Technetium-99m TRODAT-1, a tropane derivative, has shown promise as a tracer for the imaging of dopamine transporters in preliminary studies in rats and baboons. The present report concerns the first study of the use of [99mTc]TRODAT-1 for the same purpose in humans. The specific uptake of [99mTc]TRODAT-1 in dopamine transporter sites located in the basal ganglia area was confirmed: the best contrast between the basal ganglia and the occipital area, which is devoid of dopamine transporters, was achieved at 120-140 min following injection. The development of a 99mTc-based agent bypasses the need for cyclotron-produced radionuclides, which will be of benefit for routine clinical studies.

The purpose of this study was to develop a radiopharmaceutical that could be used to selectively image 5-HT1A receptors with positron emission tomography (PET). No-carrier-added 4-(2'-methoxyphenyl)-1-[2'-(N-2"-pyridinyl)-p-[18F] fluorobenzamido]ethylpiperazine (p-[18F]-MPPF, 2) was synthesized by the nucleophilic substitution of the corresponding nitro precursor 1 with K[18F]/Kryptofix 2.2.2. in dimethyl sulfoxide (DMSO) at 140 degrees C for 20 min followed by purification with high-performance liquid chromatography (HPLC) in 10% yield in a synthesis time of 90 min from end of bombardment (EOB). Specific activity was 1-4 Ci/microM. Biodistribution studies in rats showed that the initial uptake of 2 in the brain was high (0.7% dose/g tissue at 2 min). It was then rapidly eliminated. Rates of elimination were significantly slower in brain regions with high concentrations of 5-HT1A receptors (hippocampus, cortex, and hypothalamus) than in control regions. The maximum hippocampal/cerebellar ratio was 5.6:1 at 30 min postinjection. Uptake values in serotonergic, but not in control, regions were significantly reduced by prior treatment with either (+/-)-8-OH-DPAT (2 mg/kg, i.v., 5 min prior) or WAY 100635 (1 mg/kg, i.v., 5 min prior). Radioactivity in the femur did not increase with time, suggesting that in vivo defluorination may not be the major route of metabol sm. PET studies of 2 in a monkey demonstrated selective uptake and retention of 2 in the hippocampus. The hippocampal/cerebellar ratio was 3:1 at 30 min postinjection. The ratio was reduced to 1:1 by administering (+/-)-8-OH-DPAT (2 mg/kg, i.v.) 23 min postinjection of 2. Analyses of arterial plasma by HPLC revealed that 20% of radioactivity in the plasma remained as the parent compound 2 at 30 min postinjection. The results suggest that p-[18F]-MPPF may be a useful radioligand for studying cerebral 5-HT1A receptors in humans with PET techniques.

This study assessed the performance of the True X reconstruction. Spatial resolution with True X reconstruction was improved by 45 % and its % contrast was significantly improved compared to those with the conventional OSEM without PSF modeling reconstruction algorithm. The noise level was higher than that with the other reconstruction algorithm. Therefore, True X reconstruction should be used with caution when quantifying PET data.

Purpose: Sparsely sampled computed tomography (CT) has been attracting attention as a technique that can reduce the high radiation dose of conventional CT. In general, iterative reconstruction techniques have been applied to sparsely sampled CT to realize high quality images. These methodologies require high computing power due to the modeling of the system and the trajectory of radiation rays. Therefore, the purpose of this study was to obtain high quality three-dimensional (3D) reconstructed images with deep learning under sparse sampling conditions. Methods: We used a deep learning model based on a fully convolutional network and a wavelet transform to predict high quality images. To reduce the spatial resolution loss of predicted images, we replaced the pooling layer with a wavelet transform. Three different domains were evaluatedthe sinogram domain, the image domain, and the hybrid domainto optimize a reconstruction technique based on deep learning. To train and develop a deep learning model, The Cancer Imaging Archive (TCIA) dataset was used. Results: Streak artifacts, which generally occur under sparse sampling conditions, were effectively removed from deep learning-based sparsely sampled reconstructed images. However, image characteristics of fine structures varied depending on the application of deep learning technologies. The use of deep learning techniques in the sinogram domain removed streak artifacts well, but some image noise remained. Likewise, when applying deep learning technology to the image domain, a blurring effect occurred. The proposed hybrid domain sparsely sampled reconstruction based on deep learning was able to restore images to a quality similar to fully sampled images. The structural similarity (SSIM) index values of sparsely sampled CT reconstruction based on deep learning technology were 0.85 or higher. Among the three domains studied, the hybrid domain techniques achieved the highest SSIM index values (0.9 or more). Conclusion: We proposed a method of sparsely sampled CT reconstruction from a new perspective unlike iterative reconstruction. In addition, we developed an optimal deep learning-based sparse sampling reconstruction technique by evaluating image quality with deep learning technologies.

The authors estimated human-equivalent internal radiation dose of 124I-MIBG using preclinical imaging data. As a reference, the effective dose estimation showed that 124I-MIBG would deliver less radiation dose than 124I-NaI, a radiotracer already being used in patients with thyroid cancer.

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Dual-energy computed tomography (CT) techniques have been used to decompose materials and characterize tissues according to their physical and chemical compositions. However, these techniques are hampered by the limitations of conventional x-ray detectors operated in charge integrating mode. Energy-resolved photon-counting detectors provide spectral information from polychromatic x-rays using multiple energy thresholds. These detectors allow simultaneous acquisition of data in different energy ranges without spectral overlap, resulting in more efficient material decomposition and quantification for dual-energy CT. In this study, a pre-reconstruction dual-energy CT technique based on volume conservation was proposed for three-material decomposition. The technique was combined with iterative reconstruction algorithms by using a ray-driven projector in order to improve the quality of decomposition images and reduce radiation dose. A spectral CT system equipped with a CZT-based photon-counting detector was used to implement the proposed dual-energy CT technique. We obtained dual-energy images of calibration and three-material phantoms consisting of low atomic number materials from the optimal energy bins determined by Monte Carlo simulations. The material decomposition process was accomplished by both the proposed and post-reconstruction dual-energy CT techniques. Linear regression and normalized root-mean-square error (NRMSE) analyses were performed to evaluate the quantitative accuracy of decomposition images. The calibration accuracy of the proposed dual-energy CT technique was higher than that of the post-reconstruction dual-energy CT technique, with fitted slopes of 0.97-1.01 and NRMSEs of 0.20-4.50% for all basis materials. In the three-material phantom study, the proposed dual-energy CT technique decreased the NRMSEs of measured volume fractions by factors of 0.17-0.28 compared to the post-reconstruction dual-energy CT technique. It was concluded that the proposed dual-energy CT technique can potentially be used to decompose mixtures into basis materials and characterize tissues according to their composition.