In Situ Study of Fe3Pt-Fe2O3 Core-Shell Nanoparticle Formation.

We report an in situ study of Fe3Pt-Fe2O3 core-shell nanoparticle growth using liquid cell transmission electron microscopy. By controlling the Fe-to-Pt ratio in the precursor solution, we achieved the growth of nanoparticles with the formation of an iron-platinum alloy core followed by an iron oxide shell in the electron beam-induced reactions. There was no substantial change in the growth kinetics of the iron oxide shell after the Fe-Pt alloy core stopped growing. The core growth was arrested by depletion of the Pt precursor. Heteroepitaxy of Fe3Pt [101] (core)||α-Fe2O3 [111] (shell) was observed in most of the nanoparticles, while a polycrystalline iron oxide shell is developed eventually for strain relaxation. Our studies suggest that Pt atoms catalyze the reduction of Fe ions to form the Fe3Pt alloy core, and when Pt is depleted, a direct precipitation of iron oxide results in the core-shell nanostructure formation.

Longitudinal Evaluation of Sympathetic Nervous System and Perfusion in Normal and Spontaneously Hypertensive Rat Hearts with Dynamic Single-Photon Emission Computed Tomography.

The objective of this work was to evaluate the sympathetic nervous system and structure remodeling during the progression of heart failure in a rodent model using dynamic cardiac single-photon emission computed tomography (SPECT). The spontaneously hypertensive rat (SHR) model was used to study changes in the nervous system innervation and perfusion in the left ventricular (LV) myocardium with the progression of left ventricular hypertrophy (LVH) to heart failure. Longitudinal dynamic SPECT studies were performed with seven SHR and seven Wistar-Kyoto (WKY) rats over 1.5 years using a dual-head SPECT scanner with pinhole collimators. Time-activity curves (TACs) of the 123I-MIBG and 201Tl distribution in the LV blood pool and myocardium were extracted from dynamic SPECT data and fitted to compartment models to determine the influx rate, washout rate, and distribution volume (DV) of 123I-MIBG and 201Tl in the LV myocardium. The standardized uptake values (SUVs) of 123I-MIBG and 201Tl in the LV myocardium were also calculated from the static reconstructed images. The influx and washout rates of 123I-MIBG did not show a significant difference between SHRs and WKY rats. The DVs of 123I-MIBG were greater in the SHRs than in the WKY rats (p = .0028). Specifically, the DV of 123I-MIBG became greater in the SHRs by 6 months of age (p = .0017) and was still significant at the age of 22 months. The SUV of 123I-MIBG in SHRs exhibited abnormal values compared to WKY rats from the age of 18 months. There was no difference in the influx rate and the washout rate of 201Tl between the SHRs and WKY rats. The SHRs exhibited greater DV of 201Tl than WKY rats after the age of 18 months (p = .034). The SUV of 201Tl in SHRs did not show any significant difference from WKY at all ages. The higher DV of 123I-MIBG in the LV myocardium reveals abnormal nervous system activity of the SHRs at an age of 6 months, whereas a greater DV of 201Tl in the LV myocardium can only be detected at an age of 18 months. The results show that the abnormal nervous system activity appears earlier than perfusion. Furthermore, the comparison between the DV and the SUV indicates that dynamic SPECT with 123I-MIBG and 201Tl with the kinetic parameter DV is capable of detecting abnormalities of the LV at an early age.

A back-projection-and-filtering-like (BPF-like) reconstruction method with the deep learning filtration from listmode data in TOF-PET.

PURPOSE: The time-of-flight (TOF) information improves signal-to-noise ratio (SNR) for positron emission tomography (PET) imaging. Existing analytical algorithms for TOF PET usually follow a filtered back-projection process on reconstructing images from the sinogram data. This work aims to develop a back-projection-and-filtering-like (BPF-like) algorithm that reconstructs the TOF PET image directly from listmode data rapidly. METHODS: We extended the 2D conventional non-TOF PET projection model to a TOF case, where projection data are represented as line integrals weighted by the one-dimensional TOF kernel along the projection direction. After deriving the central slice theorem and the TOF back-projection of listmode data, we designed a deep learning network with a modified U-net architecture to perform the spatial filtration (reconstruction filter). The proposed BP-Net method was validated via Monte Carlo simulations of TOF PET listmode data with three different time resolutions for two types of activity phantoms. The network was only trained on the simulated full-dose XCAT dataset and then evaluated on XCAT and Jaszczak data with different time resolutions and dose levels. RESULTS: Reconstructed images show that when compared with the conventional BPF algorithm and the MLEM algorithm proposed for TOF PET, the proposed BP-Net method obtains better image quality in terms of peak signal-to-noise ratio, relative mean square error, and structure similarity index; besides, the reconstruction speed of the BP-Net is 1.75 times faster than BPF and 29.05 times faster than MLEM using 15 iterations. The results also indicate that the performance of the BP-Net degrades with worse time resolutions and lower tracer doses, but degrades less than BPF or MLEM reconstructions. CONCLUSION: In this work, we developed an analytical-like reconstruction in the form of BPF with the reconstruction filtering operation performed via a deep network. The method runs even faster than the conventional BPF algorithm and provides accurate reconstructions from listmode data in TOF-PET, free of rebinning data to a sinogram.

