm on average in the liver dataset, which indicates an improvement of over 24 % in comparison to other published methods.

Image-derived input function derived from a supervised clustering algorithm: methodology and validation in a clinical protocol using [11C](R)-rolipram.

Abstract: Image-derived input function (IDIF) obtained by manually drawing carotid arteries (manual-IDIF) can be reliably used in [11C](R)-rolipram positron emission tomography (PET) scans. However, manual-IDIF is time consuming and subject to inter- and intra-operator variability. To overcome this limitation, we developed a fully automated technique for deriving IDIF with a supervised clustering algorithm (SVCA). To validate this technique, 25 healthy controls and 26 patients with moderate to severe major depressive disorder (MDD) underwent T1-weighted brain magnetic resonance imaging (MRI) and a 90-minute [11C](R)-rolipram PET scan. For each subject, metabolite-corrected input function was measured from the radial artery. SVCA templates were obtained from 10 additional healthy subjects who underwent the same MRI and PET procedures. Cluster-IDIF was obtained as follows: 1) template mask images were created for carotid and surrounding tissue; 2) parametric image of weights for blood were created using SVCA; 3) mask images to the individual PET image were inversely normalized; 4) carotid and surrounding tissue time activity curves (TACs) were obtained from weighted and unweighted averages of each voxel activity in each mask, respectively; 5) partial volume effects and radiometabolites were corrected using individual arterial data at four points. Logan-distribution volume (VT/fP) values obtained by cluster-IDIF were similar to reference results obtained using arterial data, as well as those obtained using manual-IDIF; 39 of 51 subjects had a VT/fP error of 10%. With automatic voxel selection, cluster-IDIF curves were less noisy than manual-IDIF and free of operator-related variability. Cluster-IDIF showed widespread decrease of about 20% [11C](R)-rolipram binding in the MDD group. Taken together, the results suggest that cluster-IDIF is a good alternative to full arterial input function for estimating Logan-VT/fP in [11C](R)-rolipram PET clinical scans. This technique enables fully automated extraction of IDIF and can be applied to other radiotracers with similar kinetics.

Fast magnetic resonance parametric imaging method and system

Abstract: The invention provides a fast magnetic resonance parametric imaging method. The method comprises the first step of performing target image sequence reconstruction according to undersampling magnetic resonance signals and priori information to obtain a target image sequence, wherein the priori information is foreknown information in the process of parameter estimation, and the second step of substituting the target image sequence into a parameter estimation model to generate a parametric image. Because the priori information in the step of the parameter estimation is introduced into the step of the image sequence reconstruction, reconstruction errors caused in the process of compressed perception image reconstruction can be corrected, and the accuracy of the parametric image generated through the parameter estimation model is improved.

Noise and analyzer-crystal angular position analysis for analyzer-based phase-contrast imaging.

Abstract: The analyzer-based phase-contrast x-ray imaging (ABI) method is emerging as a potential alternative to conventional radiography. Like many of the modern imaging techniques, ABI is a computed imaging method (meaning that images are calculated from raw data). ABI can simultaneously generate a number of planar parametric images containing information about absorption, refraction, and scattering properties of an object. These images are estimated from raw data acquired by measuring (sampling) the angular intensity profile of the x-ray beam passed through the object at different angular positions of the analyzer crystal. The noise in the estimated ABI parametric images depends upon imaging conditions like the source intensity (flux), measurements angular positions, object properties, and the estimation method. In this paper, we use the Cramer–Rao lower bound (CRLB) to quantify the noise properties in parametric images and to investigate the effect of source intensity, different analyzer-crystal angular positions and object properties on this bound, assuming a fixed radiation dose delivered to an object. The CRLB is the minimum bound for the variance of an unbiased estimator and defines the best noise performance that one can obtain regardless of which estimation method is used to estimate ABI parametric images. The main result of this paper is that the variance (hence the noise) in parametric images is directly proportional to the source intensity and only a limited number of analyzer-crystal angular measurements (eleven for uniform and three for optimal non-uniform) are required to get the best parametric images. The following angular measurements only spread the total dose to the measurements without improving or worsening CRLB, but the added measurements may improve parametric images by reducing estimation bias. Next, using CRLB we evaluate the multiple-image radiography, diffraction enhanced imaging and scatter diffraction enhanced imaging estimation techniques, though the proposed methodology can be used to evaluate any other ABI parametric image estimation technique.

Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses.

Abstract: Tracer kinetic modeling in dynamic positron emission tomography (PET) has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting the spatiotemporal information in dynamic PET data. Graphical analysis (GA) is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient. In this paper, we provide an overview of recent advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest. Technical details of GA approaches for reversible and irreversible radioligands are described, considering both plasma input and reference tissue input models. Their statistical properties are discussed in view of parametric imaging.

Evaluation of a direct 4D reconstruction method using generalised linear least squares for estimating nonlinear micro-parametric maps

Abstract: Estimation of nonlinear micro-parameters is a computationally demanding and fairly challenging process, since it involves the use of rather slow iterative nonlinear fitting algorithms and it often results in very noisy voxel-wise parametric maps. Direct reconstruction algorithms can provide parametric maps with reduced variance, but usually the overall reconstruction is impractically time consuming with common nonlinear fitting algorithms. In this work we employed a recently proposed direct parametric image reconstruction algorithm to estimate the parametric maps of all micro-parameters of a two-tissue compartment model, used to describe the kinetics of [ $$^{18}$$ F]FDG. The algorithm decouples the tomographic and the kinetic modelling problems, allowing the use of previously developed post-reconstruction methods, such as the generalised linear least squares (GLLS) algorithm. Results on both clinical and simulated data showed that the proposed direct reconstruction method provides considerable quantitative and qualitative improvements for all micro-parameters compared to the conventional post-reconstruction fitting method. Additionally, region-wise comparison of all parametric maps against the well-established filtered back projection followed by post-reconstruction non-linear fitting, as well as the direct Patlak method, showed substantial quantitative agreement in all regions. The proposed direct parametric reconstruction algorithm is a promising approach towards the estimation of all individual microparameters of any compartment model. In addition, due to the linearised nature of the GLLS algorithm, the fitting step can be very efficiently implemented and, therefore, it does not considerably affect the overall reconstruction time.

Image enhancement and dynamic range compression using novel intensity-specific stochastic resonance-based parametric image enhancement model

Abstract: This paper presents a noise-aided image enhancement algorithm focussed on addressing images that have a large dynamic range, i.e., images with both dark and bright regions. The application of a new mathematical model, in a shifted double-well system exhibiting stochastic resonance, is investigated for such images. The new mathematical model addresses the shortcomings of earlier SR-based enhancement model by deriving parameters purely from input values (instead of input statistics). This model is specific to spatial domain pixel representation and operates on a revised iterative equation. This iterative processing is here applied selectively to the under-illuminated regions of the image, characterized as the De Vries-Rose (DVR) region of a human psychovisual model. The idea of suitably modifying the existing universal image quality index is also proposed for its participation in iteration termination, and to gauge the property of dynamic range compression. While the iterative algorithm is terminated using the revised image quality index, entropy maximization, and contrast quality of DVR region with constraints on perceptual quality, the performance of the proposed algorithm is also characterized by observing color enhancement and subjective scores on visual quality.

Parametric images for the assessment of cardiac kinetics by magnetic resonance imaging (MRI)

Abstract: The evaluation of Cardiac Magnetic Resonance (CMR) imaging exam is mainly based on the visual aspect. This visual evaluation depends on the level of expertise of the radiologist and it is characterized by variability within and between observers. The aim of this work is to propose a new method based on a mathematical model, “Fourier Transform” which calculates an amplitude parametric image. This image, calculated from the Cine MR images, allows the localization and quantification of abnormalities related to difference in contraction and their extent. The suggested amplitude image is likely to assist in the diagnosis through reducing the time taken by the radiologist to specify the abnormal contraction and by improving the accuracy of the examination. After testing this approach on patients (healthy and pathological), we have proven a good concordance between the results obtained by the parametric image and those collected from the routine examination.

Image processing apparatus, image processing method and medical imaging device

Abstract: An image processing apparatus, an image processing method and a medical imaging system are disclosed. The image processing apparatus comprises: an original image acquisition section configured to acquire the original image of an object; a parametric image acquisition section configured to acquire a parametric image corresponding to the original image; and an optical attribute value determination section configured to determine, based on the data value of a pixel of the original image and the parameter value of a corresponding pixel in the parametric image, optical attribute values for presenting the pixel of the original image according to a predetermined correspondence.

