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1,542 results on '"Nieminen, M."'

1. Computed Tomography Artefact Detection Using Deep Learning—Towards Automated Quality Assurance

Author

Inkinen, S. I., Kotiaho, A. O., Hanni, M., Nieminen, M. T., Brix, M. A. K., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Särestöniemi, Mariella, editor, Keikhosrokiani, Pantea, editor, Singh, Daljeet, editor, Harjula, Erkki, editor, Tiulpin, Aleksei, editor, Jansson, Miia, editor, Isomursu, Minna, editor, van Gils, Mark, editor, Saarakkala, Simo, editor, and Reponen, Jarmo, editor

Published
2024
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4. Local edge computing for radiological image reconstruction and computer-assisted detection:a feasibility study

Author

Isosalo, A. (Antti), Islam, J. (Johirul), Mustonen, H. (Henrik), Räinä, E. (Ella), Inkinen, S. I. (Satu I.), Brix, M. (Mikael), Kumar, T. (Tanesh), Reponen, J. (Jarmo), Nieminen, M. T. (Miika T.), Harjula, E. (Erkki), Isosalo, A. (Antti), Islam, J. (Johirul), Mustonen, H. (Henrik), Räinä, E. (Ella), Inkinen, S. I. (Satu I.), Brix, M. (Mikael), Kumar, T. (Tanesh), Reponen, J. (Jarmo), Nieminen, M. T. (Miika T.), and Harjula, E. (Erkki)

Abstract

Computational requirements for data processing at different stages of the radiology value chain are increasing. Cone beam computed tomography (CBCT) is a diagnostic imaging technique used in dental and extremity imaging, involving a highly demanding image reconstruction task. In turn, artificial intelligence (AI) assisted diagnostics are becoming increasingly popular, thus increasing the use of computation resources. Furthermore, the need for fully independent imaging units outside radiology departments and with remotely performed diagnostics emphasize the need for wireless connectivity between the imaging unit and hospital infrastructure. In this feasibility study, we propose an approach based on a distributed edge-cloud computing platform, consisting of small-scale local edge nodes, edge servers with traditional cloud resources to perform data processing tasks in radiology. We are interested in the use of local computing resources with Graphics Processing Units (GPUs), in our case Jetson Xavier NX, for hosting the algorithms for two use-cases, namely image reconstruction in cone beam computed tomography and AI-assisted cancer detection from mammographic images. Particularly, we wanted to determine the technical requirements for local edge computing platform for these two tasks and whether CBCT image reconstruction and breast cancer detection tasks are possible in a diagnostically acceptable time frame. We validated the use-cases and the proposed edge computing platform in two stages. First, the algorithms were validated use-case-wise by comparing the computing performance of the edge nodes against a reference setup (regular workstation). Second, we performed qualitative evaluation on the edge computing platform by running the algorithms as nanoservices. Our results, obtained through real-life prototyping, indicate that it is possible and technically feasible to run both reconstruction and AI-assisted image analysis functions in a diagnostically acceptable

Published
2023

5. Effects of a 360° virtual counselling environment on patient anxiety and CCTA process time:a randomised controlled trial

Author

Paalimäki-Paakki, K. (Karoliina), Virtanen, M. (Mari), Henner, A. (Anja), Vähänikkilä, H. (Hannu), Nieminen, M. T. (Miika T.), Schroderus-Salo, T. (Tanja), Kääriäinen, M. (Maria), Paalimäki-Paakki, K. (Karoliina), Virtanen, M. (Mari), Henner, A. (Anja), Vähänikkilä, H. (Hannu), Nieminen, M. T. (Miika T.), Schroderus-Salo, T. (Tanja), and Kääriäinen, M. (Maria)

Abstract

Introduction: This study investigated whether a 360° virtual counselling environment (360°VCE) was more effective at decreasing patients’ anxiety than routine standard of care counselling for patients undergoing coronary computed tomography angiography (CCTA), and if there was any difference in the process times for both of these groups. Methods: A total of 86 patients underwent CCTA in this randomised controlled trial. Patients were randomly assigned to intervention and control groups. The 360°VCE was developed using spherical panoramic images and non-immersive 360° technology. The primary outcome, anxiety, was measured using the State-Trait Anxiety Inventory (STAI). The secondary outcome, CCTA process time, was measured from the time of arrival in the department until end of examination. Results: Pre-scan anxiety was lower among patients in the 360°VCE group immediately before CCTA in comparison to patients in the control group (p = 0.015). Women demonstrated higher levels of anxiety than men in both groups. No between-group differences were discerned in CCTA process time. Conclusions: Access to 360°VCE can reduce patients’ pre-CCTA anxiety levels. Implications for practice: The presented results can be used to improve patient counselling and care, reduce anxiety among patients undergoing CCTA, and optimise the CCTA examination procedure.

Published
2023

6. Effect of footwear type on biomechanical risk factors for knee osteoarthritis

Author

Malus, J. (Jan), Urbaczka, J. (Jan), Rygelova, M. (Marketa), Casula, V. (Victor), Nieminen, M. (Miika), Monte, A. (Andrea), Horka, V. (Veronika), Uchytil, J. (Jaroslav), Malus, J. (Jan), Urbaczka, J. (Jan), Rygelova, M. (Marketa), Casula, V. (Victor), Nieminen, M. (Miika), Monte, A. (Andrea), Horka, V. (Veronika), and Uchytil, J. (Jaroslav)

Abstract

Background: Regular walking in different types of footwear may increase the mediolateral shear force, knee adduction moment, or vertical ground-reaction forces that could increase the risk of early development of knee osteoarthritis (OA). Purpose: To compare kinematic and kinetic parameters that could affect the development of knee OA in 3 footwear conditions. Study Design: Controlled laboratory study. Methods: A total of 40 asymptomatic participants performed walking trials in the laboratory at self-selected walking speeds under barefoot (BF), minimalistic (MF), and neutral (NF) footwear conditions. Knee joint parameters were described using discrete point values, and continuous curves were evaluated using statistical parametric mapping. A 3 × 1 repeated-measures analysis of variance was used to determine the main effect of footwear for both discrete and continuous data. To compare differences between footwear conditions, a post hoc paired t test was used. Results: Discrete point analyses showed a significantly greater knee power in NF compared with MF and BF in the weight absorption phase (P < .001 for both). Statistical parametric mapping analysis indicated a significantly greater knee angle in the sagittal plane at the end of the propulsive phase in BF compared with NF and MF (P = .043). Knee joint moment was significantly greater in the propulsive phase for the sagittal (P = .038) and frontal planes (P = .035) in BF compared with NF and MF and in the absorption phase in the sagittal plane (P = .034) in BF compared with MF and NF. A significant main effect of footwear was found for anteroposterior (propulsion, ↑MF, NF, ↓BF [P = .008]; absorption, ↑BF, MF, ↓NF P = .001]), mediolateral (propulsion, ↑MF, NF, ↓BF [P = .005]; absorption, ↑NF, MF, ↓BF [P = .044]), and vertical (propulsion, ↑NF, BF, ↓MF [P = .001]; absorption, ↑MF, BF, ↓NF [P < .001]) ground-reaction forces. Knee power showed a significant main effect of footwear (absorption, ↑NF, MF, ↓B

Published
2023

7. Assessment of articular cartilage of ankle joint in stable and unstable unilateral weber type-B/SER-type ankle fractures shortly after trauma using T2 relaxation time

Author

Lehtovirta, S. (Sami), Casula, V. (Victor), Haapea, M. (Marianne), Nortunen, S. (Simo), Lepojärvi, S. (Sannamari), Pakarinen, H. (Harri), Nieminen, M. T. (Miika T.), Lammentausta, E. (Eveliina), Niinimäki, J. (Jaakko), Lehtovirta, S. (Sami), Casula, V. (Victor), Haapea, M. (Marianne), Nortunen, S. (Simo), Lepojärvi, S. (Sannamari), Pakarinen, H. (Harri), Nieminen, M. T. (Miika T.), Lammentausta, E. (Eveliina), and Niinimäki, J. (Jaakko)

Abstract

Background: Early detection of post-traumatic cartilage damage in the ankle joint in magnetic resonance images can be difficult due to disturbances to structures usually appearing over time. Purpose: To study the articular cartilage of unilateral Weber type-B/SER-type ankle fractures shortly post-trauma using T2 relaxation time. Material and Methods: Fifty one fractured ankles were gathered from consecutively screened patients, compiled initially for RCT studies, and treated at Oulu University Hospital and classified as stable (n = 28) and unstable fractures (n = 23) based on external-rotation stress test: medial clear space of ≥5 mm was interpreted as unstable. A control group of healthy young individuals (n = 19) was also gathered. All ankles were imaged on average 9 (range: 1 to 25) days after injury on a 3.0T MRI unit for T2 relaxation time assessment, and the cartilage was divided into sub-regions for comparison. Results: Control group displayed significantly higher T2 values in tibial cartilage compared to stable (six out of nine regions, p-values = .003–.043) and unstable (six out of nine regions, p-values = .001–.037) ankle fractures. No differences were detected in talar cartilage. Also, no differences were observed between stable and unstable fractures in tibial or talar cartilage. Conclusions: Lower T2 relaxation times of tibial cartilage in fractured ankles suggest intact extra cellular matrix (ECM) of the cartilage. Severity of the ankle fracture, measured by ankle stability, does not seem to increase ECM degradation immediately after trauma.

Published
2023

8. The KNee OsteoArthritis Prediction (KNOAP2020) challenge:An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Author

Hirvasniemi, J., Runhaar, J., van der Heijden, R. A., Zokaeinikoo, M., Yang, M., Li, X., Tan, J., Rajamohan, H. R., Zhou, Y., Deniz, C. M., Caliva, F., Iriondo, C., Lee, J. J., Liu, F., Martinez, A. M., Namiri, N., Pedoia, V., Panfilov, E., Bayramoglu, N., Nguyen, H. H., Nieminen, M. T., Saarakkala, S., Tiulpin, A., Lin, E., Li, A., Li, V., Dam, E. B., Chaudhari, A. S., Kijowski, R., Bierma-Zeinstra, S., Oei, E. H.G., Klein, S., Hirvasniemi, J., Runhaar, J., van der Heijden, R. A., Zokaeinikoo, M., Yang, M., Li, X., Tan, J., Rajamohan, H. R., Zhou, Y., Deniz, C. M., Caliva, F., Iriondo, C., Lee, J. J., Liu, F., Martinez, A. M., Namiri, N., Pedoia, V., Panfilov, E., Bayramoglu, N., Nguyen, H. H., Nieminen, M. T., Saarakkala, S., Tiulpin, A., Lin, E., Li, A., Li, V., Dam, E. B., Chaudhari, A. S., Kijowski, R., Bierma-Zeinstra, S., Oei, E. H.G., and Klein, S.

Abstract

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusion: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.

Published
2023

9. Machine learning prediction of collagen fiber orientation and proteoglycan content from multiparametric quantitative MRI in articular cartilage

Author

Mirmojarabian, S. A. (Seyed Amir), Kajabi, A. W. (Abdul Wahed), Ketola, J. H. (Juuso H. J.), Nykänen, O. (Olli), Liimatainen, T. (Timo), Nieminen, M. T. (Miika T.), Nissi, M. J. (Mikko J.), and Casula, V. (Victor)

Abstract

Background: Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage. Purpose: To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content. Study type: Retrospective, animal model. Animal model: An open-source single slice MRI dataset obtained from 20 samples of 10 Shetland ponies (seven with surgically induced cartilage lesions followed by treatment and three healthy controls) yielded to 1600 data points, including 10% for test and 90% for train validation. Field strength/sequence: A 9.4 T MRI scanner/qMRI sequences: T₁, T₂, adiabatic T1ρ and T2ρ, continuous-wave T1ρ and relaxation along a fictitious field (TRAFF) maps. Assessment: Five machine learning regression models were developed: random forest (RF), support vector regression (SVR), gradient boosting (GB), multilayer perceptron (MLP), and Gaussian process regression (GPR). A nested cross-validation was used for performance evaluation. For reference, proteoglycan content and collagen fiber orientation were determined by quantitative histology from digital densitometry (DD) and polarized light microscopy (PLM), respectively. Statistical tests: Normality was tested using Shapiro–Wilk test, and association between predicted and measured values was evaluated using Spearman’s Rho test. A P-value of 0.05 was considered as the limit of statistical significance. Results: Four out of the five models (RF, GB, MLP, and GPR) yielded high accuracy (R² = 0.68–0.75 for PLM and 0.62–0.66 for DD), and strong significant correlations between the reference measurements and predicted cartilage matrix properties (Spearman’s Rho = 0.72–0.88 for PLM and 0.61–0.83 for DD). GPR algorithm had the highest accuracy (R² = 0.75 and 0.66) and lowest prediction-error (root mean squared [RMSE] = 1.34 and 2.55) for PLM and DD, respectively. Data conclusion: Multiparametric qMRIs in combination with regression models can determine cartilage compositional and structural features, with higher accuracy for collagen fiber orientation than proteoglycan content. Evidence level: 2 Technical efficacy: Stage 2

Published
2023

13. Association between common cardiovascular risk factors and clinical phenotype in patients with hypertrophic cardiomyopathy from the European Society of Cardiology (ESC) EurObservational Research Programme (EORP) Cardiomyopathy/Myocarditis registry

