Örjan Smedby

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, Maria Holstensson, Rodrigo Moreno
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, Örjan Smedby, Rimma Axelsson, Mark Lubberink, Maria Holstensson
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
Alzheimer's disease (AD) subjects usually show more profound morphological changes with time compared to cognitively normal (CN) individuals. These changes are the combination of two major biological processes: normal aging and AD pathology. Investigating normal aging and residual morphological changes separately can increase our understanding of the disease. This paper proposes two scores, the aging score (AS) and the AD-specific score (ADS), whose purpose is to measure these two components of brain atrophy independently. For this, in the first step, we estimate the atrophy due to the normal aging of CN subjects by computing the expected deformation required to match imaging templates generated at different ages. We used a state-of-the-art generative deep learning model for generating such imaging templates. In the second step, we apply deep learning-based diffeomorphic registration to align the given image of a subject with a reference imaging template. Parametrization of this deformation field is then decomposed voxel-wise into their parallel and perpendicular components with respect to the parametrization of the expected atrophy of CN individuals in one year computed in the first step. AS and ADS are the normalized scores of these two components, respectively. We evaluated these two scores on the OASIS-3 dataset with 1,014 T1-weighted MRI scans. Of these, 326 scans were from CN subjects, and 688 scans were from subjects diagnosed with AD at various stages of clinical severity, as defined by clinical dementia rating (CDR) scores. Our results reveal that AD is marked by both disease-specific brain changes and an accelerated aging process. Such changes affect brain regions differently. Moreover, the proposed scores were sensitive to detect changes in the early stages of the disease, which is promising for its potential future use in clinical studies. Our code is freely available at https://github.com/Fjr9516/DBM_with_DL .
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, Yiqun Wu, Xiaoyi Jiang, Xiaojun Chen
IEEE Journal of Biomedical and Health Informatics, 2025
Maxillary Sinus Lifting is a crucial surgical procedure for addressing insufficient alveolar bone mass andsevere resorption in dental implant therapy. To accurately analyze the geometry changesof the bone graft (BG) in the maxillary sinus (MS), it is essential to perform quantitative analysis. However, automated BG segmentation remains a major challenge due to the complex local appearance, including blurred boundaries, lesion interference, implant and artifact interference, and BG exceeding the MS. Currently, there are few tools available that can efficiently and accurately segment BG from cone beam computed tomography (CBCT) image. In this paper, we propose a distance-constrained attention network guided by prior anatomical knowledge for the automatic segmentation of BG. First, a guidance strategy of preoperative prior anatomical knowledge is added to a deep neural network (DNN), which improves its ability to identify the dividing line between the MS and BG. Next, a coordinate attention gate is proposed, which utilizes the synergy of channel and position attention to highlight salient features from the skip connections. Additionally, the geodesic distance constraint is introduced into the DNN to form multi-task predictions, which reduces the deviation of the segmentation result. In the test experiment, the proposed DNN achieved a Dice similarity coefficient of 85.48 6.38%, an average surface distance error is 0.57 0.34mm, and a 95% Hausdorff distance of 2.64 2.09mm, which is superior to the comparison networks. It markedly improves the segmentation accuracy and efficiency of BG and has potential applications in analyzing its volume change and absorption rate in the future.
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, Giovanni Volpe, Colin L. Masters, David Ames, Yoshiki Niimi, Takeshi Iwatsubo, Daniel Ferreira, Eric Westman, , and
Nature Communications, 2024
The original version of this Article contained an error in Tables 1, 3 and Supplementary Table S7 . In Table 1, the correct version of the 2nd row of the 1st column in both the Discovery dataset and Validation dataset states ‘females’ instead of the original, incorrect ‘males’. In Table 3, the correct version of the 2nd row of the 1st column states ‘females’ instead of the original, incorrect ‘males’. In Supplementary Table S7 the correct version of the 2nd row of the 1st column under both the ADNI and J-ADNI/AIBL headings states ‘females’ instead of the original, incorrect ‘males’.
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
INTRODUCTION: Low contrast resolution in abdominal computed tomography (CT) may be negatively affected by attempts to lower patient doses. Iterative reconstruction (IR) algorithms play a key role in mitigating this problem. The reconstructed slice thickness also influences image quality. The aim was to assess the interaction and influence of patient dose, slice thickness, and IR strength on image quality in abdominal CT. METHOD: With a simultaneous acquisition, images at 42 and 98 mAs were obtained in 25 patients. Multiplanar images with slice thicknesses of 1, 2, and 3 mm and advanced modeled iterative reconstruction (ADMIRE) strengths of 3 (AD3) and 5 (AD5) were reconstructed. Four radiologists evaluated the images in a pairwise manner based on five image criteria. Ordinal logistic regression with mixed effects was used to evaluate the effect of tube load, ADMIRE strength, and slice thickness using the visual grading regression technique. RESULTS: For all assessed image criteria, the regression analysis showed significantly (p < 0.001) higher image quality for AD5, but lower for tube load 42 mAs, and slice thicknesses of 1 mm and 2 mm, compared to the reference categories of AD3, 98 mAs, and 3 mm, respectively. AD5 at 2 mm was superior to AD3 at 3 mm for all image criteria studied. AD5 1 mm produced inferior image quality for liver parenchyma and overall image quality compared to AD3 3 mm. Interobserver agreement (ICC) ranged from 0.874 to 0.920. CONCLUSION: ADMIRE 5 at 2 mm slice thickness may allow for further dose reductions due to its superiority when compared to ADMIRE 3 at 3 mm slice thickness. IMPLICATIONS FOR PRACTICE: Combination of thinner slices and higher ADMIRE strength facilitates imaging at low dose.
