Image Quality Estimation Research Articles

Prospective motion detection and re-acquisition in diffusion MRI using a phase image-based method-Application to brain and tongue imaging.

To develop an image-based motion-robust diffusion MRI (dMRI) acquisition framework that is able to minimize motion artifacts caused by rigid and nonrigid motion, applicable to both brain and tongue dMRI. We developed a novel prospective motion-correction technique in dMRI using a phase image-based real-time motion-detection method (PITA-MDD) with re-acquisition of motion-corrupted images. The prospective PITA-MDD acquisition technique was tested in the brains and tongues of volunteers. The subjects were instructed to move their heads or swallow, to induce motion. Motion-detection efficacy was validated against visual inspection as the gold standard. The effect of the PITA-MDD technique on diffusion-parameter estimates was evaluated by comparing reconstructed fiber tracts using tractography with and without re-acquisition. The prospective PITA-MDD technique was able to effectively and accurately detect motion-corrupted data as compared with visual inspection. Tractography results demonstrated that PITA-MDD motion detection followed by re-acquisition helps in recovering lost and misshaped fiber tracts in the brain and tongue that would otherwise be corrupted by motion and yield erroneous estimates of the diffusion tensor. A prospective PITA-MDD technique was developed for dMRI acquisition, providing improved dMRI image quality and motion-robust diffusion estimation of the brain and tongue.

Radioprotection of eye lens using protective material in neuro cone-beam computed tomography: Estimation of dose reduction rate and image quality.

In cerebral angiography, for diagnosis and interventional neuroradiology, cone-beam computed tomography (CBCT) scan is frequently performed for evaluating brain parenchyma, cerebral hemorrhage, and cerebral infarction. However, the patient's eye lens is more frequently exposed to excessive doses in these scans than in the previous angiography and interventional neuroradiology (INR) procedures. Hence, radioprotection for the lenses is needed. This study selects the most suitable eye lens protection material for CBCT from among nine materials by evaluating the dose reduction rate and image quality. To determine the dose reduction rate, the lens doses were measured using an anthropomorphic head phantom and a real-time dosimeter. For image quality assessment, the artifact index was calculated based on the pixel value and image noise within various regions of interest in a water phantom. The protective materials exhibited dose reduction; however, streak artifacts were observed near the materials. The dose reduction rate and the degree of the artifact varied significantly depending on the protective material. The dose reduction rates were 14.6%, 14.2%, and 26.0% when bismuth shield: normal (bismuth shield in the shape of an eye mask), bismuth shield: separate (two separate bismuth shields), and lead goggles were used, respectively. The "separate" bismuth shield was found to be effective in dose reduction without lowering the image quality. We found that bismuth shields and lead goggles are suitable protective devices for the optimal reduction of lens doses.

Stopping Criterion during Rendering of Computer-Generated Images Based on SVD-Entropy.

The estimation of image quality and noise perception still remains an important issue in various image processing applications. It has also become a hot topic in the field of photo-realistic computer graphics where noise is inherent in the calculation process. Unlike natural-scene images, however, a reference image is not available for computer-generated images. Thus, classic methods to assess noise quantity and stopping criterion during the rendering process are not usable. This is particularly important in the case of global illumination methods based on stochastic techniques: They provide photo-realistic images which are, however, corrupted by stochastic noise. This noise can be reduced by increasing the number of paths, as proved by Monte Carlo theory, but the problem of finding the right number of paths that are required in order to ensure that human observers cannot perceive any noise is still open. Until now, the features taking part in the human evaluation of image quality and the remaining perceived noise are not precisely known. Synthetic image generation tends to be very expensive and the produced datasets are high-dimensional datasets. In that case, finding a stopping criterion using a learning framework is a challenging task. In this paper, a new method for characterizing computational noise for computer generated images is presented. The noise is represented by the entropy of the singular value decomposition of each block composing an image. These Singular Value Decomposition (SVD)-entropy values are then used as input to a recurrent neural network architecture model in order to extract image noise and in predicting a visual convergence threshold of different parts of any image. Thus a new no-reference image quality assessment is proposed using the relation between SVD-Entropy and perceptual quality, based on a sequence of distorted images. Experiments show that the proposed method, compared with experimental psycho-visual scores, demonstrates a good consistency between these scores and stopping criterion measures that we obtain.

