Showing papers on "Parametric Image published in 2020"

Total-Body Dynamic Reconstruction and Parametric Imaging on the uEXPLORER.

Abstract: The world's first 194-cm-long total-body PET/CT scanner (uEXPLORER) has been built by the EXPLORER Consortium to offer a transformative platform for human molecular imaging in clinical research and health care. Its total-body coverage and ultra-high sensitivity provide opportunities for more accurate tracer kinetic analysis in studies of physiology, biochemistry, and pharmacology. The objective of this study was to demonstrate the capability of total-body parametric imaging and to quantify the improvement in image quality and kinetic parameter estimation by direct and kernel reconstruction of the uEXPLORER data. Methods: We developed quantitative parametric image reconstruction methods for kinetic analysis and used them to analyze the first human dynamic total-body PET study. A healthy female subject was recruited, and a 1-h dynamic scan was acquired during and after an intravenous injection of 256 MBq of 18F-FDG. Dynamic data were reconstructed using a 3-dimensional time-of-flight list-mode ordered-subsets expectation maximization (OSEM) algorithm and a kernel-based algorithm with all quantitative corrections implemented in the forward model. The Patlak graphical model was used to analyze the 18F-FDG kinetics in the whole body. The input function was extracted from a region over the descending aorta. For comparison, indirect Patlak analysis from reconstructed frames and direct reconstruction of parametric images from the list-mode data were obtained for the last 30 min of data. Results: Images reconstructed by OSEM showed good quality with low noise, even for the 1-s frames. The image quality was further improved using the kernel method. Total-body Patlak parametric images were obtained using either indirect estimation or direct reconstruction. The direct reconstruction method improved the parametric image quality, having a better contrast-versus-noise tradeoff than the indirect method, with a 2- to 3-fold variance reduction. The kernel-based indirect Patlak method offered image quality similar to the direct Patlak method, with less computation time and faster convergence. Conclusion: This study demonstrated the capability of total-body parametric imaging using the uEXPLORER. Furthermore, the results showed the benefits of kernel-regularized reconstruction and direct parametric reconstruction. Both can achieve superior image quality for tracer kinetic studies compared with the conventional indirect OSEM for total-body imaging.

Design and Implementation of Automated Clinical Whole Body Parametric PET With Continuous Bed Motion

Abstract: We described workflows and methodologies for fully automated parametric imaging using clinical positron emission tomography (PET) systems with continuous bed motion (CBM) which aim to enable physicians to practice parametric PET routinely. The key components of our implemented methods include automatic generation of image-derived blood input functions, accurate tracking of slice imaging time during CBM acquisitions, and parametric image formation with advanced algorithms. Locations of the left ventricle and the descending aorta were automatically detected from high resolution anatomical images and registered to dynamic PET images to generate blood input functions. A method to accurately track dynamic scans and calculate time information for whole body CBM parametric PET was implemented based on bed position tags. This approach of calculating time information based on finely sampled bed position tags can be applied to flexible scan modes with various scan speeds over different body regions. We applied the calculated slice time information for whole body parametric imaging using a linear Patlak model. Parametric images were reconstructed with a nested expectation maximization (EM) algorithm. We demonstrated that parametric images generated by our automated workflow can provide quantitative parameters in addition to standardized uptake value images. We presented results for a uni-direction dynamic scan from a Biograph mCT system and a bi-direction dynamic scan from a Biograph Vision system.

