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Book ; Online: TorchRadon

Ronchetti, Matteo

Fast Differentiable Routines for Computed Tomography

2020

Abstract: This work presents TorchRadon -- an open source CUDA library which contains a set of differentiable routines for solving computed tomography (CT) reconstruction problems. The library is designed to help researchers working on CT problems to combine deep ... ...

Abstract	This work presents TorchRadon -- an open source CUDA library which contains a set of differentiable routines for solving computed tomography (CT) reconstruction problems. The library is designed to help researchers working on CT problems to combine deep learning and model-based approaches. The package is developed as a PyTorch extension and can be seamlessly integrated into existing deep learning training code. Compared to the existing Astra Toolbox, TorchRadon is up to 125 faster. The operators implemented by TorchRadon allow the computation of gradients using PyTorch backward(), and can therefore be easily inserted inside existing neural networks architectures. Because of its speed and GPU support, TorchRadon can also be effectively used as a fast backend for the implementation of iterative algorithms. This paper presents the main functionalities of the library, compares results with existing libraries and provides examples of usage.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006 ; 004
Publishing date	2020-09-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Is Gender an Antecedent to Workplace Stressors? A Systematic Review and an Empirical Study Using a Person-Centred Approach.

Fida, Roberta / Watson, David / Ghezzi, Valerio / Barbaranelli, Claudio / Ronchetti, Matteo / Di Tecco, Cristina

International journal of environmental research and public health

2023 Volume 20, Issue 8

Abstract: Objective: Work is a key domain of life in which gender inequality can manifest, yet gender is rarely the explicit focus of research seeking to understand exposure to stressors. We investigated this research gap in two studies.: Methods: Study 1 was ... ...

Abstract	Objective: Work is a key domain of life in which gender inequality can manifest, yet gender is rarely the explicit focus of research seeking to understand exposure to stressors. We investigated this research gap in two studies. Methods: Study 1 was a systematic review of the relationship between gender and key stressors (e.g., high demands, poor support, lack of clarity and control). From a total of 13,376,130 papers met our inclusion criteria. Study 2 was a cross-sectional study that included 11,289 employees nested within 71 public organisations (50.6% men). Through a latent profile analysis, we investigated the profiles of stressors separately from men and women. Results: The systematic review revealed that, for all stressors, a significant proportion of studies found no significant gender differences, and the review found mixed evidence of greater exposure for both men and women. The results of Study 2 revealed that both genders could be optimally represented by three psychosocial risk profiles reflecting medium, low and high stressors. The results also showed that while the shape of profiles was similar for both genders, men had a higher probability than women of being in the Conclusion: Gender differences in exposure to stressors are inconsistent. Although the literature on gender role theory and the gendering of work suggests different exposures to stressors in men and women, we find little empirical support for this.
MeSH term(s)	Humans ; Male ; Female ; Cross-Sectional Studies ; Workplace/psychology ; Sex Factors ; Risk Factors ; Stress, Psychological/psychology
Language	English
Publishing date	2023-04-17
Publishing country	Switzerland
Document type	Systematic Review ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph20085541
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Book ; Online: DISA

Ronchetti, Matteo / Wein, Wolfgang / Navab, Nassir / Zettinig, Oliver / Prevost, Raphael

DIfferentiable Similarity Approximation for Universal Multimodal Registration

2023

Abstract: Multimodal image registration is a challenging but essential step for numerous image-guided procedures. Most registration algorithms rely on the computation of complex, frequently non-differentiable similarity metrics to deal with the appearance ... ...

Abstract	Multimodal image registration is a challenging but essential step for numerous image-guided procedures. Most registration algorithms rely on the computation of complex, frequently non-differentiable similarity metrics to deal with the appearance discrepancy of anatomical structures between imaging modalities. Recent Machine Learning based approaches are limited to specific anatomy-modality combinations and do not generalize to new settings. We propose a generic framework for creating expressive cross-modal descriptors that enable fast deformable global registration. We achieve this by approximating existing metrics with a dot-product in the feature space of a small convolutional neural network (CNN) which is inherently differentiable can be trained without registered data. Our method is several orders of magnitude faster than local patch-based metrics and can be directly applied in clinical settings by replacing the similarity measure with the proposed one. Experiments on three different datasets demonstrate that our approach generalizes well beyond the training data, yielding a broad capture range even on unseen anatomies and modality pairs, without the need for specialized retraining. We make our training code and data publicly available. Comment: This preprint was submitted to MICCAI 2023. The Version of Record of this contribution will be published in Springer LNCS
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing
Subject code	004
Publishing date	2023-07-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Generative Tomography Reconstruction