Author

Lopes, Luis R, Losi, Maria-Angela, Sheikh, Nabeel, Laroche, Cécile, Charron, Philippe, Gimeno, Juan, Kaski, Juan P, Maggioni, Aldo P, Tavazzi, Luigi, Arbustini, Eloisa, Brito, Dulce, Celutkiene, Jelena, Hagege, Albert, Linhart, Ales, Mogensen, Jens, Garcia-Pinilla, José Manuel, Ripoll-Vera, Tomas, Seggewiss, Hubert, Villacorta, Eduardo, Caforio, Alida, Elliott, Perry M, Komissarova, S, Chakova, N, Niyazova, S, Linhart, A, Kuchynka, P, Palecek, T, Podzimkova, J, Fikrle, M, Nemecek, E, Bundgaard, H, Tfelt-Hansen, J, Theilade, J, Thune, J J, Axelsson, A, Mogensen, J, Henriksen, F, Hey, T, Nielsen, S K, Videbaek, L, Andreasen, S, Arnsted, H, Saad, A, Ali, M, Lommi, J, Helio, T, Nieminen, M S, Dubourg, O, Mansencal, N, Arslan, M, Tsieu, V Siam, Damy, T, Guellich, A, Guendouz, S, Tissot, C M, Lamine, A, Rappeneau, S, Hagege, A, Desnos, M, Bachet, A, Hamzaoui, M, Charron, P, Isnard, R, Legrand, L, Maupain, C, Gandjbakhch, E, Kerneis, M, Pruny, J-F, Bauer, A, Pfeiffer, B, Felix, S B, Dorr, M, Kaczmarek, S, Lehnert, K, Pedersen, A-L, Beug, D, Bruder, M, Böhm, M, Kindermann, I, Linicus, Y, Werner, C, Neurath, B, Schild-Ungerbuehler, M, Seggewiss, H, Neugebauer, A, Mckeown, P, Muir, A, Mcosker, J, Jardine, T, Divine, G, Elliott, P, Lorenzini, M, Watkinson, O, Wicks, E, Iqbal, H, Mohiddin, S, O'Mahony, C, Sekri, N, Carr-White, G, Bueser, T, Rajani, R, Clack, L, Damm, J, Jones, S, Sanchez-Vidal, R, Smith, M, Walters, T, Wilson, K, Rosmini, S, Anastasakis, A, Ritsatos, K, Vlagkouli, V, Forster, T, Sepp, R, Borbas, J, Nagy, V, Tringer, A, Kakonyi, K, Szabo, L A, Maleki, M, Bezanjani, F Noohi, Amin, A, Naderi, N, Parsaee, M, Taghavi, S, Ghadrdoost, B, Jafari, S, Khoshavi, M, Rapezzi, C, Biagini, E, Corsini, A, Gagliardi, C, Graziosi, M, Longhi, S, Milandri, A, Ragni, L, Palmieri, S, Olivotto, I, Arretini, A, Castelli, G, Cecchi, F, Fornaro, A, Tomberli, B, Spirito, P, Devoto, E, Bella, P Della, Maccabelli, G, Sala, S, Guarracini, F, Peretto, G, Russo, M G, Calabro, R, Pacileo, G, Limongelli, G, Masarone, D, Pazzanese, V, Rea, A, Rubino, M, Tramonte, S, Valente, F, Caiazza, M, Cirillo, A, Del Giorno, G, Esposito, A, Gravino, R, Marrazzo, T, Trimarco, B, Losi, M-A, Nardo, C Di, Giamundo, A, Musella, F, Pacelli, F, Scatteia, A, Canciello, G, Caforio, A, Iliceto, S, Calore, C, Leoni, L, Marra, M Perazzolo, Rigato, I, Tarantini, G, Schiavo, A, Testolina, M, Arbustini, E, Toro, A Di, Giuliani, L P, Serio, A, Fedele, F, Frustaci, A, Alfarano, M, Chimenti, C, Drago, F, Baban, A, Calò, L, Lanzillo, C, Martino, A, Uguccioni, M, Zachara, E, Halasz, G, Re, F, Sinagra, G, Carriere, C, Merlo, M, Ramani, F, Kavoliuniene, A, Krivickiene, A, Tamuleviciute-Prasciene, E, Viezelis, M, Celutkiene, J, Balkeviciene, L, Laukyte, M, Paleviciute, E, Pinto, Y, Wilde, A, Asselbergs, F W, Sammani, A, Van Der Heijden, J, Van Laake, L, De Jonge, N, Hassink, R, Kirkels, J H, Ajuluchukwu, J, Olusegun-Joseph, A, Ekure, E, Mizia-Stec, K, Tendera, M, Czekaj, A, Sikora-Puz, A, Skoczynska, A, Wybraniec, M, Rubis, P, Dziewiecka, E, Wisniowska-Smialek, S, Bilinska, Z, Chmielewski, P, Nieradko, B Foss, Michalak, E, Stepien-Wojno, M, Mazek, B, Lopes, L Rocha, Almeida, A R, Cruz, I, Gomes, A C, Pereira, A R, Brito, D, Madeira, H, Francisco, A R, Menezes, M, Moldovan, O, Guimaraes, T Oliveira, Silva, D, Ginghina, C, Jurcut, R, Mursa, A, Popescu, B A, Apetrei, E, Militaru, S, Coman, I Mircea, Frigy, A, Fogarasi, Z, Kocsis, I, Szabo, I A, Fehervari, L, Nikitin, I, Resnik, E, Komissarova, M, Lazarev, V, Shebzukhova, M, Ustyuzhanin, D, Blagova, O, Alieva, I, Kulikova, V, Lutokhina, Y, Pavlenko, E, Varionchik, N, Ristic, A D, Seferovic, P M, Veljic, I, Zivkovic, I, Milinkovic, I, Pavlovic, A, Radovanovic, G, Simeunovic, D, Zdravkovic, M, Aleksic, M, Djokic, J, Hinic, S, Klasnja, S, Mircetic, K, Monserrat, L, Fernandez, X, Garcia-Giustiniani, D, Larrañaga, J M, Ortiz-Genga, M, Barriales-Villa, R, Martinez-Veira, C, Veira, E, Cequier, A, Salazar-Mendiguchia, J, Manito, N, Gonzalez, J, Fernández-Avilés, F, Medrano, C, Yotti, R, Cuenca, S, Espinosa, M A, Mendez, I, Zatarain, E, Alvarez, R, Pavia, P Garcia, Briceno, A, Cobo-Marcos, M, Dominguez, F, Galvan, E De Teresa, Pinilla, J M García, Abdeselam-Mohamed, N, Lopez-Garrido, M A, Hidalgo, L Morcillo, Ortega-Jimenez, M V, Mezcua, A Robles, Guijarro-Contreras, A, Gomez-Garcia, D, Robles-Mezcua, M, Blanes, J R Gimeno, Castro, F J, Esparza, C Munoz, Molina, M Sabater, García, M Sorli, Cuenca, D Lopez, Ripoll-Vera, T, Alvarez, J, Nunez, J, Gomez, Y, Fernandez, P L Sanchez, Villacorta, E, Avila, C, Bravo, L, Diaz-Pelaez, E, Gallego-Delgado, M, Garcia-Cuenllas, L, Plata, B, Lopez-Haldon, J E, Pena Pena, M L, Perez, E M Cantero, Zorio, E, Arnau, M A, Sanz, J, Marques-Sule, E, Gale, Christopher Peter, Beleslin, Branko, Budaj, Andrzej, Chioncel, Ovidiu, Dagres, Nikolaos, Danchin, Nicolas, Erlinge, David, Emberson, Jonathan, Glikson, Michael, Gray, Alastair, Kayikcioglu, Meral, Maggioni, Aldo, Nagy, Klaudia Vivien, Nedoshivin, Aleksandr, Petronio, Anna-Sonia, Hesselink, Jolien Roo, Wallentin, Lars, Zeymer, Uwe, Caforio, Alida, Blanes, Juan Ramon Gimeno, Charron, Philippe, Elliott, Perry, Kaski, Juan Pablo, Maggioni, Aldo P, Tavazzi, Luigi, Tendera, Michal, Komissarova, S., Chakova, N., Niyazova, S., Linhart, A., Kuchynka, P., Palecek, T., Podzimkova, J., Fikrle, M., Nemecek, E., Bundgaard, H., Tfelt-Hansen, J., Theilade, J., Thune, J J, Axelsson, A., Mogensen, J., Henriksen, F., Hey, T., Nielsen, S K, Videbaek, L., Andreasen, S., Arnsted, H., Saad, A., Ali, M., Lommi, J., Helio, T., Nieminen, M S, Dubourg, O., Mansencal, N., Arslan, M., Tsieu, V Siam, Damy, T., Guellich, A., Guendouz, S., Tissot, C M, Lamine, A., Rappeneau, S., Hagege, A., Desnos, M., Bachet, A., Hamzaoui, M., Charron, P., Isnard, R., Legrand, L., Maupain, C., Gandjbakhch, E., Kerneis, M., Pruny, J-F, Bauer, A., Pfeiffer, B., Felix, S B, Dorr, M., Kaczmarek, S., Lehnert, K., Pedersen, A-L, Beug, D., Bruder, M., Böhm, M., Kindermann, I., Linicus, Y., Werner, C., Neurath, B., Schild-Ungerbuehler, M., Seggewiss, H., Neugebauer, A., McKeown, P., Muir, A., McOsker, J., Jardine, T., Divine, G., Elliott, P., Lorenzini, M., Watkinson, O., Wicks, E., Iqbal, H., Mohiddin, S., O'Mahony, C., Sekri, N., Carr-White, G., Bueser, T., Rajani, R., Clack, L., Damm, J., Jones, S., Sanchez-Vidal, R., Smith, M., Walters, T., Wilson, K., Rosmini, S., Anastasakis, A., Ritsatos, K., Vlagkouli, V., Forster, T., Sepp, R., Borbas, J., Nagy, V., Tringer, A., Kakonyi, K., Szabo, L A, Maleki, M., Bezanjani, F Noohi, Amin, A., Naderi, N., Parsaee, M., Taghavi, S., Ghadrdoost, B., Jafari, S., Khoshavi, M., Rapezzi, C., Biagini, E., Corsini, A., Gagliardi, C., Graziosi, M., Longhi, S., Milandri, A., Ragni, L., Palmieri, S., Olivotto, I., Arretini, A., Castelli, G., Cecchi, F., Fornaro, A., Tomberli, B., Spirito, P., Devoto, E., Bella, P Della, Maccabelli, G., Sala, S., Guarracini, F., Peretto, G., Russo, M G, Calabro, R., Pacileo, G., Limongelli, G., Masarone, D., Pazzanese, V., Rea, A., Rubino, M., Tramonte, S., Valente, F., Caiazza, M., Cirillo, A., Del Giorno, G., Esposito, A., Gravino, R., Marrazzo, T., Trimarco, B., Losi, M-A, Di Nardo, C., Giamundo, A., Musella, F., Pacelli, F., Scatteia, A., Canciello, G., Caforio, A., Iliceto, S., Calore, C., Leoni, L., Marra, M Perazzolo, Rigato, I., Tarantini, G., Schiavo, A., Testolina, M., Arbustini, E., Di Toro, A., Giuliani, L P, Serio, A., Fedele, F., Frustaci, A., Alfarano, M., Chimenti, C., Drago, F., Baban, A., Calò, L., Lanzillo, C., Martino, A., Uguccioni, M., Zachara, E., Halasz, G., Re, F., Sinagra, G., Carriere, C., Merlo, M., Ramani, F., Kavoliūnienė, Aušra, Krivickienė, Aušra, Tamulevičiūtė-Prascienė, Eglė, Vieželis, Mindaugas, Balkevičienė, Laura, Laukytė, M., Palevičiūtė, Eglė, Pinto, Y., Wilde, A., Asselbergs, F W, Sammani, A., Van Der Heijden, J., Van Laake, L., De Jonge, N., Hassink, R., Kirkels, J H, Ajuluchukwu, J., Olusegun-Joseph, A., Ekure, E., Mizia-Stec, K., Tendera, M., Czekaj, A., Sikora-Puz, A., Skoczynska, A., Wybraniec, M., Rubis, P., Dziewiecka, E., Wisniowska-Smialek, S., Bilinska, Z., Chmielewski, P., Foss-Nieradko, B., Michalak, E., Stepien-Wojno, M., Mazek, B., Lopes, L Rocha, Almeida, A R, Cruz, I., Gomes, A C, Pereira, A R, Brito, D., Madeira, H., Francisco, A R, Menezes, M., Moldovan, O., Guimaraes, T Oliveira, Silva, D., Ginghina, C., Jurcut, R., Mursa, A., Popescu, B A, Apetrei, E., Militaru, S., Coman, I Mircea, Frigy, A., Fogarasi, Z., Kocsis, I., Szabo, I A, Fehervari, L., Nikitin, I., Resnik, E., Komissarova, M., Lazarev, V., Shebzukhova, M., Ustyuzhanin, D., Blagova, O., Alieva, I., Kulikova, V., Lutokhina, Y., Pavlenko, E., Varionchik, N., Ristic, A D, Seferovic, P M, Veljic, I., Zivkovic, I., Milinkovic, I., Pavlovic, A., Radovanovic, G., Simeunovic, D., Zdravkovic, M., Aleksic, M., Djokic, J., Hinic, S., Klasnja, S., Mircetic, K., Monserrat, L., Fernandez, X., Garcia-Giustiniani, D., Larrañaga, J M, Ortiz-Genga, M., Barriales-Villa, R., Martinez-Veira, C., Veira, E., Cequier, A., Salazar-Mendiguchia, J., Manito, N., Gonzalez, J., Fernández-Avilés, F., Medrano, C., Yotti, R., Cuenca, S., Espinosa, M A, Mendez, I., Zatarain, E., Alvarez, R., Pavia, P Garcia, Briceno, A., Cobo-Marcos, M., Dominguez, F., Galvan, E De Teresa, Pinilla, J M García, Abdeselam-Mohamed, N., Lopez-Garrido, M A, Hidalgo, L Morcillo, Ortega-Jimenez, M V, Mezcua, A Robles, Guijarro-Contreras, A., Gomez-Garcia, D., Robles-Mezcua, M., Blanes, J R Gimeno, Castro, F J, Esparza, C Munoz, Molina, M Sabater, García, M Sorli, Cuenca, D Lopez, de Mallorca, Palma, Ripoll-Vera, T., Alvarez, J., Nunez, J., Gomez, Y., Fernandez, P L Sanchez, Villacorta, E., Avila, C., Bravo, L., Diaz-Pelaez, E., Gallego-Delgado, M., Garcia-Cuenllas, L., Plata, B., Lopez-Haldon, J E, Pena Pena, M L, Perez, E M Cantero, Zorio, E., Arnau, M A, Sanz, J., Marques-Sule, E., Repositório da Universidade de Lisboa, Lopes, Lr, Losi, Ma, Sheikh, N, Laroche, C, Charron, P, Gimeno, J, Kaski, Jp, Maggioni, Ap, Tavazzi, L, Arbustini, E, Brito, D, Celutkiene, J, Hagege, A, Linhart, A, Mogensen, J, Garcia-Pinilla, Jm, Ripoll-Vera, T, Seggewiss, H, Villacorta, E, Caforio, A, and Elliott, Pm

