Showing papers on "Parametric Image published in 2017"

Isotropic Total Variation Regularization of Displacements in Parametric Image Registration.

Abstract: Spatial regularization is essential in image registration, which is an ill-posed problem. Regularization can help to avoid both physically implausible displacement fields and local minima during optimization. Tikhonov regularization (squared l2 -norm) is unable to correctly represent non-smooth displacement fields, that can, for example, occur at sliding interfaces in the thorax and abdomen in image time-series during respiration. In this paper, isotropic Total Variation (TV) regularization is used to enable accurate registration near such interfaces. We further develop the TV-regularization for parametric displacement fields and provide an efficient numerical solution scheme using the Alternating Directions Method of Multipliers (ADMM). The proposed method was successfully applied to four clinical databases which capture breathing motion, including CT lung and MR liver images. It provided accurate registration results for the whole volume. A key strength of our proposed method is that it does not depend on organ masks that are conventionally required by many algorithms to avoid errors at sliding interfaces. Furthermore, our method is robust to parameter selection, allowing the use of the same parameters for all tested databases. The average target registration error (TRE) of our method is superior (10% to 40%) to other techniques in the literature. It provides precise motion quantification and sliding detection with sub-pixel accuracy on the publicly available breathing motion databases (mean TREs of 0.95 mm for DIR 4D CT, 0.96 mm for DIR COPDgene, 0.91 mm for POPI databases).

Parametric net influx rate images of 68Ga-DOTATOC and 68Ga-DOTATATE: quantitative accuracy and improved image contrast

Abstract: 68Ga-DOTATOC and 68Ga-DOTATATE are radiolabeled somatostatin analogs used for the diagnosis of somatostatin receptor-expressing neuroendocrine tumors (NETs), and SUV measurements are suggested for treatment monitoring However, changes in net influx rate (Ki) may better reflect treatment effects than those of the SUV, and accordingly there is a need to compute parametric images showing Ki at the voxel level The aim of this study was to evaluate parametric methods for computation of parametric Ki images by comparison to volume of interest (VOI)-based methods and to assess image contrast in terms of tumor-to-liver ratio Methods: Ten patients with metastatic NETs underwent a 45-min dynamic PET examination followed by whole-body PET/CT at 1 h after injection of 68Ga-DOTATOC and 68Ga-DOTATATE on consecutive days Parametric Ki images were computed using a basis function method (BFM) implementation of the 2-tissue-irreversible-compartment model and the Patlak method using a descending aorta image-derived input function, and mean tumor Ki values were determined for 50% isocontour VOIs and compared with Ki values based on nonlinear regression (NLR) of the whole-VOI time-activity curve A subsample of healthy liver was delineated in the whole-body and Ki images, and tumor-to-liver ratios were calculated to evaluate image contrast Correlation (R2) and agreement between VOI-based and parametric Ki values were assessed using regression and Bland-Altman analysis Results: The R2 between NLR-based and parametric image-based (BFM) tumor Ki values was 098 (slope, 081) and 097 (slope, 088) for 68Ga-DOTATOC and 68Ga-DOTATATE, respectively For Patlak analysis, the R2 between NLR-based and parametric-based (Patlak) tumor Ki was 095 (slope, 071) and 092 (slope, 074) for 68Ga-DOTATOC and 68Ga-DOTATATE, respectively There was no bias between NLR and parametric-based Ki values Tumor-to-liver contrast was 16 and 20 times higher in the parametric BFM Ki images and 23 and 30 times in the Patlak images than in the whole-body images for 68Ga-DOTATOC and 68Ga-DOTATATE, respectively Conclusion: A high R2 and agreement between NLR- and parametric-based Ki values was found, showing that Ki images are quantitatively accurate In addition, tumor-to-liver contrast was superior in the parametric Ki images compared with whole-body images for both 68Ga-DOTATOC and 68Ga DOTATATE

Total variation–based method for generation of intravoxel incoherent motion parametric images in MRI

Abstract: Purpose Total variation (TV) method has been used widely for image restoration and reconstruction In this work, we propose a TV-based algorithm for parametric image generation in intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (DW-MRI) Methods We used simulated and real data to investigate whether the proposed TV-based method can provide reliable parametric images Parametric images of IVIM parameters including perfusion fraction (PF), diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were estimated using DW-MRI data and TV through fitting the IVIM model The Levenberg-Marquardt (LM) method, which has often been used in the context of IVIM analysis, was employed as the standard method for comparison of the resulting parametric images Results The simulation results show that the proposed method outperforms the LM algorithm in terms of precision, providing a 40–81%, 90–93%, and 68–84% improvement for PF, D and D*, respectively, at signal-to-noise ratio (SNR) of 30 For real data, the proposed method showed an average five-fold, three-fold, and four-fold improvement in the SNR for PF, D and D*, respectively Conclusion We introduced the use of TV to produce parametric images, and demonstrated that the proposed TV-based method is effective in improving the parametric image quality Magn Reson Med, 2016 © 2016 International Society for Magnetic Resonance in Medicine

Simplified estimation of binding parameters based on image-derived reference tissue models for dopamine transporter bindings in non-human primates using [18F]FE-PE2I and PET.

