Örjan Smedby

@kth.se

Professor of medical image processing and visualization, Department of Biomedical Engineering and Health Systems
KTH Royal Institute of Technology

Örjan Smedby


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https://researchid.co/orjansmedby

RESEARCH INTERESTS

Medical image processing
Machine learning
Medical visualization
198

Scopus Publications

11798

Scholar Citations

50

Scholar h-index

137

Scholar i10-index

Scopus Publications

  • Combining shallow and deep neural networks on pseudo-color enhanced images for digital breast tomosynthesis lesion classification
    Zhikai Yang, Yingqing Liu, Örjan Smedby, Rodrigo Moreno
    Frontiers in Digital Health, 2026
    Introduction The classification of lesion types in Digital Breast Tomosynthesis (DBT) images is crucial for the early diagnosis of breast cancer. However, the task remains challenging due to the complexity of breast tissue and the subtle nature of lesions. To alleviate radiologists’ workload, computer-aided diagnosis (CAD) systems have been developed. The breast lesion regions vary in size and complexity, which leads to performance degradation. Methods To tackle this problem, we propose a novel DBT Dual-Net architecture comprising two complementary neural network branches that extract both low-level and high-level features. By fusing different-level feature representations, the model can better capture subtle structure. Furthermore, we introduced a pseudo-color enhancement procedure to improve the visibility of lesions on DBT. Moreover, most existing DBT classification studies rely on two-dimensional (2D) slice-level analysis, neglecting the rich three-dimensional (3D) spatial context within DBT volumes. To address this limitation, we used majority voting for image-level classification from predictions across slices. Results We evaluated our method on a public DBT dataset and compared its performance with several existing classification approaches. The results showed that our method outperforms baseline models. Discussion The use of pseudo-color enhancement, extracting high and low-level features and inter-slice majority voting proposed method is effective for lesion classification in DBT. The code is available at https://github.com/xiaoerlaigeid/DBT-Dual-Net .
  • Anatomy-aware lymphoma lesion detection in whole-body PET/CT
    Simone Bendazzoli, Antonios Tzortzakakis, Andréas Abrahamsson, Björn Engelbrekt Wahlin, Örjan Smedby, et al.
    Frontiers in Oncology, 2026
    Motivation and objectives Early cancer detection is essential for improving patient outcomes, and 18F-FDG PET/CT imaging plays a central role by combining metabolic and anatomical information. However, accurate lesion detection remains challenging due to the presence of multiple lesions with varying sizes and locations. This study investigates whether incorporating anatomical prior information can improve deep learning-based lesion detection performance. Methods Anatomical priors were incorporated by adding organ segmentation masks generated with TotalSegmentator as auxiliary input channels to two lesion detection frameworks: the CNN-based nnDetection and a transformer-based Swin UNETR implemented in MONAI. The Swin Transformer was trained using a two-stage strategy, with self-supervised pretraining performed on the autoPET dataset and supervised fine-tuning of the detector model conducted on the independent Karolinska lymphoma dataset. Model evaluation followed a single hold-out split, and performance was assessed using FROC and average precision metrics. Results Experiments were conducted on two independent PET/CT datasets covering different tracers and cancer subtypes. The autoPET dataset includes 18F-FDG PET/CT scans of lymphoma, melanoma, and lung cancer, while the Karolinska dataset focuses on lymphoma imaging. Incorporating anatomical priors consistently improved lesion detection performance within the nnDetection framework across both datasets. Specifically, nnDetection augmented with anatomical masks improved in mAP@0.1–0.5 from 0.288 to 0.335. In contrast, anatomical priors had minimal impact on the Swin Transformer, which did not demonstrate clear advantages over CNN-based encoders. Conclusions Anatomy-aware priors substantially enhance lesion detection performance in CNN-based models, highlighting the importance of explicit anatomical context for multi-lesion PET/CT analysis. However, these benefits do not readily transfer to transformer-based architectures, indicating the need for improved strategies to integrate anatomical information into vision transformers for medical image analysis.