Design study of fully wearable high-performance brain PETs for neuroimaging in free movement.

A practical wearable brain PET scanner capable of dynamic neuroimaging during free bodily movement will enable potential breakthrough basic neuroscience studies and help develop imaging-based neurological diagnoses and treatments. Weight, brain coverage, and sensitivity are three fundamental technical obstacles in the development of Fully Wearable High-Performance (FWHP) brain PET scanners. The purpose of this study is to investigate the feasibility of building a FWHP brain PET using a limited volume of lutetium-yttrium oxyorthosilicate (LYSO) scintillator crystals. Six scanners, consisted of the same volume (2.66 kg) of LYSO scintillators with combinations of 2 different crystal pitches (3 mm and 1.5 mm) and 3 different crystal lengths (20 mm, 10 mm, and 5 mm), were simulated. The performances of the six scanners were assessed and compared with Siemen's HRRT brain PET and mCT whole-body PET, in terms of aperture, axial field of views (AFOV), sensitivity, spatial resolution, count rates, and image noise property. The time-of-flight (TOF) information was included in the image reconstruction to improve the effective sensitivity. The effects of the TOF was assessed by scanning a Jaszczak phantom and reconstructing images with the maximum likelihood expectation maximization (MLEM) algorithm with different timing settings (non-TOF, 500 ps, 200 ps, 100 ps and 50 ps Coincidence Time Resolution, CTR). The signal-noise ratio (SNR) of the images were assessed and compared with those of the HRRT scanner and mCT scanner. The results show that it is possible to construct a FWHP brain PET with better spatial resolution than the dedicated HRRT brain PET, comparable effective sensitivity (with 50 ∼ 100 ps CTR), and whole-brain coverage (23.7 cm inner diameter and 13.4 cm axial field of view) using 2.66 kg of LYSO.

Enhancing the Image Quality via Transferred Deep Residual Learning of Coarse PET Sinograms.

Increasing the image quality of positron emission tomography (PET) is an essential topic in the PET community. For instance, thin-pixelated crystals have been used to provide high spatial resolution images but at the cost of sensitivity and manufacture expense. In this paper, we proposed an approach to enhance the PET image resolution and noise property for PET scanners with large pixelated crystals. To address the problem of coarse blurred sinograms with large parallax errors associated with large crystals, we developed a data-driven, single-image super-resolution (SISR) method for sinograms, based on the novel deep residual convolutional neural network (CNN). Unlike the CNN-based SISR on natural images, periodically padded sinogram data and dedicated network architecture were used to make it more efficient for PET imaging. Moreover, we included the transfer learning scheme in the approach to process cases with poor labeling and small training data set. The approach was validated via analytically simulated data (with and without noise), Monte Carlo simulated data, and pre-clinical data. Using the proposed method, we could achieve comparable image resolution and better noise property with large crystals of bin sizes of thin crystals with a bin size from to . Our approach uses external PET data as the prior knowledge for training and does not require additional information during inference. Meanwhile, the method can be added into the normal PET imaging framework seamlessly, thus potentially finds its application in designing low-cost high-performance PET systems.

Simulation study of a high-performance brain PET system with dodecahedral geometry.

