Reduced Acquisition Time [18F]GE-180 PET Scanning Protocol Replaces Gold-Standard Dynamic Acquisition in a Mouse Ischemic Stroke Model

Aim Understanding neuroinflammation after acute ischemic stroke is a crucial step on the way to an individualized post-stroke treatment. Microglia activation, an essential part of neuroinflammation, can be assessed using [ 18 F]GE-180 18 kDa translocator protein positron emission tomography (TSPO-PET). However, the commonly used 60–90 min post-injection (p.i.) time window was not yet proven to be suitable for post-stroke neuroinflammation assessment. In this study, we compare semi-quantitative estimates derived from late time frames to quantitative estimates calculated using a full 0–90 min dynamic scan in a mouse photothrombotic stroke (PT) model. Materials and Methods Six mice after PT and six sham mice were included in the study. For a half of the mice, we acquired four serial 0–90 min scans per mouse (analysis cohort) and calculated standardized uptake value ratios (SUVRs; cerebellar reference) for the PT volume of interest (VOI) in five late 10 min time frames as well as distribution volume ratios (DVRs) for the same VOI. We compared late static 10 min SUVRs and the 60–90 min time frame of the analysis cohort to the corresponding DVRs by linear fitting. The other half of the animals received a static 60–90 min scan and was used as a validation cohort. We extrapolated DVRs by using the static 60–90 min p.i. time window, which were compared to the DVRs of the analysis cohort. Results We found high linear correlations between SUVRs and DVRs in the analysis cohort for all studied 10 min time frames, while the fits of the 60–70, 70–80, and 80–90 min p.i. time frames were the ones closest to the line of identity. For the 60–90 min time window, we observed an excellent linear correlation between SUVR and DVR regardless of the phenotype (PT vs . sham). The extrapolated DVRs of the validation cohort were not significantly different from the DVRs of the analysis group. Conclusion Simplified quantification by a reference tissue ratio of the late 60–90 min p.i. [ 18 F]GE-180 PET image can replace full quantification of a dynamic scan for assessment of microglial activation in the mouse PT model

Parameter Optimization of a Digital Photon Counter Coupled to a Four-Layered DOI Crystal Block With Light Sharing

We have developed four-layered depth-of-interaction (DOI) detectors based on light sharing. Reflectors, which are inserted in every two lines of crystal segments and shifted differently depending on each layer, project 3-D crystal positions to a 2-D position histogram without any overlapping after applying the Anger-type calculation. The DOI measurement itself has the potential to improve time resolution because the depth-dependent timing delay can be corrected. However, light sharing tends to increase variance of light paths inside the crystal block, thus resulting in worsened time resolution. Although we have reported advantages of our DOI detectors in terms of position and energy resolutions, we had not evaluated their potential for time resolution. In this paper, therefore, we measured timing performance with the help of a digital photon counter (DPC), which offers precise control of event triggering. There are several studies that have reported one-to-one coupling of the scintillator to the DPC pixel, but DPCs have not been studied well for light-sharing detectors. Therefore, in this work, we optimized measurement parameters of the DPCs for our four-layered DOI detector. The DOI detector consists of 256 LGSO crystals which are arranged in four layers of 8 × 8 arrays, coupled to the DPC array. Each crystal element is 2.9 ×2.9 × 5 mm^3 . Each die of the DPC array provides an individual timestamp. Crystal identification performance largely depended on the dark count rate of the DPC array, which can be reduced by means of cell inhibition. We measured several conditions of the inhibition rate of microcells and temperature. For increased inhibition rate, we observed degraded time resolution, although positioning performance and energy resolution were improved. Regarding the temperature dependency within 10 to -7°C, we found that time resolution was insensitive. At 10 °C and 20% inhibition rate, average time resolution over all crystals was 267±32 ps (full width half maximum). Better positioning performance and energy resolution were obtained for colder temperatures

Improving depth-of-interaction resolution in pixellated PET detectors using neural networks

Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have a long axial field of view (FOV), which increases estimation uncertainty of the annihilation position and therefore degrades the spatial resolution. A way to address this issue is depth-of-interaction (DOI) estimation. In this work we propose two machine learning-based algorithms, a dense and a convolutional neural network (NN), as well as a multiple linear regression (MLR)-based method to estimate DOI in depolished PET detector arrays with single-sided readout. The algorithms were tested on an 8× 8 array of 1.53× 1.53× 15 mm3 crystals and a 4× 4 array of 3.1× 3.1× 15 mm3 crystals, both made of Ce:LYSO scintillators and coupled to a 4× 4 array of 3× 3 mm3 silicon photomultipliers (SiPMs). Using the conventional linear DOI estimation method resulted in an average DOI resolution of 3.76 mm and 3.51 mm FWHM for the 8× 8 and the 4× 4 arrays, respectively. Application of MLR outperformed the conventional method with average DOI resolutions of 3.25 mm and 3.33 mm FWHM, respectively. Using the machine learning approaches further improved the DOI resolution, to an average DOI resolution of 2.99 mm and 3.14 mm FWHM, respectively, and additionally improved the uniformity of the DOI resolution in both arrays. Lastly, preliminary results obtained by using only a section of the crystal array for training showed that the NN-based methods could be used to reduce the number of calibration steps required for each detector array