  • Kinetic modelling of [⁶⁸Ga]Ga-FAPI-46 PET in pancreaticobiliary lesions: distinguishing cancer from pancreatitis
    Ted Nilsson, Pawel Rasinski, Ernesto Sparrelid, Antonios Tzortzakakis, Thuy A Tran, et al.
    European Journal of Nuclear Medicine and Molecular Imaging, 2026
    Purpose Fibroblast activation protein (FAP)–targeted PET using [⁶⁸Ga]Ga-FAPI-46 visualizes fibroblasts abundant in pancreatic cancer (PC) but also present in pancreatitis, complicating interpretation of static images. Dynamic imaging and kinetic modeling may provide additional insight, but their diagnostic value remains unclear. This study evaluated whether kinetic parameters from dynamic [⁶⁸Ga]Ga-FAPI-46 PET can differentiate PC from pancreatitis and their relationship with standardized uptake value (SUV) and tumor-to-blood ratio (TBR). Methods Sixty-one patients with suspected pancreaticobiliary cancer underwent a 45-min dynamic [⁶⁸Ga]Ga-FAPI-46 PET scan, followed by static scans at 60 and 180 min. Time–activity curves were generated for 51 malignant and 53 benign lesions. Compartment models and Logan analysis yielded kinetic parameters (K 1 , k 2 , k 3 , k 4 , V T , V NS , V S ). SUV and TBR were correlated with V T , and group comparisons and ROC analyses assessed discriminatory performance. Results Reversible models best described the tracer kinetics. V T and V S were significantly higher in PC than pancreatitis, and k 2 and k 4 were lower, indicating higher [⁶⁸Ga]Ga-FAPI-46 binding respectively slower washout in malignant lesions. SUV correlated strongly with V T ( r ≥ 0.784)​, and TBR showed very strong correlations ( r ≥ 0.902)​ for the 0–60 min interval, with strong correlations observed across all models and time points. ROC analyses demonstrated comparable differentiation between V T , SUV max , and TBR max . Conclusions Kinetic parameters showed strong correlations with simplified methods and similar ability to differentiate PC from pancreatitis. SUV and TBR measures thus represent practical alternatives to kinetic modelling for lesion characterization. ClinicalTrials.gov ID: NCT05172310
  • Decomposing the effect of normal aging and Alzheimer’s disease in brain morphological changes via learned aging templates
    Jingru Fu, Daniel Ferreira, Ö. Smedby, R. Moreno
    Scientific Reports, 2025
  • Efficient Generation of Synthetic Breast CT Slices By Combining Generative and Super-Resolution Models
    Zhikai Yang, Mehdi Astaraki, Örjan Smedby, Rodrigo Moreno
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2025
  • Two-Stage Convolutional Neural Network for Breast CT Reconstruction
    Zhikai Yang, Yihan Xiao, Ozan Öktem, Örjan Smedby, Rodrigo Moreno
    Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2025
    In this study, we propose a deep learning based two-stage breast CT reconstruction in the image domain. Unlike most methods, we use two separate models to improve the Breast CT image quality. In the first stage, a deep learning-based denoiser was used to remove the noise. In the second stage, a deep learning based image enhancement model is used to improve the image quality. We evaluated the proposed method on the AAPM 2021 sparse view CT reconstruction challenge dataset. The experimental results demonstrate that the proposed method performs better than all comparison methods.
  • Automatic Segmentation of Bone Graft in Maxillary Sinus via Distance Constrained Network Guided by Prior Anatomical Knowledge
    Jiangchang Xu, Jie Gao, Shuanglin Jiang, Chunliang Wang, Örjan Smedby, et al.
    IEEE Journal of Biomedical and Health Informatics, 2025
  • Automated region growing-based segmentation for trabecular bone structure in fresh-frozen human wrist specimens
    Eva Klintström, Benjamin Klintström, Örjan Smedby, Rodrigo Moreno
    BMC Medical Imaging, 2024
    Bone strength depends on both mineral content and bone structure. Measurements of bone microstructure on specimens can be performed by micro-CT. In vivo measurements are reliably performed by high-resolution peripheral computed tomography (HR-pQCT) using dedicated software. In previous studies from our research group, trabecular bone properties on CT data of defatted specimens from many different CT devices have been analyzed using an Automated Region Growing (ARG) algorithm-based code, showing strong correlations to micro-CT.The aim of the study was to validate the possibility of segmenting and measuring trabecular bone structure from clinical CT data of fresh-frozen human wrist specimens. Data from micro-CT was used as reference. The hypothesis was that the ARG-based in-house