Phantom Design (United States)

Abstract AimsT1 mapping on cardiac magnetic resonance (CMR) imaging is useful for diagnosis and prognostication in patients with light-chain cardiac amyloidosis (AL-CA). We conducted this study to evaluate the performance of T1 mapping parameters for detection of cardiac amyloidosis (CA) in patients with left ventricular hypertrophy (LVH) and their prognostic values in patients with AL-CA, using a semi-automated deep learning algorithm.Methods and ResultsA total of 300 patients who underwent CMR for differential diagnosis of LVH were analyzed. CA was confirmed in 50 patients (39 with AL-CA and 11 with transthyretin amyloidosis), hypertrophic cardiomyopathy in 198, hypertensive heart disease in 47, and Fabry disease in 5. A semi-automated deep learning algorithm (Myomics-Q) was used for the analysis of the CMR images. The optimal cutoff extracellular volume fraction (ECV) for the differentiation of CA from other etiologies was 33.6% (diagnostic accuracy 85.6%). he artificial intelligence (AI)-derived ECV showed a significant prognostic value for a composite of cardiovascular death and heart failure hospitalization in patients with AL-CA (revised Mayo stage III or IV) (adjusted hazard ratio 4.247 for ECV ≥40%, 95% confidence interval 1.215–14.851, p-value=0.024). Incorporation of AI-derived ECV into the revised Mayo staging system resulted in better risk stratification (integrated discrimination index 27.9%, p=0.013; net reclassification index 13.8%, p=0.007).ConclusionsAI-assisted T1 mapping on CMR imaging allows for improved diagnosis of CA from other etiologies of LVH. Furthermore, AI-derived ECV has significant prognostic value in patients with AL-CA, suggesting its clinical usefulness.Graphical Abstract

From quencher to potent activator – multimodal luminescence thermometry with Fe3+ in the oxides MAl4O7 (M = Ca, Sr, Ba).

Precise optical temperature sensors based on luminescent Tb3+:Eu3+ tetrakis complexes with imidazolic counterions with high emission quantum yield values and low temperature uncertainty.

In this paper, we introduce a new deep learning architecture for camera and Lidar sensor fusion. The proposed scheme performs 2D object detection using the RGB camera image and the depth, height, and intensity images generated by projecting the 3D Lidar point cloud into camera image plane. The proposed object detector consists of two convolutional neural networks (CNNs) that process the RGB and Lidar images separately as well as the fusion network that combines the feature maps produced at the intermediate layers of the CNNs. We aim to develop a robust object detector that maintains good object detection accuracy even when the quality of the sensor signals is degraded for object detection. Towards this end, we devise the gated fusion unit (GFU) that adjusts the contribution of the feature maps generated by two CNN structures via gating mechanism. Using the GFU, the proposed object detector can fuse the high level feature maps drawn from two modalities with appropriate weights to achieve robust performance. Experiments conducted on the challenging KITTI benchmark show that the proposed camera and Lidar fusion network outperforms the conventional sensor fusion methods even when either of the camera and Lidar sensor signals is corrupted by missing data, occlusion, noise, and illumination change.

The Materials on the International Space Station Experiments (MISSE) provide low‐cost material exposure experiments on the exterior of the International Space Station (ISS). The original concept for a suitcase‐like box bolted to the ISS to passively expose materials to space has grown to include increasingly complex in situ characterization. As the ISS completes construction, the facilities available to MISSE experiments will increase dramatically. MISSE7 is the first MISSE to take advantage of this new infrastructure. In addition to material exposure, MISSE7 will include characterization of single‐event radiation effects on electronics and solar cell performance in LEO. MISSE7 will exploit the ISS Express Logistics Carrier power and data capabilities and will leave behind a MISSE specific infrastructure for future missions.