Tomographic image quality of rotating slat versus parallel hole-collimated SPECT

Parallel and converging hole collimators are most frequently used in nuclear medicine. Less common is the use of rotating slat collimators for single photon emission computed tomography (SPECT). The higher photon collection efficiency, inherent to the geometry of rotating slat collimators, results in much lower noise in the data. However, plane integrals contain spatial information in only one direction, whereas line integrals provide two-dimensional information. It is not a trivial question whether the initial gain in efficiency will compensate for the lower information content in the plane integrals. Therefore, a comparison of the performance of parallel hole and rotating slat collimation is needed. This study compares SPECT with rotating slat and parallel hole collimation in combination with MLEM reconstruction with accurate system modeling and correction for scatter and attenuation. A contrast-to-noise study revealed an improvement of a factor 2-3 for hot lesions and more than a factor of 4 for cold lesion. Furthermore, a clinically relevant case of heart lesion detection is simulated for rotating slat and parallel hole collimators. In this case, rotating slat collimators outperform the traditional parallel hole collimators. We conclude that rotating slat collimators are a valuable alternative for parallel hole collimators

Temperature dependence of the LabPET small-animal PET scanner

INTRODUCTION In quantitative PET imaging it is important to correct for all image-degrading effects, for example detector efficiency variation. Detector efficiency variation depends on the stability of detector efficiency when operating conditions vary within normal limits. As the efficiency of APD-based light detection strongly depends on ambient temperature, temperature-dependent detector efficiency normalization may be needed in APD-based PET scanners. We have investigated the temperature dependence of the LabPET APD-based small-animal PET scanner. MATERIALS AND METHODS First a simulation study was performed to evaluate the effect of different APD temperature coefficients on the temperature dependence of scanner sensitivity. Five experiments were also performed. First the immediate effect of temperature changes on scanner sensitivity was evaluated. Second, the effect of temperature changes that have stabilized for a few hours was investigated. In a third experiment the axial sensitivity profile was acquired at 21 degrees C and 24 degrees C. Next, two acquisitions of the NEMA image quality phantom (at 21 degrees C and 23 degrees C) were performed and absolute quantification was done based on normalization scans acquired at the correct and incorrect temperature. Finally, the feasibility of maintaining a constant room temperature and the stability of the scanner sensitivity under constant room temperature was evaluated. RESULTS Simulations showed that the relation between temperature-dependent APD gain changes and scanner sensitivity is quite complex. A temperature deviation leading to a 1 % change in APD gain corresponds to a much larger change in scanner sensitivity due to the shape of the energy histogram. In the first and second experiment a strong correlation between temperature and scanner sensitivity was observed. Changes of 2.24 kcps/MBq and 1.64 kcps/MBq per degrees C were seen for immediate and stabilized temperature changes respectively. The NEMA axial sensitivity profile also showed a decrease in sensitivity at higher temperature. The quantification experiment showed that a larger quantification error (up to 13%) results when a normalization scan acquired at the incorrect temperature is used. In the last experiment, temperature variability was 0.19 degrees C and counts varied by 10.2 Mcts (1.33%). CONCLUSION The sensitivity of the LabPET small-animal PET scanner strongly depends on room temperature. Therefore, room temperature should be kept as stable as possible and temperature-dependent detector efficiency normalization should be used. However, with constant room temperature excellent scanner stability is observed. Temperature should be kept constant within 0.5 degrees C and weekly normalization scans are recommended

Performance of digital silicon photomultipliers for time of flight PET scanners

The performance of Digital Silicon Photomultipliers (dSiPM) coupled to a LYSO array containing 15×15 pixels with a size of 2×2×22 mm3 is evaluated to determinate their potential for whole body Time of Flight (TOF) PET scanners. The detector pixels are smaller in size than the light sensors and therefore light spreading is required to determine the crystal where interaction occurred. A light guide of 1 mm was used to spread the light and neighbor logic (NL) configuration were employed to ensure correct crystals identification. We studied the energy resolution and coincidence resolving time (CRT) for different trigger levels. The measured average energy resolution across detector was 14.5 %. Prior to measurements of time resolution skew time calibration of dSiPM was performed. The average CRT achieved using trigger level 1 option was 376 ps FWHM. Finally, we studied the amount of events that are disregarded due to dark count effects for different trigger levels and temperatures. Our studies show that a trade-off must be made between the detector’s CRT and sensitivity due to its vulnerability to dark counts. To employ dSiPM in TOF PET systems without 1:1 coupling effective cooling is necessary to limit dark count influence

Reducing BCI calibration time with transfer learning: a shrinkage approach

Introduction: A brain-computer interface system (BCI) allows subjects to make use of neural control signals to drive a computer application. Therefor a BCI is generally equipped with a decoder to differentiate between types of responses recorded in the brain. For example, an application giving feedback to the user can benefit from recognizing the presence or absence of a so-called error potential (Errp), elicited in the brain of the user when this feedback is perceived as being ‘wrong’, a mistake of the system. Due to the high inter- and intra- subject variability in these response signals, calibration data needs to be recorded to train the decoder. This calibration session is exhausting and demotivating for the subject. Transfer Learning is a general name for techniques in which data from previous subjects is used as additional information to train a decoder for a new subject, thereby reducing the amount of subject specific data that needs to be recorded during calibration. In this work we apply transfer learning to an Errp detection task by applying single-target shrinkage to Linear Discriminant Analysis (LDA), a method originally proposed by Höhne et. al. to improve accuracy by compensating for inter-stimuli differences in an ERP-speller [1]. Material, Methods and Results: For our study we used the error potential dataset recorded by Perrin et al. in [2]. For 26 subjects each, 340 Errp/nonErrp responses were recorded with a #Errp to #nonErrp ratio of 0.41 to 0.94. 272 responses were available for training the decoder and the remaining 68 responses were left out for testing. For every subject separately we built three different decoders. First, a subject specific LDA decoder was built solely making use of the subject’s own train data. Second, we added the train data of the other 25 subjects to train a global LDA decoder, naively ignoring the difference between subjects. Finally, the single-target-shrinkage method (STS) [1] is used to regularize the parameters of the subject specific decoder towards those of the global decoder. Making use of cross validation this method assigns an optimal weight to the subject specific data and data from previous subjects to be used for training. Figure 1 shows the performance of the three decoders on the test data in terms of AUC as a function of the amount of subject specific calibration data used. Discussion: The subject specific decoder in Figure 1 shows how sensitive the decoding performance is to the amount of calibration data provided. Using data from previously recorded subjects the amount of calibration data, and as such the calibration time, can be reduced as shown by the global decoder. A certain amount of quality is however sacrificed. Making an optimal compromise between the subject specific and global decoder, the single-target-shrinkage decoder allows the calibration time to be reduced by 20% without any change in decoder quality (confirmed by a paired sample t-test giving p=0.72). Significance: This work serves as a first proof of concept in the use of shrinkage LDA as a transfer learning method. More specific, the error potential decoder built with reduced calibration time boosts the opportunity for error correcting methods in BCI

Adaptive SPECT: personalizing medical imaging

We develop modern techniques for image quality evaluation and optimization of imaging systems, and use them to control adaptive SPECT systems. Our results should contribute to the development of more personalized and efficient medical imaging