built software could be used for such measurements.HR-pQCT image data at two resolutions (61 and 82 µm isotropic voxels) from 23 fresh-frozen human forearms were analyzed. Correlations to micro-CT were strong, varying from 0.72 to 0.99 for all parameters except trabecular termini and nodes. The bone volume fraction had correlations varying from 0.95 to 0.98 but was overestimated compared to micro-CT, especially at the lower resolution. Trabecular separation and spacing were the most stable parameters with correlations at 0.80-0.97 and mean values in the same range as micro-CT.Results from this in vitro study show that an ARG-based software could be used for segmenting and measuring 3D trabecular bone structure from clinical CT data of fresh-frozen human wrist specimens using micro-CT data as reference. Over-and underestimation of several of the bone structure parameters must however be taken into account.
  • Correction to: Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease (Nature Communications, (2022), 13, 1, (4566), 10.1038/s41467-022-32202-6)
    Konstantinos Poulakis, Joana B. Pereira, J.-Sebastian Muehlboeck, Lars-Olof Wahlund, Örjan Smedby, et al.
    Nature Communications, 2024
  • Image quality assessments in abdominal CT: Relative importance of dose, iterative reconstruction strength and slice thickness
    B. Kataria, M. Woisetschläger, J. Nilsson Althén, M. Sandborg, Ö. Smedby
    Radiography, 2024
  • Acquisition Duration Optimization Using Visual Grading Regression in [68Ga]FAPI-46 PET Imaging of Oncologic Patients
    Ted Nilsson, Pawel Rasinski, Örjan Smedby, Siri af Burén, Ernesto Sparrelid, et al.
    Journal of Nuclear Medicine Technology, 2024
  • Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation
    Simone Bendazzoli, Emelie Bäcklin, Örjan Smedby, Birgitta Janerot-Sjoberg, Bryan Connolly, et al.
    Journal of Medical Imaging, 2024
  • Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography
    Emelie Bäcklin, Adrian Gonon, Magnus Sköld, Örjan Smedby, Eva Breznik, et al.
    Clinical Physiology and Functional Imaging, 2024
  • Image quality in CT thorax: effect of altering reconstruction algorithm and tube load
    Bharti Kataria, Mischa Woisetschläger, Jonas Nilsson Althén, Michael Sandborg, Örjan Smedby
    Radiation Protection Dosimetry, 2024
  • 3D Breast Ultrasound Image Classification Using 2.5D Deep learning
    Zhikai Yang, Tianyu Fan, Örjan Smedby, Rodrigo Moreno
    Proceedings of SPIE the International Society for Optical Engineering, 2024
  • Lesion Localization in Digital Breast Tomosynthesis with Deformable Transformers by Using 2.5D Information
    Zhikai Yang, Tianyu Fan, Örjan Smedby, Rodrigo Moreno
    Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2024
  • Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation
    Hanna Tomic, Zhikai Yang, Anders Tingberg, Sophia Zackrisson, Rodrigo Moreno, et al.
    Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2024
  • Validation of automated post-adjustments of HDR prostate brachytherapy treatment plans by quantitative measures and oncologist observer study
    Frida Dohlmar, Björn Morén, Michael Sandborg, Örjan Smedby, Alexander Valdman, et al.
    Brachytherapy, 2023
  • Synthesis of Pediatric Brain Tumor Images With Mass Effect
    Yu Zhou, Jingru Fu, Örjan Smedby, Rodrigo Moreno
    Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2023
  • Learning effects in visual grading assessment of model-based reconstruction algorithms in abdominal Computed Tomography
    Bharti Kataria, Jenny Öman, Michael Sandborg, Örjan Smedby
    European Journal of Radiology Open, 2023
  • Photon-counting detector CT and energy-integrating detector CT for trabecular bone microstructure analysis of cubic specimens from human radius
    Benjamin Klintström, Lilian Henriksson, Rodrigo Moreno, Alexandr Malusek, Örjan Smedby, et al.
    European Radiology Experimental, 2022
  • Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease
    Konstantinos Poulakis, Joana B. Pereira, J.-Sebastian Muehlboeck, Lars-Olof Wahlund, Örjan Smedby, et al.
    Nature Communications, 2022
  • A review on AI-based medical image computing in head and neck surgery
    Jiangchang Xu, Bolun Zeng, Jan Egger, Chunliang Wang, Örjan Smedby, et al.
    Physics in Medicine and Biology, 2022
  • Prior-aware autoencoders for lung pathology segmentation
    Mehdi Astaraki, Örjan Smedby, Chunliang Wang
    Medical Image Analysis, 2022
  • Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients
    Fabian Sinzinger, Mehdi Astaraki, Örjan Smedby, Rodrigo Moreno
    Frontiers in Oncology, 2022

