MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI

Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow. We present a framework for medical image fitting tasks that is included in MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth. Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.Comment: 31 pages, 11 figures URL: http://mitk.org/wiki/MITK-ModelFi

cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations

Classification of heterogeneous diseases is challenging due to their complexity, variability of symptoms and imaging findings. Chronic Obstructive Pulmonary Disease (COPD) is a prime example, being underdiagnosed despite being the third leading cause of death. Its sparse, diffuse and heterogeneous appearance on computed tomography challenges supervised binary classification. We reformulate COPD binary classification as an anomaly detection task, proposing cOOpD: heterogeneous pathological regions are detected as Out-of-Distribution (OOD) from normal homogeneous lung regions. To this end, we learn representations of unlabeled lung regions employing a self-supervised contrastive pretext model, potentially capturing specific characteristics of diseased and healthy unlabeled regions. A generative model then learns the distribution of healthy representations and identifies abnormalities (stemming from COPD) as deviations. Patient-level scores are obtained by aggregating region OOD scores. We show that cOOpD achieves the best performance on two public datasets, with an increase of 8.2% and 7.7% in terms of AUROC compared to the previous supervised state-of-the-art. Additionally, cOOpD yields well-interpretable spatial anomaly maps and patient-level scores which we show to be of additional value in identifying individuals in the early stage of progression. Experiments in artificially designed real-world prevalence settings further support that anomaly detection is a powerful way of tackling COPD classification

Interferon β-1a in relapsing multiple sclerosis: four-year extension of the European IFNβ-1a Dose-C omparison Study

Background: Multiple sclerosis (MS) is a chronic disease requiring long-term monitoring of treatment. Objective: To assess the four-year clinical efficacy of intramuscular (IM) IFNb-1a in patients with relapsing MS from the European IFNb-1a Dose-C omparison Study. Methods: Patients who completed 36 months of treatment (Part 1) of the European IFNb-1a Dose-C omparison Study were given the option to continue double-blind treatment with IFNb-1a 30 mcg or 60 mcg IM once weekly (Part 2). Analyses of 48-month data were performed on sustained disability progression, relapses, and neutralizing antibody (NA b) formation. Results: O f 608/802 subjects who completed 36 months of treatment, 493 subjects continued treatment and 446 completed 48 months of treatment and follow-up. IFNb-1a 30 mcg and 60 mcg IM once weekly were equally effective for up to 48 months. There were no significant differences between doses over 48 months on any of the clinical endpoints, including rate of disability progression, cumulative percentage of patients who progressed (48 and 43, respectively), and annual relapse rates; relapses tended to decrease over 48 months. The incidence of patients who were positive for NAbs at any time during the study was low in both treatment groups. Conclusion: C ompared with 60-mcg IM IFNb-1a once weekly, a dose of 30 mcg IM IFNb-1a once weekly maintains the same clinical efficacy over four years

Checkliste zur Unterstützung der Helmholtz-Zentren bei der Implementierung von Richtlinien für nachhaltige Forschungssoftware

Mit der voranschreitenden Digitalisierung von Forschung und Lehre steigt die Zahl an Software-Lösungen, die an wissenschaftlichen Einrichtungen entstehen und zur Erkenntnisgewinnung genutzt werden. Die – unter dem Stichwort Open Science geforderte – Zugänglichkeit und Nachnutzung von wissenschaftlichen Ergebnissen kann in vielen Fachgebieten nur sichergestellt werden, wenn neben Forschungsdaten auch Programmcode offen zugänglich gemacht wird. Die vorliegende Handreichung richtet sich an Entscheider*innen in den Helmholtz-Zentren, die sich mit der Implementierung von Richtlinien für nachhaltige Forschungssoftware befassen. Sie ergänzt eine Muster-Richtlinie, die den Zentren bereits eine richtungsweisende und nachnutzbare Vorlage zur Erstellung von Regelungen für einen nachhaltigen Umgang mit Forschungssoftware gibt

First direct mass measurements of stored neutron-rich 129,130,131Cd isotopes with FRS-ESR