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, J. Matthias Löhr, Thuy A. Tran, August Blomgren, Antonios Tzortzakakis, Rimma Axelsson, Maria Holstensson
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, Chunliang Wang
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, Birgitta Janerot‐Sjoberg
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, Örjan Smedby, Magnus Dustler, Predrag Bakic
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, Torbjörn Larsson, Åsa Carlsson Tedgren
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, Mischa Woisetschläger, Eva Klintström
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, Giovanni Volpe, Colin L. Masters, David Ames, Yoshiki Niimi, Takeshi Iwatsubo, Daniel Ferreira, Eric Westman, , and
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, Xiaoyi Jiang, Xiaojun Chen
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
MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention
Irene Brusini, Eilidh MacNicol, Eugene Kim, Örjan Smedby, Chunliang Wang, Eric Westman, Mattia Veronese, Federico Turkheimer, Diana Cash
Neurobiology of Aging, 2022
A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images
Mehdi Astaraki, Guang Yang, Yousuf Zakko, Iuliana Toma-Dasu, Örjan Smedby, Chunliang Wang
Frontiers in Oncology, 2021
Image quality and potential dose reduction using advanced modeled iterative reconstruction (ADMIRE) in abdominal CT - A review
B Kataria, J Nilsson Althén, Ö Smedby, A Persson, H Sökjer, M Sandborg
Radiation Protection Dosimetry, 2021
Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features
Mehdi Astaraki, Yousuf Zakko, Iuliana Toma Dasu, Örjan Smedby, Chunliang Wang
Physica Medica, 2021
Deriving brain imaging biomarkers with deep learning
Örjan Smedby
Proceedings of SPIE the International Society for Optical Engineering, 2021
Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema
I. Blystad, J. B. M. Warntjes, Ö Smedby, P. Lundberg, E.-M. Larsson, A. Tisell
Scientific Reports, 2020
A comparative study of trabecular bone micro-structural measurements using different CT modalities
Indranil Guha, Benjamin Klintström, Eva Klintström, Xiaoliu Zhang, Örjan Smedby, Rodrigo Moreno, Punam K Saha
Physics in Medicine and Biology, 2020
A Multi-Organ Nucleus Segmentation Challenge
Neeraj Kumar, Ruchika Verma, Deepak Anand, Yanning Zhou, Omer Fahri Onder, Efstratios Tsougenis, Hao Chen, Pheng-Ann Heng, Jiahui Li, Zhiqiang Hu, Yunzhi Wang, Navid Alemi Koohbanani, Mostafa Jahanifar, Neda Zamani Tajeddin, Ali Gooya, Nasir Rajpoot, Xuhua Ren, Sihang Zhou, Qian Wang, Dinggang Shen, Cheng-Kun Yang, Chi-Hung Weng, Wei-Hsiang Yu, Chao-Yuan Yeh, Shuang Yang, Shuoyu Xu, Pak Hei Yeung, Peng Sun, Amirreza Mahbod, Gerald Schaefer, Isabella Ellinger, Rupert Ecker, Orjan Smedby, Chunliang Wang, Benjamin Chidester, That-Vinh Ton, Minh-Triet Tran, Jian Ma, Minh N. Do, Simon Graham, Quoc Dang Vu, Jin Tae Kwak, Akshaykumar Gunda, Raviteja Chunduri, Corey Hu, Xiaoyang Zhou, Dariush Lotfi, Reza Safdari, Antanas Kascenas, Alison O'Neil, Dennis Eschweiler, Johannes Stegmaier, Yanping Cui, Baocai Yin, Kailin Chen, Xinmei Tian, Philipp Gruening, Erhardt Barth, Elad Arbel, Itay Remer, Amir Ben-Dor, Ekaterina Sirazitdinova, Matthias Kohl, Stefan Braunewell, Yuexiang Li, Xinpeng Xie, Linlin Shen, Jun Ma, Krishanu Das Baksi, Mohammad Azam Khan, Jaegul Choo, Adrian Colomer, Valery Naranjo, Linmin Pei, Khan M. Iftekharuddin, Kaushiki Roy, Debotosh Bhattacharjee, Anibal Pedraza, Maria Gloria Bueno, Sabarinathan Devanathan, Saravanan Radhakrishnan, Praveen Koduganty, Zihan Wu, Guanyu Cai, Xiaojie Liu, Yuqin Wang, Amit Sethi
IEEE Transactions on Medical Imaging, 2020
Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus
Irene Brusini, Olof Lindberg, J-Sebastian Muehlboeck, Örjan Smedby, Eric Westman, Chunliang Wang
Frontiers in Neuroscience, 2020
Multimodal brain tumor segmentation with normal appearance autoencoder
Mehdi Astaraki, Chunliang Wang, Gabriel Carrizo, Iuliana Toma-Dasu, Örjan Smedby
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020
Fully Bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression
Konstantinos Poulakis, Daniel Ferreira, Joana B. Pereira, Örjan Smedby, Prashanthi Vemuri, Eric Westman
Aging, 2020
Assessment of image quality in abdominal computed tomography: Effect of model-based iterative reconstruction, multi-planar reconstruction and slice thickness on potential dose reduction
Bharti Kataria, Jonas Nilsson Althén, Örjan Smedby, Anders Persson, Hannibal Sökjer, Michael Sandborg
European Journal of Radiology, 2020
Comparison of acquisition protocols for ventilation/perfusion SPECT - A Monte Carlo study
Maria Holstensson, Örjan Smedby, Gavin Poludniowski, Alejandro Sanchez-Crespo, Irina Savitcheva, Michael Öberg, Per Grybäck, Stefan Gabrielson, Patricia Sandqvist, Erika Bartholdson, Rimma Axelsson
Physics in Medicine and Biology, 2019
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
Xiahai Zhuang, Lei Li, Christian Payer, Darko Štern, Martin Urschler, Mattias P. Heinrich, Julien Oster, Chunliang Wang, Örjan Smedby, Cheng Bian, Xin Yang, Pheng-Ann Heng, Aliasghar Mortazi, Ulas Bagci, Guanyu Yang, Chenchen Sun, Gaetan Galisot, Jean-Yves Ramel, Thierry Brouard, Qianqian Tong, Weixin Si, Xiangyun Liao, Guodong Zeng, Zenglin Shi, Guoyan Zheng, Chengjia Wang, Tom MacGillivray, David Newby, Kawal Rhode, Sebastien Ourselin, Raad Mohiaddin, Jennifer Keegan, David Firmin, Guang Yang
Medical Image Analysis, 2019
Image quality and pathology assessment in CT Urography: When is the low-dose series sufficient?