RECENT SCHOLAR PUBLICATIONS

  • Kinetic modelling of [⁶⁸Ga] Ga-FAPI-46 PET in pancreaticobiliary lesions: distinguishing cancer from pancreatitis
    T Nilsson, P Rasinski, E Sparrelid, A Tzortzakakis, TA Tran, Ö Smedby, ...
    European Journal of Nuclear Medicine and Molecular Imaging, 1-11 , 2026
    2026
  • DynamicBUS: Restoring Temporal Dynamics from Static Ultrasound for Improved Breast Cancer Diagnosis
    Z Yang, Y Liu, T Bai, A Biguri, H Chen, Y Li, CB Schönlieb, Ö Smedby, ...
    2026
  • Anatomy-Aware Lymphoma Lesion Detection in Whole-Body PET/CT
    S Bendazzoli, A Tzortzakakis, A Abrahamsson, BE Wahlin, Ö Smedby, ...
    arXiv preprint arXiv:2511.07047 , 2025
    2025
  • Two-stage convolutional neural network for breast CT reconstruction
    Z Yang, Y Xiao, O Öktem, Ö Smedby, R Moreno
    Medical Imaging 2025: Physics of Medical Imaging 13405, 815-820 , 2025
    2025
  • Decomposing the effect of normal aging and Alzheimer’s disease in brain morphological changes via learned aging templates
    J Fu, D Ferreira, Ö Smedby, R Moreno
    Scientific Reports 15 (1), 11813 , 2025
    2025
    Citations: 8
  • Combining shallow and deep neural networks on pseudo-color enhanced images for digital breast tomosynthesis lesion classification
    Z Yang, Y Liu, Ö Smedby, R Moreno
    Frontiers in Digital Health 7, 1705044 , 2025
    2025
  • Designing Radio-dynamics Features for PCR Prediction in Breast DCE-MRI
    S Bendazzoli, M Astaraki, Y Li, R Moreno, Ö Smedby, H Lu, C Wang
    2025
  • Automatic segmentation of bone graft in maxillary sinus via distance constrained network guided by prior anatomical knowledge
    J Xu, J Gao, S Jiang, C Wang, Ö Smedby, Y Wu, X Jiang, X Chen
    IEEE journal of biomedical and health informatics 29 (3), 1995-2005 , 2024
    2024
    Citations: 4
  • Efficient Generation of Synthetic Breast CT Slices By Combining Generative and Super-Resolution Models
    Z Yang, M Astaraki, Ö Smedby, R Moreno
    Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment … , 2024
    2024
    Citations: 1
  • Image quality assessments in abdominal CT: Relative importance of dose, iterative reconstruction strength and slice thickness
    B Kataria, M Woisetschläger, JN Althén, M Sandborg, Ö Smedby
    Radiography 30 (6), 1563-1571 , 2024
    2024
    Citations: 1
