Fast and Accurate Gamma Imaging System Calibration Based on Deep Denoising Networks and Self-Adaptive Data Clustering.

Gamma imagers play a key role in both industrial and medical applications. Modern gamma imagers typically employ iterative reconstruction methods in which the system matrix (SM) is a key component to obtain high-quality images. An accurate SM could be acquired from an experimental calibration step with a point source across the FOV, but at a cost of long calibration time to suppress noise, posing challenges to real-world applications. In this work, we propose a time-efficient SM calibration approach for a 4π-view gamma imager with short-time measured SM and deep-learning-based denoising. The key steps include decomposing the SM into multiple detector response function (DRF) images, categorizing DRFs into multiple groups with a self-adaptive K-means clustering method to address sensitivity discrepancy, and independently training separate denoising deep networks for each DRF group. We investigate two denoising networks and compare them against a conventional Gaussian filtering method. The results demonstrate that the denoised SM with deep networks faithfully yields a comparable imaging performance with the long-time measured SM. The SM calibration time is reduced from 1.4 h to 8 min. We conclude that the proposed SM denoising approach is promising and effective in enhancing the productivity of the 4π-view gamma imager, and it is also generally applicable to other imaging systems that require an experimental calibration step.

A Wide Energy Range and 4π-View Gamma Camera with Interspaced Position-Sensitive Scintillator Array and Embedded Heavy Metal Bars.

(1) Background: Gamma cameras have wide applications in industry, including nuclear power plant monitoring, emergency response, and homeland security. The desirable properties of a gamma camera include small weight, good resolution, large field of view (FOV), and wide imageable source energy range. Compton cameras can have a 4π FOV but have limited sensitivity at low energy. Coded-aperture gamma cameras are operatable at a wide photon energy range but typically have a limited FOV and increased weight due to the thick heavy metal collimators and shielding. In our lab, we previously proposed a 4π-view gamma imaging approach with a 3D position-sensitive detector, with which each detector element acts as the collimator for other detector elements. We presented promising imaging performance for 99mTc, 18F, and 137Cs sources. However, the imaging performance for middle- and high-energy sources requires further improvement. (2) Methods: In this study, we present a new gamma camera design to achieve satisfactory imaging performance in a wide gamma energy range. The proposed gamma camera consists of interspaced bar-shaped GAGG (Ce) crystals and tungsten absorbers. The metal bars enhance collimation for high-energy gamma photons without sacrificing the FOV. We assembled a gamma camera prototype and conducted experiments to evaluate the gamma camera's performance for imaging 57Co, 137Cs, and 60Co point sources. (3) Results: Results show that the proposed gamma camera achieves a positioning accuracy of <3° for all gamma energies. It can clearly resolve two 137Cs point sources with 10° separation, two 57Co and two 60Co point sources with 20° separation, as well as a 2 × 3 137Cs point-source array with 20° separation. (4) Conclusions: We conclude that the proposed gamma camera design has comprehensive merits, including portability, 4π-view FOV, and good angular resolution across a wide energy range. The presented approach has promising potential in nuclear security applications.

Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation.

BACKGROUND: Attenuation correction can improve the quantitative accuracy of single-photon emission computed tomography (SPECT) images. Existing SPECT-only systems normally can only provide non-attenuation corrected (NC) images which are susceptible to attenuation artifacts. In this work, we developed a post-reconstruction attenuation correction (PRAC) approach facilitated by a deep learning-based attenuation map for myocardial perfusion SPECT imaging. METHODS: In the PRAC method, new projection data were estimated via forwardly projecting the scanner-generated NC image. Then an attenuation map, generated from NC image using a pretrained deep learning (DL) convolutional neural network, was incorporated into an offline reconstruction algorithm to obtain the attenuation-corrected images from the forwardly projected projections. We evaluated the PRAC method using 30 subjects with a DL network trained with 40 subjects, using the vendor-generated AC images and CT-based attenuation maps as the ground truth. RESULTS: The PRAC methods using DL-generated and CT-based attenuation maps were both highly consistent with the scanner-generated AC image. The globally normalized mean absolute errors were 1.1% ± .6% and .7% ± .4% and the localized absolute percentage errors were 8.9% ± 13.4% and 7.8% ± 11.4% in the left ventricular (LV) blood pool, respectively, and - 1.3% ± 8.0% and - 3.8% ± 4.5% in the LV myocardium for PRAC methods using DL-generated and CT-based attenuation maps, respectively. The summed stress scores after PRAC using both attenuation maps were more consistent with the ground truth than those of the NC images. CONCLUSION: We developed a PRAC approach facilitated by deep learning-based attenuation maps for SPECT myocardial perfusion imaging. It may be feasible for this approach to provide AC images for SPECT-only scanner data.