On the use of low-dimensional temporal subspace constraints to reduce reconstruction time and improve image quality of accelerated 4D-MRI

Background and purposeThe purpose of this work is to investigate the use of low-dimensional temporal subspace constraints for 4D-MRI reconstruction from accelerated data in the context of MR-guided online adaptive radiation therapy (MRgOART). Materials and methodsSubspace basis functions are derived directly from the accelerated golden angle radial stack-of-stars 4D-MRI data. The reconstruction times, image quality, and motion estimates are investigated as a function of the number of subspace coefficients and compared with a conventional frame-by-frame reconstruction. These experiments were performed in five patients with four 4D-MRI scans per patient on a 1.5T MR-Linac. ResultsIf two or three subspace coefficients are used, the iterative reconstruction time is reduced by 32% and 18%, respectively, compared to conventional parallel imaging with compressed sensing reconstructions. No significant difference was found between motion estimates made with the subspace-constrained reconstructions (p > 0.08). Qualitative improvements in image quality included reduction in apparent noise and reductions in streaking artifacts from the radial k-space coverage. ConclusionIncorporating subspace constraints for accelerated 4D-MRI reconstruction reduces noise and residual undersampling artifacts in the images while reducing computation time, making it a strong candidate for use in clinical MRgOART workflows.

Automated estimation of echocardiogram image quality in hospitalized patients.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients.This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

Multiperspective Ultrasound Strain Imaging of the Abdominal Aorta.

Current decision-making for clinical intervention of abdominal aortic aneurysms (AAAs) is based on the maximum diameter of the aortic wall, but this does not provide patient-specific information on rupture risk. Ultrasound (US) imaging can assess both geometry and deformation of the aortic wall. However, low lateral contrast and resolution are currently limiting the precision of both geometry and local strain estimates. To tackle these drawbacks, a multiperspective scanning mode was developed on a dual transducer US system to perform strain imaging at high frame rates. Experimental imaging was performed on porcine aortas embedded in a phantom of the abdomen, pressurized in a mock circulation loop. US images were acquired with three acquisition schemes: Multiperspective ultrafast imaging, single perspective ultrafast imaging, and conventional line-by-line scanning. Image registration was performed by automatic detection of the transducer surfaces. Multiperspective images and axial displacements were compounded for improved segmentation and tracking of the aortic wall, respectively. Performance was compared in terms of image quality, motion tracking, and strain estimation. Multiperspective compound displacement estimation reduced the mean motion tracking error over one cardiac cycle by a factor 10 compared to conventional scanning. Resolution increased in radial and circumferential strain images, and circumferential signal-to-noise ratio (SNRe) increased by 10 dB. Radial SNRe is high in wall regions moving towards the transducer. In other regions, radial strain estimates remain cumbersome for the frequency used. In conclusion, multiperspective US imaging was demonstrated to improve motion tracking and circumferential strain estimation of porcine aortas in an experimental set-up.

High-rate X-ray spectroscopy in mammography with photon counting detectors using a limited number of energy bins

Direct measurement of mammographic X-ray spectra under clinical conditions can be useful for quality control (QC) in mammography for estimating image quality and patient dose. The measurement of mammographic X-ray spectra using the traditional detectors with multi-channel pulse height analyzer (MCA) systems is challenging because of the limitations imposed on measurements with high energy resolution at high photon counting rates, as well as geometrical restrictions, especially in a hospital environment. Semiconductor photon-counting detectors equipped with faster electronics enable utilization in somewhat high count rate situations at the expense of decreased energy resolution. This work presents a method for estimating mammography X-ray spectra from measurements acquired with a multi-energy bin photon counting detector. Simulation tests were conducted out for different incident X-ray spectra, energy thresholds and for multiple noise realizations assuming an ideal photon-counting X-ray detector with three energy thresholds. In addition, experimental measurements of mammography X-ray spectra were estimated by using a high-count rate Gallium Arsenide (GaAs) photon counting detector (with two or four energy thresholds) and a high resolution X-ray spectroscopy detector with Cadmium Telluride (CdTe) sensor for comparison. Differences between half-value layer values (HVL) obtained from the estimated spectra using the proposed method were found to be within 0.04 mm of the true values. This proposed method could be an attractive tool for laboratory research and calibration of photon-counting detectors.