Subjects
Genotype, Health Policy, Diabetes, Cardiovascular risk factors, Hypertension, Hypertrophic cardiomyopathy, Obesity, Cardiomyopathy, Hypertrophic, Ventricular Dysfunction, Left, diabete, Cardiovascular Diseases, Risk Factors, Heart Disease Risk Factors, cardiovascular risk factor, Humans, Female, 03.02. Klinikai orvostan, Cardiology and Cardiovascular Medicine, Cardiomyopathies, obesity
Abstract

© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited., Aims: The interaction between common cardiovascular risk factors (CVRF) and hypertrophic cardiomyopathy (HCM) is poorly studied. We sought to explore the relation between CVRF and the clinical characteristics of patients with HCM enrolled in the EURObservational Research Programme (EORP) Cardiomyopathy registry. Methods and results: 1739 patients with HCM were studied. The relation between hypertension (HT), diabetes (DM), body mass index (BMI) and clinical traits was analyzed. Analyses were stratified according to the presence or absence of a pathogenic variant in a sarcomere gene.The prevalence of HT, DM and obesity (Ob) was 37%, 10%, and 21%, respectively. HT, DM and Ob were associated with older age (p

Published
2022

16. Quantitative evaluation of the tibiofemoral joint cartilage by T2 mapping in patients with acute anterior cruciate ligament injury vs contralateral knees : results from the subacute phase using data from the NACOX study cohort

Author

Casula, V, Edwardsson Tajik, Bashir, Kvist, Joanna, Frobell, R., Haapea, M., Nieminen, M. T., Gauffin, Håkan, Englund, M., Casula, V, Edwardsson Tajik, Bashir, Kvist, Joanna, Frobell, R., Haapea, M., Nieminen, M. T., Gauffin, Håkan, and Englund, M.

Abstract

Objective: Immediate cartilage structural alterations in the acute phase after an anterior cruciate ligament (ACL) rupture may be a precursor to posttraumatic osteoarthritis (PTOA) development. Our aim was to describe changes in cartilage matrix in the subacute phase of the acutely ACL-injured knee compared to the contralateral uninjured knee. Design: Participants (n = 118) aged 15-40 years with an acute ACL injury were consecutively included in subacute phase after acute ACL-injury and underwent MRI (mean 29 days post trauma) of both knees. Mean T2 relaxation times, T2 spatial coefficient of variation and cartilage thickness were determined for different regions of the tibiofemoral cartilage. Differences between the acutely ACL-injured and uninjured knee were evaluated using Wilcoxon signed-rank test. Results: T2 relaxation time in injured knees was increased in multiple cartilage regions from both medial and lateral compartment compared to contralateral knees, mostly in medial trochlea and posterior tibia (P-value<0.001). In the same sites of injured knees, we observed significantly thinner cartilage. Moreover, injured knees presented shorter T2 relaxation time in superficial cartilage on lateral central femur and trochlea (P-value<0.001), and decreased T2 spatial coefficient of variation in lateral trochlea and load bearing regions of medial-central femoral condyle and central tibia in both compartments. Conclusion: Small but statistically significant differences were observed in the subacute phase between ACL-injured and uninjured knee in cartilage T2 relaxation time and cartilage thickness. Future longitudinal observations of the same cohort will allow for better understanding of early development of PTOA. (C) 2022 The Author(s). Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International., Funding Agencies|Swedish Research Council; Swedish Research Council for Sport Science; Medical Research Council of Southeast; Jane & Aatos Erkko Foundation; ALF Grants Region Ostergotland; ALF Grants Region Skane

Published
2022
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17. Deep-learning-based contrast synthesis from MRF parameter maps in the knee joint

Author

Nykänen, O. (Olli), Nevalainen, M. (Mika), Casula, V. (Victor), Isosalo, A. (Antti), Inkinen, S. I. (Satu I.), Nikki, M. (Marko), Lattanzi, R. (Riccardo), Cloos, M. A. (Martijn A.), Nissi, M. J. (Mikko J.), Nieminen, M. T. (Miika T.), Nykänen, O. (Olli), Nevalainen, M. (Mika), Casula, V. (Victor), Isosalo, A. (Antti), Inkinen, S. I. (Satu I.), Nikki, M. (Marko), Lattanzi, R. (Riccardo), Cloos, M. A. (Martijn A.), Nissi, M. J. (Mikko J.), and Nieminen, M. T. (Miika T.)

Abstract

Background: Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time. Purpose: To improve clinical utility of MRF by synthesizing contrast-weighted MR images from the quantitative data provided by MRF, using U-nets that were trained for the synthesis task utilizing L1- and perceptual loss functions, and their combinations. Study type: Retrospective. Population: Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33–35, gender distribution not available). Field strength and sequence: A 3 T, multislice-MRF, proton density (PD)-weighted 3D-SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat-saturated T2-weighted 3D-space, water-excited double echo steady state (DESS). Assessment: Data were divided into training, validation, test, and radiologist’s assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist’s assessment. The synthetic and target images were evaluated using 5-point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics. Statistical tests: Friedman’s test accompanied with post hoc Wilcoxon signed-rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized. Results: The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3–4 on a 5-point scale). Qualitatively, the best synthetic images were produced with combination of L1- and perceptual loss functions and perceptual loss alone, while L1-loss alone led to significantly poorer image quality (Likert scores below 3). The in

Published
2022

18. Deep-learning-based contrast synthesis from MRF parameter maps in the knee

Author

Nykänen, O. (Olli), Isosalo, A. (Antti), Inkinen, S. (Satu), Casula, V. (Victor), Nevalainen, M. (Mika), Lattanzi, R. (Riccardo), Cloos, M. (Martijn), Nissi, M. (Mikko), Nieminen, M. T. (Miika T.), Nykänen, O. (Olli), Isosalo, A. (Antti), Inkinen, S. (Satu), Casula, V. (Victor), Nevalainen, M. (Mika), Lattanzi, R. (Riccardo), Cloos, M. (Martijn), Nissi, M. (Mikko), and Nieminen, M. T. (Miika T.)

Abstract

Synopsis In this study, deep convolutional neural networks (DCNN) are used to synthesize contrast-weighted magnetic resonance (MR) images from quantitative parameter maps of the knee joint obtained with magnetic resonance fingerprinting (MRF). Training of the neural networks was performed using data from 142 patients, for which both standard MR images and quantitative MRF maps of the knee were available. The study demonstrates that synthesizing contrast-weighted images from MRF-parameter maps is possible utilizing DCNNs. Furthermore, the study indicates a need to tune up the dictionary used in MRF so that the parameters expected from the target anatomy are well-covered.

Published
2022

19. 360°-ohjausympäristön vaikutus sepelvaltimoiden tietokonetomografiatutkimukseen tulevien potilaiden ahdistukseen

Author

Kääriäinen, M. (Maria), Nieminen, M. (Miika), Henner, A. (Anja), Paalimäki-Paakki, K. (Karoliina), Kääriäinen, M. (Maria), Nieminen, M. (Miika), Henner, A. (Anja), and Paalimäki-Paakki, K. (Karoliina)

Abstract

The aim of the study was to investigate the effect of a 360° virtual counselling environment (360°VCE) on reducing people’s anxiety levels and improving computed tomography angiography (CCTA) process time. The anxiety during CCTA can reduce patient safety, the quality of the imaging experience, and the image quality, as well as increase the radiation dose. In the intervention planning phase (phase I) the systematic review identified and synthesized the effectiveness of digital counseling environments (n=26) at reducing anxiety, depression, and adherence to treatment among patients who are chronically ill. In phase II the 360°VCE was developed using spherical panoramic images and non-immersive 360° technology. A qualitative study was then conducted to describe patients’ (n=10), radiographers’ (n=10) and radiography students’ (n=10) experiences of the developed 360°VCE for the CCTA. In the pilot study the 360°VCE relieved patients’ fear, increased knowledge and senses of patient safety and self-efficacy. In phase III the effect of a 360°VCE on patient anxiety and CCTA process time were evaluated in a randomised controlled trial. A total of 86 CCTA patients were randomly assigned to intervention (n=41) and control (n=45) groups. The primary outcome, anxiety, was measured using the State-Trait Anxiety Inventory (STAI). The secondary outcome was CCTA process time. Pre-scan anxiety was lower among patients in the 360°VCE group right before CCTA in comparison to patients in the control group (p=0.015). The change in the state anxiety was -7.34 (95% CI -9.83 to -4.86, p<0.001) for the intervention and -0.98 (95% CI -1.73 to -0.22, p=0.012) for the control group. Women demonstrated higher levels of anxiety than men in both groups. No between-group differences were discerned in CCTA process time. The presented results can be used to improve patient counselling and care, reduce anxiety among patients undergoing CCTA, and opt, Tiivistelmä Ahdistus sepelvaltimoiden tietokonetomografiatutkimuksessa (TT) voi heikentää potilasturvallisuutta, potilaan kuvantamiskokemuksen laatua, kuvanlaatua sekä lisätä potilaan säteilyannosta. Tämän tutkimuksen tarkoituksena oli kehittää ja kuvailla 360°-ohjausympäristöä sekä arvioida sen vaikuttavuutta sepelvaltimoiden TT-tutkimukseen tulevien potilaiden ahdistukseen ja tutkimuksen läpimenoaikaan. Tavoitteena oli tuottaa uutta tietoa 360°-ohjausympäristöjen kehittämiseksi kuvantamistutkimuksiin tuleville potilaille. Tutkimuksessa oli kolme vaihetta: ohjausympäristön kehittäminen, pilotointi ja vaikuttavuuden arviointi. Vaiheessa I systemaattisella kirjallisuuskatsauksella syntetisoitiin aiempi tutkimustieto pitkäaikaissairaiden digitaalisten ohjausympäristöjen (n=26) vaikuttavuudesta. Tietoa hyödynnettiin intervention kehittämisessä. Vaiheessa II kehitettiin 360°-ohjausympäristö ja kuvailtiin laadullisella pilottitutkimuksella potilaiden (n=10), röntgenhoitajien (n=10) ja röntgenhoitajaopiskelijoiden (n=10) kokemuksia siitä. Aineisto kerättiin teemahaastatteluilla ja analysoitiin sisällönanalyysillä. Vaiheessa III arvioitiin 360°-ohjausympäristön vaikuttavuutta sepelvaltimoiden TT-tutkimukseen tulevien potilaiden ahdistukseen ja tutkimuksen läpimenoaikaan. Potilaat satunnaistettiin koe- (n=41) ja kontrolliryhmiin (n=45): koeryhmä käytti ennen sairaalaan tuloa 360°-ohjausympäristöä nykykäytännön mukaisen (kirjallisen ja suullisen) ohjauksen lisäksi. Kontrolliryhmä sai nykykäytännön mukaisen ohjauksen. Aineisto kerättiin ennen ja jälkeen intervention ahdistusta mittaavalla STAI-mittarilla sekä TT-tutkimuksen läpimenoajan osalta potilastietojärjestelmästä. Aineisto analysoitiin tilastollisesti. Katsaus osoitti, että pitkäaikaissairaille kehitetyt digitaaliset ohjausympäristöt ovat tehokkaampia tai yhtä tehokkaita kuin tavanomaiset ohjausmenetelmät, kuten suullinen ohjaus. Pilottitutkimuksessa potilaat kokivat 360°-ohjausympäristön

Published
2022

20. The KNee OsteoArthritis prediction (KNOAP2020) challenge:an image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Author

Hirvasniemi, J. (J.), Runhaar, J. (J.), van der Heijden, R. (R.A.), Zokaeinikoo, M. (M.), Yang, M. (M.), Li, X. (X.), Tan, J. (J.), Rajamohan, H. (H.R.), Zhou, Y. (Y.), Deniz, C. (C.M.), Caliva, F. (F.), Iriondo, C. (C.), Lee, J. (J.J.), Liu, F. (F.), Martinez, A. (A.M.), Namiri, N. (N.), Pedoia, V. (V.), Panfilov, E. (E.), Bayramoglu, N. (N.), Nguyen, H. (H.H.), Nieminen, M. (M.T.), Saarakkala, S. (S.), Tiulpin, A. (A.), Lin, E. (E.), Li, A. (A.), Li, V. (V.), Dam, E. (E.B.), Chaudhari, A. (A.S.), Kijowski, R. (R.), Bierma-Zeinstra, S. (S.), Oei, E. (E.H.G.), Klein, S. (S.), Hirvasniemi, J. (J.), Runhaar, J. (J.), van der Heijden, R. (R.A.), Zokaeinikoo, M. (M.), Yang, M. (M.), Li, X. (X.), Tan, J. (J.), Rajamohan, H. (H.R.), Zhou, Y. (Y.), Deniz, C. (C.M.), Caliva, F. (F.), Iriondo, C. (C.), Lee, J. (J.J.), Liu, F. (F.), Martinez, A. (A.M.), Namiri, N. (N.), Pedoia, V. (V.), Panfilov, E. (E.), Bayramoglu, N. (N.), Nguyen, H. (H.H.), Nieminen, M. (M.T.), Saarakkala, S. (S.), Tiulpin, A. (A.), Lin, E. (E.), Li, A. (A.), Li, V. (V.), Dam, E. (E.B.), Chaudhari, A. (A.S.), Kijowski, R. (R.), Bierma-Zeinstra, S. (S.), Oei, E. (E.H.G.), and Klein, S. (S.)

