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Background: Magnetic particle imaging (MPI) is a recently developed, non‐invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X‐space are two commonly used MPI reconstruction methods, where the former is extremely time‐consuming and the latter usually produces blurry images. Purpose: Currently, we proposed an end‐to‐end machine learning framework to reconstruct high‐resolution MPI images from 1‐D voltage signals directly and efficiently. Methods: The proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291 597 pairs of high‐resolution 2‐D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high‐resolution MPI image reconstruction. Results: Experiment results showed that, MPIGAN exhibited remarkable abilities in high‐resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X‐space in recovering the fine‐scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high‐quality MPI images. Conclusion: Our study provides a promising AI solution for end‐to‐end, efficient, and high‐resolution magnetic particle imaging reconstruction. [ABSTRACT FROM AUTHOR]

Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework. Due to limited counts received, signal-to-noise-ratio (SNR) and resolution of parametric images produced by direct reconstruction frameworks are still limited. Recently supervised deep learning methods have been successfully applied to medical imaging denoising/reconstruction when large number of high-quality training labels are available. For static PET imaging, high-quality training labels can be acquired by extending the scanning time. However, this is not feasible for dynamic PET imaging, where the scanning time is already long enough. In this work, we proposed an unsupervised deep learning framework for direct parametric reconstruction from dynamic PET, which was tested on the Patlak model and the relative equilibrium Logan model. The training objective function was based on the PET statistical model. The patient's anatomical prior image, which is readily available from PET/CT or PET/MR scans, was supplied as the network input to provide a manifold constraint, and also utilized to construct a kernel layer to perform non-local feature denoising. The linear kinetic model was embedded in the network structure as a 1 ×1 ×1 convolution layer. Evaluations based on dynamic datasets of 18F-FDG and 11C-PiB tracers show that the proposed framework can outperform the traditional and the kernel method-based direct reconstruction methods.

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.

• A new method for direct reconstruction of biological tissue temperature field by dynamic response feature matching and steady-state correction is proposed. • The direct reconstruction method, on the premise of not depending on the specific system input information, reconstructs the transient temperature field of biological tissues during thermal therapy. • The direct reconstruction method of biological tissue temperature field can accurately reconstruct the internal temperature field of the biological tissue for different laser irradiation forms. • The direct reconstruction method has is robust, and insensitive to measurement errors. • The work can provide scientific support for online and real-time reconstruction of biological tissue temperature field during hyperthermia. In the field of oncology thermotherapy technology, online and real-time reconstruction of biological tissue temperature field is a fundamental work with important practical needs. In this paper, we proposed a new method for direct reconstruction of biological tissue temperature field by dynamic response feature matching and steady-state correction. Based on the bioheat transfer equation, the step response model of tissue temperature field during thermal therapy is established, and the dynamic response eigenvectors of biological tissue temperature and the steady-state gain of temperature response are determined offline. According to the optimal matching principle between the temperature dynamic response of the observation point and the internal space point of tissue, the optimal matching matrix of the dynamic response features between the observation point temperature and the internal temperature field of tissue is determined with the use of the dynamic response eigenvectors. Further, through the weighted synthesis of observation point temperature response by using the optimal matching matrix of dynamic response features, the tissue transient temperature field pre-estimation model is established. Finally, by correcting the tissue temperature field pre-estimation model with steady-state gain correction factor, the direct reconstruction scheme of temperature field based on dynamic response feature matching and steady-state gain correction is established. In this work, the non-destructive reconstruction of the internal temperature field of biological tissues during laser-induced thermal therapy is achieved based on the above direct reconstruction scheme. Numerical simulation experiments are performed to investigated the effects of pre-estimation time domain, the number of measurement points, laser irradiation form and measurement error on the temperature field reconstruction results. [ABSTRACT FROM AUTHOR]

