A virtual coronagraphic test bench for SHARK-NIR, the second-generation high-contrast imager for the Large Binocular Telescope

SHARK-NIR is the second-generation high-contrast coronagraphic imager for the Large Binocular Telescope (LBT). In my Ph.D. project I have been involved in the conceptual and final design phase of the instrument. In specific, I developed a simulator in IDL language that operated as a virtual test bench to make a comparative study of several coronagraphic techniques identified as suitable candidates for implementation in the instrument. The simulator is based on physical optics propagation and adopts an end-to-end approach to generate images in presence of several sources of optical aberrations, from atmospheric residuals to telescope vibrations and non common path aberrations (NCPA). In particular, a big effort has been devoted to the optimization of the software efficiency through a dedicated parallelization scheme, to modelling of NCPA spatial and temporal properties, to the investigation of the effects of telescope vibrations and of the impact of the forthcoming upgrade of LBT Adaptive Optics system. I explored the coronagraphic performance in a wide range of observing conditions and characterized the coronagraphs sensitivity to aberrations, misalignments of optical components and chromatism. I also helped developing a data reduction pipeline to process simulated data adopting several algorithms. Simulations results have been used to define a final set of coronagrahic solutions that allow to fulfill the top-level scientific requirements.\\Finally, I validated with simulations the phase diversity approach as a strategy for on-line sensing of NCPA. Simulations contributed to the final choice of the internal DM for both NCPA and fast tip-tilt correction

A Deep Learning model to segment liver metastases on CT images acquired at different time-points during chemotherapy

The aim of this study is to present a fully automatic deep learning algorithm to segment liver Colorectal cancer metastases (lmCRC) on CT images, based on a U-Net structure, comparing nets with and without the transfer learning approach. This is a bi-centric study, enrolling patients who underwent CT exam before (baseline) and after first-line therapy (TP1). Patients were divided into training (using a portion of baseline sequences from both centers) to train the DL model, and two validation sets: one with baseline (valB), and one with TP1 (valTP1) sequences. The reference standard for the automatic segmentations was defined by the manual segmentations performed by an experienced radiologist on the portal phase of the baseline and TP1 CT exam. The best performing model obtained Dice Similarity Coefficient (DSC) of 0.68±0.24, Precision (Pr) of 0.74±0.27, Recall (Re) of 0.73±0.26, Detection Rate (DR) of 93% on the valB, and DSC of 0.61±0.28, Pr of 0.68±0.31, Re of 0.65±0.29 and DR of 88% on the valTP1. These encouraging results, if confirmed on larger dataset, might provide a reliable and robust tool that can be used as first step of future radiomics analyses aimed at predicting response to therapy, improving the management of lmCRC patients

Hierarchichal-segmented AO in order to attain wide field compensation in the visible on an 8m class telescope

We describe the preliminary optimized layout for a partially optimized concept of an optical-8m class VLT-like 2x2 segmented camera where each channel is assisted by an equivalent of an MCAO system where the ground layer correction is commonly employed while the high altitude ones is performed in an open-loop fashion. While we derive the basic relationships among the Field of View and attainable correction with a pre-defined choice for the hardware, we discuss sky coverage and wavefront sensing issues employing natural and artificial references, involving the latest stateof-the-art in the development of wavefront sensing. We show that a flexible approach allow for a compensated Field of View that is variable and can be properly tuned matching the current turbulence situation and the requirement in term of quality of the compensation. A preliminary description of the overall optomechanical package is given as well along with a rough estimates of the efforts required to translates such a concept into reality.Comment: 6 pages, 4 figures, in AO4ELT5 Proceeding