Abstract

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusions: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.

Published
2022

21. Predicting knee osteoarthritis progression from structural MRI using deep learning

Author

Panfilov, E. (Egor), Saarakkala, S. (Simo), Nieminen, M. T. (Miika T.), Tiulpin, A. (Aleksei), Panfilov, E. (Egor), Saarakkala, S. (Simo), Nieminen, M. T. (Miika T.), and Tiulpin, A. (Aleksei)

Abstract

Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials. Prior art focused on manually designed imaging biomarkers, which may not fully exploit all disease-related information present in MRI scan. In contrast, our method learns relevant representations from raw data end-to-end using Deep Learning, and uses them for progression prediction. The method employs a 2D CNN to process the data slice-wise and aggregate the extracted features using a Transformer. Evaluated on a large cohort (n=4,866), the proposed method outperforms conventional 2D and 3D CNN-based models and achieves average precision of 0.58 ± 0.03 and ROC AUC of 0.78 ± 0.01. This paper sets a baseline on end-to-end KOA progression prediction from structural MRI. Our code is publicly available at https://github.com/MIPT-Oulu/OAProgressionMR.

Published
2022

22. Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS:6-year data from osteoarthritis initiative

Author

Väärälä, A. (Ari), Casula, V. (Victor), Peuna, A. (Arttu), Panfilov, E. (Egor), Mobasheri, A. (Ali), Haapea, M. (Marianne), Lammentausta, E. (Eveliina), Nieminen, M. T. (Miika T.), Väärälä, A. (Ari), Casula, V. (Victor), Peuna, A. (Arttu), Panfilov, E. (Egor), Mobasheri, A. (Ali), Haapea, M. (Marianne), Lammentausta, E. (Eveliina), and Nieminen, M. T. (Miika T.)

Abstract

In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA.

Published
2022

23. Quantitative evaluation of the tibiofemoral joint cartilage by T2 mapping in patients with acute anterior cruciate ligament injury vs contralateral knees:results from the subacute phase using data from the NACOX study cohort

Author

Casula, V. (Victor), Tajik, B. (B.E.), Kvist, J. (J.), Frobell, R. (R.), Haapea, M. (Marianne), Nieminen, M. (Miika), Gauffin, H. (H.), Englund, M. (M.), Casula, V. (Victor), Tajik, B. (B.E.), Kvist, J. (J.), Frobell, R. (R.), Haapea, M. (Marianne), Nieminen, M. (Miika), Gauffin, H. (H.), and Englund, M. (M.)

Abstract

Objective: Immediate cartilage structural alterations in the acute phase after an anterior cruciate ligament (ACL) rupture may be a precursor to posttraumatic osteoarthritis (PTOA) development. Our aim was to describe changes in cartilage matrix in the subacute phase of the acutely ACL-injured knee compared to the contralateral uninjured knee. Design: Participants (n = 118) aged 15–40 years with an acute ACL injury were consecutively included in subacute phase after acute ACL-injury and underwent MRI (mean 29 days post trauma) of both knees. Mean T2 relaxation times, T2 spatial coefficient of variation and cartilage thickness were determined for different regions of the tibiofemoral cartilage. Differences between the acutely ACL-injured and uninjured knee were evaluated using Wilcoxon signed-rank test. Results: T2 relaxation time in injured knees was increased in multiple cartilage regions from both medial and lateral compartment compared to contralateral knees, mostly in medial trochlea and posterior tibia (P-value<0.001). In the same sites of injured knees, we observed significantly thinner cartilage. Moreover, injured knees presented shorter T2 relaxation time in superficial cartilage on lateral central femur and trochlea (P-value<0.001), and decreased T2 spatial coefficient of variation in lateral trochlea and load bearing regions of medial-central femoral condyle and central tibia in both compartments. Conclusions: Small but statistically significant differences were observed in the subacute phase between ACL-injured and uninjured knee in cartilage T2 relaxation time and cartilage thickness. Future longitudinal observations of the same cohort will allow for better understanding of early development of PTOA.

Published
2022

24. Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis

Author

Bayramoglu, N. (Neslihan), Nieminen, M. T. (Miika T.), Saarakkala, S. (Simo), Bayramoglu, N. (Neslihan), Nieminen, M. T. (Miika T.), and Saarakkala, S. (Simo)

Abstract

Objective: To assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: We used lateral view knee radiographs from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder), and subsequently, these anatomical landmarks were used to extract three different texture ROIs. Hand-crafted features, based on Local Binary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the PFOA. The proposed classification models were eventually compared with more conventional reference models that use clinical assessments and participant characteristics such as age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC), the area under the precision-recall (PR) curve -average precision (AP)-, and Brier score in the stratified 5-fold cross validation setting. Results: Of the 5507 knees, 953 (17.3%) had PFOA. AUC and AP for the strongest reference model including age, sex, BMI, WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487, respectively. Textural ROI classification using CNN significantly improved the prediction performance (ROC AUC = 0.889, AP = 0.714). Conclusions: We present the first study that

Published
2022

25. Evaluation of different convolutional neural network encoder-decoder architectures for breast mass segmentation

Author

Isosalo, A. (Antti), Mustonen, H. (Henrik), Turunen, T. (Topi), Ipatti, P. S. (Pieta S.), Reponen, J. (Jarmo), Nieminen, M. T. (Miika T.), Inkinen, S. I. (Satu I.), Isosalo, A. (Antti), Mustonen, H. (Henrik), Turunen, T. (Topi), Ipatti, P. S. (Pieta S.), Reponen, J. (Jarmo), Nieminen, M. T. (Miika T.), and Inkinen, S. I. (Satu I.)

Abstract

In this work, we study convolutional neural network encoder-decoder architectures with pre-trained encoder weights for breast mass segmentation from digital screening mammograms. To automatically detect breast cancer, one fundamental task to achieve is the segmentation of the potential abnormal regions. Our objective was to find out whether encoder weights trained for breast cancer evaluation in comparison to those learned from natural images can yield a better model initialization, and furthermore improved segmentation results. We applied transfer learning and initialized the encoder, namely ResNet34 and ResNet22, with ImageNet weights and weights learned from breast cancer classification, respectively. A large clinically-realistic Finnish mammography screening dataset was utilized in model training and evaluation. Furthermore, an independent Portuguese INbreast dataset was utilized for further evaluation of the models. 5-fold cross-validation was applied for training. Soft Focal Tversky loss was used to calculate the model training time error. Dice score and Intersection over Union were used in quantifying the degree of similarity between the annotated and automatically produced segmentation masks. The best performing encoder-decoder with ResNet34 encoder tailed with U-Net decoder yielded Dice scores (mean±SD) of 0.7677±0.2134 for the Finnish dataset, and ResNet22 encoder tailed with U-Net decoder 0.8430±0.1091 for the INbreast dataset. No large differences in segmentation accuracy were found between the encoders initialized with weights pre-trained from breast cancer evaluation, and of those from natural image classification.

Published
2022

26. Effectiveness of digital counseling environments on anxiety, depression, and adherence to treatment among patients who are chronically ill:systematic review

Author

Paalimäki-Paakki, K. (Karoliina), Virtanen, M. (Mari), Henner, A. (Anja), Nieminen, M. T. (Miika T.), Kääriäinen, M. (Maria), Paalimäki-Paakki, K. (Karoliina), Virtanen, M. (Mari), Henner, A. (Anja), Nieminen, M. T. (Miika T.), and Kääriäinen, M. (Maria)

Abstract

Background: Patients who are chronically ill need novel patient counseling methods to support their self-care at different stages of the disease. At present, knowledge of how effective digital counseling is at managing patients’ anxiety, depression, and adherence to treatment seems to be fragmented, and the development of digital counseling will require a more comprehensive view of this subset of interventions. Objective: This study aims to identify and synthesize the best available evidence on the effectiveness of digital counseling environments at improving anxiety, depression, and adherence to treatment among patients who are chronically ill. Methods: Systematic searches of the EBSCO (CINAHL), PubMed, Scopus, and Web of Science databases were conducted in May 2019 and complemented in October 2020. The review considered studies that included adult patients aged ≥18 years with chronic diseases; interventions evaluating digital (mobile, web-based, and ubiquitous) counseling interventions; and anxiety, depression, and adherence to treatment, including clinical indicators related to adherence to treatment, as outcomes. Methodological quality was assessed using the standardized Joanna Briggs Institute critical appraisal tool for randomized controlled trials or quasi-experimental studies. As a meta-analysis could not be conducted because of considerable heterogeneity in the reported outcomes, narrative synthesis was used to synthesize the results. Results: Of the 2056 records screened, 20 (0.97%) randomized controlled trials, 4 (0.19%) pilot randomized controlled trials, and 2 (0.09%) quasi-experimental studies were included. Among the 26 included studies, 10 (38%) digital, web-based interventions yielded significantly positive effects on anxiety, depression, adherence to treatment, and the clinical indicators related to adherence to treatment, and another 18 (69%) studies reported positive, albeit statistically nonsignificant, changes among patients who were c

Published
2022

27. Risk of sudden cardiac death associated with QRS, QTc, and JTc intervals in the general population

Author

Tikkanen, J. T. (Jani T.), Kenttä, T. (Tuomas), Porthan, K. (Kimmo), Anttonen, O. (Olli), Eranti, A. (Antti), Aro, A. L. (Aapo L.), Kerola, T. (Tuomas), Rissanen, H. A. (Harri A.), Knekt, P. (Paul), Heliövaara, M. (Markku), Holkeri, A. (Arttu), Haukilahti, A. (Anette), Niiranen, T. (Teemu), Hernesniemi, J. (Jussi), Jula, A. (Antti), Nieminen, M. S. (Markku S.), Myerburg, R. J. (Robert J.), Albert, C. M. (Christine M.), Salomaa, V. (Veikko), Huikuri, H. V. (Heikki V.), Junttila, M. J. (M. Juhani), Tikkanen, J. T. (Jani T.), Kenttä, T. (Tuomas), Porthan, K. (Kimmo), Anttonen, O. (Olli), Eranti, A. (Antti), Aro, A. L. (Aapo L.), Kerola, T. (Tuomas), Rissanen, H. A. (Harri A.), Knekt, P. (Paul), Heliövaara, M. (Markku), Holkeri, A. (Arttu), Haukilahti, A. (Anette), Niiranen, T. (Teemu), Hernesniemi, J. (Jussi), Jula, A. (Antti), Nieminen, M. S. (Markku S.), Myerburg, R. J. (Robert J.), Albert, C. M. (Christine M.), Salomaa, V. (Veikko), Huikuri, H. V. (Heikki V.), and Junttila, M. J. (M. Juhani)

Abstract

Background: QRS duration and corrected QT (QTc) interval have been associated with sudden cardiac death (SCD), but no data are available on the significance of repolarization component (JTc interval) of the QTc interval as an independent risk marker in the general population. Objective: In this study, we sought to quantify the risk of SCD associated with QRS, QTc, and JTc intervals. Methods: This study was conducted using data from 3 population cohorts from different eras, comprising a total of 20,058 individuals. The follow-up period was limited to 10 years and age at baseline to 30–61 years. QRS duration and QT interval (Bazett’s) were measured from standard 12-lead electrocardiograms at baseline. JTc interval was defined as QTc interval — QRS duration. Cox proportional hazards models that controlled for confounding clinical factors identified at baseline were used to estimate the relative risk of SCD. Results: During a mean period of 9.7 years, 207 SCDs occurred (1.1 per 1000 person-years). QRS duration was associated with a significantly increased risk of SCD in each cohort (pooled hazard ratio [HR] 1.030 per 1-ms increase; 95% confidence interval [CI] 1.017–1.043). The QTc interval had borderline to significant associations with SCD and varied among cohorts (pooled HR 1.007; 95% CI 1.001–1.012). JTc interval as a continuous variable was not associated with SCD (pooled HR 1.001; 95% CI 0.996–1.007). Conclusions: Prolonged QRS durations and QTc intervals are associated with an increased risk of SCD. However, when the QTc interval is deconstructed into QRS and JTc intervals, the repolarization component (JTc) appears to have no independent prognostic value.