PURPOSE: In brain imaging, the spherical PET system achieves the highest sensitivity when the solid angle is concerned. However, it is not practical. In this work, we designed an alternative sphere-like scanner, the dodecahedral scanner, which has a high sensitivity in imaging and a high feasibility to manufacture. We simulated this system and compared the performance with a few other dedicated brain PET systems. METHODS: Monte Carlo simulations were conducted to generate data of the dedicated brain PET system with the dodecahedral geometry (11 regular pentagon detectors). The data were then reconstructed using the in-house developed software with the fully three-dimensional maximum-likelihood expectation maximization (3D-MLEM) algorithm. RESULTS: Results show that the proposed system has a high-sensitivity distribution for the whole field of view (FOV). With a depth-of-interaction (DOI) resolution around 6.67 mm, the proposed system achieves the spatial resolution of 1.98 mm. Our simulation study also shows that the proposed system improves the image contrast and reduces noise compared with a few other dedicated brain PET systems. Finally, simulations with the Hoffman phantom show the potential application of the proposed system in clinical applications. CONCLUSIONS: In conclusion, the proposed dodecahedral PET system is potential for widespread applications in high-sensitivity, high-resolution PET imaging, to lower the injected dose.

Design of a short nonuniform acquisition protocol for quantitative analysis in dynamic cardiac SPECT imaging - a retrospective 123 I-MIBG animal study.

PURPOSE: Our previous works have found that quantitative analysis of 123 I-MIBG kinetics in the rat heart with dynamic single-photon emission computed tomography (SPECT) offers the potential to quantify the innervation integrity at an early stage of left ventricular hypertrophy. However, conventional protocols involving a long acquisition time for dynamic imaging reduce the animal survival rate and thus make longitudinal analysis difficult. The goal of this work was to develop a procedure to reduce the total acquisition time by selecting nonuniform acquisition times for projection views while maintaining the accuracy and precision of estimated physiologic parameters. METHOD: Taking dynamic cardiac imaging with 123 I-MIBG in rats as an example, we generated time activity curves (TACs) of regions of interest (ROIs) as ground truths based on a direct four-dimensional reconstruction of experimental data acquired from a rotating SPECT camera, where TACs represented as the coefficients of B-spline basis functions were used to estimate compartmental model parameters. By iteratively adjusting the knots (i.e., control points) of B-spline basis functions, new TACs were created according to two rules: accuracy and precision. The accuracy criterion allocates the knots to achieve low relative entropy between the estimated left ventricular blood pool TAC and its ground truth so that the estimated input function approximates its real value and thus the procedure yields an accurate estimate of model parameters. The precision criterion, via the D-optimal method, forces the estimated parameters to be as precise as possible, with minimum variances. Based on the final knots obtained, a new protocol of 30 min was built with a shorter acquisition time that maintained a 5% error in estimating rate constants of the compartment model. This was evaluated through digital simulations. RESULTS: The simulation results showed that our method was able to reduce the acquisition time from 100 to 30 min for the cardiac study of rats with 123 I-MIBG. Compared to a uniform interval dynamic SPECT protocol (1 s acquisition interval, 30 min acquisition time), the newly proposed protocol with nonuniform interval achieved comparable (K1 and k2, P = 0.5745 for K1 and P = 0.0604 for k2) or better (Distribution Volume, DV, P = 0.0004) performance for parameter estimates with less storage and shorter computational time. CONCLUSION: In this study, a procedure was devised to shorten the acquisition time while maintaining the accuracy and precision of estimated physiologic parameters in dynamic SPECT imaging. The procedure was designed for 123 I-MIBG cardiac imaging in rat studies; however, it has the potential to be extended to other applications, including patient studies involving the acquisition of dynamic SPECT data.

An energy-optimized collimator design for a CZT-based SPECT camera.

In single photon emission computed tomography, it is a challenging task to maintain reasonable performance using only one specific collimator for radio-tracers over a broad spectrum of diagnostic photon energies, since photon scatter and penetration in a collimator differ with the photon energy. Frequent collimator exchanges are inevitable in daily clinical SPECT imaging, which hinders throughput while subjecting the camera to operational errors and damage. Our objective is to design a collimator, which independent of the photon energy performs reasonably well for commonly used radiotracers with low- to medium-energy levels of gamma emissions. Using the Geant4 simulation toolkit, we simulated and evaluated a parallel-hole collimator mounted to a CZT detector. With the pixel-geometry-matching collimation, the pitch of the collimator hole was fixed to match the pixel size of the CZT detector throughout this work. Four variables, hole shape, hole length, hole radius/width and the source-to-collimator distance were carefully studied. Scatter and penetration of the collimator, sensitivity and spatial resolution of the system were assessed for four radionuclides including 57Co, 99m Tc, 123I and 111In, with respect to the aforementioned four variables. An optimal collimator was then decided upon such that it maximized the total relative sensitivity (TRS) for the four considered radionuclides while other performance parameters, such as scatter, penetration and spatial resolution, were benchmarked to prevalent commercial scanners and collimators. Digital phantom studies were also performed to validate the system with the optimal square-hole collimator (23 mm hole length, 1.28 mm hole width, 0.32 mm septal thickness) in terms of contrast, contrast-to-noise ratio and recovery ratio. This study demonstrates promise of our proposed energy-optimized collimator to be used in a CZT-based gamma camera, with comparable or even better imaging performance versus commercial collimators such as low-energy high resolution (LEHR) and medium energy general purpose (MEGP) collimators.