Bharti Kataria, Jonas Nilsson Althén, Örjan Smedby, Anders Persson, Hannibal Sökjer, Michael Sandborg
BMC Medical Imaging, 2019
Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
Mehdi Astaraki, Chunliang Wang, Giulia Buizza, Iuliana Toma-Dasu, Marta Lazzeroni, Örjan Smedby
Physica Medica, 2019
Simultaneous MR knee image segmentation and bias field correction using deep learning and partial convolution
Fengkai Wan, Örjan Smedby, Chunliang Wang
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2019
Normal Appearance Autoencoder for Lung Cancer Detection and Segmentation
Mehdi Astaraki, Iuliana Toma-Dasu, Örjan Smedby, Chunliang Wang
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry
Irene Brusini, Daniel Jörgens, Örjan Smedby, Rodrigo Moreno
Mathematics and Visualization, 2019
Pelvis segmentation using multi-pass U-Net and iterative shape estimation
Chunliang Wang, Bryan Connolly, Pedro Filipe de Oliveira Lopes, Alejandro F. Frangi, Örjan Smedby
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues
Amirreza Mahbod, Gerald Schaefer, Isabella Ellinger, Rupert Ecker, Örjan Smedby, Chunliang Wang
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
Automatic rat brain segmentation from MRI using statistical shape models and random forest
Simone Bendazzoli, Irene Brusini, Peter Damberg, Örjan Smedby, Leif Andersson, Chunliang Wang
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2019
Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans
Giulia Buizza, Iuliana Toma-Dasu, Marta Lazzeroni, Chiara Paganelli, Marco Riboldi, Yongjun Chang, Örjan Smedby, Chunliang Wang
Physica Medica, 2018
Changes in brain architecture are consistent with altered fear processing in domestic rabbits
Irene Brusini, Miguel Carneiro, Chunliang Wang, Carl-Johan Rubin, Henrik Ring, Sandra Afonso, José A. Blanco-Aguiar, Nuno Ferrand, Nima Rafati, Rafael Villafuerte, Örjan Smedby, Peter Damberg, Finn Hallböök, Mats Fredrikson, Leif Andersson
Proceedings of the National Academy of Sciences of the United States of America, 2018
Direct estimation of human trabecular bone stiffness using cone beam computed tomography
Eva Klintström, Benjamin Klintström, Dieter Pahr, Torkel B. Brismar, Örjan Smedby, Rodrigo Moreno
Oral Surgery Oral Medicine Oral Pathology and Oral Radiology, 2018
Convolutional neural network-based image enhancement for X-ray percutaneous coronary intervention
Marco Pavoni, Yongjun Chang, Sang-ho Park, Orjan Smedby
Journal of Medical Imaging, 2018
Quantitative Measurements Versus Receiver Operating Characteristics and Visual Grading Regression in CT Images Reconstructed with Iterative Reconstruction: A Phantom Study
Kristin Jensen, Hilde Kjernlie Andersen, Örjan Smedby, Bjørn Helge Østerås, Anette Aarsnes, Anders Tingberg, Erik Fosse, Anne Catrine Martinsen
Academic Radiology, 2018
Assessment of image quality in abdominal CT: Potential dose reduction with model-based iterative reconstruction
Bharti Kataria, Jonas Nilsson Althén, Örjan Smedby, Anders Persson, Hannibal Sökjer, Michael Sandborg
European Radiology, 2018
Automatic whole heart segmentation using deep learning and shape context
Chunliang Wang, Örjan Smedby
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018
Effects of preprocessing in slice-level classification of interstitial lung disease based on deep convolutional networks
Yongjun Chang, Örjan Smedby
Lecture Notes in Computational Vision and Biomechanics, 2018
Learning a single step of streamline tractography based on neural networks
Daniel Jörgens, Örjan Smedby, Rodrigo Moreno
Mathematics and Visualization, 2018
Image denoising with convolutional neural networks for percutaneous transluminal coronary angioplasty
Marco Pavoni, Yongjun Chang, Örjan Smedby
Lecture Notes in Computational Vision and Biomechanics, 2018
Automatic brain segmentation using artificial neural networks with shape context
Amirreza Mahbod, Manish Chowdhury, Örjan Smedby, Chunliang Wang
Pattern Recognition Letters, 2018
Breast Cancer Histological Image Classification Using Fine-Tuned Deep Network Fusion
Amirreza Mahbod, Isabella Ellinger, Rupert Ecker, Örjan Smedby, Chunliang Wang
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018
Quantitative MRI for analysis of peritumoral edema in malignant gliomas
Ida Blystad, J. B. Marcel Warntjes, Örjan Smedby, Peter Lundberg, Elna-Marie Larsson, et al.
