Publications by authors named "Örjan Smedby"

72 Publications

A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images.

Front Oncol 2021 17;11:737368. Epub 2021 Dec 17.

Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, Sweden.

Objectives: Both radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules.

Methods: Conventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction.

Results: The best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean ± standard deviations) of 0.792 ± 0.025, 0.801 ± 0.018, and 0.817 ± 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 ± 0.010, 0.824 ± 0.021, and 0.936 ± 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 ± 0.010).

Conclusion: The end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study.
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http://dx.doi.org/10.3389/fonc.2021.737368DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718670PMC
December 2021

MRI-derived brain age as a biomarker of ageing in rats: validation using a healthy lifestyle intervention.

Neurobiol Aging 2022 01 14;109:204-215. Epub 2021 Oct 14.

Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.

The difference between brain age predicted from MRI and chronological age (the so-called BrainAGE) has been proposed as an ageing biomarker. We analyse its cross-species potential by testing it on rats undergoing an ageing modulation intervention. Our rat brain age prediction model combined Gaussian process regression with a classifier and achieved a mean absolute error (MAE) of 4.87 weeks using cross-validation on a longitudinal dataset of 31 normal ageing rats. It was then tested on two groups of 24 rats (MAE = 9.89 weeks, correlation coefficient = 0.86): controls vs. a group under long-term environmental enrichment and dietary restriction (EEDR). Using a linear mixed-effects model, BrainAGE was found to increase more slowly with chronological age in EEDR rats (p=0.015 for the interaction term). Cox regression showed that older BrainAGE at 5 months was associated with higher mortality risk (p=0.03). Our findings suggest that lifestyle-related prevention approaches may help to slow down brain ageing in rodents and the potential of BrainAGE as a predictor of age-related health outcomes.
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http://dx.doi.org/10.1016/j.neurobiolaging.2021.10.004DOI Listing
January 2022

Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features.

Phys Med 2021 Mar 25;83:146-153. Epub 2021 Mar 25.

KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, SE-14157 Huddinge, Sweden.

Purpose: Low-Dose Computed Tomography (LDCT) is the most common imaging modality for lung cancer diagnosis. The presence of nodules in the scans does not necessarily portend lung cancer, as there is an intricate relationship between nodule characteristics and lung cancer. Therefore, benign-malignant pulmonary nodule classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features.

Methods: To efficiently capture both intra-nodule heterogeneities and contextual information of the pulmonary nodules, a dual pathway model was developed to integrate the intra-nodule characteristics with contextual attributes. The proposed approach was implemented with both supervised and unsupervised learning schemes. A random forest model was added as a second component on top of the networks to generate the classification results. The discrimination power of the model was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUROC) metric.

Results: Experiments on 1297 manually segmented nodules show that the integration of context and target supervised deep features have a great potential for accurate prediction, resulting in a discrimination power of 0.936 in terms of AUROC, which outperformed the classification performance of the Kaggle 2017 challenge winner.

Conclusion: Empirical results demonstrate that integrating nodule target and context images into a unified network improves the discrimination power, outperforming the conventional single pathway convolutional neural networks.
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http://dx.doi.org/10.1016/j.ejmp.2021.03.013DOI Listing
March 2021

A comparative study of trabecular bone micro-structural measurements using different CT modalities.

Phys Med Biol 2020 Oct 21. Epub 2020 Oct 21.

Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, UNITED STATES.

Osteoporosis, characterized by reduced bone mineral density and micro-architectural degeneration, significantly enhances fracture-risk. There are several viable methods for trabecular bone micro-imaging, which widely vary in terms of technology, reconstruction principle, spatial resolution, and acquisition time. We have performed an excised cadaveric bone specimen study to evaluate different CT-imaging modalities for trabecular bone micro-structural analysis. Excised cadaveric bone specimens from the distal radius were scanned using micro-CT and four in vivo CT imaging modalities: HR-pQCT, dental CBCT, whole-body MDCT, and extremity CBCT. A new algorithm was developed to optimize soft thresholding parameters for individual in vivo CT modalities for computing quantitative bone volume fraction maps. Finally, agreement of trabecular bone micro-structural measures, derived from different in vivo CT imaging, with reference measures from micro-CT imaging was examined. Observed values of most trabecular measures, including trabecular bone volume, network area, transverse and plate-rod micro-structure, thickness, and spacing, for in vivo CT modalities were higher than their micro-CT-based reference values. In general, HR-pQCT-based trabecular bone measures were closer to their reference values as compared to other in vivo CT modalities. Despite large differences in observed values of measures among modalities, high linear correlation (r ∈ [0.94 0.99]) was found between micro-CT and in vivo CT-derived measures of trabecular bone volume, transverse and plate micro-structural volume, and network area. All HR-pQCT-derived trabecular measures, except the erosion index, showed high correlation (r ∈ [0.91 0.99]). The plate-width measure showed a higher correlation (r ∈ [0.72 0.91]) among in vivo and micro-CT modalities than its counterpart binary plate-rod characterization-based measure erosion index (r ∈ [0.65 0.81]). Although a strong correlation was observed between micro-structural measures from in vivo and micro-CT imaging, large shifts in their values for in vivo modalities warrant proper scanner calibration prior to adopting in multi-site and longitudinal studies.
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http://dx.doi.org/10.1088/1361-6560/abc367DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058110PMC
October 2020