Dynamic FDG-PET Imaging to Differentiate Malignancies from Inflammation in Subcutaneous and In Situ Mouse Model for Non-Small Cell Lung Carcinoma (NSCLC).

BACKGROUND: [18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) has been widely used in oncologic procedures such as tumor diagnosis and staging. However, false-positive rates have been high, unacceptable and mainly caused by inflammatory lesions. Misinterpretations take place especially when non-subcutaneous inflammations appear at the tumor site, for instance in the lung. The aim of the current study is to evaluate the use of dynamic PET imaging procedure to differentiate in situ and subcutaneous non-small cell lung carcinoma (NSCLC) from inflammation, and estimate the kinetics of inflammations in various locations. METHODS: Dynamic FDG-PET was performed on 33 female mice inoculated with tumor and/or inflammation subcutaneously or inside the lung. Standardized Uptake Values (SUVs) from static imaging (SUVmax) as well as values of influx rate constant (Ki) of compartmental modeling from dynamic imaging were obtained. Static and kinetic data from different lesions (tumor and inflammations) or different locations (subcutaneous, in situ and spontaneous group) were compared. RESULTS: Values of SUVmax showed significant difference in subcutaneous tumor and inflammation (p<0.01), and in inflammations from different locations (p<0.005). However, SUVmax showed no statistical difference between in situ tumor and inflammation (p = 1.0) and among tumors from different locations (subcutaneous and in situ, p = 0.91). Values of Ki calculated from compartmental modeling showed significant difference between tumor and inflammation both subcutaneously (p<0.005) and orthotopically (p<0.01). Ki showed also location specific values for inflammations (subcutaneous, in situ and spontaneous, p<0.015). However, Ki of tumors from different locations (subcutaneous and in situ) showed no significant difference (p = 0.46). CONCLUSION: In contrast to static PET based SUVmax, both subcutaneous and in situ inflammations and malignancies can be differentiated via dynamic FDG-PET based Ki. Moreover, Values of influx rate constant Ki from compartmental modeling can offer an assessment for inflammations at different locations of the body, which also implies further validation is necessary before the replacement of in situ inflammation with its subcutaneous counterpart in animal experiments.

Fast direct estimation of the blood input function and myocardial time activity curve from dynamic SPECT projections via reduction in spatial and temporal dimensions.

PURPOSE: Reconstruction of parametric images from dynamic single photon emission computed tomography (SPECT) data acquired with slow rotating cameras is a challenge because the estimation of the time-activity curves (TACs) may involve fitting data to an inconsistent underdetermined system of equations. This work presents a novel algorithm for the estimation of the blood input function and myocardial TAC with high accuracy and high efficiency directly from these projections. METHODS: In the proposed dynamic reconstruction method, the information from the segmentation of functional regions from the static reconstructed image was used as a prior to construct a sparse matrix, through which the spatial distribution of the radioactive tracer was represented. Then the temporal distribution of the radioactive tracer was modeled by nonuniform B-spline basis functions which were determined according to a new selection rule. With reduction in both the spatial and temporal dimensions of the reconstructed image, the blood input function and myocardial TAC were estimated using the 4D maximum likelihood expectation maximization algorithm. The method was validated using data from both digital phantom simulations and an experimental rat study. RESULTS: Compared with the conventional dynamic SPECT reconstruction method without the reduction in spatial dimensions, the proposed method provides more accurate TACs with less computation time in both phantom simulation studies and a rat experimental study. CONCLUSIONS: The proposed method is promising in both providing more accurate time-activity curves and reducing the computation time, which makes it practical for small animal studies using clinical systems with slow rotating cameras.