Plos One, 2017
Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation
Kristína Lidayová, Hans Frimmel, Ewert Bengtsson, Örjan Smedby
Journal of Medical Imaging, 2017
A Study of Coronary Bifurcation Shape in a Normal Population
Pau Medrano-Gracia, John Ormiston, Mark Webster, Susann Beier, Chris Ellis, et al.
Journal of Cardiovascular Translational Research, 2017
Multiorgan Segmentation Using Coherent Propagating Level Set Method Guided by Hierarchical Shape Priors and Local Phase Information
Chunliang Wang, Örjan Smedby
Cloud Based Benchmarking of Medical Image Analysis, 2017
Automatic heart and vessel segmentation using random forests and a local phase guided level set method
Chunliang Wang, Qian Wang, Örjan Smedby
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
Feature space clustering for trabecular bone segmentation
Benjamin Klintström, Eva Klintström, Örjan Smedby, Rodrigo Moreno
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
Skeleton-based fast, fully automated generation of vessel tree structure for clinical evaluation of blood vessel systems
Kristína Lidayová, Hans Frimmel, Chunliang Wang, Ewert Bengtsson, Örjan Smedby
Skeletonization Theory Methods and Applications, 2017
Granulometry-based trabecular bone segmentation
Manish Chowdhury, Benjamin Klintström, Eva Klintström, Örjan Smedby, Rodrigo Moreno
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
Segmentation of cortical bone using fast level sets
Manish Chowdhury, Daniel Jörgens, Chunliang Wang, Örjan Smedby, Rodrigo Moreno
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2017
Airway-tree segmentation in subjects with acute respiratory distress syndrome
Kristína Lidayová, Duván Alberto Gómez Betancur, Hans Frimmel, Marcela Hernández Hoyos, Maciej Orkisz, et al.
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
Kristína Lidayová, Anindya Gupta, Hans Frimmel, Ida-Maria Sintorn, Ewert Bengtsson, et al.
Communications in Computer and Information Science, 2017
An investigation of fat infiltration of the multifidus muscle in patients with severe neck symptoms associated with chronic whiplash-associated disorder
Anette Karlsson, Olof Dahlqvist Leinhard, Ulrika Åslund, Janne West, Thobias Romu, et al.
Journal of Orthopaedic and Sports Physical Therapy, 2016
A computational atlas of normal coronary artery anatomy
Pau Medrano-Gracia, John Ormiston, Mark Webster, Susann Beier, Alistair Young, et al.
Eurointervention, 2016
Prediction of apparent trabecular bone stiffness through fourth-order fabric tensors
Rodrigo Moreno, Örjan Smedby, Dieter H. Pahr
Biomechanics and Modeling in Mechanobiology, 2016
Predicting trabecular bone stiffness from clinical cone-beam CT and HR-pQCT Data; an In vitro study using finite element analysis
Eva Klintström, Benjamin Klintström, Rodrigo Moreno, Torkel B. Brismar, Dieter H. Pahr, et al.
Plos One, 2016
Vesselness estimation through higher-order orientation tensors
Rodrigo Moreno, Orjan Smedby
Proceedings International Symposium on Biomedical Imaging, 2016
Steering second-order tensor voting by vote clustering
Daniel Jorgens, Orjan Smedby, Rodrigo Moreno
Proceedings International Symposium on Biomedical Imaging, 2016
Fast vascular skeleton extraction algorithm
Kristína Lidayová, Hans Frimmel, Chunliang Wang, Ewert Bengtsson, Örjan Smedby
Pattern Recognition Letters, 2016
Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography
Olivier Bernard, Johan G. Bosch, Brecht Heyde, Martino Alessandrini, Daniel Barbosa, et al.
IEEE Transactions on Medical Imaging, 2016
Non-rigid point set registration of curves: Registration of the superficial vessel centerlines of the brain
Filipe M. M. Marreiros, Chunliang Wang, Sandro Rossitti, Örjan Smedby
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2016
An efficient radiographic Image Retrieval system using Convolutional Neural Network
Manish Chowdhury, Samuel Rota Bulo, Rodrigo Moreno, Malay Kumar Kundu, Orjan Smedby
Proceedings International Conference on Pattern Recognition, 2016
Erratum: Superficial vessel reconstruction with a Multiview camera system (Journal of Medical Imaging (2016) 3:1 (015001))
Filipe M. M Marreiros, Sandro Rossitti, Per M. Karlsson, Chunliang Wang, Torbjörn Gustafsson, et al.
Journal of Medical Imaging, 2016
Quantitative MRI for analysis of active multiple sclerosis lesions without gadolinium-based contrast agent
I. Blystad, I. Håkansson, A. Tisell, J. Ernerudh, Ö. Smedby, et al.
American Journal of Neuroradiology, 2016
Superficial vessel reconstruction with a multiview camera system
Filipe M. M. Marreiros, Sandro Rossitti, Per M. Karlsson, Chunliang Wang, Torbjörn Gustafsson, et al.
Journal of Medical Imaging, 2016
Coverage segmentation of 3D thin structures
Kristina Lidayova, Joakim Lindblad, Natasa Sladoje, Hans Frimmel, Chunliang Wang, et al.