Ronchetti, Matteo / Bacciu, Davide

2020

Abstract: We propose an end-to-end differentiable architecture for tomography reconstruction that directly maps a noisy sinogram into a denoised reconstruction. Compared to existing approaches our end-to-end architecture produces more accurate reconstructions ... ...

Abstract	We propose an end-to-end differentiable architecture for tomography reconstruction that directly maps a noisy sinogram into a denoised reconstruction. Compared to existing approaches our end-to-end architecture produces more accurate reconstructions while using less parameters and time. We also propose a generative model that, given a noisy sinogram, can sample realistic reconstructions. This generative model can be used as prior inside an iterative process that, by taking into consideration the physical model, can reduce artifacts and errors in the reconstructions. Comment: Accepted as a poster for the NeurIPS 2020 Workshop on Deep Learning and Inverse Problems
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Mathematics - Numerical Analysis
Publishing date	2020-10-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: How Much Does My Work Affect My Health? The Relationships between Working Conditions and Health in an Italian Survey.

Ronchetti, Matteo / Russo, Simone / Di Tecco, Cristina / Iavicoli, Sergio

Safety and health at work

2021 Volume 12, Issue 3, Page(s) 370–376

Abstract: Backround: Working condition surveys are widely recognized as useful tools for monitoring the quality of working life and the improvements introduced by health and safety policy frameworks at the European and national level. The Italian Workers' ... ...

Abstract	Backround: Working condition surveys are widely recognized as useful tools for monitoring the quality of working life and the improvements introduced by health and safety policy frameworks at the European and national level. The Italian Workers' Compensation Authority carried out a national survey (Insula) to investigate the employer's perceptions related to working conditions and their impact on health. Methods: The present study is based on the data collected from the Italian survey on health and safety at work (INSULA) conducted on a representative sample of the Italian workforce ( Results: Working conditions such as managerial support, job satisfaction, and role act as protective factors on mental and physical health. On the contrary, workers' risk perceptions related to personal exposure to occupational safety and health risks, concern about health conditions, and work-related stress risk exposure determine a poorer state of health. Conclusions: This study highlights the link between working conditions and self-report health, and this aims to provide a contribution in the field of health at work. Findings show that working conditions must be object of specific preventive measures to improve the workers' health and well-being.
Language	English
Publishing date	2021-04-16
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2592798-X
ISSN	2093-7997 ; 2093-7911
ISSN (online)	2093-7997
ISSN	2093-7911
DOI	10.1016/j.shaw.2021.04.002
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Article ; Online: Demand-Resource Profiles and Job Satisfaction in the Healthcare Sector: A Person-Centered Examination Using Bayesian Informative Hypothesis Testing.

Marzocchi, Ivan / Ghezzi, Valerio / Di Tecco, Cristina / Ronchetti, Matteo / Ciampa, Valeria / Olivo, Ilaria / Barbaranelli, Claudio

International journal of environmental research and public health

2023 Volume 20, Issue 2

Abstract: Work characteristics may independently and jointly affect well-being, so that whether job demands deplete or energize employees depends on the resources available in the job. However, contradictory results on their joint effects have emerged so far in ... ...