  • Acquisition Duration Optimization Using Visual Grading Regression in [68Ga] FAPI-46 PET Imaging of Oncologic Patients
    T Nilsson, P Rasinski, Ö Smedby, S Af Burén, E Sparrelid, JM Löhr, ...
    Journal of Nuclear Medicine Technology 52 (3), 221-228 , 2024
    2024
    Citations: 1
  • Author Correction: Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease
    K Poulakis, JB Pereira, JS Muehlboeck, LO Wahlund, Ö Smedby, G Volpe, ...
    nature communications 15 (1), 5784 , 2024
    2024
  • Lung vessel connectivity map as anatomical prior knowledge for deep learning-based lung lobe segmentation
    S Bendazzoli, E Bäcklin, Ö Smedby, B Janerot-Sjoberg, B Connolly, ...
    Journal of Medical Imaging 11 (4), 044001-044001 , 2024
    2024
    Citations: 5
  • Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography
    E Bäcklin, A Gonon, M Sköld, Ö Smedby, E Breznik, B Janerot‐Sjoberg
    Clinical Physiology and Functional Imaging 44 (4), 340-348 , 2024
    2024
  • Unsupervised domain adaptation for pediatric brain tumor segmentation
    J Fu, S Bendazzoli, Ö Smedby, R Moreno
    arXiv preprint arXiv:2406.16848 , 2024
    2024
    Citations: 14
  • 3D breast ultrasound image classification using 2.5 D deep learning
    Z Yang, T Fan, Ö Smedby, R Moreno
    17th International Workshop on Breast Imaging (IWBI 2024) 13174, 443-449 , 2024
    2024
    Citations: 9
  • Automated region growing-based segmentation for trabecular bone structure in fresh-frozen human wrist specimens
    E Klintström, B Klintström, Ö Smedby, R Moreno
    BMC Medical Imaging 24 (1), 101 , 2024
    2024
    Citations: 4
  • Lesion localization in digital breast tomosynthesis with deformable transformers by using 2.5 D information
    Z Yang, T Fan, Ö Smedby, R Moreno
    Medical Imaging 2024: Computer-Aided Diagnosis 12927, 84-89 , 2024
    2024
    Citations: 4
  • Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation
    H Tomic, Z Yang, A Tingberg, S Zackrisson, R Moreno, Ö Smedby, ...
    Medical Imaging 2024: Physics of Medical Imaging 12925, 279-288 , 2024
    2024
    Citations: 1
  • Image quality in CT thorax: effect of altering reconstruction algorithm and tube load
    B Kataria, M Woisetschläger, J Nilsson Althén, M Sandborg, Ö Smedby
    Radiation Protection Dosimetry 200 (5), 504-514 , 2024
    2024
    Citations: 3