High-quality and fast reconstructions are essential for the clinical application of positron emission tomography (PET) imaging. Herein, a deep-learning-based framework is proposed for PET image reconstruction directly from the sinogram domain to achieve high-quality and high-speed reconstruction at the same time. In this framework, conditional generative adversarial networks are constructed to learn a mapping from sinogram data to a reconstructed image and to generate a well-trained model. The network consists of a generator that utilizes the U-net structure and a whole-image strategy discriminator, which are alternately trained. Simulation experiments are conducted to validate the performance of the algorithm in terms of reconstruction accuracy, reconstruction efficiency, and robustness. Real patient data and Sprague Dawley rat data were used to verify the performance of the proposed method under complex conditions. The experimental results demonstrate the superior performance of the proposed method in terms of image quality, reconstruction speed, and robustness. [ABSTRACT FROM AUTHOR]

• Develop a direct reconstruction scheme for retrieving the internal temperature field of a Li-ion battery based on mapping characteristics; • Establish a direct reconstruction model of the internal temperature field of the battery; • The provided scheme can reconstruct the temperature field directly online by synthesizing the surface temperature measurements of the battery. Lithium-ion (Li-ion) batteries are susceptible to the working temperature so that the monitoring is essential to the battery internal temperature. The electrochemical-thermal coupling model completely reflects the battery internal behavior. However, the high complexity and multi-field coupling make it difficult to be applied to reconstruct the battery temperature field based on state observer. Based on mapping characteristics, this work proposes a temperature field direct reconstruction scheme. And the electrochemical-thermal coupling model plays a large role in this scheme. Mapping characteristic vectors of the transient temperature field are obtained from the step response model of the Li-ion battery temperature field to charge and discharge current. Furthermore, the quantitative connection of the mapping characteristic vectors between the surface points of and the internal nodes of the battery is characterized by a relevance degree matrix. And a direct reconstruction model of the internal transient temperature field is established. The internal temperature field is reconstructed directly online and in real time via exploiting the temperature measurements on battery surface. In numerical experiments, the three-dimensional temperature field of the Li-ion battery charge and discharge processes is reconstructed. The effects of charge and discharge rates, model mismatch, and measurement errors on the reconstruction results are investigated. During the charge and discharge processes, the maximum values of the transient average error of the reconstructed temperature field are about 0.4 K and 0.3 K, respectively. And the transient average error is generally below 0.8 K when the standard deviation of measurement errors is 0.5 K. [ABSTRACT FROM AUTHOR]

Introduction and aim: Amelogenesis imperfecta associates a clinically and genetically heterogeneous group of developmental defects affecting the appearance and structure of enamel, which more or less severely affect all dental functions. Manifestations of amelogenesis imperfecta are considered by patients to be a compromise of aesthetics and increased tooth sensitivity. The consequences of the disease have a detrimental effect on adolescents who integrate into groups and seek their place in society. However, the traditional treatment approach can be very expensive and may require a certain degree of psychological resilience of the patient due to the pain and time consuming treatment. These treatment requirements, together with the insufficient thickness of the secondary dentin layer, thus often force the physician to delay treatment, which may adversely affect the patient's psychosocial development. Self-observation: A fifteen-year-old patient was sent to the prosthetic department for complete reconstruction of permanent teeth with a diagnosis of hypoplastic amelogenesis imperfecta. Due to the nature of the disease, immediate direct reconstruction was performed. Conclusion: An unprepared approach is possible even in adolescence. Hard dental tissues are preserved, the vitality of the dental pulp is not endangered and the patient's psychological burden is reduced. Direct reconstruction can be approached provided the patient cooperates. This approach must be combined with consistent treatment of the tooth surface to reduce adhesion and plaque retention. The advantage is also that any partial failure of the reconstruction is relatively easy to repair in the case of direct reconstruction.

Parametric imaging based on dynamic positron emission tomography (PET) has wide applications in neurology. Compared to indirect methods, direct reconstruction methods, which reconstruct parametric images directly from the raw PET data, have superior image quality due to better noise modeling and richer information extracted from the PET raw data. For low-dose scenarios, the advantages of direct methods are more obvious. However, the wide adoption of direct reconstruction is inevitably impeded by the excessive computational demand and deficiency of the accessible raw data. In addition, motion modeling inside dynamic PET image reconstruction raises more computational challenges for direct reconstruction methods. In this work, we focused on the 18F-FDG Patlak model, and proposed a data-driven approach which can estimate the motion corrected full-dose direct Patlak images from the dynamic PET reconstruction series, based on a proposed novel temporal non-local convolutional neural network. During network training, direct reconstruction with motion correction based on full-dose dynamic PET sinograms was performed to obtain the training labels. The reconstructed full-dose /low-dose dynamic PET images were supplied as the network input. In addition, a temporal non-local block based on the dynamic PET images was proposed to better recover the structural information and reduce the image noise. During testing, the proposed network can directly output high-quality Patlak parametric images from the full-dose /low-dose dynamic PET images in seconds. Experiments based on 15 full-dose and 15 low-dose 18F-FDG brain datasets were conducted and analyzed to validate the feasibility of the proposed framework. Results show that the proposed framework can generate better image quality than reference methods.

