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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.
Adipose tissue-derived stromal cells (ASCs) represent a capable source for cell-based therapeutic approaches. For monitoring a cell-based application in vivo, magnetic resonance imaging (MRI) of cells labeled with iron oxide particles is a common method. It is the aim of the present study to analyze potential DNA damage, cytotoxicity and impairment of functional properties of human (h)ASCs after labeling with citrate-coated very small superparamagnetic iron oxide particles (VSOPs). Cytotoxic as well as genotoxic effects of the labeling procedure were measured in labeled and unlabeled hASCs using the MTT assay, comet assay and chromosomal aberration test. Trilineage differentiation was performed to evaluate an impairment of the differentiation potential due to the particles. Proliferation as well as migration capability were analyzed after the labeling procedure. Furthermore, the labeling of the hASCs was confirmed by Prussian blue staining, transmission electron microscopy (TEM) and high-resolution MRI. Below the concentration of 0.6 mM, which was used for the procedure, no evidence of genotoxic effects was found. At 0.6 mM, 1 mM as well as 1.5 mM, an increase in the number of chromosomal aberrations was determined. Cytotoxic effects were not observed at any concentration. Proliferation, migration capability and differentiation potential were also not affected by the procedure. Labeling with VSOPs is a useful labeling method for hASCs that does not affect their proliferation, migration and differentiation potential. Despite the absence of cytotoxicity, however, indications of genotoxic effects have been demonstrated.
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.
Objective
Growing interest in measuring the cochlear duct length (CDL) has emerged, since it can influence the selection of cochlear implant electrodes. Currently the measurements are performed with ionized radiation imaging. Only a few studies have explored CDL measurements in magnetic resonance imaging (MRI). Therefore, the presented study aims to fill this gap by estimating CDL in MRI and comparing it with multislice computed tomography (CT).
Study Design
Retrospective data analyses of 42 cochleae.
Setting
Tertiary care medical center.
Methods
Diameter (A value) and width (B value) of the cochlea were measured in HOROS software. The CDL and the 2-turn length were determined by the elliptic circular approximation (ECA). In addition, the CDL, the 2-turn length, and the angular length were determined via HOROS software by the multiplanar reconstruction (MPR) method.
Results
CDL values were significantly shorter in MRI by MPR (d = 1.38 mm, P < .001) but not by ECA. Similar 2-turn length measurements were significantly lower in MRI by MPR (d = 1.67 mm) and ECA (d = 1.19 mm, both P < .001). In contrast, angular length was significantly higher in MRI (d = 26.79°, P < .001). When the values were set in relation to the frequencies of the cochlea, no clinically relevant differences were estimated (58 Hz at 28-mm CDL).
Conclusion
In the presented study, CDL was investigated in CT and MRI by using different approaches. Since no clinically relevant differences were found, diagnostics with radiation may be omitted prior to cochlear implantation; thus, a concept of radiation-free cochlear implantation could be established.
Background
Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters.
Methods
In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen–Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson’s correlation coefficient and Bland Altman analysis.
To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer.
Results
The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson’s correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L.
Conclusions
Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.
Background
The effect of smoking on coronary vasomotion has been investigated in the past with various imaging techniques in both short- and long-term smokers. Additionally, coronary vasomotion has been shown to be normalized in long-term smokers by L-Arginine acting as a substrate for NO synthase, revealing the coronary endothelium as the major site of abnormal vasomotor response. Aim of the prospective cohort study was to investigate coronary vasomotion of young healthy short-term smokers via magnetic resonance cold pressor test with and without the administration of L-Arginine and compare obtained results with the ones from nonsmokers.
Methods
Myocardial blood flow (MBF) was quantified with first-pass perfusion MRI on a 1.5 T scanner in healthy short-term smokers (N = 10, age: 25.0 ± 2.8 years, 5.0 ± 2.9 pack years) and nonsmokers (N = 10, age: 34.3 ± 13.6) both at rest and during cold pressor test (CPT). Smokers underwent an additional examination after administration of L-Arginine within a median of 7 days of the naïve examination.
Results
MBF at rest turned out to be 0.77 ± 0.30 (smokers with no L-Arginine; mean ± standard deviation), 0.66 ± 0.21 (smokers L-Arginine) and 0.84 ± 0.08 (nonsmokers). Values under CPT were 1.21 ± 0.42 (smokers no L-Arginine), 1.09 ± 0.35 (smokers L-Arginine) and 1.63 ± 0.33 (nonsmokers). In all groups, MBF was significantly increased under CPT compared to the corresponding rest examination (p < 0.05 in all cases). Additionally, MBF under CPT was significantly different between the smokers and the nonsmokers (p = 0.002). MBF at rest was significantly different between the smokers when L-Arginine was given and the nonsmokers (p = 0.035).
Conclusion
Short-term smokers showed a reduced response to cold both with and without the administration of L-Arginine. However, absolute MBF values under CPT were lower compared to nonsmokers independently of L-Arginine administration.