Published
2022

28. Dipolar relaxation of water protons in the vicinity of a collagen-like peptide

Author

Karjalainen, J. (Jouni), Henschel, H. (Henning), Nissi, M. J. (Mikko J.), Nieminen, M. T. (Miika T.), Hanni, M. (Matti), Karjalainen, J. (Jouni), Henschel, H. (Henning), Nissi, M. J. (Mikko J.), Nieminen, M. T. (Miika T.), and Hanni, M. (Matti)

Abstract

Quantitative magnetic resonance imaging is one of the few available methods for noninvasive diagnosis of degenerative changes in articular cartilage. The clinical use of the imaging data is limited by the lack of a clear association between structural changes at the molecular level and the measured magnetic relaxation times. In anisotropic, collagen-containing tissues, such as articular cartilage, the orientation dependency of nuclear magnetic relaxation can obscure the content of the images. Conversely, if the molecular origin of the phenomenon would be better understood, it would provide opportunities for diagnostics as well as treatment planning of degenerative changes in these tissues. We study the magnitude and orientation dependence of the nuclear magnetic relaxation due to dipole–dipole coupling of water protons in anisotropic, collagenous structures. The water–collagen interactions are modeled with molecular dynamics simulations of a small collagen-like peptide dissolved in water. We find that in the vicinity of the collagen-like peptide, the dipolar relaxation of water hydrogen nuclei is anisotropic, which can result in orientation-dependent relaxation times if the water remains close to the peptide. However, the orientation-dependency of the relaxation is different from the commonly observed magic-angle phenomenon in articular cartilage MRI.

Published
2022

29. Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues:data from the Osteoarthritis Initiative

Author

Panfilov, E. (Egor), Tiulpin, A. (Aleksei), Nieminen, M. T. (Miika T.), Saarakkala, S. (Simo), Casula, V. (Victor), Panfilov, E. (Egor), Tiulpin, A. (Aleksei), Nieminen, M. T. (Miika T.), Saarakkala, S. (Simo), and Casula, V. (Victor)

Abstract

Morphological changes in knee cartilage subregions are valuable imaging-based biomarkers for understanding progression of osteoarthritis, and they are typically detected from magnetic resonance imaging (MRI). So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and subregional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two data sets of 3D double-echo steady-state (DESS) MRI derived from the Osteoarthritis Initiative were used: first, n = 88; second, n = 600, 0-/12-/24-month visits. Our method performed deep learning-based segmentation of knee cartilage tissues, their subregional division via multi-atlas registration, and extraction of subregional volume and thickness. The segmentation model was developed and assessed on the first data set. Subsequently, on the second data set, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r > 0.934) and agreement (mean difference < 116 mm³) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r = 0.845–0.973 and mean differences = 262–501 mm³ for weight-bearing cartilage volume, and r = 0.770–0.962 and mean differences = 0.513–1.138 mm for subregional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12- and 24-month subregional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extra

Published
2022

30. Relaxation anisotropy of quantitative MRI parameters in biological tissues

Author

Hänninen, N. E. (Nina Elina), Liimatainen, T. (Timo), Hanni, M. (Matti), Gröhn, O. (Olli), Nieminen, M. T. (Miika Tapio), and Nissi, M. J. (Mikko Johannes)

Subjects
Biophysics, Biological physics, Biomedical engineering
Abstract

Quantitative MR relaxation parameters vary in the sensitivity to the orientation of the tissue in the magnetic field. In this study, the orientation dependence of multiple relaxation parameters was assessed in various tissues. Ex vivo samples of each tissue type were prepared either from bovine knee (tendon, cartilage) or mouse (brain, spinal cord, heart, kidney), and imaged at 9.4 T MRI with T1, T2, continuous wave (CW‐) T1ρ, adiabatic T1ρ and T2ρ, and Relaxation along fictitious field (RAFF2‐4) sequences at five different orientations with respect to the main magnetic field. Relaxation anisotropy of the measured parameters was quantified and compared. The highly ordered collagenous tissues, i.e. cartilage and tendon, presented the highest relaxation anisotropy for T2, CW‐T1ρ with spin-lock power

Published
2022

33. Drug–gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval

Author

Avery, C L, Sitlani, C M, Arking, D E, Arnett, D K, Bis, J C, Boerwinkle, E, Buckley, B M, Ida Chen, Y-D, de Craen, A J M, Eijgelsheim, M, Enquobahrie, D, Evans, D S, Ford, I, Garcia, M E, Gudnason, V, Harris, T B, Heckbert, S R, Hochner, H, Hofman, A, Hsueh, W-C, Isaacs, A, Jukema, J W, Knekt, P, Kors, J A, Krijthe, B P, Kristiansson, K, Laaksonen, M, Liu, Y, Li, X, MacFarlane, P W, Newton-Cheh, C, Nieminen, M S, Oostra, B A, Peloso, G M, Porthan, K, Rice, K, Rivadeneira, F F, Rotter, J I, Salomaa, V, Sattar, N, Siscovick, D S, Slagboom, P E, Smith, A V, Sotoodehnia, N, Stott, D J, Stricker, B H, Stürmer, T, Trompet, S, Uitterlinden, A G, van Duijn, C, Westendorp, R G J, Witteman, J C, Whitsel, E A, and Psaty, B M

Published
2014
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34. Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis

Author

Bayramoglu, N. (Neslihan), Nieminen, M. T. (Miika T.), and Saarakkala, S. (Simo)

Subjects
FOS: Computer and information sciences, musculoskeletal diseases, Computer Science - Machine Learning, Texture analysis, Computer Vision and Pattern Recognition (cs.CV), Patellofemoral osteoarthritis, Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Computer Vision and Pattern Recognition, Deep learning, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)
Abstract

Objective: To assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: We used lateral view knee radiographs from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder), and subsequently, these anatomical landmarks were used to extract three different texture ROIs. Hand-crafted features, based on Local Binary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the PFOA. The proposed classification models were eventually compared with more conventional reference models that use clinical assessments and participant characteristics such as age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC), the area under the precision-recall (PR) curve -average precision (AP)-, and Brier score in the stratified 5-fold cross validation setting. Results: Of the 5507 knees, 953 (17.3%) had PFOA. AUC and AP for the strongest reference model including age, sex, BMI, WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487, respectively. Textural ROI classification using CNN significantly improved the prediction performance (ROC AUC = 0.889, AP = 0.714). Conclusions: We present the first study that analyses patellar bone texture for diagnosing PFOA. Our results demonstrates the potential of using texture features of patella to predict PFOA.

Published
2021

35. Simulation modelling of greenhouse gas balance in continuous-cover forestry of Norway spruce stands on nutrient-rich drained peatlands

Author

Shanin, V., primary, Juutinen, A., additional, Ahtikoski, A., additional, Frolov, P., additional, Chertov, O., additional, Rämö, J., additional, Lehtonen, A., additional, Laiho, R., additional, Mäkiranta, P., additional, Nieminen, M., additional, Laurén, A., additional, Sarkkola, S., additional, Penttilä, T., additional, Ťupek, B., additional, and Mäkipää, R., additional

Published
2021
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40. Effects of alirocumab on cardiovascular and metabolic outcomes after acute coronary syndrome in patients with or without diabetes: a prespecified analysis of the ODYSSEY OUTCOMES randomised controlled trial