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression.

Aging (Albany NY) 2020 07 9;12(13):12622-12647. Epub 2020 Jul 9.

Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.

Tau pathology and brain atrophy are the closest correlate of cognitive decline in Alzheimer's disease (AD). Understanding heterogeneity and longitudinal progression of atrophy during the disease course will play a key role in understanding AD pathogenesis. We propose a framework for longitudinal clustering that simultaneously: 1) incorporates whole brain data, 2) leverages unequal visits per individual, 3) compares clusters with a control group, 4) allows for study confounding effects, 5) provides cluster visualization, 6) measures clustering uncertainty. We used amyloid-β positive AD and negative healthy subjects, three longitudinal structural magnetic resonance imaging scans (cortical thickness and subcortical volume) over two years. We found three distinct longitudinal AD brain atrophy patterns: one typical diffuse pattern (n=34, 47.2%), and two atypical patterns: minimal atrophy (n=23 31.9%) and hippocampal sparing (n=9, 12.5%). We also identified outliers (n=3, 4.2%) and observations with uncertain classification (n=3, 4.2%). The clusters differed not only in regional distributions of atrophy at baseline, but also longitudinal atrophy progression, age at AD onset, and cognitive decline. A framework for the longitudinal assessment of variability in cohorts with several neuroimaging measures was successfully developed. We believe this framework may aid in disentangling distinct subtypes of AD from disease staging.
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http://dx.doi.org/10.18632/aging.103623DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377879PMC
July 2020

Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus.

Front Neurosci 2020 24;14:15. Epub 2020 Jan 24.

Division of Biomedical Imaging, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden.

Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer's disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.
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http://dx.doi.org/10.3389/fnins.2020.00015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081773PMC
January 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.

Eur J Radiol 2020 Jan 18;122:108703. Epub 2019 Oct 18.

Department of Medical Physics, Department of Medical & Health Sciences, Center for Medical Image Science & Visualization (CMIV), Linköping University, S-581 85, Linköping, Sweden. Electronic address:

Purpose: To determine the effect of tube load, model-based iterative reconstruction (MBIR) strength and slice thickness in abdominal CT using visual comparison of multi-planar reconstruction images.

Method: Five image criteria were assessed independently by four radiologists on two data sets at 42- and 98-mAs tube loads for 25 patients examined on a 192-slice dual-source CT scanner. Effect of tube load, MBIR strength, slice thickness and potential dose reduction was estimated with Visual Grading Regression (VGR). Objective image quality was determined by measuring noise (SD), contrast-to-noise (CNR) ratio and noise-power spectra (NPS).

Results: Comparing 42- and 98-mAs tube loads, improved image quality was observed as a strong effect of log tube load regardless of MBIR strength (p < 0.001). Comparing strength 5 to 3, better image quality was obtained for two criteria (p < 0.01), but inferior for liver parenchyma and overall image quality. Image quality was significantly better for slice thicknesses of 2mm and 3mm compared to 1mm, with potential dose reductions between 24%-41%. As expected, with decrease in slice thickness and algorithm strength, the noise power and SD (HU-values) increased, while the CNR decreased.

Conclusion: Increasing slice thickness from 1 mm to 2 mm or 3 mm allows for a possible dose reduction. MBIR strength 5 shows improved image quality for three out of five criteria for 1 mm slice thickness. Increasing MBIR strength from 3 to 5 has diverse effects on image quality. Our findings do not support a general recommendation to replace strength 3 by strength 5 in clinical abdominal CT protocols. However, strength 5 may be used in task-based protocols.
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http://dx.doi.org/10.1016/j.ejrad.2019.108703DOI Listing
January 2020

A Multi-Organ Nucleus Segmentation Challenge.