5th International Conference on Image Processing Theory Tools and Applications 2015 Ipta 2015, 2015
Gradient-based enhancement of tubular structures in medical images
Rodrigo Moreno, Örjan Smedby
Medical Image Analysis, 2015
Real-time interactive 3D tumor segmentation using a fast level-set algorithm
Chunliang Wang, Nils Dahlström, Sven-Göran Fransson, Claes Lundström, örjan Smedby
Journal of Medical Imaging and Health Informatics, 2015
Regression models for analyzing radiological visual grading studies - an empirical comparison
S. Ehsan Saffari, Áskell Löve, Mats Fredrikson, Örjan Smedby
BMC Medical Imaging, 2015
How to measure renal artery stenosis - a retrospective comparison of morphological measurement approaches in relation to hemodynamic significance
Malin Andersson, Karl Jägervall, Per Eriksson, Anders Persson, Göran Granerus, et al.
BMC Medical Imaging, 2015
Consistent intensity inhomogeneity correction in water-fat MRI
Thord Andersson, Thobias Romu, Anette Karlsson, Bengt Norén, Mikael F. Forsgren, et al.
Journal of Magnetic Resonance Imaging, 2015
Visual assessment of biliary excretion of Gd-EOB-DTPA in patients with suspected diffuse liver disease - A biopsy-verified prospective study
Bengt Norén, Nils Dahlström, Mikael Fredrik Forsgren, Olof Dahlqvist Leinhard, Stergios Kechagias, et al.
European Journal of Radiology Open, 2015
Multi-organ segmentation using shape model guided local phase analysis
Chunliang Wang, Örjan Smedby
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015
Anisotropy estimation of trabecular bone in gray-scale: Comparison between cone beam and micro computed tomography data
Rodrigo Moreno, Magnus Borga, Eva Klintström, Torkel Brismar, Örjan Smedby
Lecture Notes in Computational Vision and Biomechanics, 2015
Visualization of liver lesions in standardized video-documented ultrasonography-inter-observer agreement and effect of contrast injection
Carina Stenman, Robert Glavaš, Joachim Davidsson, Anders Knutsson, Örjan Smedby
Medical Ultrasonography, 2015
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
Adriënne M. Mendrik, Koen L. Vincken, Hugo J. Kuijf, Marcel Breeuwer, Willem H. Bouvy, et al.
Computational Intelligence and Neuroscience, 2015
Automatic multi-organ segmentation in non-enhanced CT datasets using hierarchical shape priors
Chunliang Wang, Orjan Smedby
Proceedings International Conference on Pattern Recognition, 2014
Volume-based fabric tensors through lattice-Boltzmann simulations
Rodrigo Moreno, Orjan Smedby
Proceedings International Conference on Pattern Recognition, 2014
Trabecular bone histomorphometric measurements and contrast-to-noise ratio in CBCT
E Klintström, Ö Smedby, B Klintström, T B Brismar, R Moreno
Dentomaxillofacial Radiology, 2014
Normal appearing and diffusely abnormal white matter in patients with multiple sclerosis assessed with quantitative MR
Janne West, Anne Aalto, Anders Tisell, Olof Dahlqvist Leinhard, Anne-Marie Landtblom, et al.
Plos One, 2014
Trabecular bone structure parameters from 3D image processing of clinical multi-slice and cone-beam computed tomography data
Eva Klintström, Örjan Smedby, Rodrigo Moreno, Torkel B. Brismar
Skeletal Radiology, 2014
Automatic multi-organ segmentation using fast model based level set method and hierarchical shape priors
Ceur Workshop Proceedings, 2014
Techniques for computing fabric tensors: A review
Rodrigo Moreno, Magnus Borga, Örjan Smedby
Mathematics and Visualization, 2014
Fast level-set based image segmentation using coherent propagation
Chunliang Wang, Hans Frimmel, Örjan Smedby
Medical Physics, 2014
Correlations between fabric tensors computed on cone beam and micro computed tomography images
Rodrigo Moreno, Magnus Borga, Eva Klintström, Torkel Brismar, Örjan Smedby
Computational Vision and Medical Image Processing IV Proceedings of Eccomas Thematic Conference on Computational Vision and Medical Image Processing Vipimage 2013, 2014
Non-rigid deformation pipeline for compensation of superficial brain shift
Filipe M. M. Marreiros, Sandro Rossitti, Chunliang Wang, Örjan Smedby
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013
Vessel wall segmentation using implicit models and total curvature penalizers
Rodrigo Moreno, Chunliang Wang, Örjan Smedby
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013
Stereoscopic static depth perception of enclosed 3D objects
Filipe M. M. Marreiros, Örjan Smedby
Proceedings Sap 2013 ACM Symposium on Applied Perception, 2013
Patient dose and image quality in low-dose abdominal CT: A comparison between iterative reconstruction and filtered back projection
Bharti Kataria, Örjan Smedby
Acta Radiologica, 2013
Increased Concentrations of Glutamate and Glutamine in Normal-Appearing White Matter of Patients with Multiple Sclerosis and Normal MR Imaging Brain Scans
Anders Tisell, Olof Dahlqvist Leinhard, Jan Bertus Marcel Warntjes, Anne Aalto, Örjan Smedby, et al.
Plos One, 2013
Do radiologists agree on findings in radiographer-acquired sonographic examinations?
Carina Stenman, Shazia Jamil, Lars Thorelius, Anders Knutsson, Örjan Smedby
Journal of Ultrasound in Medicine, 2013
Erratum: Quantifying the potential for dose reduction with visual grading regression (British Journal of Radiology (2013) 86 (20110784) DOI: 10.1259/bjr/31197714)
British Journal of Radiology, 2013
Real-time intraoperative visualization of myocardial circulation using augmented reality temperature display
Zoltán Szabó, Sören Berg, Stefan Sjökvist, Torbjörn Gustafsson, Per Carleberg, et al.
International Journal of Cardiovascular Imaging, 2013
Non-rigid deformation pipeline for compensation of superficial brain shift.