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images

Abstract: Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from high levels of noise, drastically reducing the precision of parametric image analysis. In this paper, we investigate the use of machine learning and artificial neural networks to denoise dynamic PET images. We train a deep denoising autoencoder (DAE) using noisy and noise-free spatiotemporal image patches, extracted from the simulated images of [11C]raclopride, a dopamine D2 receptor agonist. The DAE-processed dynamic and corresponding parametric images (simulated and acquired) are compared with those obtained with conventional denoising techniques, including temporal and spatial Gaussian smoothing, iterative spatiotemporal smoothing/deconvolution, and the highly constrained backprojection processing (HYPR). The simulated (acquired) parametric image non-uniformity was 7.75% (19.49%) with temporal and 5.90% (14.50%) with spatial smoothing, 5.82% (16.21%) with smoothing/deconvolution, 5.49% (13.38%) with HYPR, and 3.52% (11.41%) with DAE. The DAE also produced the best results in terms of the coefficient of variation of voxel values and structural similarity index. Denoising-induced bias in the regional mean binding potential was 7.8% with temporal and 26.31% with spatial smoothing, 28.61% with smoothing/deconvolution, 27.63% with HYPR, and 14.8% with DAE. When the test data did not match the training data, erroneous outcomes were obtained. Our results demonstrate that a deep DAE can provide a substantial reduction in the voxel-level noise compared with the conventional spatiotemporal denoising methods while introducing a similar or lower amount of bias. The better DAE performance comes at the cost of lower generality and requiring appropriate training data.

All-Pass Parametric Image Registration

Abstract: Image registration is a required step in many practical applications that involve the acquisition of multiple related images. In this paper, we propose a methodology to deal with both the geometric and intensity transformations in the image registration problem. The main idea is to modify an accurate and fast elastic registration algorithm (Local All-Pass—LAP) so that it returns a parametric displacement field, and to estimate the intensity changes by fitting another parametric expression. Although we demonstrate the methodology using a low-order parametric model, our approach is highly flexible and easily allows substantially richer parametrisations, while requiring only limited extra computation cost. In addition, we propose two novel quantitative criteria to evaluate the accuracy of the alignment of two images (“salience correlation”) and the number of degrees of freedom (“parsimony”) of a displacement field, respectively. Experimental results on both synthetic and real images demonstrate the high accuracy and computational efficiency of our methodology. Furthermore, we demonstrate that the resulting displacement fields are more parsimonious than the ones obtained in other state-of-the-art image registration approaches.

Direct List Mode Parametric Reconstruction for Dynamic Cardiac SPECT

Abstract: Recently introduced stationary dedicated cardiac SPECT scanners provide new opportunities to quantify myocardial blood flow (MBF) using dynamic SPECT. However, comparing to PET, the low sensitivity of SPECT scanners affects MBF quantification due to the high noise level, especially for 201 Thallium (201Tl) due to its typically low injected dose. The conventional indirect method for generating parametric images typically starts by reconstructing a time series of frame images followed by fitting the time-activity curve (TAC) for each voxel or segment with an appropriate kinetic model. The indirect method is simple and easy to implement; however, it usually suffers from substantial image noise that could also lead to bias. In this paper, we developed a list mode direct parametric image reconstruction algorithm to substantially reduce noise in MBF quantification using dynamic SPECT and allow for patient radiation dose reduction. GPU-based parallel computing was used to achieve more than 2000-fold acceleration. The proposed method was evaluated in both simulation and in vivo canine studies. Compared with the indirect method, the proposed direct method achieved substantially lower image noise and variability, particularly at large number of iterations and at low-count levels.

Kernel-based curve-fitting method with spatial regularization for generation of parametric images in dynamic PET.

Abstract: Due to high levels of noise in pixel-wise time-activity curves, the indirect method that generates kinetic parametric images from dynamic positron emission tomography (PET) images often results in poor parametric image quality. We have demonstrated that the quality of parametric images can be improved by denoising dynamic PET images, using gradient-free curve-fitting and applying a kernel-based post-filtering to parametric images. However, many gradient-free curve-fitting methods are time-consuming. Moreover, some parameter estimates (e.g. k2 and k3) have large variability. To provide high-quality PET parametric images with low computational cost, we propose a curve-fitting method that incorporates the kernel-based denoising method and the highly constrained backprojection technique into the Levenberg-Marquardt (LM) algorithm. We conducted a simulation study to evaluate the performance of the proposed curve-fitting method. Dynamic PET images were reconstructed using the expectation-maximization (EM) algorithm and were denoised before parameter estimation. Compared to the LM algorithm with and without the kernel-based post-filtering, the proposed method achieved superior performance, offering a decrease in both bias and coefficient of variation (CV) on all parametric images. Overall, the proposed method exhibited lower bias and slightly higher CV than the gradient-free pattern search method with the kernel-based post-filtering (PatS-K). Moreover, the computation time of the proposed method was about 18 times lower than that of the PatS-K method. Finally, we show that the proposed method can further improve the quality of parametric images when dynamic PET images are reconstructed using the kernel-based EM algorithm.