Abstract: The aim of this study on dopamine transporter binding by [18F]FE-PE2I and PET was to describe an image-derived approach using reference tissue models: the Logan DVR approach and simplified reference tissue model (SRTM), the features of which were simple to operate and precise in the measurements. Using the approach, the authors sought to obtain binding images and parameters. [18F]FE-PE2I and dynamic PET as well as an MRI was performed on three rhesus monkeys, and metabolite corrected arterial plasma inputs were obtained. After co-registering of PET to MR images, both image sets were resliced. The time-activity curve of the cerebellum was used as indirect input, and binding parametric images were computed voxel-by-voxel. Voxel-wise linear calculations were used for the Logan DVR approach, and nonlinear least squares fittings for the SRTM. To determine the best linear regression in the Logan DVR approach, the distribution volume ratio was obtained using the optimal starting frame analysis. The obtained binding parameters were compared with those obtained by the other independent ROI-based numerical approaches: two-tissue compartment model (2TCM), Logan DVR approach and SRTM using PMOD software. Binding potentials (BP) obtained by the present approach agreed well with those obtained by ROI-based numerical approaches, although reference tissue models tended to underestimate the BP value than 2TCM. Image-derived Logan approach provided a low-noise image, the computation time was short, and the error in the optimal starting frame analysis was small. The present approach provides a high-quality binding parametric image and reliable parameter value easily.

Nonlinear PET parametric image reconstruction with MRI information using kernel method

Abstract: Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.

PET Image Reconstruction: Methodology and Quantitative Accuracy

Abstract: This chapter reviews the techniques developed for positron emission tomography (PET) image reconstruction and image property analysis. Both mathematical theory and practical considerations are introduced. We focus on the commonly used methods on commercial PET scanners, in particular model-based statistical reconstruction methods. We also briefly describe data corrections necessary for PET image reconstruction, which are important for reducing artifacts and improving quantitative accuracy. Finally some recent developments are described, including the reconstruction of time-of-flight (TOF) PET data and direct parametric image reconstruction.

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.

Unifying framework for decomposition models of parametric and non-parametric image registration

Abstract: Image registration aims to find spatial transformations such that the so-called given template image becomes similar in some sense to the reference image. Methods in image registration can be divided into two classes (parametric or non-parametric) based on the degree of freedom of the given method. In parametric image registration, the transformation is governed by a finite set of image features or by expanding the transformation in terms of basis functions. Meanwhile, in non-parametric image registration, the problem is modelled as a functional minimisation problem via the calculus of variations. In this paper, we provide a unifying framework for decomposition models for image registration which combine parametric and non-parametric models. Several variants of the models are presented with focus on the affine, diffusion and linear curvature models. An effective numerical solver is provided for the models as well as experimental results to show the effectiveness, robustness and accuracy of the models. The...

GPU-based List-mode Direct Parametric Reconstruction for Dynamic Cardiac SPECT

Abstract: Noninvasive quantification of myocardial blood flow (MBF) using Single-Photon Emission Computed Tomography (SPECT) is an important clinical tool for the diagnosis and characterization of coronary artery disease. Current indirect MBF estimation approach using SPECT suffers from inaccurate quantification due to substantial image noise. Existing direct parametric reconstruction methods for conventional parallel-hole rotational scanners estimate kinetic parameters only for specific ROIs. In this paper we developed an innovative voxel-by-voxel based direct parametric image reconstruction algorithm to substantially reduce noise in MBF quantification using dynamic SPECT/CT and allow for patient radiation dose reduction. GPU-based parallel computing was used to achieve >2000-fold acceleration. The proposed method was evaluated in both simulation and in-vivo studies. Compared with the indirect method, the direct method achieved substantially lower image noise, particularly at high iterations and with low count levels.

Method for forming composite parameter image from series of angiographic digital subtraction frames

Abstract: FIELD: medicine.SUBSTANCE: in a series of angiographic digital subtraction frames, sets of diagnostic significant subtraction frames are isolated. For each set of diagnostically significant subtraction frames, a parametric image is formed taking into account a colour or halftone scale synchronized with reference time points selected in accordance with the phases of the physiological cycles in the patient's body. A composite image for a series of angiographic digital subtraction frames is formed by combining the values of elements located at coincident positions in parametric images.EFFECT: method allows to improve the visualization quality of the patient's vascular system by preserving the repeatability of the result of encoding the parametric image, eliminating the loss of information about the vessels in the places where their projections are placed, and increasing the contrast of images of the arteries and veins in the composite parametric image.5 cl, 6 dwg

Slice-to-volume parametric image registration models with applications to MRI-guided cardiac procedures

Abstract: A mathematical formulation for intensity-based slice-to-volume registration is proposed. The approach is flexible and accommodates various regularization schemes, similarity measures, and optimizers. The framework is evaluated by registering 2D and 3D cardiac magnetic resonance (MR) images obtained in vivo, aimed at real- time MR-guided applications. Rigid-body and affine transformations are used to validate the parametric model. Target registration error (TRE), Jaccard, and Dice indices are used to evaluate the algorithm and demonstrate the accuracy of the registration scheme on both simulated and clinical data. Registration with the affine model appeared to be more robust than with the rigid model in controlled cases. By simply extending the rigid model to an affine model, alignment of the cardiac region generally improved, without the need for complex dissimilarity measures or regularizers.