1. |
- Chesmore, Grace E., et al.
(författare)
-
Simons Observatory : characterizing the Large Aperture Telescope Receiver with radio holography
- 2022
-
Ingår i: Applied Optics. - 1559-128X .- 2155-3165. ; 61:34, s. 10309-10319
-
Tidskriftsartikel (refereegranskat)abstract
- We present near-field radio holography measurements of the Simons Observatory Large Aperture Telescope Receiver optics. These measurements demonstrate that radio holography of complex millimeter-wave optical systems comprising cryogenic lenses, filters, and feed horns can provide detailed characterization of wave propagation before deployment. We used the measured amplitude and phase, at 4 K, of the receiver near-field beam pattern to predict two key performance parameters: 1) the amount of scattered light that will spill past the telescope to 300 K and 2) the beam pattern expected from the receiver when fielded on the telescope. These cryogenic measurements informed the removal of a filter, which led to improved optical efficiency and reduced sidelobes at the exit of the receiver. Holography measurements of this system suggest that the spilled power past the telescope mirrors will be less than 1%, and the main beam with its near sidelobes are consistent with the nominal telescope design. This is the first time such parameters have been confirmed in the lab prior to deployment of a new receiver. This approach is broadly applicable to millimeter and submillimeter instruments.
|
|
2. |
- Helland, Ragnhild Holden, et al.
(författare)
-
Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.
- 2023
-
Ingår i: Scientific reports. - 2045-2322. ; 13:1
-
Tidskriftsartikel (refereegranskat)abstract
- Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
|
|