Author

Ray, K, Colhoun, H, Szarek, M, Baccara-Dinet, M, Bhatt, D, Bittner, V, Budaj, A, Diaz, R, Goodman, S, Hanotin, C, Harrington, R, Jukema, J, Loizeau, V, Lopes, R, Moryusef, A, Murin, J, Pordy, R, Ristic, A, Roe, M, Tunon, J, White, H, Zeiher, A, Schwartz, G, Steg, P, Tricoci, P, Mahaffey, K, Edelberg, J, Lecorps, G, Sasiela, W, Tamby, J, Aylward, P, Drexel, H, Sinnaeve, P, Dilic, M, Gotcheva, N, Prieto, J, Yong, H, Lopez-Jaramillo, P, Pecin, I, Reiner, Z, Ostadal, P, Viigimaa, M, Nieminen, M, Chumburidze, V, Marx, N, Danchin, N, Liberopoulos, E, Montenegro Valdovinos, P, Tse, H, Kiss, R, Xavier, D, Zahger, D, Valgimigli, M, Kimura, T, Kim, H, Kim, S, Erglis, A, Laucevicius, A, Kedev, S, Yusoff, K, Ramos Lopez, G, Alings, M, Halvorsen, S, Correa Flores, R, Morais, J, Dorobantu, M, Karpov, Y, Chua, T, Fras, Z, Dalby, A, de Silva, H, Hagstrom, E, Landmesser, U, Chiang, C, Sritara, P, Guneri, S, Parkhomenko, A, Moriarty, P, Vogel, R, Chaitman, B, Kelsey, S, Olsson, A, Rouleau, J, Simoons, M, Alexander, K, Meloni, C, Rosenson, R, Sijbrands, E, Alexander, J, Armaganijan, L, Bagai, A, Bahit, M, Brennan, J, Clifton, S, Devore, A, Deloatch, S, Dickey, S, Dombrowski, K, Ducrocq, G, Eapen, Z, Endsley, P, Eppinger, A, Harrison, R, Hess, C, Hlatky, M, Jordan, J, Knowles, J, Kolls, B, Kong, D, Leonardi, S, Lillis, L, Maron, D, Marcus, J, Mathews, R, Mehta, R, Mentz, R, Moreira, H, Patel, C, Bernardez-Pereira, S, Perkins, L, Povsic, T, Puymirat, E, Schuyler Jones, W, Shah, B, Sherwood, M, Stringfellow, K, Sujjavanich, D, Toma, M, Trotter, C, Van Diepen, S, Wilson, M, Yan, A, Schiavi, L, Garrido, M, Alvarisqueta, A, Sassone, S, Bordonava, A, Alves De Lima, A, Schmidberg, J, Duronto, E, Caruso, O, Novaretto, L, Hominal, M, Montana, O, Caccavo, A, Gomez Vilamajo, O, Lorenzatti, A, Cartasegna, L, Paterlini, G, Mackinnon, I, Caime, G, Amuchastegui, M, Salomone, O, Codutti, O, Jure, H, Bono, J, Hrabar, A, Vallejos, J, Ahuad Guerrero, R, Novoa, F, Patocchi, C, Zaidman, C, Giuliano, M, Dran, R, Vico, M, Carnero, G, Guzman, P, Medrano Allende, J, Garcia Brasca, D, Bustamante Labarta, M, Nani, S, Blumberg, E, Colombo, H, Liberman, A, Fuentealba, V, Luciardi, H, Waisman, G, Berli, M, Garcia Duran, R, Cestari, H, Luquez, H, Giordano, J, Saavedra, S, Zapata, G, Costamagna, O, Llois, S, Waites, J, Collins, N, Soward, A, Hii, C, Shaw, J, Arstall, M, Horowitz, J, Ninio, D, Rogers, J, Colquhoun, D, Oqueli Flores, R, Roberts-Thomson, P, Raffel, O, Lehman, S, Aroney, C, Coverdale, S, Garrahy, P, Starmer, G, Sader, M, Carroll, P, Dick, R, Zweiker, R, Hoppe, U, Huber, K, Berger, R, Delle-Karth, G, Frey, B, Weidinger, F, Faes, D, Hermans, K, Pirenne, B, Leone, A, Hoffer, E, Vrolix, M, De Wolf, L, Wollaert, B, Castadot, M, Dujardin, K, Beauloye, C, Vervoort, G, Striekwold, H, Convens, C, Roosen, J, Barbato, E, Claeys, M, Cools, F, Terzic, I, Barakovic, F, Midzic, Z, Pojskic, B, Fazlibegovic, E, Kulic, M, Durak-Nalbantic, A, Vulic, D, Muslibegovic, A, Goronja, B, Reis, G, Sousa, L, Nicolau, J, Giorgeto, F, Silva, R, Nigro Maia, L, Rech, R, Rossi, P, Cerqueira, M, Duda, N, Kalil, R, Kormann, A, Abrantes, J, Pimentel Filho, P, Soggia, A, de Santos, M, Neuenschwander, F, Bodanese, L, Michalaros, Y, Eliaschewitz, F, Vidotti, M, Leaes, P, Botelho, R, Kaiser, S, Manenti, E, Precoma, D, Moura Jorge, J, de B Silva, P, Silveira, J, Saporito, W, Marin-Neto, J, Feitosa, G, Ritt, L, de Souza, J, Costa, F, Souza, W, Reis, H, Machado, L, Ayoub, J, Todorov, G, Nikolov, F, Velcheva, E, Tzekova, M, Benov, H, Petranov, S, Tumbev, H, Shehova-Yankova, N, Markov, D, Raev, D, Mollov, M, Kichukov, K, Ilieva-Pandeva, K, Ivanova, R, Gospodinov, M, Mincheva, V, Lazov, P, Dimov, B, Senaratne, M, Stone, J, Kornder, J, Pearce, S, Dion, D, Savard, D, Pesant, Y, Pandey, A, Robinson, S, Gosselin, G, Vizel, S, Hoag, G, Bourgeois, R, Morisset, A, Sabbah, E, Sussex, B, Kouz, S, Macdonald, P, Diaz, A, Michaud, N, Fell, D, Leung, R, Vuurmans, T, Lai, C, Nigro, F, Davies, R, Nogareda, G, Vijayaraghavan, R, Ducas, J, Lepage, S, Mehta, S, Cha, J, Dupuis, R, Fong, P, Lutchmedial, S, Rodes-Cabau, J, Fadlallah, H, Cleveland, D, Huynh, T, Bata, I, Hameed, A, Pincetti, C, Potthoff, S, Acevedo, M, Aguirre, A, Vejar, M, Yanez, M, Araneda, G, Fernandez, M, Perez, L, Varleta, P, Florenzano, F, Huidobro, L, Raffo, C, Olivares, C, Nahuelpan, L, Montecinos, H, Chen, J, Dong, Y, Huang, W, Wang, J, Huang, S, Yao, Z, Li, X, Cui, L, Lin, W, Sun, Y, Li, J, Zhang, X, Zhu, H, Chen, D, Huang, L, Dong, S, Su, G, Xu, B, Su, X, Cheng, X, Lin, J, Zong, W, Li, H, Feng, Y, Xu, D, Yang, X, Ke, Y, Lin, X, Zhang, Z, Zheng, Z, Luo, Z, Chen, Y, Ding, C, Zhong, Y, Zheng, Y, Peng, D, Zhao, S, Li, Y, Liu, X, Wei, M, Liu, S, Yu, Y, Qu, B, Jiang, W, Zhou, Y, Zhao, X, Yuan, Z, Guo, Y, Xu, X, Shi, X, Ge, J, Fu, G, Bai, F, Fang, W, Shou, X, Xiang, M, Lu, Q, Zhang, R, Zhu, J, Xu, Y, Fan, Z, Li, T, Wu, C, Jaramillo, N, Sanchez Vallejo, G, Luna Botia, D, Botero Lopez, R, Molina De Salazar, D, Cadena Bonfanti, A, Cotes Aroca, C, Diego Higuera, J, Blanquicett, M, Barrera Silva, S, Garcia Lozada, H, Coronel Arroyo, J, Accini Mendoza, J, Fernandez Ruiz, R, Quintero Ossa, A, Manzur Jatin, F, Sotomayor Herazo, A, Castellanos Parada, J, Suarez Arambula, R, Urina Triana, M, Fernandez Trujillo, A, Strozzi, M, Car, S, Jeric, M, Milicic, D, Bencic, M, Pintaric, H, Prvulovic, D, Sikic, J, Persic, V, Mileta, D, Stambuk, K, Babic, Z, Tomulic, V, Lukenda, J, Mejic-Krstulovic, S, Starcevic, B, Spinar, J, Horak, D, Velicka, Z, Stasek, J, Alan, D, Machova, V, Linhart, A, Novotny, V, Kaucak, V, Rokyta, R, Naplava, R, Coufal, Z, Adamkova, V, Podpera, I, Zizka, J, Motovska, Z, Marusincova, I, Svab, P, Heinc, P, Kuchar, J, Povolny, P, Matuska, J, Poulsen, S, Raungaard, B, Clemmensen, P, Bang, L, May, O, Bottcher, M, Hove, J, Frost, L, Gislason, G, Larsen, J, Betton Johansen, P, Hald, F, Johansen, P, Jeppesen, J, Nielsen, T, Kristensen, K, Walichiewicz, P, Lomholdt, J, Klausen, I, Nielsen, P, Davidsen, F, Videbaek, L, Soots, M, Vahula, V, Hedman, A, Soopold, U, Martsin, K, Jurgenson, T, Kristjan, A, Helsinki, J, Vikman, S, Huikuri, H, Airaksinen, J, Coste, P, Ferrari, E, Morel, O, Montalescot, G, Machecourt, J, Barone-Rochette, G, Mansourati, J, Cottin, Y, Leclercq, F, Belhassane, A, Delarche, N, Boccara, F, Paganelli, F, Clerc, J, Schiele, F, Aboyans, V, Probst, V, Berland, J, Lefevre, T, Citron, B, Khintibidze, I, Shaburishvili, T, Pagava, Z, Ghlonti, R, Lominadze, Z, Khabeishvili, G, Hemetsberger, R, Edward, K, Rauch-Krohnert, U, Stratmann, M, Appel, K, Schmidt, E, Omran, H, Stellbrink, C, Dorsel, T, Lianopoulos, E, Vohringer, H, Marx, R, Zirlik, A, Schellenberg, D, Heitzer, T, Laufs, U, Werner, C, Gielen, S, Nuding, S, Winkelmann, B, Behrens, S, Sydow, K, Karakas, M, Simonis, G, Muenzel, T, Werner, N, Leggewie, S, Bocker, D, Braun-Dullaeus, R, Toursarkissian, N, Jeserich, M, Weissbrodt, M, Schaeufele, T, Weil, J, Voller, H, Waltenberger, J, Natour, M, Schmitt, S, Muller-Wieland, D, Steiner, S, Heidenreich, L, Offers, E, Gremmler, U, Killat, H, Rieker, W, Patsilinakos, S, Kartalis, A, Manolis, A, Sionis, D, Chachalis, G, Skoumas, I, Athyros, V, Vardas, P, Parthenakis, F, Alexopoulos, D, Hahalis, G, Lekakis, J, Hatzitolios, A, Fausto Ovando, S, Arango Benecke, J, Rodriguez De Leon, E, Yan, B, Siu, D, Turi, T, Merkely, B, Ungi, I, Lupkovics, G, Nagy, L, Katona, A, Edes, I, Muller, G, Horvath, I, Kapin, T, Szigeti, Z, Falukozy, J, Kumbla, M, Sandhu, M, Annam, S, Proddutur, N, Regella, R, Premchand, R, Mahajan, A, Pawar, S, Abhyanakar, A, Kerkar, P, Govinda, R, Oomman, A, Sinha, D, Patil, S, Kahali, D, Sawhney, J, Joshi, A, Chaudhary, S, Harkut, P, Guha, S, Porwal, S, Jujjuru, S, Pothineni, R, Monteiro, M, Khan, A, Iyengar, S, Grewal, J, Chopda, M, Fulwani, M, Patange, A, Sachin, P, Chopra, V, Goyal, N, Shinde, R, Manakshe, G, Patki, N, Sethi, S, Munusamy, V, Karna, S, Thanvi, S, Adhyapak, S, Patil, C, Pandurangi, U, Mathur, R, Gupta, J, Kalashetti, S, Bhagwat, A, Raghuraman, 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M., Schmalfuss C., Picone M., Pederson R., Herzog W., Friedman K., Lindsey J., Nowins R., Timothy E., Leonard P., Lepor N., El Shahawy M., Weintraub H., Irimpen A., Alonso A., May W., Christopher D., Galski T., Chu A., Mody F., Ramin E., Hodes Z., Rossi J., Rose G., Fairlamb J., Lambert C., Raisinghani A., Abbate A., Vetrovec G., King M., Carey C., Gerber J., Younis L., Park H. T., Vidovich M., Knutson T., Friedman D., Chaleff F., Loussararian A., Rozeman P., Kimmelstiel C., Kuvin J., Silver K., Foster M., Tonnessen G., Espinoza A., Amlani M., Wali A., Malozzi C., Jong G. T., Massey C., Wattanakit K., O'Donnell P. J., Singal D., Jaffrani N., Banuru S., Fisher D., Xenakis M., Perlmutter N., Bhagwat R., Strader J., Blonder R., Akyea-Djamson A., Labroo A., Marais H. J., Claxton E., Weiss R., Kathryn R., Berk M., Rossi P., Joshi P., Khera A., Khaira A. S., Kumkumian G., Lupovitch S., Purow J., Welka S., Hoffman D., Fischer S., Soroka E., Eagerton D., Pancholy S., Ray M., Erenrich N., Farrar M., Pollock S., French W. J., Diamantis S., Guy D., Gimple L., Neustel M., Schwartz S., Pereira E., Albert S., Spriggs D., Strain J., Mittal S., Vo A., Chane M., Hall J., Vijay N., Lotun K., Lester F. M., Nahhas A., Pope T., Nager P., Vohra R., Sharma M., Bashir R., Ahmed H., Berlowitz M., Fishberg R., Barrucco R., Yang E., Radin M., Sporn D., Stapleton D., Eisenberg S., Landzberg J., Mcgough M., Turk S., Schwartz M., Sundram P. S., Jain D., Zainea M., Bayron C., Karlsberg R., Dohad S., Lui H., Keen W., Westerhausen D., Khurana S., Agarwal H., Birchem J., Penny W., Chang M., Murphy S., Henry J., Schifferdecker B., Gilbert J. M., Chalavarya G., Eaton C., Schmedtje J. F., Christenson S., Dotani I., Denham D., Macdonell A., Gibson P., Rahman A., Al Joundi T., Assi N., Conrad G., Kotha P., Love M., Giesler G., Rubenstein H., Gamil D., Akright L., Krawczyk J., Cobler J., Wells T., Welker J., Foster R., Gilmore R., Anderson J., Jacoby D., Gardner G., Dandillaya R., Vora K., Kostis J., Hunter J., Laxson D., Ball E., Lopes R., Egydio F., Kawakami A., Oliveira J., Wozniak J., Matthews A., Ratky C., Valiris J., Berdan L., Hepditch A., Quintero K., Rorick T., Westbrook M., Pascual A., Rovito C., Bezault M., Drouet E., Simon T., Alsweiler C., Luyten A., Butters J., Griffith L., Shaw M., Grunberg L., Islam S., Bregeault M. -F., Bougon N., Faustino D., Fontecave S., Murphy J., Verrier M., Agnetti V., Andersen D., Badreddine E., Bekkouche M., Bouancheau C., Brigui I., Brocklehurst M., Cianciarulo J., Devaul D., Domokos S., Gache C., Gobillot C., Guillou S., Healy J., Heath M., Jaiwal G., Javierre C., Labeirie J., Monier M., Morales U., Mrabti A., Mthombeni B., Okan B., Smith L., Sheller J., Sopena S., Pellan V., Benbernou F., Bengrait N., Lamoureux M., Kralova K., Scemama M., Bejuit R., Coulange A., Berthou C., Repincay J., Lorenzato C., Etienne A., Gouet V., Normand M., Ourliac A., Rondel C., Adamo A., Beltran P., Barraud P., Dubois-Gache H., Halle B., Metwally L., Mourgues M., Sotty M., Vincendet M., Cotruta R., Chengyue Z., Fournie-Lloret D., Morrello C., Perthuis A., Picault P., Zobouyan I., Dempsey M. A., McClanahan M. 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B., Berti S., Cavallini C., Puccioni E., Galvani M., Tespili M., Piatti P., Palvarini M., De Luca G., Violini R., De Leo A., Olivari Z., Perrone Filardi P., Ferratini M., Racca V., Dai K., Shimatani Y., Kamiya H., Ando K., Takeda Y., Morino Y., Hata Y., Kimura K., Kishi K., Michishita I., Uehara H., Higashikata T., Hirayama A., Hirooka K., Doi Y., Sakagami S., Taguchi S., Koike A., Fujinaga H., Koba S., Kozuma K., Kawasaki T., Ono Y., Shimizu M., Katsuda Y., Wada A., Shinke T., Ako J., Fujii K., Takahashi T., Sakamoto T., Nakao K., Furukawa Y., Sugino H., Tamura R., Mano T., Uematsu M., Utsu N., Ito K., Haraguchi T., Sato K., Ueda Y., Nishibe A., Fujimoto K., Masutani M., Yoon J. H., Kim H. -L., Park H. S., Chae I. -H., Kim M. H., Jeong M. H., Rha S., Kim C., Kim H. -S., Kim H. 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R., Troquay R. P., Viergever E. P., Al-Windy N. Y., Bartels G. L., Cornel J. H., Hermans W. R., Herrman J. P., Bos R. J., Groutars R. G., Van Der Zwaan C. C., Kaplan R., Lionarons R., Ronner E., Groenemeijer B. E., Bronzwaer P. N., Liem A. A., Rensing B. J., Bokern M. J., Nijmeijer R., Hersbach F. M., Willems F. F., Gosselink A. T., Rasoul S., Elliott J., Wilkins G., Fisher R., Scott D., Hart H., Stewart R., Harding S., Ternouth I., Fisher N., Wilson S., Aitken D., Anscombe R., Davidson L., Tomala T., Nygard O., Sparby J. A., Andersen K., Gullestad L., Jortveit J., Munk P. S., Singsaas E. G., Hurtig U., Calderon Ticona J. R., Durand Velasquez J. R., Negron Miguel S. A., Sanabria Perez E. S., Carrion Chambilla J. M., Chavez Ayala C. A., Castillo Leon R. P., Vargas Gonzales R. J., Hernandez Zuniga J. D., Camacho Cosavalente L. A., Bravo Mannucci J. E., Heredia Landeo J., Llerena Navarro N. C., Roldan Concha Y. M., Rodriguez Chavez V. E., Anchante Hernandez H. A., Zea Nunez C. A., Mogrovejo Ramos W., Ferrolino A., Sy R. A. G., Tirador L., Sy R. G., Matiga G., Coching R. M., Bernan A., Rogelio G., Morales D. D., Tan E., Sulit D. J., Wlodarczak A., Jaworska K., Skonieczny G., Pawlowicz L., Wojewoda P., Busz-Papiez B., Bednarski J., Goch A., Staneta P., Dulak E., Saminski K., Krasowski W., Sudnik W., Zurakowski A., Skorski M., Miklaszewicz B., Kubica J., Andrzej Lipko J., Kostarska-Srokosz E., Piepiorka M., Drzewiecka A., Stasiewski A., Blicharski T., Bystryk L., Szpajer M., Korol M., Czerski T., Mirek-Bryniarska E., Gniot J., Lubinski A., Gorny J., Franek E., Raczak G., Szwed H., Monteiro P., Mesquita Bastos J., Pereira H. H., Martins D., Seixo F., Mendonca C., Botelho A., Caetano F., Minescu B., Istratoaie O., Tesloianu D. N., Cristian G., Dumitrescu S., Podoleanu C. G., Constantinescu M. C., Bengus C. M., Militaru C., Rosu D., Parepa I. R., Matei A. V., Alexandru T. M., Malis M., Coman I., Stanescu-Cioranu R., Dimulescu D., Shvarts Y., Orlikova O., Kobalava Z., Barbarash O. L., Markov V., Lyamina N., Gordienko A., Zrazhevsky K., Vishnevsky A. Y., Gurevich V., Stryuk R., Lomakin N. V., Bokarev I., Khlevchuk T., Shalaev S., Khaisheva L., Chizhov P., Viktorova I., Osokina N., Shchekotov V., Akatova E., Chumakova G., Libov I., Voevoda M. I., Tretyakova T. V., Baranov E., Shustov S., Yakushin S., Gordeev I., Khasanov N., Reshetko O., Sotnikova T., Molchanova O., Nikolaev K., Gapon L., Baranova E., Shogenov Z., Kosmachova E., Povzun A., Egorova L., Tyrenko V. V., Ivanov I. G., Ilya M., Kanorsky S., Simic D., Ivanovic N., Davidovic G., Tasic N., Asanin M. R., Stojic S., Apostolovic S. R., Ilic S., Putnikovic Tosic B., Stankovic A., Arandjelovic A., Radovanovic S., Todic B., Balinovac J., Dincic D. V., Seferovic P., Karadzic A., Dodic S., Dimkovic S., Jakimov T., Poh K. -K., Ong H. Y., Tang I-Shing J., Micko K., Nociar J., Pella D., Fulop P., Hranai M., Palka J., Mazur J., Majercak I., Dzupina A., Fazekas F., Gonsorcik J., Bugan V., Selecky J., Kamensky G., Strbova J., Smik R., Dukat A., Zuran I., Poklukar J., Cernic Suligoj N., Cevc M., Cyster H. P., Ranjith N., Corbett C., Bayat J., Makotoko E. M., du Toit Theron H., Kapp I. E., de V Basson M. M., Lottering H., Van Aswegen D., Van Zyl L. J., Sebastian P. J., Pillay T., Saaiman J. A., Commerford P. J., Cassimjee S., Riaz G., Ebrahim I. O., Sarvan M., Mynhardt J. H., Reuter H., Moodley R., Vida M., Cequier Fillat A. R., Bodi Peris V., Fuentes Jimenez F., Marin F., Cruz Fernandez J. M., Hidalgo Urbano R. J., Gil-Extremera B., Toledo P., Worner Diz F., Garcia-Dorado D., Iniguez A., Gonzalez-Juanatey J. R., Fernandez Portales J., Civeira Murillo F., Matas Pericas L., Zamorano J. L., De Mora Martin M., Bruguera Cortada J., Alonso Martin J. J., Serrano Antolin J. M., De Berrazueta Fernandez J. R., Vazquez de Prada J. A., Diaz Fernandez J. F., Garcia Lledo J. A., Cosin Sales J., Botas Rodriguez J., Gusi Tragant G., Benedicto A., Gonzalez-Juanatey C., Camprubi Potau M., Plaza Perez I., De La Tassa C. M., Loma-Osorio Rincon P., Balaguer Recena J., Escudier J. M., Payeras A. C., Alonso Orcajo N., Valdivielso P., Constantine G., Haniffa R., Tissera N., Amarasekera S., Ponnamperuma C., Fernando N., Fernando K., Jayawardena J., Wijeyasingam S., Ranasinghe G., Ekanayaka R., Mendis S., Senaratne V., Mayurathan G., Sirisena T., Rajapaksha A., Herath J. I., Amarasena N., Berglund S., Rasmanis G., Vedin O., Witt N., Mourtzinis G., Nicol P., Hansen O., Romeo S., Agergaard Jensen S., Torstensson I., Ahremark U., Sundelin T., Moccetti T., Muller C., Mach F., Binde R., Tsai W. -C., Ueng K. -C., Lai W. -T., Liu M. -E., Hwang J. -J., Yin W. -H., Hsieh I. -C., Hsieh M. -J., Lin W. H., Kuo J. -Y., Huang T. -Y., Fang C. -Y., Kaewsuwanna P., Soonfuang W., Jintapakorn W., Sukonthasarn A., Wongpraparut N., Sastravaha K., Sansanayudh N., Kehasukcharoen W., Piyayotai D., Chotnoparatpat P., Camsari A., Kultursay H., Mutlu B., Ersanli M., Demirtas M., Kirma C., Ural E., Koldas L., Karpenko O., Prokhorov A., Vakaluyk I., Myshanych H., Reshotko D., Batushkin V., Rudenko L., Kovalskyi I., Kushnir M., Tseluyko V., Mostovoy Y., Stanislavchuk M., Kyiak Y., Karpenko Y., Malynovsky Y., Klantsa A., Kutniy O., Amosova E., Tashchuk V., Leshchuk O., Rishko M., Kopytsya M., Yagensky A., Vatutin M., Bagriy A., Barna O. M., Ushakov O., Dzyak G., Goloborodko B., Rudenko A., Zheleznyy V., Trevelyan J., Zaman A., Lee K., Moriarty A., Aggarwal R. K., Clifford P., Wong Y. -K., Iqbal S. M., Subkovas E., Braganza D., Sarkar D., Storey R., Griffiths H., McClure S., Muthusamy R., Smith S., Kurian J., Levy T., Barr C., Kadr H., Gerber R., Simaitis A., Soran H., Mathur A., Brodison A., Ayaz M., Cheema M., Oliver R., Thackray S., Mudawi T., Rahman G., Sultan A., Sharman D., Sprigings D., Butler R., Wilkinson P., Lip G. Y., Halcox J., Gallagher S., Ossei-Gerning N., Vardi G., Baldari D., Brabham D., Treasure II C., Dahl C., Palmer B., Wiseman A., Puri S., Mohart A. E., Ince C., Flores E., Wright S., Cheng S. -C., Rosenberg M., Rogers W., Kosinski E., Forgosh L., Waltman J., Khan M., Shoukfeh M., Dagher G., Cambier P., Lieber I., Kumar P., East C., Krichmar P., Hasan M., White L., Knickelbine T., Haldis T., Gillespie E., Amidon T., Suh D., Arif I., Abdallah M., Akhter F., Carlson E., D'Urso M., El-Ahdab F., Nelson W., Moriarty K., Harris B., Cohen S., Carter L., Doty D., Sabatino K., Haddad T., Malik A., Rao S., Mulkay A., Jovin I., Klancke K., Malhotra V., Devarapalli S. K., Koren M., Chandna H., Dodds III G., Goraya T., Bengston J., Janik M., Moran J., Sumner A., Kobayashi J., Davis W., Yazdani S., Pasquini J., Thakkar M., Vedere A., Leimbach W., Rider J., Fenton S., Singh N., Shah A. V., Janosik D., Pepine C., Berman B., Gelormini J., Daniels C., Richard K., Keating F., Kondo N. I., Shetty S., Levite H., Waider W., Takata T., Abu-Fadel M., Shah V., Aggarwal R., Izzo M., Kumar A., Hattler B., Do R., Link C., Bortnick A., Kinzfogl III G., Ghitis A., Larry J., Teufel E., Kuhlman P., Mclaurin B., Zhang W., Thew S., Abbas J., White M., Islam O., Subherwal S., Ranadive N., Vakili B., Gring C., Henderson D., Schuchard T., Farhat N., Kline G., Mahal S., Whitaker J., Speirs S., Andersen R., Daboul N., Horwitz P., Zahr F., Ponce G., Jafar Z., Mcgarvey J., Panchal V., Voyce S., Blok T., Sheldon W., Azizad M. M., Schmalfuss C., Picone M., Pederson R., Herzog W., Friedman K., Lindsey J., Nowins R., Timothy E., Leonard P., Lepor N., El Shahawy M., Weintraub H., Irimpen A., Alonso A., May W., Christopher D., Galski T., Chu A., Mody F., Ramin E., Hodes Z., Rossi J., Rose G., Fairlamb J., Lambert C., Raisinghani A., Abbate A., Vetrovec G., King M., Carey C., Gerber J., Younis L., Park H. T., Vidovich M., Knutson T., Friedman D., Chaleff F., Loussararian A., Rozeman P., Kimmelstiel C., Kuvin J., Silver K., Foster M., Tonnessen G., Espinoza A., Amlani M., Wali A., Malozzi C., Jong G. T., Massey C., Wattanakit K., O'Donnell P. J., Singal D., Jaffrani N., Banuru S., Fisher D., Xenakis M., Perlmutter N., Bhagwat R., Strader J., Blonder R., Akyea-Djamson A., Labroo A., Marais H. J., Claxton E., Weiss R., Kathryn R., Berk M., Rossi P., Joshi P., Khera A., Khaira A. S., Kumkumian G., Lupovitch S., Purow J., Welka S., Hoffman D., Fischer S., Soroka E., Eagerton D., Pancholy S., Ray M., Erenrich N., Farrar M., Pollock S., French W. J., Diamantis S., Guy D., Gimple L., Neustel M., Schwartz S., Pereira E., Albert S., Spriggs D., Strain J., Mittal S., Vo A., Chane M., Hall J., Vijay N., Lotun K., Lester F. M., Nahhas A., Pope T., Nager P., Vohra R., Sharma M., Bashir R., Ahmed H., Berlowitz M., Fishberg R., Barrucco R., Yang E., Radin M., Sporn D., Stapleton D., Eisenberg S., Landzberg J., Mcgough M., Turk S., Schwartz M., Sundram P. S., Jain D., Zainea M., Bayron C., Karlsberg R., Dohad S., Lui H., Keen W., Westerhausen D., Khurana S., Agarwal H., Birchem J., Penny W., Chang M., Murphy S., Henry J., Schifferdecker B., Gilbert J. M., Chalavarya G., Eaton C., Schmedtje J. F., Christenson S., Dotani I., Denham D., Macdonell A., Gibson P., Rahman A., Al Joundi T., Assi N., Conrad G., Kotha P., Love M., Giesler G., Rubenstein H., Gamil D., Akright L., Krawczyk J., Cobler J., Wells T., Welker J., Foster R., Gilmore R., Anderson J., Jacoby D., Gardner G., Dandillaya R., Vora K., Kostis J., Hunter J., Laxson D., Ball E., Lopes R., Egydio F., Kawakami A., Oliveira J., Wozniak J., Matthews A., Ratky C., Valiris J., Berdan L., Hepditch A., Quintero K., Rorick T., Westbrook M., Pascual A., Rovito C., Bezault M., Drouet E., Simon T., Alsweiler C., Luyten A., Butters J., Griffith L., Shaw M., Grunberg L., Islam S., Bregeault M. -F., Bougon N., Faustino D., Fontecave S., Murphy J., Verrier M., Agnetti V., Andersen D., Badreddine E., Bekkouche M., Bouancheau C., Brigui I., Brocklehurst M., Cianciarulo J., Devaul D., Domokos S., Gache C., Gobillot C., Guillou S., Healy J., Heath M., Jaiwal G., Javierre C., Labeirie J., Monier M., Morales U., Mrabti A., Mthombeni B., Okan B., Smith L., Sheller J., Sopena S., Pellan V., Benbernou F., Bengrait N., Lamoureux M., Kralova K., Scemama M., Bejuit R., Coulange A., Berthou C., Repincay J., Lorenzato C., Etienne A., Gouet V., Normand M., Ourliac A., Rondel C., Adamo A., Beltran P., Barraud P., Dubois-Gache H., Halle B., Metwally L., Mourgues M., Sotty M., Vincendet M., Cotruta R., Chengyue Z., Fournie-Lloret D., Morrello C., Perthuis A., Picault P., Zobouyan I., Dempsey M. A., and McClanahan M. A.