High-quality and fast reconstructions are essential for the clinical application of positron emission tomography (PET) imaging. Herein, a deep-learning-based framework is proposed for PET image reconstruction directly from the sinogram domain to achieve high-quality and high-speed reconstruction at the same time. In this framework, conditional generative adversarial networks are constructed to learn a mapping from sinogram data to a reconstructed image and to generate a well-trained model. The network consists of a generator that utilizes the U-net structure and a whole-image strategy discriminator, which are alternately trained. Simulation experiments are conducted to validate the performance of the algorithm in terms of reconstruction accuracy, reconstruction efficiency, and robustness. Real patient data and Sprague Dawley rat data were used to verify the performance of the proposed method under complex conditions. The experimental results demonstrate the superior performance of the proposed method in terms of image quality, reconstruction speed, and robustness.

Receptor ligand-based dynamic Positron Emission Tomography (PET) permits the measurement of neurotransmitter release in the human brain. For single-scan paradigms, the conventional method of estimating changes in neurotransmitter levels relies on fitting a pharmacokinetic model to activity concentration histories extracted after PET image reconstruction. However, due to the statistical fluctuations of activity concentration data at the voxel scale, parametric images computed using this approach often exhibit low signal-to-noise ratio, impeding characterization of neurotransmitter release. Numerous studies have shown that direct parametric reconstruction (DPR) approaches, which combine image reconstruction and kinetic analysis in a unified framework, can improve the signal-to-noise ratio of parametric mapping. However, there is little experience with DPR in imaging of neurotransmission and the performance of the approach in this application has not been evaluated before in humans. In this report, we present and evaluate a DPR methodology that computes 3-D distributions of ligand transport, binding potential (BPND) and neurotransmitter release magnitude (γ) from a dynamic sequence of PET sinograms. The technique employs the linear simplified reference region model (LSRRM) of Alpert et al. (2003), which represents an extension of the simplified reference region model that incorporates time-varying binding parameters due to radioligand displacement by release of neurotransmitter. Estimation of parametric images is performed by gradient-based optimization of a Poisson log-likelihood function incorporating LSRRM kinetics and accounting for the effects of head movement, attenuation, detector sensitivity, random and scattered coincidences. A 11C-raclopride simulation study showed that the proposed approach substantially reduces the bias and variance of voxel-wise γ estimates as compared to standard methods. Moreover, simulations showed that detection of release could be made more reliable and/or conducted using a smaller sample size using the proposed DPR estimator. Likewise, images of BPND computed using DPR had substantially improved bias and variance properties. Application of the method in human subjects was demonstrated using 11C-raclopride dynamic scans and a reward task, confirming the improved quality of the estimated parametric images using the proposed approach.

Introduction and aim: Amelogenesis imperfecta associates a clinically and genetically heterogeneous group of developmental defects affecting the appearance and structure of enamel, which more or less severely affect all dental functions. Manifestations of amelogenesis imperfecta are considered by patients to be a compromise of aesthetics and increased tooth sensitivity. The consequences of the disease have a detrimental effect on adolescents who integrate into groups and seek their place in society. However, the traditional treatment approach can be very expensive and may require a certain degree of psychological resilience of the patient due to the pain and time consuming treatment. These treatment requirements, together with the insufficient thickness of the secondary dentin layer, thus often force the physician to delay treatment, which may adversely affect the patient's psychosocial development. Self-observation: A fifteen-year-old patient was sent to the prosthetic department for complete reconstruction of permanent teeth with a diagnosis of hypoplastic amelogenesis imperfecta. Due to the nature of the disease, immediate direct reconstruction was performed. Conclusion: An unprepared approach is possible even in adolescence. Hard dental tissues are preserved, the vitality of the dental pulp is not endangered and the patient's psychological burden is reduced. Direct reconstruction can be approached provided the patient cooperates. This approach must be combined with consistent treatment of the tooth surface to reduce adhesion and plaque retention. The advantage is also that any partial failure of the reconstruction is relatively easy to repair in the case of direct reconstruction. [ABSTRACT FROM AUTHOR]