Multiple Spatial Frequencies Pyramid WaveFront Sensing

A modification of the pyramid wavefront sensor is described. In this conceptually new class of devices, the perturbations are split at the level of the focal plane depending upon their spatial frequencies, and then measured separately. The aim of this approach is to increase the accuracy in the determination of some range of spatial frequency perturbations, or a certain classes of modes, disentangling them from the noise associated to the Poissonian fluctuations of the light coming from the perturbations outside of the range of interest or from the background in the pupil planes; the latter case specifically when the pyramid wavefront sensor is used with a large modulation. While the limits and the effectiveness of this approach should be further investigated, a number of variations on the concept are shown, including a generalization of the spatial filtering in the point-diffraction wavefront sensor. The simplest application, a generalization to the pyramid of the well-known spatially filtering in wavefront sensing, is showing promise as a significant limiting magnitude advance. Applications are further speculated in the area of extreme adaptive optics and when serving spectroscopic instrumentation where “light in the bucket” rather than Strehl performance is required

Ingot Laser Guide Stars Wavefront Sensing

We revisit one class of z-invariant WaveFront sensor where the LGS is fired aside of the telescope aperture. In this way there is a spatial dependence on the focal plane with respect to the height where the resonant scattering occurs. We revise the basic parameters involving the geometry and we propose various merit functions to define how much improvement can be attained by a z-invariant approach. We show that refractive approaches are not viable and we discuss several solutions involving reflective ones in what has been nicknamed "ingot wavefront sensor" discussing the degrees of freedom required to keep tracking and the basic recipe for the optical design.Comment: 6 pages, 4 figures, AO4ELT5 Conference Proceeding, 201

MAVIS: system modelling and performance prediction

The MCAO Assisted Visible Imager and Spectrograph (MAVIS) Adaptive Optics Module has very demanding goals to support science in the optical: providing 15% SR in V band on a large FoV of 30arcsec diameter in standard atmospheric conditions at Paranal. It will be able to work in closed loop on up to three natural guide stars down to H=19, providing a sky coverage larger than 50% in the south galactic pole. Such goals and the exploration of a large MCAO system parameters space have required a combination of analytical and end- to-end simulations to assess performance, sky coverage and drive the design. In this work we report baseline performance, statistical sky coverage and parameters sensitivity analysis done in the phase-A instrument study.Comment: 12 pages, 9 figures, 7 tables. SPIE conference Astronomical Telescopes and Instrumentation, 14 - 18 December 2020, digital foru

A Holographic Diffuser Generalised Optical Differentiation Wavefront Sensor

The wavefront sensors used today at the biggest World's telescopes have either a high dynamic range or a high sensitivity, and they are subject to a linear trade off between these two parameters. A new class of wavefront sensors, the Generalised Optical Differentiation Wavefront Sensors, has been devised, in a way not to undergo this linear trade off and to decouple the dynamic range from the sensitivity. This new class of WFSs is based on the light filtering in the focal plane from a dedicated amplitude filter, which is a hybrid between a linear filter, whose physical dimension is related to the dynamic range, and a step in the amplitude, whose size is related to the sensitivity. We propose here a possible technical implementation of this kind of WFS, making use of a simple holographic diffuser to diffract part of the light in a ring shape around the pin of a pyramid wavefront sensor. In this way, the undiffracted light reaches the pin of the pyramid, contributing to the high sensitivity regime of the WFS, while the diffused light is giving a sort of static modulation of the pyramid, allowing to have some signal even in high turbulence conditions. The holographic diffuser zeroth order efficiency is strictly related to the sensitivity of the WFS, while the diffusing angle of the diffracted light gives the amount of modulation and thus the dynamic range. By properly choosing these two parameters it is possible to build a WFS with high sensitivity and high dynamic range in a static fashion. Introducing dynamic parts in the setup allows to have a set of different diffuser that can be alternated in front of the pyramid, if the change in the seeing conditions requires it.Comment: 11 pages, 5 figure

A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images

The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

Background: Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact of manual and automatic segmentations on the radiomics models. Methods: Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the construction dataset, while 28 the external validation. Tumour volumes were manually and automatically segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four machine learning classifiers. Results: Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%, and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the automatic versus manual segmentation. Conclusion: Our study showed that radiomics models can pave the way to help clinicians in the prediction of tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the external validation dataset are promising for further research into radiomics approaches using both manual and automatic segmentations