PPARγ Activation-Mediated Egr-1 Inhibition Benefits Against Brain Injury in an Experimental Ischaemic Stroke Model.

BACKGROUND: Inflammatory response is a critical contributor to cerebral ischaemia injuries and blood-brain barrier (BBB) dysfunction. Early growth response-1 (Egr-1), an oxygen-sensing transcription factor which is rapidly and markedly triggered in ischaemic events, acts as a master switch coordinating the upregulation of multiple target proinflammatory genes. Here, we explored whether peroxisome proliferator-activated receptor-gamma (PPARγ) activation by telmisartan can modulate Egr-1 expression and the subsequent inflammatory responses in a rat model of cerebral ischaemia. METHODS: Cerebral ischaemia was induced in rats by middle cerebral artery occlusion (MCAO). Brain injury was evaluated by brain water content, infarct volume, and Evans blue dye extravasation. Egr-1 and claudin-5 levels were assessed by western blot and real-time polymerase chain reaction. RESULTS: MCAO-provoked Egr-1 expression was time dependent, peaking at 24 h and continuing to 72 h. The elevation in Egr-1 was coupled with a reduction in claudin-5. Telmisartan treatment significantly corrected the alterations of Egr-1 and claudin-5, alleviated the neurological deficits, and reduced brain water content, infarct volume, and Evans blue dye extravasation 24 h after MCAO. However, all the benefits of telmisartan were reversed by antagonising PPARγ with GW9662. CONCLUSION: Egr-1, a proinflammatory factor, is positively associated with post-ischaemic inflammation and the associated BBB dysfunction. PPARγ serves as an upstream transcription factor of the Egr-1 cascade. Targeting Egr-1 may emerge as a potential strategy to suppress inflammatory responses following ischaemic stroke.

Improvement of Whole-body Bone Planar Images on a Bone-dedicated Single-photon Emission Computed Tomography Scanner by Blind Deconvolution Algorithm.

Purpose: We have developed a bone-dedicated collimator with higher sensitivity but slightly degraded resolution on single-photon emission computed tomography (SPECT) for planar bone scintigraphy, compared with conventional low-energy high-resolution collimator. In this work, we investigated the feasibility of using the blind deconvolution algorithm to improve the resolution of planar images on bone scintigraphy. Materials and Methods: Monte Carlo simulation was performed with the NCAT phantom for modeling bone scintigraphy on the clinical dual-head SPECT scanner (Imagine NET 632, Beijing Novel Medical Equipment Ltd.) equipped with the bone-dedicated collimator. Maximum likelihood estimation method was used for the blind deconvolution algorithm. The initial estimation of point spread function (PSF) and iteration number for the method were determined by comparing the deblurred images obtained from different input parameters. We simulated different tumors in five different locations and with five different diameters to evaluate the robustness of the initial inputs. Furthermore, we performed chest phantom studies on the clinical SPECT scanner. The quantified increased contrast ratio (CR) between the tumor and the background was evaluated. Results: The 2 mm PSF kernel and 10 iterations provided a practical and robust deblurred image on our system. Those two inputs can generate robust deblurred images in terms of the tumor location and size with an average increased CR of 21.6%. The phantom studies also demonstrated the ability of blind deconvolution, using those two inputs, with increased CRs of 17%, 17%, 22%, 20%, and 13% for lesions with diameters of 1 cm, 2 cm, 3 cm, 4 cm, and 5 cm, respectively. Conclusions: It is feasible to use the blind deconvolution algorithm to deblur the planar images for SPECT bone scintigraphy. The appropriate values of the PSF kernel and the iteration number for the blind deconvolution can be determined using simulation studies.

Efficient image reconstruction for a small animal PET system with dual-layer-offset detector design.