Medical Image Computing and Computer Assisted Intervention Miccai International Conference on Medical Image Computing and Computer Assisted Intervention, 2013
Quantifying the potential for dose reduction with visual grading regression
Ö Smedby, M Fredrikson, J De Geer, L Borgen, M Sandborg
British Journal of Radiology, 2013
Separation of advanced from mild hepatic fibrosis by quantification of the hepatobiliary uptake of Gd-EOB-DTPA
Bengt Norén, Mikael Fredrik Forsgren, Olof Dahlqvist Leinhard, Nils Dahlström, Johan Kihlberg, et al.
European Radiology, 2013
Postangioplasty restenosis followed with magnetic resonance imaging in an atherosclerotic rabbit model
Mari Hänni, Olli Leppänen, Örjan Smedby
International Journal of Biomedical Imaging, 2012
Synthetic MRI of the brain in a clinical setting
I Blystad, JBM Warntjes, O Smedby, A-M Landtblom, P Lundberg, et al.
Acta Radiologica, 2012
Can segmented 3D images be used for stenosis evaluation in coronary CT angiography?
Chunliang Wang, Anders Persson, Jan Engvall, Jakob De Geer, Waldemar Czekierda, et al.
Acta Radiologica, 2012
Estimation of trabecular thickness in gray-scale images through granulometric analysis
Rodrigo Moreno, Magnus Borga, Örjan Smedby
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2012
Application of adaptive non-linear 2D and 3D postprocessing filters for reduced dose abdominal CT
Lars Borgen, Mannudeep K Kalra, Frode Lærum, Isabelle W Hachette, Carina H Fredriksson, et al.
Acta Radiologica, 2012
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, T. B. Brismar, et al.
European Radiology, 2012
Generalizing the mean intercept length tensor for gray-level images
Rodrigo Moreno, Magnus Borga, Örjan Smedby
Medical Physics, 2012
Evaluation of the plate-rod model assumption of trabecular bone
Rodrigo Moreno, Magnus Borga, Orjan Smedby
Proceedings International Symposium on Biomedical Imaging, 2012
Quantifying effects of post-processing with visual grading regression
Örjan Smedby, Mats Fredrikson, Jakob De Geer, Michael Sandborg
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2012
Visual grading regression with random effects
Örjan Smedby, Mats Fredrikson, Jakob De Geer, Michael Sandborg
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2012
Effects of moderate red wine consumption on liver fat and blood lipids: A prospective randomized study
Stergios Kechagias, Sepehr Zanjani, Solveig Gjellan, Olof Dahlqvist Leinhard, Johan Kihlberg, et al.
Annals of Medicine, 2011
The efficacy of 2D, non-linear noise reduction filtering in cardiac imaging: A pilot study
Jakob De Geer, Michael Sandborg, Örjan Smedby, Anders Persson
Acta Radiologica, 2011
Level-set based vessel segmentation accelerated with periodic monotonic speed function
Chunliang Wang, Hans Frimmel, Örjan Smedby
Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2011
Radiographer-acquired and radiologist-reviewed ultrasound examination - Agreement with radiologist's bedside evaluation
Carina Stenman, Lars Thorelius, Anders Knutsson, Örjan Smedby
Acta Radiologica, 2011
Erratum: Visual grading regression - Analysing data from visual grading experiments with regression models (The British Journal of Radiology (2010) 83, (767-775) doi: 10.1259/bjr/35254923)
British Journal of Radiology, 2011
Soft classification of trabeculae in trabecular bone
Rodrigo Moreno, Orjan Smedby, Magnus Borga
Proceedings International Symposium on Biomedical Imaging, 2011
Non-invasive investigations of potential renal artery stenosis in renal insufficiency
P. Eriksson, A. A. Mohammed, J. De Geer, J. Kihlberg, A. Persson, et al.
Nephrology Dialysis Transplantation, 2010
Visual grading of 2D and 3D functional MRI compared with image-based descriptive measures
Mattias Ragnehed, Olof Dahlqvist Leinhard, Johan Pihlsgård, Staffan Wirell, Hannibal Sökjer, et al.
European Radiology, 2010
Iodinated contrast opacification gradients in normal coronary arteries imaged with prospectively ECG-gated single heart beat 320-detector row computed tomography
Michael L. Steigner, Dimitrios Mitsouras, Amanda G. Whitmore, Hansel J. Otero, Chunliang Wang, et al.
Circulation Cardiovascular Imaging, 2010
Integrating automatic and interactive methods for coronary artery segmentation: let the PACS workstation think ahead
Chunliang Wang, Örjan Smedby
International Journal of Computer Assisted Radiology and Surgery, 2010
Visual grading regression: Analysing data from visual grading experiments with regression models
Ö Smedby, M Fredrikson
British Journal of Radiology, 2010
Making the PACS workstation a browser of image processing software: A feasibility study using inter-process communication techniques
Chunliang Wang, Felix Ritter, Örjan Smedby
International Journal of Computer Assisted Radiology and Surgery, 2010
Soft tissue discrimination ex vivo by dual energy computed tomography
H. Zachrisson, E. Engström, J. Engvall, L. Wigström, Ö. Smedby, et al.
European Journal of Radiology, 2010
Liver vessel enhancement by Gd-BOPTA and Gd-EOB-DTPA: A comparison in healthy volunteers
T. B. Brismar, N. Dahlström, N. Edsborg, A. Persson, Ö. Smedby, et al.
Acta Radiologica, 2009
Web-based interactive 3D visualization as a tool for improved anatomy learning
Helge Petersson, David Sinkvist, Chunliang Wang, Örjan Smedby
Anatomical Sciences Education, 2009
Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms
Michiel Schaap, Coert T. Metz, Theo van Walsum, Alina G. van der Giessen, Annick C. Weustink, et al.