Usually, the sensitivity matrix-based algorithms are used for image reconstruction in electrical tomography. These algorithms assume that the inverse problem is like that in the well-established medical CT and find successful applications. In the medical CT, rather than using the matrix-based algorithms, the inverse Radon transform and its derivatives dominate. However, these inverse transforms have not been introduced to electrical tomography due to a lack of the link between the parallel projections and electrical distributions. This article presents a map from electrical field lines to parallel lines in the inverse Radon transform and a novel image reconstruction algorithm by using the transform for dual-modality electrical tomography. The transform is realized through mapping the coordinates in electric field to the assumed parallel projections. The points of intersections between electrical field lines from different exciting electrodes are mapped to those of parallel lines from the related projections in a typical inverse Radon transform. Unlike the medical CT, different excitation schemes are related to distinct equivalent projections, and iterative scanning is used to apply all available projected data. Simulated and experimental data are implemented to validate the feasibility and effectiveness of the proposed algorithm, and performance comparisons are made with three algorithms, such as Calderon’s method, iterative method based on electrical field lines, and typical Landweber method. The proposed method yields good quality images in the least time and dynamic flames are monitored. Dual-modality images of material distributions are obtained to capture the ignition and blowout of the flame evolution in a Bunsen burner. [ABSTRACT FROM AUTHOR]

Treatment of renal artery aneurysms (RAA) remains controversial. Endovascular treatment has increased for main trunk and for very distal aneurysms, whereas for lesions located at the bifurcation surgical treatment seems to be a valid option. The goal of this study was to describe the technique of direct reconstruction of RAA and to report on outcomes. This study comprised single centre prospective collection of data with retrospective analysis (January 2015 to August 2018) of patients operated on for distal RAA using direct reconstruction. A total of 24 RAA in 21 patients (seven men and 14 women, mean age 59 ± 13 years) was included. History of hypertension was found in 15 patients and renal insufficiency was present in one. Mean pre-operative systolic and diastolic blood pressures were 134 ± 21 mmHg and 74 ± 10 mmHg, and mean pre-operative rates of creatinine and glomerular filtration rate were 67 ± 13 μmol/L and 93 ± 49 mL/min/1.73 m2, respectively. Indications for repair were a diameter >20 mm in seven cases (mean diameter = 25 ± 2 mm) or rapid growth in one case, symptomatic aneurysm in 12 cases (hypertension, haematuria, pain), and a concomitant lesion in four cases (splenic aneurysm, abdominal aortic aneurysm, occlusive visceral artery lesions). All lesions were distal, main artery bifurcation in 22 cases and hilar in two cases. The main aetiology was fibromuscular dysplasia (16 cases) followed by atherosclerosis (seven cases) and one case of Ehlers Danlos Syndrome. In situ reconstruction was possible for 22 RAA, while two cases required kidney autotransplantation. The mean renal ischaemia time was 18 ± 5 min. At two years, the patency rate was 100%, and mean systolic blood pressure decreased (134 mmHg–122 mmHg, p =.047). Renal function was stable from 93 ± 49 pre-operatively to 95 ± 35 mL/min/1.73 m2 in the post-operative course (p =.56). Direct reconstruction appears to be efficient for most RAA. This technique is complementary to ex vivo autotransplantation and endovascular treatment. [ABSTRACT FROM AUTHOR]

• For heat conduction system, a response spatiotemporal correlation (RSTC) model of temperatures between measurement space and estimation space is proposed. • A RSTC based direct reconstruction (RSTC-DR) method is developed for transient temperature field of heat conduction system. • The proposed method does not rely on specific system heat source and can achieve non-destructive reconstruction of temperature field in real-time. In this paper, according to a spatiotemporal correlation characteristic of the temperature response of a transient heat conduction system, a response spatiotemporal correlation (RSTC) model of the temperatures between measurement space and estimation space of the system is proposed. Furthermore, a RSTC based direct reconstruction (RSTC-DR) method is developed for a transient temperature field reconstruction problem of a heat conduction system with unknown heat sources. In the offline stage, firstly, mapping eigenvectors of a heat conduction system are determined by the transfer equations without specific heat source information. Then, according to the correlation degree between the mapping eigenvectors, a finite number of representative spatial points (RPs) are extracted. Meanwhile, a fuzzy membership relationship between the mapping characteristics of the RPs and that of the temperature field is determined. Namely, effective mapping characteristic coverage of the RPs to the whole temperature field is realized. Moreover, a RSTC model between the measurement spatial points (MPs) and the RPs is obtained by the step response model of the temperature field. In the online stage, based on the RSTC model, the temperature responses of the RPs are estimated by employing the time series vector of the temperature measurements of the MPs. Next, according to the fuzzy membership relationship, the temperature field can be reconstructed in real time by synthesizing the temperature estimations of the RPs. The RSTC-DR method is employed to achieve the non-destructive reconstruction of the transient temperature field of a two-dimensional heat conduction system. The temperature field is reconstructed by the numerical experiment to verify the effectiveness of the proposed method and to discuss its adaptability. Furthermore, the practical effectiveness of the method is demonstrated by applying the existing experimental data. [ABSTRACT FROM AUTHOR]

Computational methods, such as the linear parametric neurotransmitter PET (lp-ntPET) method, have been developed to characterize the transient changes in radiotracer kinetics in the target tissue during endogenous neurotransmitter release. In this paper, we describe and evaluate a parametric reconstruction algorithm that uses an expectation maximization framework, along with the lp-ntPET model, to estimate the endogenous neurotransmitter response to stimuli directly from the measured PET data. Computer simulations showed that the proposed direct reconstruction method offers improved accuracy and precision for the estimated timing parameters of the neurotransmitter response at the voxel level (${t}_{d}=1\pm 2 $ min, for activation onset bias and standard deviation) compared with conventional post reconstruction modeling (${t}_{d}=4\pm 7 $ min). In addition, we applied the proposed direct parameter estimation methodology to a [11C]raclopride displacement study of an awake rat and generated parametric maps illustrating the magnitude of ligand displacement from striatum. Although the estimated parametric maps of activation magnitude obtained from both direct and post reconstruction methodologies suffered from false positive activations, the proposed direct reconstruction framework offered more reliable parametric maps when the activation onset parameter was constrained. [ABSTRACT FROM AUTHOR]

In this paper, the shortcut D-bar method is introduced to electrical capacitance tomography to directly reconstruct the permittivity distribution within the region of interest. The method reveals the analytic relationship between the permittivity distribution and the Dirichlet-to-Neumann (DN) map. The DN map can be directly estimated with measured capacitances. However, the direct estimate of the DN map is inaccurate in the cases of small electrode numbers. To better adapt the method to real applications of small electrode numbers, the original shortcut D-bar method is modified by splitting estimation of the DN map into the analytical expression of a homogeneous background and the estimation of the other variation part. Comparisons are made among the original, modified shortcut D-bar methods, and the classical Landweber iteration method through simulation and experiment, respectively. Results confirm the improved tomographic imaging quality by using the modified shortcut D-bar method. [ABSTRACT FROM AUTHOR]

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Detecting inhomogeneities in the electrical conductivity is a special case of the inverse problem in electrical impedance tomography, that leads to fast direct reconstruction methods. One such method can, under reasonable assumptions, exactly characterize the inhomogeneities based on monotonicity properties of either the Neumann-to-Dirichlet map (non-linear) or its Fréchet derivative (linear). We give a comparison of the non-linear and linear approach in the presence of measurement noise, and show numerically that the two methods give essentially the same reconstruction in the unit disk domain. For a fair comparison, exact matrix characterizations are used when probing the monotonicity relations to avoid errors from numerical solution to PDEs and numerical integration. Using a special factorization of the Neumann-to-Dirichlet map also makes the non-linear method as fast as the linear method in the unit disk geometry. [ABSTRACT FROM PUBLISHER]

