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Purpose: Xtrim‐PET is a newly designed Silicon Photomultipliers (SiPMs)‐based prototype PET scanner dedicated for small laboratory animal imaging. We present the performance evaluation of the Xtrim‐PET scanner following NEMA NU‐4 2008 standards to help optimizing scanning protocols which can be achieved through standard and reliable system performance characterization. Methods: The performance assessment was conducted according to the National Electrical Manufacturers Association (NEMA) NU‐4 2008 standards in terms of spatial resolution, sensitivity, counting rate performance, scatter fraction and image quality. The in vivo imaging capability of the scanner is also showcased through scanning a normal mouse injected with 18F‐FDG. Furthermore, the performance characteristics of the developed scanner are compared with commercially available systems and current prototypes. Results: The volumetric spatial resolution at 5 mm radial offset from the central axis of the scanner is 6.81 µl, whereas a peak absolute sensitivity of 2.99% was achieved using a 250–650 keV energy window and a 10 ns timing window. The peak noise‐equivalent count rate (NECR) using a mouse‐like phantom is 113.18 kcps at 0.34 KBq/cc with 12.5% scatter fraction, whereas the NECR peaked at 82.76 kcps for an activity concentration level of 0.048 KBq/cc with a scatter fraction of 25.8% for rat‐like phantom. An excellent uniformity (3.8%) was obtained using NEMA image quality phantom. Recovery coefficients of 90%, 86%, 68%, 40% and 12% were calculated for rod diameters of 5, 4, 3, 2 and 1 mm, respectively. Spill‐over ratios for air‐filled and water‐filled chambers were 35% and 25% without applying any correction for attenuation and Compton scattering effects. Conclusion: Our findings revealed that beyond compactness, lightweight, easy installation and good energy resolution, the Xtrim‐PET prototype presents a reasonable performance making it suitable for preclinical molecular imaging‐based research. [ABSTRACT FROM AUTHOR]

Introduction: The position estimation in gamma detection system will have constant misplacements which can be corrected in the calibration procedure. In the pixelated crystal uniformly irradiation of detector will produce irregular shape due to position estimation errors. This image is called flood field image and is used to calibrate the position estimation. In this work we present a novel approach to automatically calibrate pixelated crystal array position estimation. Methods: In the flood image of a pixelated crystal array the local peaks represent the estimation of position for the gamma photons that interacted in a single crystal pixel. First, the method detects 2-D peak locations automatically in the case of blurred pixel responses in the presence of noise and disturbance of the image. The algorithm consists of a filtering step for smoothing the image followed by two rounds of local peak detection. After localizing image peaks, the correction routine will map the image locations to the crystal pixels using the thin-plate spline interpolation method. Results: The algorithm is tested for two flood images obtained from developed detector with different irregularity levels. By configuring constant parameters according to the detector configuration the method detected all crystal pixels in the image and map them correctly. The method further has been tested for10 identical blocks and the result showed automatic peak detection routine for all the blocks. Conclusion: An automatic peak detection is presented to work instead of time consuming manual calibration routines. The method shows robust performance in the presence of image noise. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate the biodistribution of polyethylene glycol (PEG) coated superparamagnetic iron oxide nanoparticles radiolabeled with Ga in normal mice after intravenous administration of this probe. Three mice were sacrificed at specific time intervals. The biodistribution data revealed high uptake by liver and spleen (60.62 and 12.65 %ID/g at 120 min post injection for liver and spleen, respectively). The clearance of other organs was fast. These results suggest that Ga-PEG-SPIONs has magnificent capabilities for applying in (PET-MRI) as a theranostic agent for detection of liver and spleen malignancies. [ABSTRACT FROM AUTHOR]

