A Framework for the Objective Assessment of Registration Accuracy

Validation and accuracy assessment are themain bottlenecks preventing the adoption of image processing algorithms in the clinical practice. In the classical approach, a posteriori analysis is performed through objective metrics. In this work, a different approach based on Petri nets is proposed.The basic idea consists in predicting the accuracy of a given pipeline based on the identification and characterization of the sources of inaccuracy. The concept is demonstrated on a case study: the intrasubject rigid and affine registration of magnetic resonance images. A choice of possible sources of inaccuracies that can affect the registration process is accounted for, and an estimation of the overall inaccuracy is provided through Petri nets. Both synthetic and real data are considered. While synthetic data allow the benchmarking of the performance with respect to the ground truth, real data enable to assess the robustness of the methodology in real contexts as well as to determine the suitability of the use of synthetic data in the training phase. Results revealed a higher correlation and a lower dispersion among the metrics for simulated data, while the opposite trend was observed for pathologic ones. Results show that the proposedmodel not only provides a good prediction performance but also leads to the optimization of the end-to-end chain in terms of accuracy and robustness, setting the ground for its generalization to different and more complex scenarios

Increasing differential diagnosis between lipoma and liposarcoma through radiomics: a narrative review

Soft tissue sarcomas (STSs) are rare, heterogeneous, and very often asymptomatic diseases. Their diagnosis is fundamental, as is the identification of the degree of malignancy, which may be high, medium, or low. The Italian Medical Oncology Association and European Society of Medical Oncology (ESMO) guidelines recommend magnetic resonance imaging (MRI) because the clinical examination is typically ineffective. The diagnosis of these rare diseases with artificial intelligence (AI) techniques presents reduced datasets and therefore less robust methods. However, the combination of AI techniques with radiomics may be a new angle in diagnosing rare diseases such as STSs. Results obtained are promising within the literature, not only for the performance but also for the explicability of the data. In fact, one can make tumor classification, site localization, and prediction of the risk of developing metastasis. Thanks to the synergy between computer scientists and radiologists, linking numerical features to radiological evidence with excellent performance could be a new step forward for the diagnosis of rare diseases

ECMO for COVID-19 patients in Europe and Israel

Since March 15th, 2020, 177 centres from Europe and Israel have joined the study, routinely reporting on the ECMO support they provide to COVID-19 patients. The mean annual number of cases treated with ECMO in the participating centres before the pandemic (2019) was 55. The number of COVID-19 patients has increased rapidly each week reaching 1531 treated patients as of September 14th. The greatest number of cases has been reported from France (n = 385), UK (n = 193), Germany (n = 176), Spain (n = 166), and Italy (n = 136) .The mean age of treated patients was 52.6 years (range 16–80), 79% were male. The ECMO configuration used was VV in 91% of cases, VA in 5% and other in 4%. The mean PaO2 before ECMO implantation was 65 mmHg. The mean duration of ECMO support thus far has been 18 days and the mean ICU length of stay of these patients was 33 days. As of the 14th September, overall 841 patients have been weaned from ECMO support, 601 died during ECMO support, 71 died after withdrawal of ECMO, 79 are still receiving ECMO support and for 10 patients status n.a. . Our preliminary data suggest that patients placed on ECMO with severe refractory respiratory or cardiac failure secondary to COVID-19 have a reasonable (55%) chance of survival. Further extensive data analysis is expected to provide invaluable information on the demographics, severity of illness, indications and different ECMO management strategies in these patients

A multi-view approach to multi-modal MRI cluster ensembles

It has been shown that the combination of multi-modal MRI images improve the discrimination of diseased tissue. However the fusion of dissimilar imaging data for classification and segmentation purposes is not a trivial task, there is an inherent difference in information domains, dimensionality and scales. This work proposes a multi-view consensus clustering methodology for the integration of multi-modal MR images into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view imaging approach calculates multiple vectorial dissimilarity-spaces for each one of the MRI modalities and makes use of the concepts behind cluster ensembles to combine a set of base unsupervised segmentations into an unified partition of the voxel-based data. The methodology is specially designed for combining DCE-MRI and DTI-MR, for which a manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information

Validation through Accuracy Prediction in Neuroimage Registration

Validation and accuracy assessment are the main bottlenecks preventing the adoption of many medical image processing algorithms in the clinical practice. In the classical approach, a-posteriori analysis is performed based on some predefined objective metrics. The main limitation of this methodology is in the fact that it does not provide a mean to estimate what the performance would be a-priori, and thus to shape the processing workflow in the most suitable way. In this paper, we propose a different approach based on Petri Nets. The basic idea consists in predicting the accuracy that will result from a given processing on a given type of data based on the identification and characterization of the sources of inaccuracy intervening along the whole chain. Here we propose a proof of concept in the specific case of image registration. A Petri Net is constructed after the detection of the possible sources of inaccuracy and the evaluation of their respective impact on the estimation of the deformation field. A training set of five different synthetic volumes is used. Afterward, validation is performed on a different set of five synthetic volumes by comparing the estimated inaccuracy with the posterior measurements according to a set of predefined metrics. Two real cases are also considered. Results show that the proposed model provides a good prediction performance. An extended set of clinical data will allow the complete characterization of the system for the considered task

Method and apparatus to increase the field of view in a cone beam computerized tomography acquisition

The present invention relates to a method and an apparatus for Cone-Beam Computerized Tomography,(CBCT) capable of increasing the maximum Field-Of-View (FOV) through a composite scanningprotocol, based on the acquisition and reconstruction of multiple volumes relating to partially overlappingdifferent anatomic areas, and on the subsequent stitching of said volumes, so as to obtain, as final result, asingle final volume having dimensions larger than what would be allowed by the geometry of theacquisition system.CBCT apparatuses are known in the art, and allow to obtain tomographic images of an anatomic portion,by acquiring a sequence of bi-dimensional radiographic images during the rotation of a systemcomprising an X-ray source and a X-ray detector around the anatomic part to be acquired.A CBCT apparatus comprises substantially: an X-ray source projecting a conic X-ray beam (unless it issubsequently collimated) through an object to be acquired, a bi-dimensional X-ray detector positioned soas to measure the intensity of radiation after passing through the object, a mechanical support on whichsaid X-ray source and detector are fixed, a mechanical system allowing the rotation and the translation ofsaid support around the object, so as to acquire radiographic images from different positions; an electronicsystem capable of regulating and synchronizing the functioning of the various components of theapparatus; a computer or similar, capable of allowing the operator to control the functions of theapparatus, and of reconstructing and visualizing the acquired images. On the market there aresubstantially two kinds of such apparatuses: a first kind where the patient stands or sits vertically during the acquisition, described e.g. in patent EP2018121B1 Sirona; and a second kind where the patient lies on a table, described e.g. in patent IT1277796 QR

A novel Burkholderia ambifaria strain able to degrade the mycotoxin fusaric acid and to inhibit Fusarium spp. growth

Fusaric acid (FA) is a fungal metabolite produced by several Fusarium species responsible for wilts and root rot diseases of a great variety of plants. Bacillus spp. and Pseudomonas spp. have been considered as promising biocontrol agents against phytopathogenic Fusarium spp., however it has been demonstrated that FA negatively affects growth and production of some antibiotics in these bacteria. Thus, the capability to degrade FA would be a desirable characteristic in bacterial biocontrol agents of Fusarium wilt. Taking this into account, bacteria isolated from the rhizosphere of barley were screened for their ability to use FA as sole carbon and energy source. One strain that fulfilled this requirement was identified according to sequence analysis of 16S rRNA, gyrB and recA genes as Burkholderia ambifaria. This strain, designated T16, was able to grow with FA as sole carbon, nitrogen and energy source and also showed the ability to detoxify FA in barley seedlings. This bacterium also exhibited higher growth rate, higher cell densities, longer survival, higher levels of indole-3-acetic acid (IAA) production, enhanced biofilm formation and increased resistance to different antibiotics when cultivated in Luria Bertani medium at pH 5.3 compared to pH 7.3. Furthermore, B. ambifaria T16 showed distinctive plant growth-promoting features, such as siderophore production, phosphate-solubilization, 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity, in vitro antagonism against Fusarium spp. and improvement of grain yield when inoculated to barley plants grown under greenhouse conditions. This strain might serve as a new source of metabolites or genes for the development of novel FA-detoxification systems.Fil: Simonetti, Ester. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; ArgentinaFil: Roberts, Irma. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; ArgentinaFil: Montecchia, Marcela Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; ArgentinaFil: Gutiérrez Boem, Flavio Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; ArgentinaFil: Gómez, Federico Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; ArgentinaFil: Ruiz, Jimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; Argentin