BACKGROUND: A compact PET/SPECT/CT system Inliview-3000B has been developed to provide multi-modality information on small animals for biomedical research. Its PET subsystem employed a dual-layer-offset detector design for depth-of-interaction capability and higher detection efficiency, but the irregular design caused some difficulties in calculating the normalization factors and the sensitivity map. Besides, the relatively larger (2 mm) crystal cross-section size also posed a challenge to high-resolution image reconstruction. PURPOSE: We present an efficient image reconstruction method to achieve high imaging performance for the PET subsystem of Inliview-3000B. METHODS: List mode reconstruction with efficient system modeling was used for the PET imaging. We adopt an on-the-fly multi-ray tracing method with random crystal sampling to model the solid angle, crystal penetration and object attenuation effect, and modify the system response model during each iteration to improve the reconstruction performance and computational efficiency. We estimate crystal efficiency with a novel iterative approach that combines measured cylinder phantom data with simulated line-of-response (LOR)-based factors for normalization correction before reconstruction. Since it is necessary to calculate normalization factors and the sensitivity map, we stack the two crystal layers together and extend the conventional data organization method here to index all useful LORs. Simulations and experiments were performed to demonstrate the feasibility and advantage of the proposed method. RESULTS: Simulation results showed that the iterative algorithm for crystal efficiency estimation could achieve good accuracy. NEMA image quality phantom studies have demonstrated the superiority of random sampling, which is able to achieve good imaging performance with much less computation than traditional uniform sampling. In the spatial resolution evaluation based on the mini-Derenzo phantom, 1.1 mm hot rods could be identified with the proposed reconstruction method. Reconstruction of double mice and a rat showed good spatial resolution and a high signal-to-noise ratio, and organs with higher uptake could be recognized well. CONCLUSION: The results validated the superiority of introducing randomness into reconstruction, and demonstrated its reliability for high-performance imaging. The Inliview-3000B PET subsystem with the proposed image reconstruction can provide rich and detailed information on small animals for preclinical research.

Robust H∞ Control for Semilinear Parabolic Distributed Parameter Systems With External Disturbances via Mobile Actuators and Sensors.

This article presents a robust H∞ feedback compensator design approach for semilinear parabolic distributed parameter systems (DPSs) with external disturbances via mobile actuators and sensors. An H∞ performance constraint is introduced to deal with the external disturbances from the model and measurement noise. Two types of feedback compensators are designed in terms of the collocated and noncollocated mobile actuators and sensors. By the Lyapunov direct technique, some sufficient conditions based on LMI constraints are proposed for the exponential stability under H∞ performance constraints in the L2 -norm. Moreover, the open-loop and closed-loop well-posedness of the semilinear DPSs with external disturbances are analyzed via the C0 -semigroup theory approach. Finally, extensive numerical simulation results for semilinear DPSs with external disturbances via collocated and noncollocated mobile actuators and sensors are shown to verify the effectiveness of the proposed method.

Characterization and Assessment of Projection Probability Density Function and Enhanced Sampling in Self-Collimation SPECT.

We have recently reported a self-collimation SPECT (SC-SPECT) design concept that constructs sensitive detectors in a multi-ring interspaced mosaic architecture to simultaneously improve system spatial resolution and sensitivity. In this work, through numerical and Monte-Carlo simulation studies, we investigate this new design concept by analyzing its projection probability density functions (PPDF) and the effects of enhanced sampling, i.e. having rotational and translational object movements during imaging. We first quantitatively characterize PPDFs by their widths and edge slopes. Then we compare the PPDFs of an SC-SPECT and a series of multiple-pinhole SPECT (MPH-SPECT) systems and assess the impact of PPDFs - combined with enhanced sampling - on image contrast recovery coefficient and variance through phantom studies. We show the PPDFs of SC- SPECT have steeper edges and a wider range of width, and these attributes enable SC-SPECT to achieve better performance.

Study of 99mTc absorption on micro-sized ion exchange resins to achieve high activity for SPECT.

In single-photon emission computed tomography (SPECT), a micro-sized 99mTc source is routinely used for performance measurement, geometry calibration, and system matrix generation. Therefore, a micro-sized source is critical in nuclear instrument production and quality control. Standard methods can only produce a point source with a large size and low total activity, as they are limited by the concentration of the 99mTc solution. The absorption of 99mTc on ion exchange resins has been used; however, few studies have quantitatively evaluated the absorption process and optimized the source activity. This paper proposes a procedure for producing a micro-sized 99mTc resin source with a super-high concentration, as well as a method for the fast measurement of the point source time-activity curve (TAC). Experiments on two resin point sources with diameters of 0.681 mm and 0.326 mm were carried out. Two semi-empirical models, including the first kinetic model and the pseudo-second-order rate equation model, were used to fit TACs. The results show the first kinetic model fit better, which suggests an acquisition time of 2-4 h is needed for optimization. The verification experiment demonstrates a resin point source with a diameter of 0.35 mm and total activity of 10.6 mCi (i.e., 59.1 Ci/mL concentration) was produced.