Using 2D contour sketches as input is an attractive solution for easing the creation of 3D models. This paper tackles the problem of creating 3D models of animals from a single, side-view sketch. We use the a priori assumptions of smoothness and structural symmetry of the animal about the sagittal plane to inform the 3D reconstruction. Our contributions include methods for identifying and inferring the contours of shape parts from the input sketch, a method for identifying the hierarchy of these structural parts including the detection of approximate symmetric pairs, and a hierarchical algorithm for positioning and blending these parts into a consistent 3D implicit-surface-based model. We validate this pipeline by showing that a number of plausible animal shapes can be automatically constructed from a single sketch. [ABSTRACT FROM AUTHOR]

The Magnetic Resonance Imaging (MRI) signal can be made sensitive to functional parameters that provide information about tissues. In dynamic contrast enhanced (DCE) MRI these functional parameters are related to the microvasculature environment and the concentration changes that occur rapidly after the injection of a contrast agent. Typically DCE images are reconstructed individually and kinetic parameters are estimated by fitting a pharmacokinetic model to the time-enhancement response; these methods can be denoted as “indirect”. If undersampling is present to accelerate the acquisition, techniques such as kt-FOCUSS can be employed in the reconstruction step to avoid image degradation. This paper suggests a Bayesian inference framework to estimate functional parameters directly from the measurements at high temporal resolution. The current implementation estimates pharmacokinetic parameters (related to the extended Tofts model) from undersampled ( k , t )-space DCE MRI. The proposed scheme is evaluated on a simulated abdominal DCE phantom and prostate DCE data, for fully sampled, 4 and 8-fold undersampled ( k , t )-space data. Direct kinetic parameters demonstrate better correspondence (up to 70% higher mutual information) to the ground truth kinetic parameters (of the simulated abdominal DCE phantom) than the ones derived from the indirect methods. For the prostate DCE data, direct kinetic parameters depict the morphology of the tumour better. To examine the impact on cancer diagnosis, a peripheral zone prostate cancer diagnostic model was employed to calculate a probability map for each method. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method to form pharmacokinetic-rate images of optical fluorophores directly from near infra-red (NIR) boundary measurements. We first derive a mapping from spatially resolved pharmacokinetic rates to NIR boundary measurements by combining compartmental modeling with a diffusion based NIR photon propagation model. We express this mapping as a state-space equation. Next, we introduce a spatio-temporal prior model for the pharmacokinetic-rate images and combine it with the state-space equation. We address the image formation problem using the extended Kalman filtering framework. We analyze the computational complexity of the resulting algorithms and evaluate their performance in numerical simulations. An important feature of our approach is that the reconstruction of fluorescence concentrations and compartmental modeling are combined into a single step 1) to take advantage of the inherent temporal correlations in dynamic NIR measurements, and 2) to incorporate spatio-temporal a priori information on pharmacokinetic-rate images. Simulation results show that the resulting algorithms are more robust and lead to higher signal-to-noise ratio as compared to existing approaches where the reconstruction of concentrations and compartmental modeling are treated separately. Additionally, we reconstructed pharmacokinetic-rate images using in vivo data obtained from three patients with breast tumors. The reconstruction results show that the pharmacokinetic rates of indocyanine green are higher inside the tumor region as compared to the surrounding tissue. [ABSTRACT FROM AUTHOR]

Mathematical interest in electrical impedance tomography has been strong since the publication of Calderón's foundational paper. This paper introduced the idea of applying external voltage patterns to a medium such that, assuming that the medium is sufficiently close to a constant admittivity, the reconstruction can be accomplished directly by inverse Fourier transform. Motivated by Calderón's method, we have developed a variant of the algorithm which is applicable to the case of measurement on only a part of the boundary and on discrete electrodes. Here we determine voltage or current patterns to apply to the electrodes which optimally approximate Calderón's special functions in the interior. Furthermore, in three dimensions and higher, Calderón's method allows each point in Fourier space to be computed in a multiplicity of ways. We show that by making use of the inherent redundancy in our measurements, we can significantly improve the quality of the static images produced by our algorithm. [ABSTRACT FROM AUTHOR]