Abstract

Background: After acute coronary syndrome, diabetes conveys an excess risk of ischaemic cardiovascular events. A reduction in mean LDL cholesterol to 1·4–1·8 mmol/L with ezetimibe or statins reduces cardiovascular events in patients with an acute coronary syndrome and diabetes. However, the efficacy and safety of further reduction in LDL cholesterol with an inhibitor of proprotein convertase subtilisin/kexin type 9 (PCSK9) after acute coronary syndrome is unknown. We aimed to explore this issue in a prespecified analysis of the ODYSSEY OUTCOMES trial of the PCSK9 inhibitor alirocumab, assessing its effects on cardiovascular outcomes by baseline glycaemic status, while also assessing its effects on glycaemic measures including risk of new-onset diabetes. Methods: ODYSSEY OUTCOMES was a randomised, double-blind, placebo-controlled trial, done at 1315 sites in 57 countries, that compared alirocumab with placebo in patients who had been admitted to hospital with an acute coronary syndrome (myocardial infarction or unstable angina) 1–12 months before randomisation and who had raised concentrations of atherogenic lipoproteins despite use of high-intensity statins. Patients were randomly assigned (1:1) to receive alirocumab or placebo every 2 weeks; randomisation was stratified by country and was done centrally with an interactive voice-response or web-response system. Alirocumab was titrated to target LDL cholesterol concentrations of 0·65–1·30 mmol/L. In this prespecified analysis, we investigated the effect of alirocumab on cardiovascular events by glycaemic status at baseline (diabetes, prediabetes, or normoglycaemia)—defined on the basis of patient history, review of medical records, or baseline HbA1c or fasting serum glucose—and risk of new-onset diabetes among those without diabetes at baseline. The primary endpoint was a composite of death from coronary heart disease, non-fatal myocardial infarction, fatal or non-fatal ischaemic stroke, or unstable angina requiring

Published
2019

41. Quantitative dual contrast photon-counting computed tomography for assessment of articular cartilage health

Author

Paakkari, P. (Petri), Inkinen, S. I. (Satu I.), Honkanen, M. K. (Miitu K. M.), Prakash, M. (Mithilesh), Shaikh, R. (Rubina), Nieminen, M. T. (Miika T.), Grinstaff, M. W. (Mark W.), Mäkelä, J. T. (Janne T. A.), Töyräs, J. (Juha), and Honkanen, J. T. (Juuso T. J.)