A 3-dimensional stationary cascade gamma-ray coincidence imager.

Objective.For certain radionuclides that decay through emitting two or more gamma photons consecutively within a short time interval-called cascade gamma-rays, the location where a radiopharmaceutical molecule emits cascade gamma-rays can be identified through coincidence detection of the photons. If each cascade photon is detected through a collimation mechanism, the location of the molecule can be inferred from the intersection of the back-projections of the two photons.Approach.In this work, we report the design and evaluation of a three-dimensional stationary imager based on this concept for imaging distributions of cascade-emitting radionuclides in radiopharmaceutical therapy. The imager was composed of two gamma-ray cameras assembled in an L-shape. Both cameras were NaI(Tl) scintillator based, one with a multi-slit collimator, the other with a multi-pinhole collimator. The field of view (FOV) was 100 mm (∅) × 100 mm (L). Based on the unique characteristics of the cascade coincidence events, we used a direct back-projection algorithm to reconstruct point source images for assessing the imager's intrinsic spatial resolution and the standard maximum likelihood expectation maximization algorithm for reconstructing phantom images.Main results.We evaluated the performance of the imager in both simulated and prototype form with radionuclide177Lu (cascade photon emitter). On the simulated imager, the coincidence detection efficiency at the center of FOV was 3.85 × 10-6, the spatial resolution was 7.0 mm. On the prototype imager, the corresponding values were 3.20 × 10-6and 6.7 mm, respectively. Simulated hot-rod and experimental cardiac phantom studies demonstrate the first three-dimensional cascade gamma coincidence imager is fully functional.

Self-Collimating SPECT With Multi-Layer Interspaced Mosaic Detectors.

Conventional single photon emission computed tomography (SPECT) relies on mechanical collimation whose resolution and sensitivity are interdependent, the best performance a SPECT system can attain is only a compromise of these two equally desired properties. To simultaneously achieve high resolution and sensitivity, we propose to use sensitive detectors constructed in a multi-layer in ter spaced mosaicdetectors (MATRICES) architecture to accomplish part of the collimation needed. We name this new approach self-collimation. We evaluate three self-collimating SPECT systems and report their imaging performance: 1) A simulated human brain SPECT achieves 3.88% sensitivity, it clearly resolves 0.5-mm and 1.0-mm hot-rod patterns at noise-free and realistic count-levels, respectively; 2) a simulated mouse SPECT achieves 1.25% sensitivity, it clearly resolves 50- [Formula: see text] and 100- [Formula: see text] hot-rod patterns at noise-free and realistic count-levels, respectively; 3) a SPECT prototype achieves 0.14% sensitivity and clearly separates 0.3-mm-diameter point sources of which the center-to-center neighbor distance is also 0.3 mm. Simulated contrast phantom studies show excellent resolution and signal-to-noise performance. The unprecedented system performance demonstrated by these 3 SPECT scanners is a clear manifestation of the superiority of the self-collimating approach over conventional mechanical collimation. It represents a potential paradigm shift in SPECT technology development.

Generation of synthetic PET images of synaptic density and amyloid from 18 F-FDG images using deep learning.