IEEE Trans Med Imaging 2020 05 23;39(5):1380-1391. Epub 2019 Oct 23.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.
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http://dx.doi.org/10.1109/TMI.2019.2947628DOI Listing
May 2020

Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.

Med Image Anal 2019 12 1;58:101537. Epub 2019 Aug 1.

School of Biomedical Engineering and Imaging Sciences, Kings College London, London, U.K.

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).
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http://dx.doi.org/10.1016/j.media.2019.101537DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839613PMC
December 2019

Image quality and pathology assessment in CT Urography: when is the low-dose series sufficient?

BMC Med Imaging 2019 08 9;19(1):64. Epub 2019 Aug 9.

Department of Medical & Health Sciences, Linköping University, Linköping, Sweden.

Background: Our aim was to compare CT images from native, nephrographic and excretory phases using image quality criteria as well as the detection of positive pathological findings in CT Urography, to explore if the radiation burden to the younger group of patients or patients with negative outcomes can be reduced.

Methods: This is a retrospective study of 40 patients who underwent a CT Urography examination on a 192-slice dual source scanner. Image quality was assessed for four specific renal image criteria from the European guidelines, together with pathological assessment in three categories: renal, other abdominal, and incidental findings without clinical significance. Each phase was assessed individually by three radiologists with varying experience using a graded scale. Certainty scores were derived based on the graded assessments. Statistical analysis was performed using visual grading regression (VGR). The limit for significance was set at p = 0.05.

Results: For visual reproduction of the renal parenchyma and renal arteries, the image quality was judged better for the nephrogram phase (p < 0.001), whereas renal pelvis/calyces and proximal ureters were better reproduced in the excretory phase compared to the native phase (p < 0.001). Similarly, significantly higher certainty scores were obtained in the nephrogram phase for renal parenchyma and renal arteries, but in the excretory phase for renal pelvis/calyxes and proximal ureters. Assessment of pathology in the three categories showed no statistically significant differences between the three phases. Certainty scores for assessment of pathology, however, showed a significantly higher certainty for renal pathology when comparing the native phase to nephrogram and excretory phase and a significantly higher score for nephrographic phase but only for incidental findings.

Conclusion: Visualisation of renal anatomy was as expected with each post-contrast phase showing favourable scores compared to the native phase. No statistically significant differences in the assessment of pathology were found between the three phases. The low-dose CT (LDCT) seems to be sufficient in differentiating between normal and pathological examinations. To reduce the radiation burden in certain patient groups, the LDCT could be considered a suitable alternative as a first line imaging method. However, radiologists should be aware of its limitations.
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http://dx.doi.org/10.1186/s12880-019-0363-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688276PMC
August 2019

Comparison of acquisition protocols for ventilation/perfusion SPECT-a Monte Carlo study.

Phys Med Biol 2019 12 5;64(23):235018. Epub 2019 Dec 5.

Medical Radiation Physics and Nuclear Medicine, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden. Department of Clinical Science, Intervention and Technology (CLINTEC), Division of Functional Imaging and Technology, Karolinska Institutet, Stockholm, Sweden. Author to whom any corrrespondence should be addressed.

One of the most commonly used imaging techniques for diagnosing pulmonary embolism (PE) is ventilation/perfusion (V/P) scintigraphy. The aim of this study was to evaluate the performance of the currently used imaging protocols for V/P single photon emission computed tomography (V/P SPECT) at two nuclear medicine department sites and to investigate the effect of altering important protocol parameters. The Monte Carlo technique was used to simulate 4D digital phantoms with perfusion defects. Six imaging protocols were included in the study and a total of 72 digital patients were simulated. Six dually trained radiologists/nuclear medicine physicians reviewed the images and reported all perfusion mismatch findings. The radiologists also visually graded the image quality. No statistically significant differences in diagnostic performance were found between the studied protocols, but visual grading analysis pointed out one protocol as significantly superior to four of the other protocols. Considering the study results, we have decided to harmonize our clinical protocols for imaging patients with suspected PE. The administered Technegas and macro aggregated albumin activities have been altered, a low energy all purpose collimator is used instead of a low energy high resolution collimator and the acquisition times have been lowered.
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http://dx.doi.org/10.1088/1361-6560/ab36eeDOI Listing
December 2019

Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method.

Phys Med 2019 Apr 27;60:58-65. Epub 2019 Mar 27.

KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden.

Purpose: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy.

Methods: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC).

Results: The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROC = 0.90 vs. AUROC = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values.

Conclusion: A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.
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http://dx.doi.org/10.1016/j.ejmp.2019.03.024DOI Listing
April 2019

Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans.