Medical Image Analysis, 2009
Advanced 3D visualization in student-centred medical education
Charlotte Silén, Staffan Wirell, Joanna Kvist, Eva Nylander, Örjan Smedby
Medical Teacher, 2008
Separation of advanced from mild fibrosis in diffuse liver disease using 31P magnetic resonance spectroscopy
Bengt Noren, Olof Dahlqvist, Peter Lundberg, Sven Almer, Stergios Kechagias, et al.
European Journal of Radiology, 2008
An interactive software module for visualizing coronary arteries in CT angiography
Chunliang Wang, Hans Frimmel, Anders Persson, Örjan Smedby
International Journal of Computer Assisted Radiology and Surgery, 2008
Renal artery stenosis: Extracting quantitative parameters with a mathematical model pitted to magnetic resonance blood plow data
Martin Larsson, Anders Persson, Per Eriksson, Johan Kihlberg, Örjan Smedby
Journal of Magnetic Resonance Imaging, 2008
Quantitative abdominal fat estimation using MRI
O. Dahlqvist Leinhard, A. Johansson, J. Rydell, O. Smedby, F. Nystrom, et al.
Proceedings International Conference on Pattern Recognition, 2008
Brain magnetic resonance imaging does not contribute to the diagnosis of chronic neuroborreliosis
A. Aalto, J. Sjöwall, L. Davidsson, P. Forsberg, Ö. Smedby
Acta Radiologica, 2007
Contrast-enhanced magnetic resonance cholangiography with Gd-BOPTA and Gd-EOB-DTPA in healthy subjects
N. Dahlström, A. Persson, N. Albiin, Ö. Smedby, T. B. Brismar
Acta Radiologica, 2007
Coronary artery segmentation and skeletonization based on competing fuzzy connectedness tree
Chunliang Wang, Örjan Smedby
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
A blending technique for enhanced depth perception in medical x-ray vision applications
Studies in Health Technology and Informatics, 2007
Three-dimensional drip infusion CT cholangiography in patients with suspected obstructive biliary disease: A retrospective analysis of feasibility and adverse reaction to contrast material
A Persson, N Dahlström, Ö Smedby, TB Brismar
BMC Medical Imaging, 2006
Standardized volume rendering for magnetic resonance angiography measurements in the abdominal aorta
A. Persson, T. B. Brismar, C. Lundström, N. Dahlström, F. Othberg, et al.
Acta Radiologica, 2006
Analysis of skeletal microstructure with clinical multislice CT
Joel Petersson, Torkel Brismar, Örjan Smedby
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006
Efficient 3-D adaptive filtering for medical image enhancement
B. Svensson, M. Andersson, O. Smedby, H. Knutsson
2006 3rd IEEE International Symposium on Biomedical Imaging from Nano to Macro Proceedings, 2006
Volume rendering of three-dimensional drip infusion CT cholangiography in patients with suspected obstructive biliary disease: A retrospective study
A Persson, N Dahlström, Ö Smedby, T B Brismar
British Journal of Radiology, 2005
Computed tomographic colonography: Comparison of two workstations
E. Sörstedt, A. Persson, B. Norén, U. Björnlert, P. Malcherek, et al.
Acta Radiologica, 2005
Standardized volume-rendering of contrast-enhanced renal magnetic resonance angiography
Ö. Smedby, R. Öberg, B. Åsberg, H. Stenström, P. Eriksson
Acta Radiologica, 2005
The vertical infraclavicular brachial plexus block: A simulation study using magnetic resonance imaging
??ivind Klaastad, ??rjan Smedby, Trygve Kjelstrup, Hans-J??rgen Smith
Anesthesia and Analgesia, 2005
Vessel size estimation in peripheral artery interventions: Are angiographic measurements reliable?
Hugo Stenström, Anders Knutsson, Örjan Smedby
Acta Radiologica, 2005
Absolute quantification of human liver metabolite concentrations by localized in vivo 31P NMR spectroscopy in diffuse liver disease
Bengt Nor�n, Peter Lundberg, Marcus Ressner, Staffan Wirell, Sven Almer, et al.
European Radiology, 2005
Color-coded depth information in volume-rendered magnetic resonance angiography
Orjan Smedby, Karin Edsborg, John Henriksson
Proceedings of SPIE the International Society for Optical Engineering, 2004
Quantification of atherosclerosis with MRI and image processing in spontaneously hyperlipidemic rabbits
Mari H�nni, H. Edvardsson, M. W�gberg, K. Pettersson, �. Smedby
Journal of Cardiovascular Magnetic Resonance, 2004
Volume rendering compared with maximum intensity projection for magnetic resonance angiography measurements of the abdominal aorta
A. Persson, N. Dahlström, L. Engellau, E.‐M. Larsson, T. B. Brismar, et al.
Acta Radiologica, 2004
A Novel Infraclavicular Brachial Plexus Block: The Lateral and Sagittal Technique, Developed by Magnetic Resonance Imaging Studies
??ivind Klaastad, Hans-J??rgen Smith, ??rjan Smedby, Eldrid H. Winther-Larssen, Per Brodal, et al.
Anesthesia and Analgesia, 2004
A novel approach to infraclavicular brachial plexus block: The ultrasound experience [4] (multiple letters)
Richard Brull, Colin J. L. McCartney, Vincent W. S. Chan
Anesthesia and Analgesia, 2004
Compact and efficient 3D shape description through radial function approximation
Hannes Edvardson, Örjan Smedby
Computer Methods and Programs in Biomedicine, 2003
An evaluation of the supraclavicular plumb-bob technique for brachial plexus block by magnetic resonance imaging
Anesthesia and Analgesia, 2003
Distribution of local anesthetic in axillary brachial plexus block: A clinical and magnetic resonance imaging study
Øivind Klaastad, Örjan Smedby, Gale E. Thompson, Terje Tillung, Per Kristian Hol, et al.