PURPOSE: Positron emission tomography (PET) imaging with various tracers is increasingly used in Alzheimer's disease (AD) studies. However, access to PET scans using new or less-available tracers with sophisticated synthesis and short half-life isotopes may be very limited. Therefore, it is of great significance and interest in AD research to assess the feasibility of generating synthetic PET images of less-available tracers from the PET image of another common tracer, in particular 18 F-FDG. METHODS: We implemented advanced deep learning methods using the U-Net model to predict 11 C-UCB-J PET images of synaptic vesicle protein 2A (SV2A), a surrogate of synaptic density, from 18 F-FDG PET data. Dynamic 18 F-FDG and 11 C-UCB-J scans were performed in 21 participants with normal cognition (CN) and 33 participants with Alzheimer's disease (AD). Cerebellum was used as the reference region for both tracers. For 11 C-UCB-J image prediction, four network models were trained and tested, which included 1) 18 F-FDG SUV ratio (SUVR) to 11 C-UCB-J SUVR, 2) 18 F-FDG Ki ratio to 11 C-UCB-J SUVR, 3) 18 F-FDG SUVR to 11 C-UCB-J distribution volume ratio (DVR), and 4) 18 F-FDG Ki ratio to 11 C-UCB-J DVR. The normalized root mean square error (NRMSE), structure similarity index (SSIM), and Pearson's correlation coefficient were calculated for evaluating the overall image prediction accuracy. Mean bias of various ROIs in the brain and correlation plots between predicted images and true images were calculated for ROI-based prediction accuracy. Following a similar training and evaluation strategy, 18 F-FDG SUVR to 11 C-PiB SUVR network was also trained and tested for 11 C-PiB static image prediction. RESULTS: The results showed that all four network models obtained satisfactory 11 C-UCB-J static and parametric images. For 11 C-UCB-J SUVR prediction, the mean ROI bias was -0.3% ± 7.4% for the AD group and -0.5% ± 7.3% for the CN group with 18 F-FDG SUVR as the input, -0.7% ± 8.1% for the AD group, and -1.3% ± 7.0% for the CN group with 18 F-FDG Ki ratio as the input. For 11 C-UCB-J DVR prediction, the mean ROI bias was -1.3% ± 7.5% for the AD group and -2.0% ± 6.9% for the CN group with 18 F-FDG SUVR as the input, -0.7% ± 9.0% for the AD group, and -1.7% ± 7.8% for the CN group with 18 F-FDG Ki ratio as the input. For 11 C-PiB SUVR image prediction, which appears to be a more challenging task, the incorporation of additional diagnostic information into the network is needed to control the bias below 5% for most ROIs. CONCLUSIONS: It is feasible to use 3D U-Net-based methods to generate synthetic 11 C-UCB-J PET images from 18 F-FDG images with reasonable prediction accuracy. It is also possible to predict 11 C-PiB SUVR images from 18 F-FDG images, though the incorporation of additional non-imaging information is needed.

The Neuroprotective Effect of Irisin in Ischemic Stroke.

Irisin is a PGC-1α-dependent myokine that causes increased energy expenditure by driving the development of white adipose tissue into brown fat-like tissue. Exercise can improve irisin levels and lead to its release into the blood. In ischemic stroke, neurons are always sensitive to energy supply; after a series of pathophysiological processes, reactive oxygen species that are detrimental to cell survival via mitochondrial dysfunction are generated in large quantities. As a protein associated with exercise, irisin can alleviate brain injury in the pathogenesis of ischemic stroke. It is thought that irisin can upregulate the levels of brain-derived neurotrophic factor (BDNF), which protects nerve cells from injury during ischemic stroke. Furthermore, the release of irisin into the blood via exercise influences the mitochondrial dynamics crucial to maintaining the normal function of nerve cells. Consequently, we intended to summarize the known effects of irisin during ischemic stroke.

Maximum likelihood activity and attenuation estimation using both emission and transmission data with application to utilization of Lu-176 background radiation in TOF PET.

PURPOSE: We present a new method for joint reconstruction of activity and attenuation images using both emission and transmission data and demonstrate its advantage over the standard maximum likelihood activity and attenuation (MLAA) reconstruction using emission data alone. METHODS: We define a joint likelihood function including both time-of-flight (TOF) emission data and transmission data. The latter can be obtained from an external source or from Lu-176 background radiation. Activity and attenuation images are estimated jointly by maximizing the likelihood function. The proposed method solves the undetermined scale problem in the conventional MLAA. A monotonically convergent algorithm was derived to optimize the objective function. Furthermore, we present a theoretical analysis of the noise propagation in the joint reconstruction. Simulations and phantom experiments were conducted to validate the feasibility of the proposed method. RESULTS: Quantitatively correct and less noisy images were reconstructed with the proposed method. Artifacts in the attenuation map reconstructed from the standard MLAA were removed by incorporating transmission data. Noise analysis was validated with different transmission sources and transmission count levels. The theoretical prediction indicated that noise of activity map would not change in a large range of transmission count level and a very low transmission count level could result in good estimation. CONCLUSIONS: The results demonstrate the feasibility of obtaining quantitatively correct images in TOF PET by using both emission and (weak) transmission data. The noise analysis also provides guidance for choosing a proper transmission source configuration to reduce noise propagation.