Phys Med 2018 Oct 22;54:21-29. Epub 2018 Sep 22.

KTH Royal Institute of Technology, Department of Biomedical Engineering and Health Systems, Hälsovägen 11C, SE-14157 Huddinge, Sweden. Electronic address:

Purpose: A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested.

Methods: The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border. Mean intensity changes are computed within each zone, for CT and PET scans separately, and used as image features for tumor response assessment. Doing so, tumors are described by accounting for temporal and spatial changes at the same time. Using linear support vector machines, the new features were tested on 30 non-small cell lung cancer patients who underwent sequential or concurrent chemoradiotherapy. Prediction of 2-years overall survival was based on two PET-CT scans, acquired before the start and during the first 3 weeks of treatment. The predictive power of the newly proposed longitudinal pattern features was compared to that of previously proposed radiomics features and radiobiological parameters.

Results: The highest areas under the receiver operating characteristic curves were 0.98 and 0.93 for patients treated with sequential and concurrent chemoradiotherapy, respectively. Results showed an overall comparable performance with respect to radiomics features and radiobiological parameters.

Conclusions: A novel set of quantitative image features, based on underlying tumor physiology, was computed from PET/CT scans and successfully employed to distinguish between early responders and non-responders to chemoradiotherapy.
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http://dx.doi.org/10.1016/j.ejmp.2018.09.003DOI Listing
October 2018

Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention.

J Med Imaging (Bellingham) 2018 Apr 28;5(2):024006. Epub 2018 Jun 28.

KTH Royal Institute of Technology MTH, Halsovagen, Huddinge, Sweden.

Percutaneous coronary intervention (PCI) uses x-ray images, which may give high radiation dose and high concentrations of contrast media, leading to the risk of radiation-induced injury and nephropathy. These drawbacks can be reduced by using lower doses of x-rays and contrast media, with the disadvantage of noisier PCI images with less contrast. Vessel-edge-preserving convolutional neural networks (CNN) were designed to denoise simulated low x-ray dose PCI images, created by adding artificial noise to high-dose images. Objective functions of the designed CNNs have been optimized to achieve an edge-preserving effect of vessel walls, and the results of the proposed objective functions were evaluated qualitatively and quantitatively. Finally, the proposed CNN-based method was compared with two state-of-the-art denoising methods: K-SVD and block-matching and 3D filtering. The results showed promising performance of the proposed CNN-based method for PCI image enhancement with interesting capabilities of CNNs for real-time denoising and contrast enhancement tasks.
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http://dx.doi.org/10.1117/1.JMI.5.2.024006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021716PMC
April 2018

Changes in brain architecture are consistent with altered fear processing in domestic rabbits.

Proc Natl Acad Sci U S A 2018 07 25;115(28):7380-7385. Epub 2018 Jun 25.

Science for Life Laboratory Uppsala, Department of Medical Biochemistry and Microbiology, Uppsala University, 752 36 Uppsala, Sweden;

The most characteristic feature of domestic animals is their change in behavior associated with selection for tameness. Here we show, using high-resolution brain magnetic resonance imaging in wild and domestic rabbits, that domestication reduced amygdala volume and enlarged medial prefrontal cortex volume, supporting that areas driving fear have lost volume while areas modulating negative affect have gained volume during domestication. In contrast to the localized gray matter alterations, white matter anisotropy was reduced in the corona radiata, corpus callosum, and the subcortical white matter. This suggests a compromised white matter structural integrity in projection and association fibers affecting both afferent and efferent neural flow, consistent with reduced neural processing. We propose that compared with their wild ancestors, domestic rabbits are less fearful and have an attenuated flight response because of these changes in brain architecture.
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http://dx.doi.org/10.1073/pnas.1801024115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6048517PMC
July 2018

Direct estimation of human trabecular bone stiffness using cone beam computed tomography.

Oral Surg Oral Med Oral Pathol Oral Radiol 2018 07 10;126(1):72-82. Epub 2018 Apr 10.

Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden.

Objectives: The aim of this study was to evaluate the possibility of estimating the biomechanical properties of trabecular bone through finite element simulations by using dental cone beam computed tomography data.

Study Design: Fourteen human radius specimens were scanned in 3 cone beam computed tomography devices: 3-D Accuitomo 80 (J. Morita MFG., Kyoto, Japan), NewTom 5 G (QR Verona, Verona, Italy), and Verity (Planmed, Helsinki, Finland). The imaging data were segmented by using 2 different methods. Stiffness (Young modulus), shear moduli, and the size and shape of the stiffness tensor were studied. Corresponding evaluations by using micro-CT were regarded as the reference standard.