Non-Parametric Clustering Using Deep Neural Networks

Abstract: In this paper, a novel algorithm for non-parametric image clustering, is proposed. Non-parametric clustering methods operate by considering the number of clusters unknown as opposed to parametric clustering, where the number of clusters is known a priori. In the present work, a deep neural network is trained, in order to decide whether an arbitrary sized group of elements can be considered as a unique cluster or it consists of more than one clusters. Using this trained neural network as clustering criterion, an iterative algorithm is built, able to cluster any given dataset. Evaluation of the proposed method on several public datasets shows that the proposed method is either on par or outperforms state-of-the-art methods even when compared to parametric image clustering methods. The proposed method is additionally able to correctly cluster input samples from a completely different dataset than the one it has been trained on, as well as data coming from different modalities. Results on cross-dataset clustering show evidence of the generalization potential of the proposed method.

Parametric Image-based Breast Tumor Classification Using Convolutional Neural Network in the Contourlet Transform Domain

Abstract: Automated visual identification of Benign and Malignant breast tumors even with ultrasound (US) B-Mode image is still an open area of research. This paper presents parametric image-based approach to classify and detect benign and malignant breast tumors from ultrasound images using a custom-made convolutional neural network architecture. The Rician Inverse Gaussian (RiIG) distribution is presented here as a suitable model for describing the statistics of the ultrasound images in the Contourlet Transform domain. Locally computed values of the dispersion parameters of the RiIG distribution in various contourlet sub-bands yield parametric images those are classified using the proposed convolutional neural network. Experiments are conducted on a publicly available dataset of 250 images, of 100 belong to the benign Fibroadenoma and 150 in the malignant category. The proposed method provides accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV) of 96%, 97.3%, 94.12%, 96% and 96%, respectively. It is also shown that the accuracy obtained by the Proposed Method is higher than several recently reported results.

Nested Parametric Image Reconstruction using Time-of-Flight PET Histoimages

Abstract: Due to limited counts in voxel-wise time activity curves, the indirect methods that generate kinetic parametric image from dynamic PET reconstructions often have poor image quality. The TOF PET data can be naturally and efficiently stored in histoimage, and 4D tracer distribution can be efficiently reconstructed using the DIRECT (Direct Image REConstruction for Tof) approaches. We aim to develop efficient dynamic/parametric reconstruction with improved quantitative quality from time-of-flight PET data by taking advantage of its intrinsic kinetic models in our DIRECT frameworks. We have implemented volume of interest (VOI) based and voxel-wise parametric fitting using the linearized Patlak model. We further modified DIRECT reconstructions to support 4D nested reconstruction approach with interleaving tomographic reconstruction and parametric fitting at each iteration. To evaluate proposed dynamic reconstruction approaches, we generate 4D dynamic data sets using the synthetic lesion embedding technique. First, a human subject injected with FDG was scanned on the PennPET Explorer scanner configured with 70 cm axial FOV. Then lung and liver lesions were synthetically embedded using pre-scanned sphere data sets with predetermined time-activity curves. We showed that VOI based method can accurately estimate the Patlak parameters from the frame based reconstructions after corrections. The 4D nested DIRECT reconstruction with Patlak fitting can substantially reduce noise in the reconstructed image frames and provide efficient and feasible tool for direct reconstruction of kinetic parametric image.