Abstract	Work characteristics may independently and jointly affect well-being, so that whether job demands deplete or energize employees depends on the resources available in the job. However, contradictory results on their joint effects have emerged so far in the literature. We argue that these inconsistencies can be partially explained by two arguments in the contemporary literature in the field. First, most studies in the job design domain are based on classic variable-centered methodologies which, although informative, are not well suited to investigate complex patterns of interactions among multiple variables. Second, these studies have mainly focused on generic work characteristics (e.g., workload, control, support), and are lacking in occupational specificity. Thus, to overcome these limitations, in the current research we include generic and occupation-specific work characteristics and adopt a person-centered approach to (a) identify different patterns of interactions of job demands and resources in a sample of healthcare employees, and (b) determine the degree to which these patterns are associated with employee well-being. We involved a sample of 1513 Italian healthcare providers and collected data on key job demands (workload, emotional dissonance, patient demands and physical demands) and resources (control, management support and peers' support). We focused on job satisfaction as a broad indicator of well-being. Latent profile analysis revealed four profiles of job demands and resources: high strain-isolated, resourceless, resourceful and active job on the ward. The results of Bayesian informative hypothesis testing showed the highest support for the hypothesis stating that healthcare employees belonging to the active job on the ward profile (medium-high demands, high resources) were the most satisfied. Conversely, employees belonging to the high strain-isolated profile (high demands, low resources) and the resourceless profile (medium-low demands, low resources) were the least satisfied. Overall, our study confirms the key role played by job resources in determining well-being in high-risk sectors, demonstrating that job satisfaction can develop both in challenging and less demanding situations. On a practical level, mapping the complexity of the healthcare psychosocial work environment has important implications, allowing for a better assessment process of employee well-being and helping to identify the most effective and fitting interventions.
MeSH term(s)	Humans ; Job Satisfaction ; Health Care Sector ; Bayes Theorem ; Health Personnel/psychology ; Workload/psychology ; Surveys and Questionnaires
Language	English
Publishing date	2023-01-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph20020967
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Book ; Online: Global Multi-modal 2D/3D Registration via Local Descriptors Learning

Markova, Viktoria / Ronchetti, Matteo / Wein, Wolfgang / Zettinig, Oliver / Prevost, Raphael

2022

Abstract: Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all require a good ... ...

Abstract	Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all require a good initialization to succeed due to the challenging appearance of ultrasound images and the arbitrary coordinate system they are acquired in. In this paper, we present a novel approach to solve the problem of registration of an ultrasound sweep to a pre-operative image. We learn dense keypoint descriptors from which we then estimate the registration. We show that our method overcomes the challenges inherent to registration tasks with freehand ultrasound sweeps, namely, the multi-modality and multidimensionality of the data in addition to lack of precise ground truth and low amounts of training examples. We derive a registration method that is fast, generic, fully automatic, does not require any initialization and can naturally generate visualizations aiding interpretability and explainability. Our approach is evaluated on a clinical dataset of paired MR volumes and ultrasound sequences. Comment: This preprint was submitted to MICCAI 2022 and has not undergone post-submission improvements or corrections. The Version of Record of this contribution will be published in Springer LNCS
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2022-05-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: PRO-TIP

Ronchetti, Matteo / Rackerseder, Julia / Tirindelli, Maria / Salehi, Mehrdad / Navab, Nassir / Wein, Wolfgang / Zettinig, Oliver

Phantom for RObust automatic ultrasound calibration by TIP detection

2022

Abstract: We propose a novel method to automatically calibrate tracked ultrasound probes. To this end we design a custom phantom consisting of nine cones with different heights. The tips are used as key points to be matched between multiple sweeps. We extract them ...

Abstract	We propose a novel method to automatically calibrate tracked ultrasound probes. To this end we design a custom phantom consisting of nine cones with different heights. The tips are used as key points to be matched between multiple sweeps. We extract them using a convolutional neural network to segment the cones in every ultrasound frame and then track them across the sweep. The calibration is robustly estimated using RANSAC and later refined employing image based techniques. Our phantom can be 3D-printed and offers many advantages over state-of-the-art methods. The phantom design and algorithm code are freely available online. Since our phantom does not require a tracking target on itself, ease of use is improved over currently used techniques. The fully automatic method generalizes to new probes and different vendors, as shown in our experiments. Our approach produces results comparable to calibrations obtained by a domain expert. Comment: This preprint was submitted to MICCAI 2022. The Version of Record of this contribution will be published in Springer LNCS
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Physics - Medical Physics
Subject code	006
Publishing date	2022-06-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Developing a cost-estimation model for work-related stress: An absence-based estimation using data from two Italian case studies.