Multifunction bismuth gadolinium oxide nanoparticles as radiosensitizer in radiation therapy and imaging.

Multifunction bismuth-based nanoparticles with the ability to display diagnostic and therapeutic functions have drawn extensive attention as theranostic agents in radiation therapy and imaging due to their high atomic number, low toxicity, and low cost. Herein, we tried to introduce multifunction bismuth gadolinium oxide nanoparticles (BiGdO3) as a new theranostic agent for radiation therapy, computed tomography (CT) and magnetic resonance imaging (MRI). After synthesis of BiGdO3 nanoparticles and surface modifications of them with PEG, biocompatibility of the nanoparticles was evaluated by a CCK-8 assay. We investigated dose amplification properties of the nanoparticles using gel dosimetry and in vitro and in vivo assays. According to clonogenic assay radiation, a sensitizer enhancement ratio (SER) of 1.75 and 1.66 (100 µg ml-1-nanoparticles), for MCF-7 and 4T1 cell lines at low energy x-ray was achieved, respectively. Radiation dose enhancement effect of the nanoparticles was proven for high concentrations (500 µg ml-1) by gel dosimetry. For further investigation, in vivo cancer radiotherapy was carried out using female BALB/c mice with 4T1 breast tumors. In vivo results emphasized the radiosensitizing effect of BiGdO3-PEG nanoparticles. Both bismuth and gadolinium provide CT contrast, while gadolinium can be employed for MRI T1 contrast, so we evaluated contrast enhancement of BiGdO3-PEG nanoparticles as a dual-modal imaging agent in MR and CT imaging. Collectively, our experimental results clearly display properties of BiGdO3-PEG nanoparticles as multimodal imaging and radiosensitizing agents. The results show that the nanoparticles deserve further study as a new theranostic agent.

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SNR), subsequently affecting diagnostic and quantitative accuracies. Deep learning methods have shown a great potential to reduce the noise and improve the SNR in low dose PET data. In this work, we comprehensively investigated the quantitative accuracy of small lung nodules, in addition to visual image quality, using deep learning based denoising methods for oncological PET imaging. We applied and optimized an advanced deep learning method based on the U-net architecture to predict the standard dose PET image from 10% low-dose PET data. We also investigated the effect of different network architectures, image dimensions, labels and inputs for deep learning methods with respect to both noise reduction performance and quantitative accuracy. Normalized mean square error (NMSE), SNR, and standard uptake value (SUV) bias of different nodule regions of interest (ROIs) were used for evaluation. Our results showed that U-net and GAN are superior to CAE with smaller SUVmean and SUVmax bias at the expense of inferior SNR. A fully 3D U-net has optimal quantitative performance compared to 2D and 2.5D U-net with less than 15% SUVmean bias for all the ten patients. U-net outperforms Residual U-net (r-U-net) in general with smaller NMSE, higher SNR and lower SUVmax bias. Fully 3D U-net is superior to several existing denoising methods, including Gaussian filter, anatomical-guided non-local mean (NLM) filter, and MAP reconstruction with Quadratic prior and relative difference prior, in terms of superior image quality and trade-off between noise and bias. Furthermore, incorporating aligned CT images has the potential to further improve the quantitative accuracy in multi-channel U-net. We found the optimal architectures and parameters of deep learning based methods are different for absolute quantitative accuracy and visual image quality. Our quantitative results demonstrated that fully 3D U-net can both effectively reduce image noise and control bias even for sub-centimeter small lung nodules when generating standard dose PET using 10% low count down-sampled data.

Crystal identification for a dual-layer-offset LYSO based PET system via Lu-176 background radiation and mean shift algorithm.