Results: The 3-D Accuitomo 80 (J. Morita MFG., Kyoto, Japan) showed good performance in estimating stiffness and shear moduli but was sensitive to the choice of segmentation method. NewTom 5 G (QR Verona, Verona, Italy) and Verity (Planmed, Helsinki, Finland) yielded good correlations, but they were not as strong as Accuitomo 80 (J. Morita MFG., Kyoto, Japan). The cone beam computed tomography devices overestimated both stiffness and shear compared with the micro-CT estimations.

Conclusions: Finite element-based calculations of biomechanics from cone beam computed tomography data are feasible, with strong correlations for the Accuitomo 80 scanner (J. Morita MFG., Kyoto, Japan) combined with an appropriate segmentation method. Such measurements might be useful for predicting implant survival by in vivo estimations of bone properties.
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http://dx.doi.org/10.1016/j.oooo.2018.03.014DOI Listing
July 2018

Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction.

Eur Radiol 2018 Jun 24;28(6):2464-2473. Epub 2018 Jan 24.

Department of Medical Physics, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.

Purpose: To estimate potential dose reduction in abdominal CT by visually comparing images reconstructed with filtered back projection (FBP) and strengths of 3 and 5 of a specific MBIR.

Material And Methods: A dual-source scanner was used to obtain three data sets each for 50 recruited patients with 30, 70 and 100% tube loads (mean CTDI 1.9, 3.4 and 6.2 mGy). Six image criteria were assessed independently by five radiologists. Potential dose reduction was estimated with Visual Grading Regression (VGR).

Results: Comparing 30 and 70% tube load, improved image quality was observed as a significant strong effect of log tube load and reconstruction method with potential dose reduction relative to FBP of 22-47% for MBIR strength 3 (p < 0.001). For MBIR strength 5 no dose reduction was possible for image criteria 1 (liver parenchyma), but dose reduction between 34 and 74% was achieved for other criteria. Interobserver reliability showed agreement of 71-76% (κ 0.201-0.286) and intra-observer reliability of 82-96% (κ 0.525-0.783).

Conclusion: MBIR showed improved image quality compared to FBP with positive correlation between MBIR strength and increasing potential dose reduction for all but one image criterion.

Key Points: • MBIR's main advantage is its de-noising properties, which facilitates dose reduction. • MBIR allows for potential dose reduction in relation to FBP. • Visual Grading Regression (VGR) produces direct numerical estimates of potential dose reduction. • MBIR strengths 3 and 5 dose reductions were 22-34 and 34-74%. • MBIR strength 5 demonstrates inferior performance for liver parenchyma.
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http://dx.doi.org/10.1007/s00330-017-5113-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938296PMC
June 2018

Quantitative Measurements Versus Receiver Operating Characteristics and Visual Grading Regression in CT Images Reconstructed with Iterative Reconstruction: A Phantom Study.

Acad Radiol 2018 Apr 29;25(4):509-518. Epub 2017 Nov 29.

Department of Diagnostic Physics, Oslo University Hospital, P.O. Box, 0454 Oslo, Norway; Department of Physics, University of Oslo, P. O. Box 1048 Blindern, N-0316 Oslo, Norway.

Rationale And Objectives: This study aimed to evaluate the correlation of quantitative measurements with visual grading regression (VGR) and receiver operating characteristics (ROC) analysis in computed tomography (CT) images reconstructed with iterative reconstruction.

Materials And Methods: CT scans on a liver phantom were performed on CT scanners from GE, Philips, and Toshiba at three dose levels. Images were reconstructed with filtered back projection (FBP) and hybrid iterative techniques (ASiR, iDose, and AIDR 3D of different strengths). Images were visually assessed by five readers using a four- and five-grade ordinal scale for liver low contrast lesions and for 10 image quality criteria. The results were analyzed with ROC and VGR. Standard deviation, signal-to-noise ratios, and contrast-to-noise ratios were measured in the images.

Results: All data were compared to FBP. The results of the quantitative measurements were improved for all algorithms. ROC analysis showed improved lesion detection with ASiR and AIDR and decreased lesion detection with iDose. VGR found improved noise properties for all algorithms, increased sharpness with iDose and AIDR, and decreased artifacts from the spine with AIDR, whereas iDose increased the artifacts from the spine. The contrast in the spine decreased with ASiR and iDose.