Dental fractures of the anterior teeth are a relatively frequent accident during childhood. However until maxillary maturation is completed, the employment of porcelain crowns is not recommended, the treatment of choice, instead, would be direct composite restoration. The procedure for restoration of two extensive superior central incisor fractures is presented. Composite crowns were customized directly over the teeth. Root canal treatment was performed in one of the pieces and subsequently a post was cemented, around which a composite core was constructed. Acetate crowns were filled with composite, adapted to the dental contour and photo cured before removal to reproduce the anatomy. After follow-up of 8 years restorations remained esthetic and fully functional. [ABSTRACT FROM AUTHOR]

Magnetic Particle Imaging is a tomographic imaging technique that measures the voltage induced due to magnetization changes of magnetic nanoparticle distributions. The relationship between the received signal and the distribution of the nanoparticels is described by the system function. A common method for image reconstruction is using a measured system function to create a system matrix and set up a regularized linear system of equations. Since the measurement of the system matrix is time-consuming, different methods for acceleration have been proposed. These include modeling the system matrix or using a direct reconstruction method in time, known as X-space reconstruction. In this work, based on the simplified Langevin model of paramagnetism and certain approximations, a direct reconstruction technique for Magnetic Particle Imaging in the frequency domain with two- and three-dimensional Lissajous trajectory excitation is presented. The approach uses Chebyshev polynomials of second kind. During reconstruction, they are weighted with the frequency components of the voltage signal and additional factors and then summed up. To obtain the final nanoparticle distribution, this result is rescaled and deconvolved. It is shown that the approach works for both simulated data and real measurements. The obtained image quality is comparable to a modeled system matrix approach using the same simplified physical assumptions and no relaxation effects. The reconstruction of a 31 × 31 × 31 volume takes less than a second and is up to 25 times faster than the state-of-the-art Kaczmarz reconstruction. Besides, the derivation of the proposed method shows some new theoretical aspects of the system function and its well-known observed similarity to tensor products of Chebyshev polynomials of second kind., (© 2022 Institute of Physics and Engineering in Medicine.)

Parametric imaging based on dynamic positron emission tomography (PET) has wide applications in neurology. Compared to indirect methods, direct reconstruction methods, which reconstruct parametric images directly from the raw PET data, have superior image quality due to better noise modeling and richer information extracted from the PET raw data. For low-dose scenarios, the advantages of direct methods are more obvious. However, the wide adoption of direct reconstruction is inevitably impeded by the excessive computational demand and deficiency of the accessible raw data. In addition, motion modeling inside dynamic PET image reconstruction raises more computational challenges for direct reconstruction methods. In this work, we focused on the 18F-FDG Patlak model, and proposed a data-driven approach which can estimate the motion corrected full-dose direct Patlak images from the dynamic PET reconstruction series, based on a proposed novel temporal non-local convolutional neural network. During network training, direct reconstruction with motion correction based on full-dose dynamic PET sinograms was performed to obtain the training labels. The reconstructed full-dose /low-dose dynamic PET images were supplied as the network input. In addition, a temporal non-local block based on the dynamic PET images was proposed to better recover the structural information and reduce the image noise. During testing, the proposed network can directly output high-quality Patlak parametric images from the full-dose /low-dose dynamic PET images in seconds. Experiments based on 15 full-dose and 15 low-dose 18F-FDG brain datasets were conducted and analyzed to validate the feasibility of the proposed framework. Results show that the proposed framework can generate better image quality than reference methods. [ABSTRACT FROM AUTHOR]

