A New High-intensity, Low-momentum Muon Beam for the Generation of Low-energy Muons at PSI

At the Paul Scherrer Institute (PSI, Villigen, Switzerland) a new high-intensity muon beam line with momentum p < 40MeV/c is currently being commissioned. The beam line is especially designed to serve the needs of the low-energy, polarized positive muon source (LE-μ+) and LE-μ SR spectrometer at PSI. The beam line replaces the existing μ E4 muon decay channel. A large acceptance is accomplished by installing two solenoidal magnetic lenses close to the muon production target E that is hit by the 590-MeV PSI proton beam. The muons are then transported by standard large aperture quadrupoles and bending magnets to the experiment. Several slit systems and an electrostatic separator allow the control of beam shape, momentum spread, and to reduce the background due to beam positrons or electrons. Particle intensities of up to 3.5 × 108 μ+/s and 107 μ−/s are expected at 28MeV/c beam momentum and 1.8mA proton beam current. This will translate into a LE-μ+ rate of 7,000/s being available at the LE-μ SR spectrometer, thus achieving μ+ fluxes, that are comparable to standard μ SR facilitie

Foramen Mandibulae as an Indicator of Successful Conduction Anesthesia

Comparative measurements were made of 144 orthopantomographs in 50 patients with successful and 94 patients with unsuccessful inferior alveolar nerve block anesthesia. The results show that the bony lingula is prominent in 28.5% of all patients, or in 56.0% of those with unsuccessful anesthesia. The variables mandibular notch vs. mandibular foramen (MN-MF) and the anterior ramus ridge vs. mandibular foramen (ARR-MF) show greater distances in the group of patients with successful anesthesia, while the variables of posterior ramus ridge vs. mandibular foramen (PRR-MF) and mandibular angle vs. mandibular foramen (MA-MF) were greater in the group of patients with unsuccessful anesthesia (p > 0.05). It is concluded that the variability in position of the mandibular foramen among others may be responsible for an occasional failure of inferior alveolar nerve block

Epidemiological Analysis of Oral Surgery Procedures

The epidemiological study was conducted to assess oral health of patients referred to the Department of Oral Surgery at Clinical Hospital Center in Rijeka. The distribution of particular diagnoses and surgical interventions in relation to frequency of occurrence was tested. The total of 1,268 patients aged from 5 to 89 years, both sexes, were included in the study. All the patients were treated under local anesthesia. The most common reason for referral to oral surgery was chronic periapical lesion (33.3%), followed by retained root (26.7%), impacted tooth (12.7%), and radicular cyst (8.3%). The majority of patients, residents of Rijeka city area, were treated for the diagnosis of adult periodontitis, while the radicular cysts and hypertrophy of the upper frenulum were more frequent referral diagnoses in patients coming from the areas around Rijeka. Extractions were performed more frequently in patients from Rijeka, while cystectomies with apicectomies and frenulectomies in other patients

Fast neurotransmitter release regulated by the endocytic scaffold intersectin.

Sustained fast neurotransmission requires the rapid replenishment of release-ready synaptic vesicles (SVs) at presynaptic active zones. Although the machineries for exocytic fusion and for subsequent endocytic membrane retrieval have been well characterized, little is known about the mechanisms underlying the rapid recruitment of SVs to release sites. Here we show that the Down syndrome-associated endocytic scaffold protein intersectin 1 is a crucial factor for the recruitment of release-ready SVs. Genetic deletion of intersectin 1 expression or acute interference with intersectin function inhibited the replenishment of release-ready vesicles, resulting in short-term depression, without significantly affecting the rate of endocytic membrane retrieval. Acute perturbation experiments suggest that intersectin-mediated vesicle replenishment involves the association of intersectin with the fissioning enzyme dynamin and with the actin regulatory GTPase CDC42. Our data indicate a role for the endocytic scaffold intersectin in fast neurotransmitter release, which may be of prime importance for information processing in the brain

Preparation of Fe3O4Spherical Nanoporous Particles Facilitated by Polyethylene Glycol 4000

Much interest has been attracted to the magnetic materials with porous structure because of their unique properties and potential applications. In this report, Fe3O4nanoporous particles assembled from small Fe3O4nanoparticles have been prepared by thermal decomposition of iron acetylacetonate in the presence of polyethylene glycol 4000. The size of the spherical nanoporous particles is 100–200 nm. Surface area measurement shows that these Fe3O4nanoporous particles have a high surface area of 87.5 m2/g. Magnetization measurement and Mössbauer spectrum indicate that these particles are nearly superparamagnetic at room temperature. It is found that the morphology of the products is greatly influenced by polyethylene glycol concentration and the polymerization degree of polyethylene glycol. Polyethylene glycol molecules are believed to facilitate the formation of the spherical assembly

Signal Transmission in the Auditory System

Contains table of contents for Section 3, an introduction, and reports on seven research projects.National Institutes of Health Grant 5 R01 DC00194National Institutes of Health Grant P01 DC00119National Institutes of Health Grant F32 DC00073National Institutes of Health Grant 5 R01 DC00473National Institutes of Health Grant 2 R01 DC00238National Institutes of Health Grant 2 R01 DC00235National Institutes of Health Grant 5 P01 DC00361National Institutes of Health Grant T32 DC00006Whitaker Health Sciences Fun

Deep-learning-based reconstruction of undersampled MRI to reduce scan times:a multicentre, retrospective, cohort study

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p&lt;0·0001). INTERPRETATION: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING: Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.</p

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

Background: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. Methods: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. Findings: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). Interpretation: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation