TY - JOUR A1 - Eissler, Cristoph A1 - Werner, Rudolf A. A1 - Arias-Loza, Paula A1 - Nose, Naoko A1 - Chen, Xinyu A1 - Pomper, Martin G. A1 - Rowe, Steven P. A1 - Lapa, Constantin A1 - Buck, Andreas K. A1 - Higuchi, Takahiro T1 - The number of frames on ECG-gated \(^{18}\)F-FDG small animal PET has a significant impact on LV systolic and diastolic functional parameters JF - Molecular Imaging N2 - Objectives. This study is aimed at investigating the impact of frame numbers in preclinical electrocardiogram- (ECG-) gated \(^{18}\)F-fluorodeoxyglucose (\(^{18}\)F-FDG) positron emission tomography (PET) on systolic and diastolic left ventricular (LV) parameters in rats. Methods. \(^{18}\)F-FDG PET imaging using a dedicated small animal PET system with list mode data acquisition and continuous ECG recording was performed in diabetic and control rats. The list-mode data was sorted and reconstructed with different numbers of frames (4, 8, 12, and 16) per cardiac cycle into tomographic images. Using an automatic ventricular edge detection software, left ventricular (LV) functional parameters, including ejection fraction (EF), end-diastolic (EDV), and end-systolic volume (ESV), were calculated. Diastolic variables (time to peak filling (TPF), first third mean filling rate (1/3 FR), and peak filling rate (PFR)) were also assessed. Results. Significant differences in multiple parameters were observed among the reconstructions with different frames per cardiac cycle. EDV significantly increased by numbers of frames (353.8 & PLUSMN; 57.7 mu l*, 380.8 & PLUSMN; 57.2 mu l*, 398.0 & PLUSMN; 63.1 mu l*, and 444.8 & PLUSMN; 75.3 mu l at 4, 8, 12, and 16 frames, respectively; *P < 0.0001 vs. 16 frames), while systolic (EF) and diastolic (TPF, 1/3 FR and PFR) parameters were not significantly different between 12 and 16 frames. In addition, significant differences between diabetic and control animals in 1/3 FR and PFR in 16 frames per cardiac cycle were observed (P < 0.005), but not for 4, 8, and 12 frames. Conclusions. Using ECG-gated PET in rats, measurements of cardiac function are significantly affected by the frames per cardiac cycle. Therefore, if you are going to compare those functional parameters, a consistent number of frames should be used. KW - Myocardial-perfusion SPECT KW - left-ventricular function KW - ejection fraction KW - MRI Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-265778 VL - 2021 ER - TY - JOUR A1 - Schadt, Fabian A1 - Israel, Ina A1 - Samnick, Samuel T1 - Development and Validation of a Semi-Automated, Preclinical, MRI-Template Based PET Image Data Analysis Tool for Rodents JF - Frontiers in Neuroinformatics N2 - AimIn PET imaging, the different types of radiotracers and accumulations, as well as the diversity of disease patterns, make the analysis of molecular imaging data acquired in vivo challenging. Here, we evaluate and validate a semi-automated MRI template-based data analysis tool that allows preclinical PET images to be aligned to a self-created PET template. Based on the user-defined volume-of-interest (VOI), image data can then be evaluated using three different semi-quantitative parameters: normalized activity, standardized uptake value, and uptake ratio. Materials and MethodsThe nuclear medicine Data Processing Analysis tool (NU_DPA) was implemented in Matlab. Testing and validation of the tool was performed using two types of radiotracers in different kinds of stroke-related brain diseases in rat models. The radiotracers used are 2-[\(^{18}\)F]fluoro-2-deoxyglucose ([\(^{18}\)F]FDG), a metabol\(^{68}\)Ga]Ga-Fucoidan, a target-selective radioligand specifically binding to p-selectin. After manual image import, the NU_DPA tool automatically creates an averaged PET template out of the acquired PET images, to which all PET images are then aligned onto. The added MRI template-based information, resized to the lower PET resolution, defines the VOI and also allows a precise subdivision of the VOI into individual sub-regions. The aligned PET images can then be evaluated semi-quantitatively for all regions defined in the MRI atlas. In addition, a statistical analysis and evaluation of the semi-quantitative parameters can then be performed in the NU_DPA tool. ResultsUsing ischemic stroke data in Wistar rats as an example, the statistical analysis of the tool should be demonstrated. In this [\(^{18}\)F]FDG-PET experiment, three different experimental states were compared: healthy control state, ischemic stroke without electrical stimulation, ischemic stroke with electrical stimulation. Thereby, statistical data evaluation using the NU_DPA tool showed that the glucose metabolism in a photothrombotic lesion can be influenced by electrical stimulation. ConclusionOur NU_DPA tool allows a very flexible data evaluation of small animal PET data in vivo including statistical data evaluation. Using the radiotracers [\(^{18}\)F]FDG and [\(^{68}\)Ga]Ga-Fucoidan, it was shown that the semi-automatic MRI-template based data analysis of the NU_DPA tool is potentially suitable for both metabolic radiotracers as well as target-selective radiotracers. KW - data analysis KW - Matlab KW - MRI KW - PET KW - positron emission tomography KW - preclinical imaging Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-240289 SN - 1662-5196 VL - 15 ER - TY - JOUR A1 - Kazuhino, Koshino A1 - Werner, Rudolf A. A1 - Toriumi, Fuijo A1 - Javadi, Mehrbod S. A1 - Pomper, Martin G. A1 - Solnes, Lilja B. A1 - Verde, Franco A1 - Higuchi, Takahiro A1 - Rowe, Steven P. T1 - Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images JF - Tomography N2 - Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%-60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for "data-hungry" technologies, such as supervised machine learning approaches, in various clinical applications. KW - AI KW - Magnetresonanztomografie KW - artificial intelligence KW - magnetic resonance imaging KW - MRI KW - DCGAN KW - GAN KW - stroke KW - machine learning Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172185 VL - 4 IS - 4 ER -