Russo, Simone / Ronchetti, Matteo / Di Tecco, Cristina / Valenti, Antonio / Jain, Aditya / Mennini, Francesco Saverio / Leka, Stavroula / Iavicoli, Sergio

Scandinavian journal of work, environment & health

2021 Volume 47, Issue 4, Page(s) 318–327

Abstract: Objectives: This paper discusses the development of a cost-estimation model for work-related stress based on psychosocial risk exposure and absence from work. It presents findings from its implementation and evaluation in two organizations in Italy, ... ...

Abstract	Objectives: This paper discusses the development of a cost-estimation model for work-related stress based on psychosocial risk exposure and absence from work. It presents findings from its implementation and evaluation in two organizations in Italy, using national-level tools developed by the Italian Workers' Compensation Authority (INAIL). It also provides recommendations for the development of similar cost-calculation methods in other countries. Methods: The cost-estimation model was based on the human capital approach using an indirect cost indicator: loss of productivity due to days of absence attributable to work-related stress. Furthermore, the population attributable fraction (PAF) epidemiological measure was used to calculate the impact of exposure to work-related stress on the basis of data collected through validated tools developed by INAIL and salary cost data. Results: The developed model was implemented and evaluated in two organizations, the first in healthcare (N=1014) and the second in public administration (N=534). In the first case, it was found that absence related to work-related stress cost the organization €445 000. In the second case, the cost was €360 000. Conclusions: The proposed model provides an example of how organizations can incorporate well-established indicators associated with work-related stress (eg, various types of absence, psychosocial risk perception, loss of productivity on the basis of salary costs) in a practical way in cost estimations of work-related stress. Such cost estimation can be applied in other countries and organizations to establish the economic and business case of managing work-related stress.
MeSH term(s)	Efficiency ; Humans ; Italy ; Occupational Stress ; Workers' Compensation
Language	English
Publishing date	2021-02-17
Publishing country	Finland
Document type	Journal Article
ZDB-ID	191563-0
ISSN	1795-990X ; 0355-3140
ISSN (online)	1795-990X
ISSN	0355-3140
DOI	10.5271/sjweh.3948
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Article: Implementing Smart Working in Public Administration: a follow up study.

Di Tecco, Cristina / Ronchetti, Matteo / Russo, Simone / Ghelli, Monica / Rondinone, Bruna Maria / Persechino, Benedetta / Iavicoli, Sergio

La Medicina del lavoro

2021 Volume 112, Issue 2, Page(s) 141–152

Abstract: Background: Starting from February 2020, in Italy most organizations have had a forced transition to flexible working practice - called "smart working in emergency" - due to the Covid-19 epidemic outbreak. This allowed to continue work activities and ... ...

Abstract	Background: Starting from February 2020, in Italy most organizations have had a forced transition to flexible working practice - called "smart working in emergency" - due to the Covid-19 epidemic outbreak. This allowed to continue work activities and services and contributed to contain the risk of infection in different sectors, particularly in the public administration. Objectives: This follow up study focussed on a panel of 187 workers from the Italian Workers' Compensation Authority taking part to a pilot project "Smart Working in INAIL" from January 2019 to December 2019. The aim was to investigate the effects of work organization on work attitudes, work-life balance and health outcomes before and after the introduction of the smart working. Methods: The data were collected at two time points through a web-based questionnaire. The first wave aimed to collect information up to one month before the implementation of the smart working. The second wave aimed to collect information about potential changes occurred after one year of smart working. Results: This study showed that high demands, low control and low social support might lead to reduced well-being and less satisfaction with work, and have an effect on work engagement and work-life balance. Particularly, improving social support can moderate the negative impact of high strain on well-being, preventing work-life imbalance and risk of isolation. Discussion: Findings and future perspectives are discussed to support stakeholders in defining policies and practices concerning health and wellbeing at work while preserving productivity, for a successful implementation of smart working in the public administration.
MeSH term(s)	COVID-19 ; Follow-Up Studies ; Humans ; Italy ; Pilot Projects ; SARS-CoV-2
Language	English
Publishing date	2021-04-20
Publishing country	Italy
Document type	Journal Article
ZDB-ID	123678-7
ISSN	0025-7818
ISSN	0025-7818
DOI	10.23749/mdl.v112i2.10595
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