Direct Estimation of Input Function Based on Fine-tuned Deep Learning Method in Dynamic PET Imaging

Abstract: 1394 Objectives: Dynamic PET provides more accurate quantitative information and richer disease information than static PET. However, routine clinical application of dynamic PET imaging is limited by invasive arterial blood sampling (or manually annotated image-derived blood activity) for use as an input function. This study aimed to develop a deep learning-based method to directly estimate the input function from dynamic PET data without any manual assistance. Methods: Our study consists of two clinical datasets. The first group of data includes dynamic FDG scans performed on the United Imaging uMI 510 PET/CT system in the Chinese PLA General Hospital. 35 subjects (25 male, 10 females; 13 healthy; age 15-73y) were scanned. Image-derived blood activity was used as input function. The second dataset (n= 26 healthy subjects) includes 90-min dynamic brain data after bolus injection of 11C-DPA-713(DPA) performed on a Siemens HRRT PET system at Johns Hopkins University. All DPA PET data in this study were acquired from individuals with high affinity binding genotype for the 18 kDa translocator protein target and included input function data acquired through arterial blood sampling and radiometabolite measurements. There were 30 dynamic frames in each scan. The input function value for each dynamic frame was interpolated from the measured input function curve. Each dataset was split into 3 parts: 70% as training set, 20% as validation set and 10% as test set. Two deep learning networks, namely the raw model and the fine-tuned model were investigated. The raw model contains a down-sampling convolutional module to extract image features and a fully connected regression module to predict the input function. The 3-D dynamic image and reference image are stacked to form a two-channel 4-D input. For FDG tracer, the reference image refers to image reconstructed from data acquired in 0 to 20 minutes scan. For DPA, the mean image of all dynamic images served as the reference image. L1-loss is chosen as the loss function based on controlled experiment results. In the fine-tuned model, the 1-D vector acquired from the convolutional module are concatenated with medical information including patient’s age, weight, height, injected dose, frame time. Data augmentation methods such as random center crop & resize, rotation, translation and scaling were used in training fine-tuned model. The input function for DPA was normalized according to its integral value before reconstruction. Indirect voxel-based reconstruction methods were used: Patlak analysis with start time (t*) of 20 minutes for Ki (FDG) and Logan analysis with t* of 30 minutes for VT (DPA). To investigate the performance of the proposed method, the root mean square error (RMSE) was calculated between the parametric images reconstructed with true and predicted input function. Results: The predicted input function shows similarity in shape to the standard input function. The prediction error in early stage was larger than in later stage, especially for the peak values in early stage. The fine-tuned model enhances prediction accuracy in the early stage, while the improvement in later stage was not obvious. The visual quality of reconstructed Ki and VT parametric image of the fine-tuned model was apparently better than that of raw model. The RMSE of the fine-tuned model was much lower than the raw model. Conclusions: The proposed fine-tuned deep learning-based method was able to estimate the input function directly from dynamic images. The high accuracy of the reconstructed parametric image using the predicted input function supports pursuit of this method for clinical application. In future work we will investigate to further improve the accuracy of the predicted input function. Support: The research was supported by the National Natural Science Foundation of China (No. 81727807, No.11575096, No. 11605008) and National Key Research and Development (R&D) Plan of China (Grant ID. 2019YFF0302503 and 2016YFC0105405).