Anesthesiology, 2002
Segmentation with gray-scale connectedness can separate arteries and veins in MRA
Xavier Tizon, Örjan Smedby
Journal of Magnetic Resonance Imaging, 2002
Skeletonization of Volumetric Vascular Images - Distance Information Utilized for Visualization
Ingela Nyström, Örjan Smedby
Journal of Combinatorial Optimization, 2001
The supraclavicular lateral paravascular approach for brachial plexus regional anesthesia: A simulation study using magnetic resonance imaging
Øivind Klaastad, Örjan Smedby
Anesthesia and Analgesia, 2001
Allergic reactions at repeat femoral angiography with ioxaglate
S. Nilsson, L. Bergstrand, U. Erikson, J. Johansson, Smedby Ö., et al.
Acta Radiologica, 2001
MRI-guided celiac plexus block
Per Kristian Hol, Gunnvald Kvarstein, Oddbj�rn Viken, �rjan Smedby, Tor Inge T�nnessen
Journal of Magnetic Resonance Imaging, 2000
New presentation method for magnetic resonance angiography images based on skeletonization
Proceedings of SPIE the International Society for Optical Engineering, 2000
Functional imaging of the thoracic outlet syndrome in an open MR scanner
Ö. Smedby, H. Rostad, Ø. Klaastad, F. Lilleås, T. Tillung, et al.
European Radiology, 2000
Characterization of human head vasculature by percolation parameters
Jan Weis, Örjan Smedby, Anders Hemmingsson
Magnetic Resonance Imaging, 1999
Timing Adjustment in Gadolinium MR Angiography by Raw Data Recombination: Technical note
Ö. Smedby, J. Weis
Acta Radiologica, 1999
Greyscale connectivity concept for visualizing MRA and CTA volumes
Proceedings of SPIE the International Society for Optical Engineering, 1999
Geometrical risk factors for atherosclerosis in the femoral artery: A longitudinal angiographic study
Örjan Smedby
Annals of Biomedical Engineering, 1998
2D inflow MR angiography in severe chronic leg ischemia
H. Eklöf, Ö. Smedby, C. Ljungman, S. Karacagil, D. Bergqvist, et al.
Acta Radiologica, 1998
Do plaques grow upstream or downstream?: An angiographic study in the femoral artery
Örjan Smedby
Arteriosclerosis Thrombosis and Vascular Biology, 1997
Magnetic resonance angiography in the resectability assessment of suspected pancreatic tumours
Ö. Smedby, V. Riesenfeld, B. M. Karlson, G. Jacobson, A. M. Löfberg, et al.
European Radiology, 1997
Anatomical variations of the human vestibular aqueduct: Part I. A radioanatomical study
Acta Radiologica Supplement, 1996
Anatomical variations of the human vestibular aqueduct: Part II. A radioanatomical study
Acta Radiologica Supplement, 1996
Geometric risk factors for atherosclerosis in the aortic bifurcation: A digitized angiography study
Örjan Smedby
Annals of Biomedical Engineering, 1996
Tortuosity and atherosclerosis in the femoral artery: What is cause and what is effect?
Ö. Smedby, L. Bergstrand
Annals of Biomedical Engineering, 1996
Development of femoral atherosclerosis in relation to flow disturbances
Örjan Smedby, Sven Nilsson, Lott Bergstrand
Journal of Biomechanics, 1996
Anatomical variations of the tympanic and mastoid portions of the facial nerve: Canal a radioanatomical investigation
Acta Radiologica Supplement, 1996
Predilection of atherosclerosis for the inner curvature in the femoral artery: A digitized angiography study
Ö. Smedby, J. Johansson, J. Mölgaard, A. G. Olsson, G. Walldius, et al.
Arteriosclerosis Thrombosis and Vascular Biology, 1995
Flow disturbances in early femoral atherosclerosis-An in vivo study with digitized cineangiography
Örjan Smedby, Nils Högman, Sven Nilsson, Uno Erikson
Journal of Biomechanics, 1993
Two-dimensional tortuosity of the superficial femoral artery in early atherosclerosis
Örjan Smedby, Nils Högman, Sven Nilsson, Uno Erikson, Anders G. Olsson, et al.
Journal of Vascular Research, 1993
A scanning system for digital analysis of cineangiography films
Örjan Smedby
Computer Methods and Programs in Biomedicine, 1992
Viscosity of some contemporary contrast media before and after mixing with whole blood
Ö. Smedby
Acta Radiologica, 1992
Angiographic methods for the study of fluid mechanical factors in atherogenesis.
Acta Radiologica Supplementum, 1992
Separated flow demonstrated by digitized cineangiography compared with LDV
O¨. Smedby, L. Fuchs, N. Tillmark
Journal of Biomechanical Engineering, 1991
Measures of continuity of care: A register-based correlation study
??rjan Smedby, Gunnar Eklund, Erik Anders Eriksson, Bj??rn Smedby
Medical Care, 1986
A methodologic study compares 2 measures of physician-patient continuity
Lakartidningen, 1984
Continuity of care: An application of visit-based measures
Bj??rn Smedby, ??rjan Smedby, Erik Anders Eriksson, Lars-G??ran Mattsson, ??ke Lindgren
Medical Care, 1984
A mathematical model for assessing mitral incompetence by videodensitometry
Annales De Radiologie Medecine Nucleaire, 1981

Örjan Smedby

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