Cardiovascular disease (CVD) is the number one cause of death worldwide, and coronary artery disease (CAD) is the most prevalent CVD, accounting for 42% of these deaths. In view of the limitations of the anatomical evaluation of CAD, Fractional Flow Reserve (FFR) has been introduced as a functional diagnostic index. Herein, we evaluate the feasibility of using deep neural networks (DNN) in an ensemble approach to predict the invasively measured FFR from raw anatomical information that is extracted from optical coherence tomography (OCT). We evaluate the performance of various DNN architectures under different formulations: regression, classification—standard, and few-shot learning (FSL) on a dataset containing 102 intermediate lesions from 80 patients. The FSL approach that is based on a convolutional neural network leads to slightly better results compared to the standard classification: the per-lesion accuracy, sensitivity, and specificity were 77.5%, 72.9%, and 81.5%, respectively. However, since the 95% confidence intervals overlap, the differences are statistically not significant. The main findings of this study can be summarized as follows: (1) Deep-learning (DL)-based FFR prediction from reduced-order raw anatomical data is feasible in intermediate coronary artery lesions; (2) DL-based FFR prediction provides superior diagnostic performance compared to baseline approaches that are based on minimal lumen diameter and percentage diameter stenosis; and (3) the FFR prediction performance increases quasi-linearly with the dataset size, indicating that a larger train dataset will likely lead to superior diagnostic performance.

Intraoperative Computer Tomographs (iCT) provide near real time visualizations which can be registered with high-quality preoperative images to improve the confidence of surgical instrument navigation. However, intraoperative images have a small field of view making the registration process error prone due to the reduced amount of mutual information. We herein propose a method to extrapolate thin acquisitions as a prior step to registration, to increase the field of view of the intraoperative images, and hence also the robustness of the guiding system. The method is based on a deep neural network which is trained adversarially using self-supervision to extrapolate slices from the existing ones. Median landmark detection errors are reduced by approximately 40%, yielding a better initial alignment. Furthermore, the intensity-based registration is improved; the surface distance errors are reduced by an order of magnitude, from 5.66 mm to 0.57 mm (p-value = $4.18\times {10}^{-6}$). The proposed extrapolation method increases the registration robustness, which plays a key role in guiding the surgical intervention confidently.

COSINUS is a dark matter direct detection experiment using NaI crystals as cryogenic scintillating calorimeters. If no signal is observed, this will constrain the dark matter scattering rate in sodium iodide. We investigate how this constraint can be used to infer that the annual modulation signal observed in the DAMA/LIBRA experiment cannot originate from dark matter nuclear recoil events, independently of the dark matter model. We achieve this by unfolding the DAMA modulation spectrum to obtain the implied unquenched nuclear recoil spectrum, which we then compare to the expected COSINUS sensitivity. We find that assuming zero background in the signal region, a 1σ, 2σ or 3σ confidence limit exclusion can be obtained with 57, 130 or 250 kg day of exposure, respectively. A simple background model indicates that in the presence of background, the exposure requirements may increase by ∼ 30%.

While neutrinos are often treated as a background for many dark matter experiments, these particles offer a new avenue for physics: the detection of core-collapse supernovae. Supernovae are extremely energetic, violent and complex events that mark the death of massive stars. During their collapse stars emit a large number of neutrinos in a short burst. These neutrinos carry 99% of the emitted energy which makes their detection fundamental in understanding supernovae. This paper illustrates how COSINUS (Cryogenic Observatory for SIgnatures seen in Next-generation Underground Searches), a sodium iodide (NaI) based dark matter search, will be sensitive to the next galactic core-collapse supernova. The experiment is composed of two separate detectors which will respond to far away and nearby supernovae. The inner core of the experiment will consist of NaI crystals operating as scintillating calorimeters. These crystals will mainly be sensitive to the Coherent Elastic Neutrino-Nucleus Scattering (CEνNS) against Na and I nuclei. The low mass of the cryogenic detectors enables the experiment to identify close supernovae within 1 kpc without pileup. The crystals will see up to hundreds of CEνNS events from a supernova happening at 200 pc. They reside at the center of a large cylindrical 230 T water tank, instrumented with 30 photomultiplier tubes. This tank acts simultaneously as a passive and active shield able to detect the Cherenkov radiation induced by impinging charged particles from ambient and cosmogenic radioactivity. A supernova near the Milky Way Center (10 kpc) will be easily detected inducing ∼60 measurable events, and the water tank will have a 3σ sensitivity to supernovae up to 22 kpc, seeing ∼10 events. This paper shows how, even without dedicated optimization, modern dark matter experiments will also be able to play their part in the multi-messenger effort to detect the next galactic core-collapse supernova.