A 410 MeV/u 238U projectile beam was used to create cadmium isotopes via abrasion-fission in a beryllium target placed at the entrance of the in-flight separator FRS at GSI. The fission fragments were separated by the FRS and injected into the isochronous storage ring ESR for mass measurements. Isochronous Mass Spectrometry (IMS) was performed under two different experimental conditions, with and without B\u3c1-tagging at the high-resolution central focal plane of the FRS. In the experiment with B\u3c1-tagging the magnetic rigidity of the injected fragments was determined with an accuracy of 2 c510-4. A new method of data analysis, which uses a correlation matrix for the combined data set from both experiments, has provided experimental mass values of 25 rare isotopes for the first time. The high sensitivity and selectivity of the method have given access to nuclides detected with a rate of a few atoms per week. In this letter we present for the 129,130,131Cd isotopes mass values directly measured for the first time. The experimental mass values of cadmium as well as for tellurium and tin isotopes show a pronounced shell effect towards and at N=82. Shell quenching cannot be deduced from a single new mass value, nor by a better agreement with a theoretical model which explicitly takes into account a quenching feature. This is in agreement with the conclusion from \u3b3-ray spectroscopy and confirms modern shell-model calculations

Recent smell loss is the best predictor of COVID-19 among individuals with recent respiratory symptoms

In a preregistered, cross-sectional study we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC=0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0-10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable

More than smell - COVID-19 is associated with severe impairment of smell, taste, and chemesthesis

Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, generally lacked quantitative measurements, were mostly restricted to data from single countries. Here, we report the development, implementation and initial results of a multi-lingual, international questionnaire to assess self-reported quantity and quality of perception in three distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, 8 other, ages 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change+/-100) revealed a mean reduction of smell (-79.7+/- 28.7, mean+/- SD), taste (-69.0+/- 32.6), and chemesthetic (-37.3+/- 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell, but also affects taste and chemesthesis. The multimodal impact of COVID-19 and lack of perceived nasal obstruction suggest that SARS-CoV-2 infection may disrupt sensory-neural mechanisms.Additional co-authors: Veronica Pereda-Loth, Shannon B Olsson, Richard C Gerkin, Paloma Rohlfs Domínguez, Javier Albayay, Michael C. Farruggia, Surabhi Bhutani, Alexander W Fjaeldstad, Ritesh Kumar, Anna Menini, Moustafa Bensafi, Mari Sandell, Iordanis Konstantinidis, Antonella Di Pizio, Federica Genovese, Lina Öztürk, Thierry Thomas-Danguin, Johannes Frasnelli, Sanne Boesveldt, Özlem Saatci, Luis R. Saraiva, Cailu Lin, Jérôme Golebiowski, Liang-Dar Hwang, Mehmet Hakan Ozdener, Maria Dolors Guàrdia, Christophe Laudamiel, Marina Ritchie, Jan Havlícek, Denis Pierron, Eugeni Roura, Marta Navarro, Alissa A. Nolden, Juyun Lim, KL Whitcroft, Lauren R. Colquitt, Camille Ferdenzi, Evelyn V. Brindha, Aytug Altundag, Alberto Macchi, Alexia Nunez-Parra, Zara M. Patel, Sébastien Fiorucci, Carl M. Philpott, Barry C. Smith, Johan N Lundström, Carla Mucignat, Jane K. Parker, Mirjam van den Brink, Michael Schmuker, Florian Ph.S Fischmeister, Thomas Heinbockel, Vonnie D.C. Shields, Farhoud Faraji, Enrique Enrique Santamaría, William E.A. Fredborg, Gabriella Morini, Jonas K. Olofsson, Maryam Jalessi, Noam Karni, Anna D'Errico, Rafieh Alizadeh, Robert Pellegrino, Pablo Meyer, Caroline Huart, Ben Chen, Graciela M. Soler, Mohammed K. Alwashahi, Olagunju Abdulrahman, Antje Welge-Lüssen, Pamela Dalton, Jessica Freiherr, Carol H. Yan, Jasper H. B. de Groot, Vera V. Voznessenskaya, Hadar Klein, Jingguo Chen, Masako Okamoto, Elizabeth A. Sell, Preet Bano Singh, Julie Walsh-Messinger, Nicholas S. Archer, Sachiko Koyama, Vincent Deary, Hüseyin Yanik, Samet Albayrak, Lenka Martinec Novákov, Ilja Croijmans, Patricia Portillo Mazal, Shima T. Moein, Eitan Margulis, Coralie Mignot, Sajidxa Mariño, Dejan Georgiev, Pavan K. Kaushik, Bettina Malnic, Hong Wang, Shima Seyed-Allaei, Nur Yoluk, Sara Razzaghi, Jeb M. Justice, Diego Restrepo, Julien W Hsieh, Danielle R. Reed, Thomas Hummel, Steven D Munger, John E Haye