Modern positron emission tomography (PET) detectors are made from pixelated scintillation crystal arrays and readout by Anger logic. The interaction position of the gamma-ray should be assigned to a crystal using a crystal position map or look-up table. Crystal identification is a critical procedure for pixelated PET systems. In this paper, we propose a novel crystal identification method for a dual-layer-offset LYSO based animal PET system via Lu-176 background radiation and mean shift algorithm. Single photon event data of the Lu-176 background radiation are acquired in list-mode for 3 h to generate a single photon flood map (SPFM). Coincidence events are obtained from the same data using time information to generate a coincidence flood map (CFM). The CFM is used to identify the peaks of the inner layer using the mean shift algorithm. The response of the inner layer is deducted from the SPFM by subtracting CFM. Then, the peaks of the outer layer are also identified using the mean shift algorithm. The automatically identified peaks are manually inspected by a graphical user interface program. Finally, a crystal position map is generated using a distance criterion based on these peaks. The proposed method is verified on the animal PET system with 48 detector blocks on a laptop with an Intel i7-5500U processor. The total runtime for whole system peak identification is 67.9 s. Results show that the automatic crystal identification has 99.98% and 99.09% accuracy for the peaks of the inner and outer layers of the whole system respectively. In conclusion, the proposed method is suitable for the dual-layer-offset lutetium based PET system to perform crystal identification instead of external radiation sources.

Multifunctional Bismuth Ferrite Nanoparticles as Magnetic Localized Dose Enhancement in Radiotherapy and Imaging.

Recently bismuth-based nanoparticles have drawn extensive attention as radiosensitizer in radiotherapy due to high atomic number, low toxicity, and low cost. This study aims to introduce the applicability of bismuth ferrite nanoparticles (BFO, BiFeO3) as a new multifunctional theranostic agent for radiotherapy, magnetic resonance imaging (MRI), and computed tomography (CT) as well as magnetic hyperthermia mediator. After evaluation of BFO nanoparticles biocompatibility which were synthesized by conventional sol-gel method, we investigated dose enhancement property of BFO nanoparticles with gel dosimetry, clonogenic, and cck8 assay. According to clonogenic assay, sensitizer enhancement ratios (SERs) were obtained as 1.35 and 1.76 for nanoparticle concentrations of 0.05 mg/ml and 0.1 mg/ml, respectively. For high concentration (0.5 mg/ml), dose enhancement effect of BFO nanoparticles was demonstrated by gel dosimetry. To prove the contrast enhancement of BFO nanoparticles in MR and CT imaging, the relaxation time rate (R2) and Hounsfield unit (HU) were measured, respectively. It was found that the R2 and Hu have linear relationship with the nanoparticle concentrations. Moreover, whereas BFO nanoparticles have magnetic properties, we measured inductive heating property of the nanoparticles in external alternative magnetic field to evaluate their applicability as magnetic hyperthermia mediator. A rapid temperature increment was detected under alternative magnetic field (12.2 kAm-1 and 17.2 kAm-1, frequency 480 kHz) owing to the high concentration of BFO nanoparticles. Collectively, our experimental investigation results proved that the multifunctional BFO nanoparticles could be employed as a multimodal imaging and radio-thermotherapeutic agent to enhance theranostic efficacy.

Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static 18F-FDG PET as a function of injected dose.

Lung cancer mortality rate can be significantly reduced by up to 20% through routine low-dose computed tomography (LDCT) screening, which, however, has high sensitivity but low specificity, resulting in a high rate of false-positive nodules. Combining PET with CT may provide more accurate diagnosis for indeterminate screening-detected nodules. In this work, we investigated low-dose dynamic 18F-FDG PET in discrimination between benign and malignant nodules using a virtual clinical trial based on patient study with ground truth. Six patients with initial LDCT screening-detected lung nodules received 90 min single-bed PET scans following a 10 mCi FDG injection. Low-dose static and dynamic images were generated from under-sampled list-mode data at various count levels (100%, 50%, 10%, 5%, and 1%). A virtual clinical trial was performed by adding nodule population variability, measurement noise, and static PET acquisition start time variability to the time activity curves (TACs) of the patient data. We used receiver operating characteristic (ROC) analysis to estimate the classification capability of standardized uptake value (SUV) and net uptake constant K i from their simulated benign and malignant distributions. Various scan durations and start times (t *) were investigated in dynamic Patlak analysis to optimize simplified acquisition protocols with a population-based input function (PBIF). The area under curve (AUC) of ROC analysis was higher with increased scan duration and earlier t *. Highly similar results were obtained using PBIF to those using image-derived input function (IDIF). The AUC value for K i using optimized t * and scan duration with 10% dose was higher than that for SUV with 100% dose. Our results suggest that dynamic PET with as little as 1 mCi FDG could provide discrimination between benign and malignant lung nodules with higher than 90% sensitivity and specificity for patients similar to the pilot and simulated population in this study, with LDCT screening-detected indeterminate lung nodules.

A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy.

Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6 ± 15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size.