Conclusions: Improved quantitative measurements in images reconstructed with iterative reconstruction compared to FBP are not equivalent to improved diagnostic image accuracy.
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http://dx.doi.org/10.1016/j.acra.2017.10.020DOI Listing
April 2018

Quantitative MRI for analysis of peritumoral edema in malignant gliomas.

PLoS One 2017 23;12(5):e0177135. Epub 2017 May 23.

Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.

Background And Purpose: Damage to the blood-brain barrier with subsequent contrast enhancement is a hallmark of glioblastoma. Non-enhancing tumor invasion into the peritumoral edema is, however, not usually visible on conventional magnetic resonance imaging. New quantitative techniques using relaxometry offer additional information about tissue properties. The aim of this study was to evaluate longitudinal relaxation R1, transverse relaxation R2, and proton density in the peritumoral edema in a group of patients with malignant glioma before surgery to assess whether relaxometry can detect changes not visible on conventional images.

Methods: In a prospective study, 24 patients with suspected malignant glioma were examined before surgery. A standard MRI protocol was used with the addition of a quantitative MR method (MAGIC), which measured R1, R2, and proton density. The diagnosis of malignant glioma was confirmed after biopsy/surgery. In 19 patients synthetic MR images were then created from the MAGIC scan, and ROIs were placed in the peritumoral edema to obtain the quantitative values. Dynamic susceptibility contrast perfusion was used to obtain cerebral blood volume (rCBV) data of the peritumoral edema. Voxel-based statistical analysis was performed using a mixed linear model.

Results: R1, R2, and rCBV decrease with increasing distance from the contrast-enhancing part of the tumor. There is a significant increase in R1 gradient after contrast agent injection (P < .0001). There is a heterogeneous pattern of relaxation values in the peritumoral edema adjacent to the contrast-enhancing part of the tumor.

Conclusion: Quantitative analysis with relaxometry of peritumoral edema in malignant gliomas detects tissue changes not visualized on conventional MR images. The finding of decreasing R1 and R2 means shorter relaxation times closer to the tumor, which could reflect tumor invasion into the peritumoral edema. However, these findings need to be validated in the future.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0177135PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5441583PMC
September 2017

Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation.

J Med Imaging (Bellingham) 2017 Apr 28;4(2):024004. Epub 2017 Apr 28.

KTH Royal Institute of Technology, School of Technology and Health, Stockholm, Sweden.

Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70 s, and the technique was successful also in detecting very distal artery branches, e.g., in the foot.
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http://dx.doi.org/10.1117/1.JMI.4.2.024004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408161PMC
April 2017

A Study of Coronary Bifurcation Shape in a Normal Population.

J Cardiovasc Transl Res 2017 Feb 27;10(1):82-90. Epub 2016 Dec 27.

Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.

During percutaneous coronary intervention, stents are placed in narrowings of the arteries to restore normal blood flow. Despite improvements in stent design, deployment techniques and drug-eluting coatings, restenosis and stent thrombosis remain a significant problem. Population stent design based on statistical shape analysis may improve clinical outcomes. Computed tomographic (CT) coronary angiography scans from 211 patients with a zero calcium score, no stenoses and no intermediate artery, were used to create statistical shape models of 446 major coronary artery bifurcations (left main, first diagonal and obtuse marginal and right coronary crux). Coherent point drift was used for registration. Principal component analysis shape scores were tested against clinical risk factors, quantifying the importance of recognised shape features in intervention including size, angles and curvature. Significant differences were found in (1) vessel size and bifurcation angle between the left main and other bifurcations; (2) inlet and curvature angle between the right coronary crux and other bifurcations; and (3) size and bifurcation angle by sex. Hypertension, smoking history and diabetes did not appear to have an association with shape. Physiological diameter laws were compared, with the Huo-Kassab model having the best fit. Bifurcation coronary anatomy can be partitioned into clinically meaningful modes of variation showing significant shape differences. A computational atlas of normal coronary bifurcation shape, where disease is common, may aid in the design of new stents and deployment techniques, by providing data for bench-top testing and computational modelling of blood flow and vessel wall mechanics.
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http://dx.doi.org/10.1007/s12265-016-9720-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5323506PMC
February 2017

A computational atlas of normal coronary artery anatomy.

EuroIntervention 2016 Sep;12(7):845-54

University of Auckland, Auckland, New Zealand.

Aims: The aim of this study was to define the shape variations, including diameters and angles, of the major coronary artery bifurcations.

