The PET component of NanoPET™/CT (Mediso) contains dual-module detectors, in which every detector module has been built up from two Hamamatsu H-9500 position sensitive photomultipliers (PSPMTs), optically coupled to the same crystal matrix using a thin BK-7 optical glass lightguide, allowing 95 mm AFOV. This arrangement allows to detect the signals of the crystal needles between the two PSPMTs, enlarging the effective area. However, the two PSPMTs of a detector module can have different transit time. The triggering signal of a module is the sum of the dynode signals of the two PSPMTs. For the higher slope of the triggering signal it can be helpful to delay one of the PSPMT dynode signals before the summation, to correct the possible transit time differences. With the use of this delay the effective transit times of the two PSPMTs will be synchronized and the time point of the triggering will be more accurate due to the higher slope, in other words the time resolution of the modules can be improved. The second step of the timing calibration is the correction of the difference of transit times of the 12 detector modules. For this purpose there is a digital timestamp delay instead of signal cables with different lengths. We describe the measurements and data processing methods we use to perform these calibrations illustrated with measured data.
The aim of this study was to evaluate how different iterative and filtered back projection kernels affect the computed tomography (CT) numbers and low contrast detectability.Five different scans were performed at 6 different tube potentials on the same Catphan 600 phantom using approximately the same dose level and otherwise identical settings. The scans were reconstructed using all available filtered back projection body kernels and with iterative reconstruction techniques.The CT numbers and the contrast-to-noise ratios were reported and how they are affected by the kernel choice and strength of iterative reconstruction.Iterative reconstruction improved contrast-to-noise ratio in most cases, but in certain situations, it decreased it. Variations in CT numbers can be large between kernels with similar sharpness for certain densities.
Measuring the mass balance of ice sheets is important with respect to understanding among others sea level rise, glacier dynamics, global ocean circulation and marine ecosystems. One important parameter of the mass balance is surface melt, which can be estimated from different satellite data sources. In this study we investigate the potential of utilizing machine learning techniques for CryoSat-2 (CS2) radar altimeter waveform classification in order to derive melt information. Training data is derived by spatio-temporally matching of CS2 measurements with MODIS land surface temperature measurements. We propose a time convolution network with a fully connected classifier tail for CS2 waveform classifcation. In addition a non-deep learning model is implemented, providing a baseline. One of the main challenges is the high class imbalance, as surface temperatures on the interior of Greenland rarely reach the freezing point. The model performance is measured by several metrics: F1 score, average recall and Matthews correlation coefficient. The results of this proof of concept study indicate feasibility.
The PET component of NanoPET TM /CT designed by Mediso Ltd. is a high resolution small animal PET system, in which every detector module contains 81×39 LYSO crystal needles of the size of 1.12×1.12×13 mm 3 . Two Hamamatsu H-9500 position sensitive photomultipliers (PSPMT-s) are optically coupled to the same crystal matrix using a thin BK-7 optical glass lightguide, allowing 95 mm AFOV. The gains of the 2×256 anodes of the PSPMTs are not uniform. The gap between the two PSPMTs results in restricted light collection efficiency for the crystal needles at the middle of the module. These two effects cause non-uniform gain, that could cause improper energy gating, the loss of 511 keV (photopeak) events and the imperfect filtering of the scattered photons. We have aimed to determine a local gain map for the detector to perform a uniformity correction to avoid these disadvantages. We have developed and implemented an application called LUT-QT to solve the crystal positioning and the gain uniformity correction pixel-by-pixel. Considering the large number of crystal elements in the detector ring (37908 pcs), all of the methods were designed to run automatically, although all of them allow the possibility of manual modification in every step. This method can be applied for every PET detector module based on pixelated scintillation crystal matrix and multianode PSPMT-s.
Computed Tomography (CT) images have a high dynamic range, which makes visualization challenging. Histogram equalization methods either use spatially invariant weights or limited kernel size due to the complexity of pairwise contribution calculation. We present a weighted histogram equalization-based tone mapping algorithm which utilizes Fast Fourier Transform for distance-dependent contribution calculation and distance-based weights. The weights follow power-law without distance-based cut-off. The resulting images have good local contrast without noticeable artefacts. The results are compared to eight popular tone mapping operators.
