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ning and the selection of an excitation angle set that balances reproducibility and SNR performance over the target imaging volume.

This work identifies regions and optimal excitation angles (θP and θL ) within the 13 C clamshell coil where deviations in B1 + field inhomogeneity or imaging biomarker errors imparted by the B1 + field were within ±10% of the respective value at the isocenter, and thus where excitation angles are reproducible and well-calibrated. Semi-quantitative and quantitative metabolic imaging biomarkers can vary with position in the clamshell coil as a result of B1 + field inhomogeneity, necessitating care in patient positioning and the selection of an excitation angle set that balances reproducibility and SNR performance over the target imaging volume.

Artificial intelligence diagnosis and triage of large vessel occlusion may quicken clinical response for a subset of time-sensitive acute ischemic stroke patients, improving outcomes. Differences in architectural elements within data-driven convolutional neural network (CNN) models impact performance. Foreknowledge of effective model architectural elements for domain-specific problems can narrow the search for candidate models and inform strategic model design and adaptation to optimize performance on available data. Here, we study CNN architectures with a range of learnable parameters and which span the inclusion of architectural elements, such as parallel processing branches and residual connections with varying methods of recombining residual information.

We compare five CNNs ResNet-50, DenseNet-121, EfficientNet-B0, PhiNet, and an Inception module-based network, on a computed tomography angiography large vessel occlusion detection task. The models were trained and preliminarily evaluated with 10-fold catenation, which causes feature maps from earlier layers to be used deeper in the network, while aiding in gradient flow and regularization.

The number of learnable parameters in our five models and best-ablated PhiNet directly related to cross-validated test performance-the smaller the model the better. However, this pattern did not hold when looking at generalization on the withheld external validation set. DenseNet-121 generalized the best; we posit this was due to its heavy use of residual connections utilizing concatenation, which causes feature maps from earlier layers to be used deeper in the network, while aiding in gradient flow and regularization.

Designing and optimizing scintillator-based gamma detector using Monte Carlo simulation is of great importance in nuclear medicine and high energy physics. In scintillation detectors, understanding the light transport in the scintillator and the light collection by the photodetector plays a crucial role in achieving high performance. Thus, accurately modeling them is critical.

In previous works, we developed a model to compute crystal reflectance from the crystal 3D surface measurement and store it in look-up tables to be used in the Monte Carlo simulation software GATE. The relative light output comparison showed excellent agreement between simulations and experiments for both polished and rough surfaces in several configurations, that is, without and with reflector. However, when comparing them at the irradiation depth closest to the photodetector face, rough crystals with a reflector overestimated the predicted light output. Investigating the cause of this overestimation, we optimized the LUT algorithmion. To perform an accurate light output comparison and ultimately have a reliable detector performance estimation, all potential sources of practical limitations must be carefully considered. To broadly enable high-fidelity modeling, we developed an interface for users to compute their own LUTs, using their surface, scintillator, and reflector characteristics.

Our results indicate that when studying scintillation detector performance with different finishes, performing simulations in ideal coupling conditions can lead to light output overestimation. To perform an accurate light output comparison and ultimately have a reliable detector performance estimation, all potential sources of practical limitations must be carefully considered. To broadly enable high-fidelity modeling, we developed an interface for users to compute their own LUTs, using their surface, scintillator, and reflector characteristics.

The aim was to evaluate long-term effectiveness and safety of lanadelumab in patients ≥12y old with hereditary angioedema (HAE) 1/2 (NCT02741596).

Rollover patients completing the HELP Study and continuing into HELP OLE received one lanadelumab 300mg dose until first attack (dose-and-wait period), then 300mg q2wks (regular dosing stage). Nonrollovers (newly enrolled) received lanadelumab 300mg q2wks from day0. Baseline attack rate for rollovers ≥1 attack/4weeks (based on run-in period attack rate during HELP Study); for nonrollovers historical attack rate ≥1 attack/12weeks. The planned treatment period was 33months.

212patients participated (109rollovers, 103nonrollovers); 81.6% completed ≥30months on study (mean [SD], 29.6 [8.2] months). Lanadelumab markedly reduced mean HAE attack rate (reduction vs baseline 87.4% overall). Patients were attack free for a mean of 97.7% of days during treatment; 81.8% and 68.9% of patients were attack free for ≥6 and ≥12months, respectively. Angioedema Quality-of-Life total and domain scores improved from day 0 to end of study. Treatment-emergent adverse events (TEAEs) (excluding HAE attacks) were reported by 97.2% of patients; most commonly injection site pain (47.2%) and viral upper respiratory tract infection (42.0%). Treatment-related TEAEs were reported by 54.7% of patients. Most injection site reactions resolved within 1hour (70.2%) or 1day (92.6%). Six (2.8%) patients discontinued due to TEAEs. check details No treatment-related serious TEAEs or deaths were reported. Eleven treatment-related TEAEs of special interest were reported by seven (3.3%) patients.

Lanadelumab demonstrated sustained efficacy and acceptable tolerability with long-term use in HAE patients.

Lanadelumab demonstrated sustained efficacy and acceptable tolerability with long-term use in HAE patients.