Introduction: Reliable quantification in positron emission tomography (PET) requires accurate attenuation correction of emission data, which in turn entails accurate determination of the attenuation map (µ-map) of the object under study. One of the main steps involved in CT-based attenuation correction (CTAC) is energy-mapping, or the conversion of linear attenuation coefficients (µ) calculated at the effective CT energy to those corresponding to 511 keV.Materials and Methods: The aim of this study is to compare different energy-mapping techniques including scaling, segmentation, the hybrid method, the bilinear calibration curve technique and the dual-energy approach to generate the µ-maps required for attenuation correction. In addition, our newly proposed method involving a quadratic polynomial calibration curve was also assessed. The µ-maps generated for both phantom and clinical studies were assessed qualitatively and quantitatively. A cylindrical polyethylene phantom containing different concentrations of K(2)HPO(4) in water was scanned and the µ-maps calculated from the corresponding CT images using the above-referenced energy-mapping methods. The CT images of five whole-body data sets acquired on a GE Discovery LS PET/CT scanner were employed to generate µ-maps using different energy-mapping approaches that were compared with the µ-maps generated at 511 keV using (68)Ge/(68)Ga rod sources. In another experiment, the evaluation was performed on PET images of a clinical study corrected for attenuation using µ-maps generated using the above described methods. The evaluation was performed for three different tissue types, namely, soft tissue, lung, and bone.Results and Discussion: All energy-mapping methods yielded almost similar results for soft tissues. The mean relative differences between scaling, segmentation, hybrid, bilinear, and quadratic polynomial calibration curve methods and the transmission scan serving as reference were 6.60%, 6.56%, 6.60%, 5.96%, and 7.36%, respectively. However, the scaling method produced the largest difference (16%) for bone tissues. For lung tissues, the segmentation method produced the largest difference (14.9%). The results for reconstructed PET images followed a similar trend. For soft tissues, all energy-mapping methods yield results in nearly the same range. However, in bone tissues, the scaling method resulted in considerable bias in the µ-maps and the reconstructed PET images. The segmentation method also produced noticeable bias especially in regions with variable densities such as the lung, since a single µ is assigned to the lungs. Apart from the aforementioned case, despite small differences in the generated µ-maps, the use of different energy-mapping methods does not affect, to a visible or measurable extent, the reconstructed PET images. [ABSTRACT FROM AUTHOR]

The amount of radiotracer injected into laboratory animals is still the most daunting challenge facing translational PET studies. Since low-dose imaging is characterized by a higher level of noise, the quality of the reconstructed images leaves much to be desired. Being the most ubiquitous techniques in denoising applications, edge-aware denoising filters, and reconstruction-based techniques have drawn significant attention in low-count applications. However, for the last few years, much of the credit has gone to deep-learning (DL) methods, which provide more robust solutions to handle various conditions. Albeit being extensively explored in clinical studies, to the best of our knowledge, there is a lack of studies exploring the feasibility of DL-based image denoising in low-count small animal PET imaging. Therefore, herein, we investigated different DL frameworks to map low-dose small animal PET images to their full-dose equivalent with quality and visual similarity on a par with those of standard acquisition. The performance of the DL model was also compared to other well-established filters, including Gaussian smoothing, nonlocal means, and anisotropic diffusion. Visual inspection and quantitative assessment based on quality metrics proved the superior performance of the DL methods in low-count small animal PET studies, paving the way for a more detailed exploration of DL-assisted algorithms in this domain. [Display omitted] • Low-dose imaging is characterized by higher noise level, thus impacting image quality. • The feasibility of deep-learning-based image denoising in low-count small animal PET studies has never been reported. • We assessed the potential of various deep-learning models to generate full-dose mice PET scans from the undersampled data. • Compared to other methods, deep-learning brings a multitude of advantages in terms of quality, implementation, and speed. [ABSTRACT FROM AUTHOR]

Oral contrast is usually administered in most X-ray computed tomography (CT) examinations of the abdomen and the pelvis as it allows more accurate identification of the bowel and facilitates the interpretation of abdominal and pelvic CT studies. However, the misclassification of contrast medium with high-density bone in CT-based attenuation correction (CTAC) is known to generate artifacts in the attenuation map ( μmap), thus resulting in overcorrection for attenuation of positron emission tomography (PET) images. In this study, we developed an automated algorithm for segmentation and classification of regions containing oral contrast medium to correct for artifacts in CT-attenuation-corrected PET images using the segmented contrast correction (SCC) algorithm. The proposed algorithm consists of two steps: first, high CT number object segmentation using combined region- and boundary-based segmentation and second, object classification to bone and contrast agent using a knowledge-based nonlinear fuzzy classifier. Thereafter, the CT numbers of pixels belonging to the region classified as contrast medium are substituted with their equivalent effective bone CT numbers using the SCC algorithm. The generated CT images are then down-sampled followed by Gaussian smoothing to match the resolution of PET images. A piecewise calibration curve was then used to convert CT pixel values to linear attenuation coefficients at 511 keV. The visual assessment of segmented regions performed by an experienced radiologist confirmed the accuracy of the segmentation and classification algorithms for delineation of contrast-enhanced regions in clinical CT images. The quantitative analysis of generated μmaps of 21 clinical CT colonoscopy datasets showed an overestimation ranging between 24.4% and 37.3% in the 3D-classified regions depending on their volume and the concentration of contrast medium. Two PET/CT studies known to be problematic demonstrated the applicability of the technique in clinical setting. More importantly, correction of oral contrast artifacts improved the readability and interpretation of the PET scan and showed substantial decrease of the SUV (104.3%) after correction. An automated segmentation algorithm for classification of irregular shapes of regions containing contrast medium was developed for wider applicability of the SCC algorithm for correction of oral contrast artifacts during the CTAC procedure. The algorithm is being refined and further validated in clinical setting. [ABSTRACT FROM AUTHOR]

In this work, a correlation function based on linograms and sinograms of the projection data was introduced, implemented and evaluated in order to estimate and compensate for the patient motion. Parabolic fitting of the peak of the correlation function was utilized to improve in the motion-estimation task. In dynamic planar imaging, the method checked for motion via its separate frames similar to the SPECT work; on the other hand, in static planar imaging, the data acquisition protocol was first exchanged from static to the dynamic modality, followed by application of the motion-correction scheme, and a final combination via summation of the motion-compensated frames. The method was successfully evaluated in many cases. Our tests showed that in SPECT imaging, the time of motion starting in a specific projection and the duration of motion are very important in the motion detection process. Measuring relative error showed that the error in the images with presence of motion along the axis of patient bed was 21.1%, being reduced to 1.4% while the 24.4% error along the perpendicular axis to the patient bed was reduced to 1.5% with the inclusion of our motion correction scheme. Experimental results in planar imaging also demonstrated the ability of this method to reduce error in the maximum count from 40.7% to 9.7%, wherein intended sudden motion was introduced in static planar acquisition. Similarly, the error produced by gradual simulated motion was reduced from 37% to 1.6%. This method was clinically examined in the bone scan (static planar data) of an old man for an Osteomyelitis study, as well as in a kidney washout study (dynamic planar data) for a child. As a result, the motion artifacts shown in the images were reduced considerably. [ABSTRACT FROM AUTHOR]

We report on development of Xtrim-PET, a prototype low-cost preclinical PET system using high-density analog silicone photomultiplier (SiPM) arrays and pixelated scintillator LYSO:Ce crystals. Channel reduction method is employed to reduce 144 SiPM signals per detector block to 16 energy and 9 fast signals in the detector head. Using these signals, a Digital Front-End (DFE) board calculates energy, position, and time information of incident gamma photon and transmits data packets to a downstream Digital Coincidence Processing Unit (DCPU) in which the coincidence events are extracted in a fully pipelined digital processing fashion for maximum count throughput. The system consists of 10 detector blocks (detector head and DFE board), a DCPU board, and an acquisition computer. Prompt and delayed coincidence events are stored in list mode in the acquisition computer where first hand corrections are applied and 3D sinogram is generated in offline mode. Two-dimensional sinograms are then generated using single-slice rebinning algorithm and iterative image reconstruction methods are employed to generate the 3D image. The developed scanner works in room temperature without additional cooling system and has 180 mm ring diameter. Tomographic image resolution of 1.6, 1.7, and 1.8 mm FWHM was calculated along radial, tangential, and axial directions, respectively. Preliminary investigations were performed using NEMA image quality phantom in addition to rat cardiac and bone scans for preclinical evaluation of the scanner. [ABSTRACT FROM AUTHOR]