Natural scene statistics model independent no-reference image quality assessment using patch based discrete cosine transform

Most of no-reference image quality assessment (NR-IQA) techniques reported in literature have utilized transform coefficients, which are modeled using curve fitting to extract features based on natural scene statistics (NSS). The performance of NR-IQA techniques that utilize curve-fitting suffers from degradation in performance because the distribution of curve fitted NSS features deviate from the statistical distribution of a distorted image. Although deep convolutional neural networks (DCNNs) have been used for NR-IQA that are NSS model-independent but their performance is dependent upon the size of training data. The available datasets for NR-IQA are small, therefore data augmentation is used that affects the performance of DCNN based NR-IQA techniques and is also computationally expensive. This work proposes a new patch-based NR-IQA technique, which utilizes features extracted from discrete cosine transform coefficients. The proposed technique is curve fitting independent and helps in avoiding errors in the statistical distribution of NSS features. It relies on global statistics to estimate image quality based on local patches, which allow us to decompose the statistics of images. The proposed technique divides the image into patches and extracts nine handcrafted features i.e., entropy, mean, variance, skewness, kurtosis, mobility, band power, energy, complexity, and peak to peak value. The extracted features are used with a support vector regression model to predict the image quality score. The experimental results have shown that the proposed technique is database and image content-independent. It shows better performance over a majority of distortion types and on images taken in real-time.

The Effect of Scan Length on the Assessment of BOLD Delay in Ischemic Stroke.

Objectives: To evaluate the impact of resting-state functional MRI scan length on the diagnostic accuracy, image quality and lesion volume estimation of BOLD delay maps used for brain perfusion assessment in acute ischemic stroke.Methods: Sixty-three acute ischemic stroke patients received a 340 s resting-state functional MRI within 24 h of stroke symptom onset. BOLD delay maps were calculated from the full scan and four shortened versions (68 s, 136 s, 204 s, 272 s). The BOLD delay lesions on these maps were compared in terms of spatial overlap and volumetric agreement with the lesions derived from the full scans and with time-to-maximum (Tmax) lesions derived from DSC-MRI in a subset of patients (n = 10). In addition, the interpretability and quality of these maps were compared across different scan lengths using mixed models.Results: Shortened BOLD delay scans showed a small volumetric bias (ranging from 0.05 to 5.3 mL; between a 0.13% volumetric underestimation and a 7.7% overestimation relative to the mean of the volumes, depending on scan length) compared to the full scan. Decreased scan length was associated with decreased spatial overlap with both the BOLD delay lesions derived from the full scans and with Tmax lesions. Only the two shortest scan lengths (68 and 136 s) were associated with substantially decreased interpretability, decreased structure clarity, and increased noisiness of BOLD delay maps.Conclusions: BOLD delay maps derived from resting-state fMRI scans lasting 272 and 204 s provide sufficient diagnostic quality and adequate assessment of perfusion lesion volumes. Such shortened scans may be helpful in situations where quick clinical decisions need to be made.

Off-the-shelf Convolutional Neural Network (CNN) features for Automatic Face Quality Prediction

Estimation of face image quality helps in correctly recognizing faces which in turn helps in many practical applications related to face. This paper presents a face quality prediction approach using Off-the-Shelf CNN features. Here we evaluated three image descriptors-binary patterns (LBP), Histogram of oriented gradients (HOG), Oriented Fast and Rotated Brief (ORB), and deep Convolution Neural Network (CNN) Networks pre-trained on ImageNet-VGG19, ResNet50, and VGG Small (4 layers) for feature extraction to detect face region image quality. Furthermore, to classify extracted features, we have evaluated three classifiers, that are different from each other in their own ways (SVM, DT and MLP) For experimental analysis, we created a face quality dataset by collecting images from web and publicly available face datasets and manually labeled images under seven categories-Good and six bad quality classes (e.g. Expression, Makeup, Pose, Occlusion, Illumination and Blur). The accuracy of face image classification using VGG19 along with MLP as a classifier was the highest (i.e.98.76%) followed by ResNet50 and MLP at 98.69% of accuracy. The lowest accuracy was obtained with LBP and SVM, this shows that deep features gives a better solution.

Relation Between Image Quality and Scan Resolution: Part I

Currently, a particular scan resolution has to be defined before a scanner starts working. Two problems arise from this process. Firstly, no matter how different two pages contents are, they will be scanned into the same resolution. For example, after scanning, a blank page and a fine-detailed drawing will have the same resolution. Secondly, for one scanned page, every part of its output would have the same resolution, whatever their contents are. These problems will cause unnecessary waste of memory used to store scanned images. So a method to decide the minimum acceptable scan resolution is needed. But current image quality estimators are not suitable for estimating image quality at different resolutions. This paper proposes four features to assess image qualities at different resolutions, namely 75, 100, 150, 200 and 300 dpi. The features are tile-SSIM mean, tile-SSIM standard deviation, horizontal transition density, and vertical transition density. Tests on images containing different contents show that these features are promising in evaluate image qualities across different scan resolutions.

