Imaging of Myocardial αvß3 Integrin Expression for Evaluation of Myocardial Injury After Acute Myocardial Infarction.

[68Ga]Ga-NODAGA-Arg-Gly-Asp (RGD) is a PET tracer targeting αvß3 integrin, which is upregulated during angiogenesis soon after acute myocardial infarction (AMI). We prospectively evaluated determinants of myocardial uptake of [68Ga]Ga-NODAGA-RGD and its associations with left ventricular (LV) function in patients after AMI. Methods: Myocardial blood flow and [68Ga]Ga-NODAGA-RGD uptake (60 min after injection) were evaluated by PET in 31 patients 7.7 ± 3.8 d after primary percutaneous coronary intervention for ST-elevation AMI. Transthoracic echocardiography of LV function was performed on the day of PET and at the 6-mo follow-up. Results: PET images showed increased uptake of [68Ga]Ga-NODAGA-RGD in the ischemic area at risk (AAR), predominantly in injured myocardial segments. The SUV in the segment with the highest uptake (SUVmax) in the ischemic AAR was higher than the SUVmean of the remote myocardium (0.73 ± 0.16 vs. 0.51 ± 0.11, P < 0.001). Multivariable predictors of [68Ga]Ga-NODAGA-RGD uptake in the AAR included high peak N-terminal pro-B-type natriuretic peptide (P < 0.001), low LV ejection fraction, low global longitudinal strain (P = 0.01), and low longitudinal strain in the AAR (P = 0.01). [68Ga]Ga-NODAGA-RGD uptake corrected for myocardial blood flow and perfusable tissue fraction in the AAR predicted improvement in global longitudinal strain at follow-up (P = 0.002), independent of peak troponin, N-terminal pro-B-type natriuretic peptide, and LV ejection fraction. Conclusion: [68Ga]Ga-NODAGA-RGD uptake shows increased αvß3 integrin expression in the ischemic AAR early after AMI that is associated with regional and global systolic dysfunction, as well as increased LV filling pressure. Increased [68Ga]Ga-NODAGA-RGD uptake predicts improvement of global LV function 6 mo after AMI.

Small animal PET with spontaneous inhalation of 15O-labelled oxygen gases: Longitudinal assessment of cerebral oxygen metabolism in a rat model of neonatal hypoxic-ischaemic encephalopathy.

Perinatal hypoxic-ischaemic encephalopathy (HIE) is the leading cause of irreversible brain damage resulting in serious neurological dysfunction among neonates. We evaluated the feasibility of positron emission tomography (PET) methodology with 15O-labelled gases without intravenous or tracheal cannulation for assessing temporal changes in cerebral blood flow (CBF) and cerebral metabolic rate for oxygen (CMRO2) in a neonatal HIE rat model. Sequential PET scans with spontaneous inhalation of 15O-gases mixed with isoflurane were performed over 14 days after the hypoxic-ischaemic insult in HIE pups and age-matched controls. CBF and CMRO2 in the injured hemispheres of HIE pups remarkably decreased 2 days after the insult, gradually recovering over 14 days in line with their increase found in healthy controls according to their natural maturation process. The magnitude of hemispheric tissue loss histologically measured after the last PET scan was significantly correlated with the decreases in CBF and CMRO2.This fully non-invasive imaging strategy may be useful for monitoring damage progression in neonatal HIE and for evaluating potential therapeutic outcomes.

Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset.

Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by evaluating its localisation performance on 165 heterogeneous cone beam computed tomography (CBCT) scans from 72 patients in comparison to an experienced radiologist's annotations. We evaluated the performance of the DLS using the symmetric mean curve distance (SMCD), the average symmetric surface distance (ASSD), and the Dice similarity coefficient (DSC). The reproducibility of the SMCD was assessed using the within-subject coefficient of repeatability (RC). Three other experts rated the diagnostic validity twice using a 0-4 Likert scale. The reproducibility of the Likert scoring was assessed using the repeatability measure (RM). The RC of SMCD was 0.969 mm, the median (interquartile range) SMCD and ASSD were 0.643 (0.186) mm and 0.351 (0.135) mm, respectively, and the mean (standard deviation) DSC was 0.548 (0.138). The DLS performance was most affected by postoperative changes. The RM of the Likert scoring was 0.923 for the radiologist and 0.877 for the DLS. The mean (standard deviation) Likert score was 3.94 (0.27) for the radiologist and 3.84 (0.65) for the DLS. The DLS demonstrated proficient qualitative and quantitative reproducibility, temporal generalisability, and clinical validity.

Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans.

Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce. To validate the mandibular canal localization accuracy of a deep learning system (DLS) we trained it with 982 CBCT scans and evaluated using 150 scans of five scanners from clinical workflow patients of European and Southeast Asian Institutes, annotated by four radiologists. The interobserver variability was compared to the variability between the DLS and the radiologists. In addition, the generalisation of DLS to CBCT scans from scanners not used in the training data was examined to evaluate its out-of-distribution performance. The DLS had a statistically significant difference (p < 0.001) with lower variability to the radiologists with 0.74 mm than the interobserver variability of 0.77 mm and generalised to new devices with 0.63 mm, 0.67 mm and 0.87 mm (p < 0.001). For the radiologists' consensus segmentation, used as a gold standard, the DLS showed a symmetric mean curve distance of 0.39 mm, which was statistically significantly different (p < 0.001) compared to those of the individual radiologists with values of 0.62 mm, 0.55 mm, 0.47 mm, and 0.42 mm. These results show promise towards integration of DLS into clinical workflow to reduce time-consuming and labour-intensive manual tasks in implantology.

Influence of head positioning during cone-beam CT imaging on the accuracy of virtual 3D models.

OBJECTIVE: Cone beam computed tomography (CBCT) images are being increasingly used to acquire three-dimensional (3D) models of the skull for additive manufacturing purposes. However, the accuracy of such models remains a challenge, especially in the orbital area. The aim of this study is to assess the impact of four different CBCT imaging positions on the accuracy of the resulting 3D models in the orbital area. METHODS: An anthropomorphic head phantom was manufactured by submerging a dry human skull in silicon to mimic the soft tissue attenuation and scattering properties of the human head. The phantom was scanned on a ProMax 3D MAX CBCT scanner using 90 and 120 kV for four different field of view positions: standard; elevated; backwards tilted; and forward tilted. All CBCT images were subsequently converted into 3D models and geometrically compared with a "gold-standard" optical scan of the dry skull. RESULTS: Mean absolute deviations of the 3D models ranged between 0.15 ± 0.11 mm and 0.56 ± 0.28 mm. The elevated imaging position in combination with 120 kV tube voltage resulted in an improved representation of the orbital walls in the resulting 3D model without compromising the accuracy. CONCLUSIONS: Head positioning during CBCT imaging can influence the accuracy of the resulting 3D model. The accuracy of such models may be improved by positioning the region of interest (e.g. the orbital area) in the focal plane (Figure 2a) of the CBCT X-ray beam.

The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions.

Any change in the light-source spectrum modifies the color information of an object. The spectral distribution of the light source can be optimized to enhance specific details of the obtained images; thus, using information-enhanced images is expected to improve the image recognition performance via machine vision. However, no studies have applied light spectrum optimization to reduce the training loss in modern machine vision using deep learning. Therefore, we propose a method for optimizing the light-source spectrum to reduce the training loss using neural networks. A two-class classification of one-vs-rest among the classes, including enamel as a healthy condition and dental lesions, was performed to validate the proposed method. The proposed convolutional neural network-based model, which accepts a 5 × 5 small patch image, was compared with an alternating optimization scheme using a linear-support vector machine that optimizes classification weights and lighting weights separately. Furthermore, it was compared with the proposed neural network-based algorithm, which inputs a pixel and consists of fully connected layers. The results of the five-fold cross-validation revealed that, compared to the previous method, the proposed method improved the F1-score and was superior to the models that were using the immutable standard illuminant D65.

Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes.

Accurate localisation of mandibular canals in lower jaws is important in dental implantology, in which the implant position and dimensions are currently determined manually from 3D CT images by medical experts to avoid damaging the mandibular nerve inside the canal. Here we present a deep learning system for automatic localisation of the mandibular canals by applying a fully convolutional neural network segmentation on clinically diverse dataset of 637 cone beam CT volumes, with mandibular canals being coarsely annotated by radiologists, and using a dataset of 15 volumes with accurate voxel-level mandibular canal annotations for model evaluation. We show that our deep learning model, trained on the coarsely annotated volumes, localises mandibular canals of the voxel-level annotated set, highly accurately with the mean curve distance and average symmetric surface distance being 0.56 mm and 0.45 mm, respectively. These unparalleled accurate results highlight that deep learning integrated into dental implantology workflow could significantly reduce manual labour in mandibular canal annotations.

Comprehensive two-dimensional field-flow fractionation-liquid chromatography in the analysis of large molecules.

A novel, comprehensive two-dimensional asymmetric field-flow fractionation-liquid chromatographic system is described (AsFlFFF-RPLC). The interface is based on a switching valve, and the whole sample is analyzed in both dimensions. The system proved to be repeatable and quantitative in the characterization of egg white proteins. Four peaks at 4, 5.5-6.0, 7.5-8.0, and 10.0-11.0 nm, and corresponding to lysozyme, ovalbumin, transferrin, and a dimer of transferrin, were obtained in the AsFlFFF first-dimension system. Lysozyme also produced an additional peak, which overlapped with ovalbumin. Twelve compounds were separated in the LC second-dimension system. Identifications were made with the help of standards (ovalbumin, ovotransferrin, lysozyme) and by comparison of the peak areas, particle sizes, and retention data with values given in the literature. The effect of heat on egg white denaturation was studied, and the unfolding of peptide bonds of the protein was found to be pronounced when the sample was heated in phosphate solution.