Methods And Results: Computed tomographic angiograms from 300 adults with a zero calcium score and no stenoses were segmented for centreline and luminal models. A computational atlas was constructed enabling automatic quantification of 3D angles, diameters and lengths of the coronary tree. The diameter (mean±SD) of the left main coronary was 3.5±0.8 mm and the length 10.5±5.3 mm. The left main bifurcation angle (distal angle or angle B) was 89±21° for cases with, and 75±23° for those without an intermediate artery (p<0.001). Analogous measurements of diameter and angle were tabulated for the other major bifurcations (left anterior descending/diagonal, circumflex/obtuse marginal and right coronary crux). Novel 3D angle definitions are proposed and analysed.

Conclusions: A computational atlas of normal coronary artery anatomy provides distributions of diameter, lengths and bifurcation angles as well as more complex shape analysis. These data define normal anatomical variation, facilitating stent design, selection and optimal treatment strategy. These population models are necessary for accurate computational flow dynamics, can be 3D printed for bench testing bifurcation stents and deployment strategies, and can aid in the discussion of different approaches to the treatment of coronary bifurcations.
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http://dx.doi.org/10.4244/EIJV12I7A139DOI Listing
September 2016

An Investigation of Fat Infiltration of the Multifidus Muscle in Patients With Severe Neck Symptoms Associated With Chronic Whiplash-Associated Disorder.

J Orthop Sports Phys Ther 2016 Oct 2;46(10):886-893. Epub 2016 Sep 2.

Study Design Cross-sectional study. Background Findings of fat infiltration in cervical spine multifidus, as a sign of degenerative morphometric changes due to whiplash injury, need to be verified. Objectives To develop a method using water/fat magnetic resonance imaging (MRI) to investigate fat infiltration and cross-sectional area of multifidus muscle in individuals with whiplash-associated disorders (WADs) compared to healthy controls. Methods Fat infiltration and cross-sectional area in the multifidus muscles spanning the C4 to C7 segmental levels were investigated by manual segmentation using water/fat-separated MRI in 31 participants with WAD and 31 controls, matched for age and sex. Results Based on average values for data spanning C4 to C7, participants with severe disability related to WAD had 38% greater muscular fat infiltration compared to healthy controls (P = .03) and 45% greater fat infiltration compared to those with mild to moderate disability related to WAD (P = .02). There were no significant differences between those with mild to moderate disability and healthy controls. No significant differences between groups were found for multifidus cross-sectional area. Significant differences were observed for both cross-sectional area and fat infiltration between segmental levels. Conclusion Participants with severe disability after a whiplash injury had higher fat infiltration in the multifidus compared to controls and to those with mild/moderate disability secondary to WAD. Earlier reported findings using T1-weighted MRI were reproduced using refined imaging technology. The results of the study also indicate a risk when segmenting single cross-sectional slices, as both cross-sectional area and fat infiltration differ between cervical levels. J Orthop Sports Phys Ther 2016;46(10):886-893. Epub 2 Sep 2016. doi:10.2519/jospt.2016.6553.
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http://dx.doi.org/10.2519/jospt.2016.6553DOI Listing
October 2016

Predicting Trabecular Bone Stiffness from Clinical Cone-Beam CT and HR-pQCT Data; an In Vitro Study Using Finite Element Analysis.

PLoS One 2016 11;11(8):e0161101. Epub 2016 Aug 11.

Department of Medical and Health Science, Division of Radiology, Linköping University, Linköping, Sweden.

Stiffness and shear moduli of human trabecular bone may be analyzed in vivo by finite element (FE) analysis from image data obtained by clinical imaging equipment such as high resolution peripheral quantitative computed tomography (HR-pQCT). In clinical practice today, this is done in the peripheral skeleton like the wrist and heel. In this cadaveric bone study, fourteen bone specimens from the wrist were imaged by two dental cone beam computed tomography (CBCT) devices and one HR-pQCT device as well as by dual energy X-ray absorptiometry (DXA). Histomorphometric measurements from micro-CT data were used as gold standard. The image processing was done with an in-house developed code based on the automated region growing (ARG) algorithm. Evaluation of how well stiffness (Young's modulus E3) and minimum shear modulus from the 12, 13, or 23 could be predicted from the CBCT and HR-pQCT imaging data was studied and compared to FE analysis from the micro-CT imaging data. Strong correlations were found between the clinical machines and micro-CT regarding trabecular bone structure parameters, such as bone volume over total volume, trabecular thickness, trabecular number and trabecular nodes (varying from 0.79 to 0.96). The two CBCT devices as well as the HR-pQCT showed the ability to predict stiffness and shear, with adjusted R2-values between 0.78 and 0.92, based on data derived through our in-house developed code based on the ARG algorithm. These findings indicate that clinically used CBCT may be a feasible method for clinical studies of bone structure and mechanical properties in future osteoporosis research.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0161101PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981445PMC
August 2017

Erratum: Superficial vessel reconstruction with a Multiview camera system.