The Influence of Signal to Noise Ratio on the Pharmacokinetic Analysis in DCE-MRI Studies

Purpose: Recruiting the Pharmacokinetic (PK) parameters estimated from non-invasive methods such as Dynamic Contrast Enhanced MRI (DCE-MRI) to evaluate or plan treatment procedure is widely investigated in clinical practices. Interpretation of the DCE-MRI data is highly dependent to precision and accuracy of the estimated parameters. One of the most effective factors on the DCE-MR images and on the contrast concentration profile is the Signal to Noise Ratio (SNR). This work focuses on the analytical evaluation of the noise effect on accuracy of the estimated PK parameters in DCE-MRI studies. Materials and Methods: Tofts model as a popular pharmacokinetic model and model selection technique was used to simulate 3470 time curves of contrast concentration. Maximum likelihood estimator as a minimum variance unbiased estimator was recruited to estimate the PK parameters. Eleven levels of signal to noise ratios (SNR= 5, 8, 10, 13, 15, 20, 25, 30, 35, 50, Noiseless) were added to the simulated CA concentration profiles. The PK parameters were estimated for 11 series data and then Mean Percentage Error (MPE) was calculated for estimated parameters. Results: The results indicate that the most sensitive parameter to the SNR of the DCE-MR images is inverse transfer constant. A SNR greater than 25 was found to ensure a reasonable error (MPE <5%) in all models parameters. Conclusion: Clinical decision based on the DCE-MRI data analysis and estimated PK parameters needs a good image quality (SNR>25), an accurate and robust estimator and correct pharmacokinetic model selection

An Effective feature set in Quality Estimation of Natural Scene Statistics Images

Quality estimation in images is an area which demands high attention of researchers. Many recent algorithms in Image quality assessment relies on the computation of definite values from the image or comparison with the original pristine image. Here, we propose the extraction of a set of specific features from image and processing is done on these extracted features to obtain the objective quality score. The detailed inspection of behaviour of this set of highly specific image features extracted through less complex mathematical procedure from a collection good quality and low quality set of Natural Scene Statistics images available in LIVE dataset is elaborated in this work. Our studies and results are compared with the subjective opinion value and is proven to be accurate. The obtained results are demonstrated using statistical and graphical manner for promptness in understanding the nature of quality of the image. Thus the proposed feature set is proven to be complete in assessing the quantitative quality value of any Natural image.

Learning No-Reference Quality Assessment of Multiply and Singly Distorted Images with Big Data.

Previous research on no-reference (NR) quality assessment of multiply-distorted images focused mainly on three distortion types (white noise, Gaussian blur, and JPEG compression), while in practice images can be contaminated by many other common distortions due to the various stages of processing. Although MUSIQUE (MUltiply-and Singly-distorted Image QUality Estimator) Zhang et al., TIP 2018 is a successful NR algorithm, this approach is still limited to the three distortion types. In this paper, we extend MUSIQUE to MUSIQUE-II to blindly assess the quality of images corrupted by five distortion types (white noise, Gaussian blur, JPEG compression, JPEG2000 compression, and contrast change) and their combinations. The proposed MUSIQUE-II algorithm builds upon the classification and parameter-estimation framework of its predecessor by using more advanced models and a more comprehensive set of distortion-sensitive features. Specifically, MUSIQUE-II relies on a three-layer classification model to identify 19 distortion types. To predict the five distortion parameter values, MUSIQUE-II extracts an additional 14 contrast features and employs a multi-layer probability-weighting rule. Finally, MUSIQUE-II employs a new most-apparent-distortion strategy to adaptively combine five quality scores based on outputs of three classification models. Experimental results tested on three multiply-distorted and six singly-distorted image quality databases show that MUSIQUE-II yields not only a substantial improvement in quality predictive performance as compared with its predecessor, but also highly competitive performance relative to other state-of-the-art FR/NR IQA algorithms.

Back-of-the-envelope image quality estimation using the national image interpretability rating scale: erratum.

This paper corrects several errata in Appl. Opt.58, 6586 (2019)APOPAI0003-693510.1364/AO.58.006586.

Three-dimensional integral imaging in photon-starved environments with high-sensitivity image sensors.