Feasibility of Nakagami parametric image for texture analysis

Abstract: Purpose: Evaluate the feasibility of using a Nakagami model to create an accurate parametric image from ultrasound imaging data for the differentiation of homogenous and heterogeneous texture phantoms. Analysis was done on the raw data i.e., radiofrequency (RF) data collected before any post processing that can affect the images. Materials and methods: The Nakagami parametric image was constructed on demodulated RF data with the sliding window technique to create a map of local parameters. The Nakagami parameter (m) for the entire image was found by averaging all values. By design, when m is greater than 1, the distribution is post-Rayleigh. When m is equal to 1, the distribution is Rayleigh. To test the technique, two agar phantoms were constructed, using varying amounts of flour as the scatterer. The higher amount of flour scatterer was meant to mimic heterogeneous texture and the lesser amount meant to mimic homogeneous texture. Scans were done on each phantom and analyzed for differences in the Nakagami parameter. Results: Phantom 1 displayed a post-Rayleigh distribution (m = 36.1±7.0), while phantom 2 did so, to a lesser extent (m = 1.64±0.12). As the distribution transitions from Rayleigh to post Rayleigh, the scatterers in the sample go from being periodically located/randomly distributed to large numbers of randomly distributed scatterers. Conclusion: Our study suggests that Nakagami parametric based metrics may be used to increase robustness of texture analysis, considering the analysis is done on the raw data before any post processing that can affect the images is introduced.

Ultrasonic tomographic reconstruction considering wood anisotropy

Abstract: We proposed an approach to solve the inverse problem for tree imaging, using a method based on ultrasound travel-time tomography and adapted to the anisotropy of wood material. The proposed inversion procedure was developed between 2015-2018. The method considers the dependency of the wave velocity on the angle of propagation and the wood elastic constants, as described by the Christoffel equation. This dependency leads to curved trajectories (or rays), that are followed by the wave from the source to the receivers, affecting the time-of-flight (TOF) measurements. A first inversion is performed considering an hypothesis of straight-line rays, using an algebraic reconstruction algorithm (SIRT), adapting the matrix formulation to obtain the slowness for every pixel as a polynomial approximation of the Christoffel equation. Then, the obtained set of slowness functions are used to perform the direct (or forward) problem, simulating the wave propagation using a raytracing approach. This simulation allows to estimate the curved trajectories and synthetic TOF values, that are then compared to the experimental measurements. This process is repeated iteratively until convergence. From the final iteration, the polynomial approximations of the Christoffel equations for each pixel are used to obtain the elastic constants via a non-linear regression. Finally, the parametric image is presented. The data folder contains the time-of-flight measurements (in seconds) and the sensors positions (in meters) from an experimental setting using pine samples. A photography of the cross-section is included. TOF values are organized as a matrix (sinogram), where columns correspond to each source, and receivers to each row. Sensors positions are presented as a column vector, specifying the [X,Y] coordinates. Three different configurations were tested: (1) a healthy case, (2) a centered defect case and (3) an off-centered defect case. Defects were simulated by drilling a circular hole of diameter 7.6 cm. The forward folder contains the code to perform the raytracing simulation (direct problem), used in the iterative inversion schema. The main file is rayPix.m, that performs the raytracing. The inverse folder contains the code to perform the image reconstruction (inverse problem). The main file is optimFinal.m, where the iterative schema was implemented. To perform an inversion, the directives are in mainInv.m

Residual Simplified Reference Tissue Model with Covariance Estimation

Abstract: The simplified reference tissue model (SRTM) can robustly estimate binding potential (BP) without a measured arterial blood input function. Although a voxel-wise estimation of BP, so-called parametric image, is more useful than the region of interest (ROI) based estimation of BP, it is challenging to calculate the accurate parametric image due to lower signal-to-noise ratio (SNR) of dynamic PET images. To achieve reliable parametric imaging, temporal images are commonly smoothed prior to the kinetic parameter estimation, which degrades the resolution significantly. To address the problem, we propose a residual simplified reference tissue model (ResSRTM) using an approximate covariance matrix to robustly compute the parametric image with a high resolution. We define the residual dynamic data as full data except for each frame data, which has higher SNR and can achieve the accurate estimation of parametric image. Since dynamic images have correlations across temporal frames, we propose an approximate covariance matrix using neighbor voxels by assuming the noise statistics of neighbors are similar. In phantom simulation and real experiments, we demonstrate that the proposed method outperforms the conventional SRTM method.