Abstract

Photon-counting detector computed tomography (PCD-CT) is a modern spectral imaging technique utilizing photon-counting detectors (PCDs). PCDs detect individual photons and classify them into fixed energy bins, thus enabling energy selective imaging, contrary to energy integrating detectors that detects and sums the total energy from all photons during acquisition. The structure and composition of the articular cartilage cannot be detected with native CT imaging but can be assessed using contrast-enhancement. Spectral imaging allows simultaneous decomposition of multiple contrast agents, which can be used to target and highlight discrete cartilage properties. Here we report, for the first time, the use of PCD-CT to quantify a cationic iodinated CA4+ (targeting proteoglycans) and a non-ionic gadolinium-based gadoteridol (reflecting water content) contrast agents inside human osteochondral tissue (n = 53). We performed PCD-CT scanning at diffusion equilibrium and compared the results against reference data of biomechanical and optical density measurements, and Mankin scoring. PCD-CT enables simultaneous quantification of the two contrast agent concentrations inside cartilage and the results correlate with the structural and functional reference parameters. With improved soft tissue contrast and assessment of proteoglycan and water contents, PCD-CT with the dual contrast agent method is of potential use for the detection and monitoring of osteoarthritis.

Published
2021

42. Virtual monochromatic imaging reduces beam hardening artefacts in cardiac interior photon counting computed tomography:a phantom study with cadaveric specimens

Author

Inkinen, S. I. (Satu I.), Juntunen, M. A. (Mikael A. K.), Ketola, J. (Juuso), Korhonen, K. (Kristiina), Sepponen, P. (Pasi), Kotiaho, A. (Antti), Pohjanen, V.-M. (Vesa-Matti), and Nieminen, M. (Miika)

Subjects
calcium scoring, interior computed tomography, photon counting detector, material decomposition, computed tomography, spectral imaging, cardiac imaging
Abstract

In interior cardiac computed tomography (CT) imaging, the x-ray beam is collimated to a limited field-of-view covering the heart volume, which decreases the radiation exposure to surrounding tissues. Spectral CT enables the creation of virtual monochromatic images (VMIs) through a computational material decomposition process. This study investigates the utility of VMIs for beam hardening (BH) reduction in interior cardiac CT, and further, the suitability of VMIs for coronary artery calcium (CAC) scoring and volume assessment is studied using spectral photon counting detector CT (PCD-CT). Ex vivo coronary artery samples (N = 18) were inserted in an epoxy rod phantom. The rod was scanned in the conventional CT geometry, and subsequently, the rod was positioned in a torso phantom and re-measured in the interior PCD-CT geometry. The total energy (TE) 10–100 keV reconstructions from PCD-CT were used as a reference. The low energy 10–60 keV and high energy 60–100 keV data were used to perform projection domain material decomposition to polymethyl methacrylate and calcium hydroxylapatite basis. The truncated basis-material sinograms were extended using the adaptive detruncation method. VMIs from 30–180 keV range were computed from the detruncated virtual monochromatic sinograms using filtered back projection. Detrending was applied as a post-processing method prior to CAC scoring. The results showed that BH artefacts from the exterior structures can be suppressed with high (≥100 keV) VMIs. With appropriate selection of the monoenergy (46 keV), the underestimation trend of CAC scores and volumes shown in Bland-Altman (BA) plots for TE interior PCD-CT was mitigated, as the BA slope values were −0.02 for the 46 keV VMI compared to −0.21 the conventional TE image. To conclude, spectral PCD-CT imaging using VMIs could be applied to reduce BH artefacts interior CT geometry, and further, optimal selection of VMI may improve the accuracy of CAC scoring assessment in interior PCD-CT.

Published
2021

43. Soiden ennallistamisen suoluonto-, vesistö-, ja ilmastovaikutukset:vertaisarvioitu raportti

Author

Kareksela, S. (Santtu), Ojanen, P. (Paavo), Aapala, K. (Kaisu), Haapalehto, T. (Tuomas), Ilmonen, J. (Jari), Koskinen, M. (Markku), Laiho, R. (Raija), Laine, A. (Anna), Maanavilja, L. (Liisa), Marttila, H. (Hannu), Minkkinen, K. (Kari), Nieminen, M. (Mika), Ronkanen, A.-K. (Anna-Kaisa), Sallantaus, T. (Tapani), Sarkkola, S. (Sakari), Tolvanen, A. (Anne), Tuittila, E.-S. (Eeva-Stiina), and Vasander, H. (Harri)

Published
2021

44. Functional and structural properties of human patellar articular cartilage in osteoarthritis

Author

Nissinen, M. T. (Mikko T.), Hänninen, N. (Nina), Prakash, M. (Mithilesh), Mäkelä, J. T. (Janne T. A.), Nissi, M. J. (Mikko J.), Töyräs, J. (Juha), Nieminen, M. T. (Miika T.), Korhonen, R. K. (Rami K.), and Tanska, P. (Petri)

Subjects
Fibril-reinforced poroelastic, Proteoglycan, Finite element analysis, Mechanical testing, Collagen
Abstract

Changes in the fibril-reinforced poroelastic (FRPE) mechanical material parameters of human patellar cartilage at different stages of osteoarthritis (OA) are not known. Further, the patellofemoral joint loading is thought to include more sliding and shear compared to other knee joint locations, thus, the relations between structural and functional changes may differ in OA. Thus, our aim was to determine the patellar cartilage FRPE properties followed by associating them with the structure and composition. Osteochondral plugs (n = 14) were harvested from the patellae of six cadavers. Then, the FRPE material properties were determined, and those properties were associated with proteoglycan content, collagen fibril orientation angle, optical retardation (fibril parallelism), and the state of OA of the samples. The initial fibril network modulus and permeability strain-dependency factor were 72% and 63% smaller in advanced OA samples when compared to early OA samples. Further, we observed a negative association between the initial fibril network modulus and optical retardation (r = -0.537, p < 0.05). We also observed positive associations between 1) the initial permeability and optical retardation (r = 0.547, p < 0.05), and 2) the initial fibril network modulus and optical density (r = 0.670, p < 0.01).These results suggest that the reduced pretension of the collagen fibrils, as shown by the reduced initial fibril network modulus, is linked with the loss of proteoglycans and cartilage swelling in human patellofemoral OA. The characterization of these changes is important to improve the representativeness of knee joint models in tissue and cell scale.

Published
2021

45. Automated detection of patellofemoral osteoarthritis from knee lateral view radiographs using deep learning:data from the Multicenter Osteoarthritis Study (MOST)

Author

Bayramoglu, N. (N.), Nieminen, M. (M.T.), and Saarakkala, S. (S.)

Subjects
Radiograph, Patellofemoral osteoarthritis, Deep learning, Knee, Radiographic PFOA prediction
Abstract

Objective: To assess the ability of imaging-based deep learning to detect radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 18,436 knees). Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA. Patellar ROI was detected using deep-learning-based object detection method. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of classification models was assessed using the area under the receiver operating characteristic curve (ROC AUC) and the average precision (AP) obtained from the Precision-Recall (PR) curve in the stratified 5-fold cross validation setting. Results: Of the 18,436 knees, 3,425 (19%) had PFOA. AUC and AP for the reference model including age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren–Lawrence (KL) grade to detect PFOA were 0.806 and 0.478, respectively. The CNN model that used only image data significantly improved the classifier performance (ROC AUC = 0.958, AP = 0.862). Conclusion: We present the first machine learning based automatic PFOA detection method. Furthermore, our deep learning based model trained on patella region from knee lateral view radiographs performs better at detecting PFOA than models based on patient characteristics and clinical assessments.

Published
2021

46. T₂-weighted magnetic resonance imaging texture as predictor of low back pain:a texture analysis-based classification pipeline to symptomatic and asymptomatic cases

Author

Ketola, J. H. (Juuso H. J.), Inkinen, S. I. (Satu I.), Karppinen, J. (Jaro), Niinimäki, J. (Jaakko), Tervonen, O. (Osmo), and Nieminen, M. T. (Miika T.)

Subjects
machine learning, lumbar spine, magnetic resonance imaging, equipment and supplies, human activities, low back pain, texture analysis
Abstract

Low back pain is a very common symptom and the leading cause of disability throughout the world. Several degenerative imaging findings seen on magnetic resonance imaging are associated with low back pain but none of them is specific for the presence of low back pain as abnormal findings are prevalent among asymptomatic subjects as well. The purpose of this population-based study was to investigate if more specific magnetic resonance imaging predictors of low back pain could be found via texture analysis and machine learning. We used this methodology to classify T₂-weighted magnetic resonance images from the Northern Finland Birth Cohort 1966 data to symptomatic and asymptomatic groups. Lumbar spine magnetic resonance imaging was performed using a fast spin-echo sequence at 1.5 T. Texture analysis pipeline consisting of textural feature extraction, principal component analysis, and logistic regression classifier was applied to the data to classify them into symptomatic (clinically relevant pain with frequency ≥30 days and intensity ≥6/10) and asymptomatic (frequency ≤7 days, intensity ≤3/10, and no previous pain episodes in the follow-up period) groups. Best classification results were observed applying texture analysis to the two lowest intervertebral discs (L4-L5 and L5-S1), with accuracy of 83%, specificity of 83%, sensitivity of 82%, negative predictive value of 94%, precision of 56%, and receiver operating characteristic area-under-curve of 0.91. To conclude, textural features from T₂-weighted magnetic resonance images can be applied in low back pain classification.

Published
2021

47. Generative adversarial networks improve interior computed tomography angiography reconstruction

Author

Ketola, J. H. (Juuso H. J.), Heino, H. (Helinä), Juntunen, M. A. (Mikael A. K.), Nieminen, M. T. (Miika T.), Siltanen, S. (Samuli), and Inkinen, S. I. (Satu I.)

Subjects
convolutional neural networks, computed tomography, sinogram extension, generative adversarial networks, image reconstruction, interior tomography
Abstract

In interior computed tomography (CT), the x-ray beam is collimated to a limited field-of-view (FOV) (e.g. the volume of the heart) to decrease exposure to adjacent organs, but the resulting image has a severe truncation artifact when reconstructed with traditional filtered back-projection (FBP) type algorithms. In some examinations, such as cardiac or dentomaxillofacial imaging, interior CT could be used to achieve further dose reductions. In this work, we describe a deep learning (DL) method to obtain artifact-free images from interior CT angiography. Our method employs the Pix2Pix generative adversarial network (GAN) in a two-stage process: (1) An extended sinogram is computed from a truncated sinogram with one GAN model, and (2) the FBP reconstruction obtained from that extended sinogram is used as an input to another GAN model that improves the quality of the interior reconstruction. Our double GAN (DGAN) model was trained with 10 000 truncated sinograms simulated from real computed tomography angiography slice images. Truncated sinograms (input) were used with original slice images (target) in training to yield an improved reconstruction (output). DGAN performance was compared with the adaptive de-truncation method, total variation regularization, and two reference DL methods: FBPConvNet, and U-Net-based sinogram extension (ES-UNet). Our DGAN method and ES-UNet yielded the best root-mean-squared error (RMSE) (0.03 ± 0.01), and structural similarity index (SSIM) (0.92 ± 0.02) values, and reference DL methods also yielded good results. Furthermore, we performed an extended FOV analysis by increasing the reconstruction area by 10% and 20%. In both cases, the DGAN approach yielded best results at RMSE (0.03 ± 0.01 and 0.04 ± 0.01 for the 10% and 20% cases, respectively), peak signal-to-noise ratio (PSNR) (30.5 ± 2.6 dB and 28.6 ± 2.6 dB), and SSIM (0.90 ± 0.02 and 0.87 ± 0.02). In conclusion, our method was able to not only reconstruct the interior region with improved image quality, but also extend the reconstructed FOV by 20%.

Published
2021

48. Optimizing iterative reconstruction for quantification of calcium hydroxyapatite with photon counting flat-detector computed tomography:a cardiac phantom study

Author

Juntunen, M. A. (Mikael A. K.), Kotiaho, A. O. (Antti O.), Nieminen, M. T. (Miika T.), and Inkinen, S. I. (Satu I.)

Subjects
iterative reconstruction, nutritional and metabolic diseases, image quality, computed tomography, phantom study, cardiovascular diseases, coronary artery calcium, photon counting flat-detector
Abstract

Purpose: Coronary artery calcium (CAC) scoring with computed tomography (CT) has been proposed as a screening tool for coronary artery disease, but concerns remain regarding the radiation dose of CT CAC scoring. Photon counting detectors and iterative reconstruction (IR) are promising approaches for patient dose reduction, yet the preservation of CAC scores with IR has been questioned. The purpose of this study was to investigate the applicability of IR for quantification of CAC using a photon counting flat-detector. Approach: We imaged a cardiac rod phantom with calcium hydroxyapatite (CaHA) inserts with different noise levels using an experimental photon counting flat-detector CT setup to simulate the clinical CAC scoring protocol. We applied filtered back projection (FBP) and two IR algorithms with different regularization strengths. We compared the air kerma values, image quality parameters [noise magnitude, noise power spectrum, modulation transfer function (MTF), and contrast-to-noise ratio], and CaHA quantification accuracy between FBP and IR. Results: IR regularization strength influenced CAC scores significantly (p

Published
2021

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