Detection of subresolution sources of coherent scattering for parametric image formation

Abstract: This work investigates the performance of speckle statistics parameters to detect coherent scattering with stationary features in echo signals. We focus on the task of parametric image formation for medical applications and the implicit tradeoff between spatial resolution and detection performance. Parameters include the model-free envelope and intensity point-wise SNRs, Nakagami shape parameter m and a Generalized Likelihood Ratio Test T, and the Homodyned-K clustering parameter and the ratio of coherent to incoherent scattering amplitudes. Media with periodic scatterers as a source of coherent scattering embedded in a diffuse scattering background were simulated and mimicked in phantoms. Model-free parameters offered better detection in terms of the parameters' contrast-to-noise ratio with respect to a medium with diffuse scattering conditions. This was observed for a wide range of spatial resolutions of parametric images, scatterer spacing values, and regardless of distorting the periodic array or reducing the scattering power of the periodic scatterers. Poorest performance was achieved with the Homodyned-K parameters, although their physical insight motivates their use when compounding methods are available or for the task of bulk parameter estimation. These results are to be merged with the detection of coherent scattering with non-stationary features and corresponding scatterer structure descriptors, as well as detection of false coherence from low scatterer number densities.

A Parametric Image Enhancement Technique for Contrast-Enhanced Ultrasonography

Abstract: The transit time of contrast agents and the parameters of time-intensity curves in ultrasonography are important factors to diagnose various diseases of a digestive organ. We have implemented an automatic parametric imaging method to overcome the difficulty of the diagnosis by naked eyes. However, the micro-bubble noise and the respiratory motions may degrade the reliability of the parameter images. In this paper, we introduce an optimization technique based on MRF(Markov Random Field) model to enhance the quality of the parameter images, and present an image tracking algorithm to compensate the image distortion by respiratory motions. A method to extract the respiration periods from the ultrasound image sequence has been developed. We have implemented the ROI(Region of Interest) tracking algorithm using the dynamic weights and a momentum factor based on these periods. An energy function is defined for the Gibbs sampler of the image enhancement method. Through the experiments using the data to diagnose liver lesions, we have shown that the proposed method improves the quality of the parametric images.

Detection of Subresolution Sources of Coherent Scattering for Parametric Image Formation Model-Free vs. Model-Based Parameters

Abstract: This work investigates the performance of speckle statistics parameters to detect coherent scattering with stationary features in echo signals. We focus on the task of parametric image formation for medical applications and the implicit tradeoff between spatial resolution and detection performance. Parameters include the model-free envelope and intensity point- wise SNRs, Nakagami shape parameter m and a Generalized Likelihood Ratio Test T, and the Homodyned-K clustering parameter and the ratio of coherent to incoherent scattering amplitudes. Media with periodic scatterers as a source of coherent scattering embedded in a diffuse scattering background were simulated and mimicked in phantoms. Model-free parameters offered better detection in terms of the parameters' contrast-to-noise ratio with respect to a medium with diffuse scattering conditions. This was observed for a wide range of spatial resolutions of parametric images, scatterer spacing values, and regardless of distorting the periodic array or reducing the scattering power of the periodic scatterers. Poorest performance was achieved with the Homodyned-K parameters, although their physical insight motivates their use when compounding methods are available or for the task of bulk parameter estimation. These results are to be merged with the detection of coherent scattering with non-stationary features and corresponding scatterer structure descriptors, as well as detection of false coherence from low scatterer number densities.

Machine learning based parametric image estimation for Analyzer-based phase contrast imaging

Abstract: An X-ray beam passing through biological tissue is deflected (i.e., refracted) by a small angle typically <10 µrad. Analyzer-based phase contrast imaging (ABI) systems are capable of measuring this tinny refraction by sampling the intensity of the beam at different propagation directions. An Analyzer crystal is the key element for this task as it acts as a narrow angular filter. Since refraction effects are highly dependent of the radiation wavelength, X-ray beam must be quasi-monochromatic. Therefore the amount of photons that reach the object and detector is much lower then that in traditional radiography. Using a reasonable exposure time, noisy reconstructions of refraction images are obtained. In this manuscript, we present a machine learning parametric image estimation approach to obtain accurate refraction images from noisy raw data.