Fully automatic binary glioma grading based on pre-therapy MRI using 3D Convolutional Neural Networks

The optimal treatment strategy of newly diagnosed glioma is strongly influenced by tumour malignancy. Manual non-invasive grading based on MRI is not always accurate and biopsies to verify diagnosis negatively impact overall survival. In this paper, we propose a fully automatic 3D computer-aided diagnosis (CAD) system to non-invasively differentiate high-grade glioblastoma from lower-grade glioma. The approach consists of an automatic segmentation step to extract the tumour ROI followed by classification using a 3D convolutional neural network. Segmentation was performed using a 3D U-Net achieving a dice score of 88.53% which matches top performing algorithms in the BraTS 2018 challenge. The classification network was trained and evaluated on a large heterogeneous dataset of 549 patients reaching an accuracy of 91%. Additionally, the CAD system was evaluated on data from the Ghent University Hospital and achieved an accuracy of 92% which shows that the algorithm is robust to data from different centres

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

Brain Tumour Classification Using Deep Features from Structural MRI

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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