Imaging in poorly illuminated environments using three-dimensional (3D) imaging with passive imaging sensors that operate in the visible spectrum is a formidable task due to the low number of photons detected. 3D integral imaging, which integrates multiple two-dimensional perspectives, is expected to perform well in the presence of noise, as well as statistical fluctuation in the detected number of photons. In this paper, we present an investigation of 3D integral imaging in low-light-level conditions, where as low as a few photons and as high as several tens of photons are detected on average per pixel. In the experimental verification, we use an electron multiplying charge-coupled device (EM-CCD) and a scientific complementary metal-oxide-semiconductor (sCMOS) camera. For the EM-CCD, a theoretical model for the probability distribution of the pixel values is derived, then fitted with the experimental data to determine the camera parameters. Likewise, pixelwise calibration is performed on the sCMOS to determine the camera parameters for further analysis. Theoretical derivation of the expected signal-to-noise-ratio is provided for each image sensor and corroborated by the experimental findings. Further comparison between the cameras includes analysis of the contrast-to-noise ratio (CNR) as well as the perception-based image quality estimator (PIQE). Improvement of image quality metrics in the 3D reconstructed images is successfully confirmed compared with those of the 2D images. To the best of our knowledge, this is the first experimental report of low-light-level 3D integral imaging with as little as a few photons detected per pixel on average to improve scene visualization including occlusion removal from the scene.

Perception Evaluation: A New Solar Image Quality Metric Based on the Multi-fractal Property of Texture Features

Next-generation ground-based solar observations require good image quality metrics for post-facto processing techniques. Based on the assumption that texture features in solar images are multi-fractal which can be extracted by a trained deep neural network as feature maps, a new reduced-reference objective image quality metric, the perception evaluation is proposed. The perception evaluation is defined as cosine distance of Gram matrix between feature maps extracted from high resolution reference image and that from blurred images. We evaluate performance of the perception evaluation with simulated and real observation images. The results show that with a high resolution image as reference, the perception evaluation can give robust estimate of image quality for solar images of different scenes.

Back-of-the-envelope image quality estimation using the national image interpretability rating scale.

For an optical imaging system, the critical output is the image itself, and therefore, the quality of that image is of utmost importance. To estimate or predict the image quality (IQ), a simulation/model is typically created to yield an output image, given an imaging system and an object/scene. The IQ is typically graded based on the imaging system along with the scene. Developing an imaging simulation and creating input scenes to produce IQ results can be time-consuming, leading to a desire for a simple method to estimate the predicted IQ. This work develops a national image interpretability rating scale (NIIRS) IQ value based on simplifying assumptions for remote-sensing purposes. While the results are on the optimistic side, this back-of-the-envelope IQ estimation allows the process of developing a new imaging system to move forward more in parallel rather than in series, i.e., developing an imaging full simulation/model in parallel with designing/procuring hardware.

Evaluation the effect of different collimators and energy window on Y-90 bremsstrahlung SPECT imaging by SIMIND Monte Carlo program.

Recently, the treatment efficiency of Yttrium 90 (Y-90) and providing reliable estimates of activity by single photon emission computed tomography (SPECT) imaging of bremsstrahlung radiation released during beta therapy have been evaluated. In the Y-90 bremsstrahlung SPECT imaging, the resulting energy spectrum is very complex and continuous, which creates many difficulties in the imaging protocol and image reconstruction. Furthermore, image quality and quantitative accuracy in the bremsstrahlung SPECT imaging are affected by collimator penetration and scatter. So, the collimator type and its geometry have impressive effects on the spatial resolution, system sensitivity and image contrast. Hereby, in this paper, we evaluated the effect of the energy window (three energy windows: 60 to 160 keV, 160 to 400 keV, and 60 to 400 keV) and the commercial parallel-hole collimators with different geometric parameters on the Y-90 bremsstrahlung spectrum and the image quality of the liver tumors based on criteria such as system sensitivity and image contrast. SIMIND Monte Carlo simulation code was used to generate the Y-90 bremsstrahlung SPECT images of the liver tumor with different diameters: 1.36, 2.04, 2.72, 3.4, 4.08, and 4.76 cm by use of the digital Zubal phantom. Furthermore, the tumor size was estimated by evaluating pixel intensity profile on the line drawn through the activity distribution image. Our results showed that the collimator choice and energy window setting in the bremsstrahlung SPECT imaging have significant effects on the image quality and tumor size estimation. Optimal image quality could be acquired by the energy window of 60 to 400 keV and the SPECT system equipped with a Medium-Energy General-Purpose (MEGP) collimator of Millennium VG Kameran (GV) Company. Moreover, the estimation of distribution size was close to the actual value for tumor sizes larger than 2.04 cm, especially by using the SPECT system equipped with the GV-MEGP collimator in the wide energy window. We found an optimal collimator to be more appropriate for improving the imaging quality of Y-90 bremsstrahlung photons, which can be used for reliable activity distribution estimates after radiation therapy.

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

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.