Receptor ligand-based dynamic Positron Emission Tomography (PET) permits the measurement of neurotransmitter release in the human brain. For single-scan paradigms, the conventional method of estimating changes in neurotransmitter levels relies on fitting a pharmacokinetic model to activity concentration histories extracted after PET image reconstruction. However, due to the statistical fluctuations of activity concentration data at the voxel scale, parametric images computed using this approach often exhibit low signal-to-noise ratio, impeding characterization of neurotransmitter release. Numerous studies have shown that direct parametric reconstruction (DPR) approaches, which combine image reconstruction and kinetic analysis in a unified framework, can improve the signal-to-noise ratio of parametric mapping. However, there is little experience with DPR in imaging of neurotransmission and the performance of the approach in this application has not been evaluated before in humans. In this report, we present and evaluate a DPR methodology that computes 3-D distributions of ligand transport, binding potential (BP ND) and neurotransmitter release magnitude (γ) from a dynamic sequence of PET sinograms. The technique employs the linear simplified reference region model (LSRRM) of Alpert et al. (2003), which represents an extension of the simplified reference region model that incorporates time-varying binding parameters due to radioligand displacement by release of neurotransmitter. Estimation of parametric images is performed by gradient-based optimization of a Poisson log-likelihood function incorporating LSRRM kinetics and accounting for the effects of head movement, attenuation, detector sensitivity, random and scattered coincidences. A 11C-raclopride simulation study showed that the proposed approach substantially reduces the bias and variance of voxel-wise γ estimates as compared to standard methods. Moreover, simulations showed that detection of release could be made more reliable and/or conducted using a smaller sample size using the proposed DPR estimator. Likewise, images of BP ND computed using DPR had substantially improved bias and variance properties. Application of the method in human subjects was demonstrated using 11C-raclopride dynamic scans and a reward task, confirming the improved quality of the estimated parametric images using the proposed approach. [ABSTRACT FROM AUTHOR]

The method of reconstructing parametric images from dynamic positron emission tomography (PET) data with the linear Patlak model has been widely used in scientific research and clinical practice. Whether for direct or indirect image reconstruction, researchers have deeply investigated the associated methods and effects. Among the existing methods, the traditional maximum likelihood expectation maximization (MLEM) reconstruction algorithm is fast but produces a substantial amount of noise. If the parameter images obtained by the MLEM algorithm are postfiltered, a large amount of image edge information is lost. Additionally, although the kernel method has a better noise reduction effect, its calculation costs are very high due to the complexity of the algorithm. Therefore, to obtain parametric images with a high signal-to-noise ratio (SNR) and good retention of detailed information, here, we use guided kernel means (GKM) and dynamic PET image information to conduct guided filtering and perform parametric image reconstruction. We apply this method to direct and indirect reconstruction, and through computer simulations, we show that our proposed method has higher identifiability and a greater SNR than conventional direct and indirect reconstruction methods. We also show that our method produces better images with direct than with indirect reconstruction., (© 2021 IOP Publishing Ltd.)

Purpose: The aim of this study is to develop and evaluate a novel direct reconstruction method to improve the signal-to-noise ratio (SNR) of parametric images in dynamic positron-emission tomography (PET), especially for applications in myocardial perfusion studies. Methods: Simulation studies were used to test the performance in SNR and computational efficiency for different methods. The NCAT phantom was used to generate simulated dynamic data. Noise realization was performed in the sinogram domain and repeated for 30 times with four different noise levels by varying the injection dose (ID) from standard ID to 1/8 of it. The parametric images were calculated by (1) three direct methods that compute the kinetic parameters from the sinogram and (2) an indirect method, which computes the kinetic parameter with pixel-by-pixel curve fitting in image space using weighted least-squares. The first direct reconstruction maximizes the likelihood function using trust-region-reflective (TRR) algorithm. The second approach uses tabulated parameter sets to generate precomputed time-activity curves for maximizing the likelihood functions. The third approach, as a newly proposed method, assumes separable complete data to derive the M-step for maximizing the likelihood. Results: The proposed method with the separable complete data performs similarly to the other two direct reconstruction methods in terms of the SNR, providing a 5%-10% improvement as compared to the indirect parametric reconstruction under the standard ID. The improvement of SNR becomes more obvious as the noise level increases, reaching more than 30% improvement under 1/8 ID. Advantage of the proposed method lies in the computation efficiency by shortening the time requirement to 25% of the indirect approach and 3%-6% of other direct reconstruction methods. Conclusions: With results provided from this simulation study, direct reconstruction of myocardial blood flow shows a high potential for improving the parametric image quality for clinical use. [ABSTRACT FROM AUTHOR]