MOST CITED SCHOLAR PUBLICATIONS

  • Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
    S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ...
    arXiv preprint arXiv:1811.02629 , 2018
    2018
    Citations: 2829
  • A multi-organ nucleus segmentation challenge
    N Kumar, R Verma, D Anand, Y Zhou, OF Onder, E Tsougenis, H Chen, ...
    IEEE transactions on medical imaging 39 (5), 1380-1391 , 2019
    2019
    Citations: 687
  • Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms
    M Schaap, CT Metz, T van Walsum, AG van der Giessen, AC Weustink, ...
    Medical image analysis 13 (5), 701-714 , 2009
    2009
    Citations: 458
  • Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge
    X Zhuang, L Li, C Payer, D Štern, M Urschler, MP Heinrich, J Oster, ...
    Medical image analysis 58, 101537 , 2019
    2019
    Citations: 427
  • MRBrainS challenge: online evaluation framework for brain image segmentation in 3T MRI scans
    AM Mendrik, KL Vincken, HJ Kuijf, M Breeuwer, WH Bouvy, J De Bresser, ...
    Computational intelligence and neuroscience 2015 (1), 813696 , 2015
    2015
    Citations: 344
  • Web‐based interactive 3D visualization as a tool for improved anatomy learning
    H Petersson, D Sinkvist, C Wang, Ö Smedby
    Anatomical sciences education 2 (2), 61-68 , 2009
    2009
    Citations: 342
  • Advanced 3D visualization in student-centred medical education
    C Silén, S Wirell, J Kvist, E Nylander, Ö Smedby
    Medical teacher 30 (5), e115-e124 , 2008
    2008
    Citations: 215
  • Synthetic MRI of the brain in a clinical setting
    I Blystad, JBM Warntjes, O Smedby, AM Landtblom, P Lundberg, ...
    Acta radiologica 53 (10), 1158-1163 , 2012
    2012
    Citations: 197
  • Iodinated contrast opacification gradients in normal coronary arteries imaged with prospectively ECG-gated single heart beat 320-detector row computed tomography
    ML Steigner, D Mitsouras, AG Whitmore, HJ Otero, C Wang, O Buckley, ...
    Circulation: Cardiovascular Imaging 3 (2), 179-186 , 2010
    2010
    Citations: 197
  • A novel infraclavicular brachial plexus block: the lateral and sagittal technique, developed by magnetic resonance imaging studies
    Ø Klaastad, HJ Smith, Ö Smedby, EH Winther-Larssen, P Brodal, ...
    Anesthesia & Analgesia 98 (1), 252-256 , 2004
    2004
    Citations: 138
  • Two-dimensional tortuosity of the superficial femoral artery in early atherosclerosis
    Ö Smedby, N Högman, S Nilsson, U Erikson, AG Olsson, G Walldius
    Journal of vascular research 30 (4), 181-191 , 1993
    1993
    Citations: 134
  • Quantifying differences in hepatic uptake of the liver specific contrast agents Gd-EOB-DTPA and Gd-BOPTA: a pilot study
    O Dahlqvist Leinhard, N Dahlström, J Kihlberg, P Sandström, TB Brismar, ...
    European radiology 22 (3), 642-653 , 2012
    2012
    Citations: 130
  • Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography
    O Bernard, JG Bosch, B Heyde, M Alessandrini, D Barbosa, ...
    IEEE transactions on medical imaging 35 (4), 967-977 , 2015
    2015
    Citations: 128
  • Quantitative MRI for analysis of peritumoral edema in malignant gliomas
    I Blystad, JBM Warntjes, Ö Smedby, P Lundberg, EM Larsson, A Tisell
    PLoS One 12 (5), e0177135 , 2017
    2017
    Citations: 122
  • Contrast-enhanced magnetic resonance cholangiography with Gd-BOPTA and Gd-EOB-DTPA in healthy subjects
    N Dahlström, A Persson, N Albiin, Ö Smedby, TB Brismar
    Acta Radiologica 48 (4), 362-368 , 2007
    2007
    Citations: 122
  • Visual grading regression: analysing data from visual grading experiments with regression models
    Ö Smedby, M Fredrikson
    The British journal of radiology 83 (993), 767-775 , 2010
    2010
    Citations: 109
  • Quantitative abdominal fat estimation using MRI
    OD Leinhard, A Johansson, J Rydell, O Smedby, F Nystrom, P Lundberg, ...
    2008 19th international conference on pattern recognition, 1-4 , 2008
    2008
    Citations: 108
  • A computational atlas of normal coronary artery anatomy
    P Medrano-Gracia, J Ormiston, M Webster, S Beier, A Young, C Ellis, ...
    EuroIntervention 12 (7), 845-854 , 2016
    2016
    Citations: 106
  • Do plaques grow upstream or downstream? An angiographic study in the femoral artery
    O Smedby
    Arteriosclerosis, thrombosis, and vascular biology 17 (5), 912-918 , 1997
    1997
    Citations: 104
  • Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease
    K Poulakis, JB Pereira, JS Muehlboeck, LO Wahlund, Ö Smedby, G Volpe, ...
    Nature communications 13 (1), 4566 , 2022
    2022
    Citations: 103