J Med Imaging (Bellingham) 2016 Jan 15;3(1):019801. Epub 2016 Feb 15.

Linköping University, Center for Medical Image Science and Visualization, Campus US, Linköping SE-581 85, Sweden; Linköping University, Department of Science and Technology-Media and Information Technology, Campus Norrköping, Norrköping SE-601 74, Sweden; Linköping University, Department of Medical and Health Sciences, Campus US, Linköping SE-581 85, Sweden; Royal Institute of Technology, School of Technology and Health, Alfred Nobels Allé 10, Huddinge SE-141 52, Sweden.

[This corrects the article DOI: 10.1117/1.JMI.3.1.015001.].
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http://dx.doi.org/10.1117/1.JMI.3.1.019801DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4753828PMC
January 2016

Superficial vessel reconstruction with a multiview camera system.

J Med Imaging (Bellingham) 2016 Jan 5;3(1):015001. Epub 2016 Jan 5.

Linköping University, Center for Medical Image Science and Visualization, Campus US, Linköping SE-581 85, Sweden; Linköping University, Department of Science and Technology-Media and Information Technology, Campus Norrköping, Norrköping SE-601 74, Sweden; Linköping University, Department of Medical and Health Sciences, Campus US, Linköping SE-581 85, Sweden; Royal Institute of Technology, School of Technology and Health, Alfred Nobels Allé 10, Huddinge SE-141 52, Sweden.

We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformation methods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstruction using three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlines are manually selected in the images. Using the properties of the Hessian matrix, the centerline points are assigned direction information. For correspondence matching, a combination of methods was used. The process starts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labeling and an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline with decreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation in virtual, phantom, and experimental images, including intraoperative data from patient experiments, shows that, with appropriate camera positions, the error estimates (root-mean square error and mean error) are [Formula: see text].
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http://dx.doi.org/10.1117/1.JMI.3.1.015001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700519PMC
January 2016

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans.

Comput Intell Neurosci 2015 2;2015:813696. Epub 2015 Dec 2.

Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands.

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.
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http://dx.doi.org/10.1155/2015/813696DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4680055PMC
September 2016

Visualization of liver lesions in standardized video-documented ultrasonography - inter-observer agreement and effect of contrast injection.

Med Ultrason 2015 Dec;17(4):437-43

Department of Radiology and Department of Medical and Health Sciences; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.

Unlabelled: The AIM of this study was to evaluate the inter-observer agreement and effect of contrast injection on the visibility of liver lesions by radiologists reviewing ultrasound examinations acquired by a radiographer using a standardized examination protocol.

Material And Method: A retrospective review was conducted by two radiologists, independently of each other, of 115 ultrasound examinations of the liver with standardized examination protocols between January 2008 and December 2012. All patients included in the study had undergone surgery for colorectal cancer. Patients attending the two-year follow-up were included.

Results: Focal findings, the most common of which were cysts, were seen in 42-43 out of the 115 patients before intravenous contrast and in 46-47 patients after intravenous contrast (p=0.012). The inter-observer agreement for focal findings was 86.1% before contrast, and 90.4% after contrast (n.s.), and the corresponding kappa values were 0.72 and 0.84, respectively.

Conclusions: A good inter-observer agreement between two radiologists reviewing ultrasound examinations (standardized ultrasound cine-loop method acquired by a radiographer) after surgery for colorectal cancer was obtained. Injection of contrast medium increased the visibility of liver lesions.
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http://dx.doi.org/10.11152/mu.2013.2066.174.visDOI Listing
December 2015

Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.

IEEE Trans Med Imaging 2016 Apr 25;35(4):967-77. Epub 2015 Nov 25.

Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.
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http://dx.doi.org/10.1109/TMI.2015.2503890DOI Listing
April 2016

Regression models for analyzing radiological visual grading studies--an empirical comparison.

BMC Med Imaging 2015 Oct 30;15:49. Epub 2015 Oct 30.

Department of Medical and Health Sciences (IMH), Linköping University, Linköping, Sweden.

Background: For optimizing and evaluating image quality in medical imaging, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading data, several regression methods are available, and this study aimed at empirically comparing such techniques, in particular when including random effects in the models, which is appropriate for observers and patients.

Methods: Data were taken from a previous study where 6 observers graded or ranked in 40 patients the image quality of four imaging protocols, differing in radiation dose and image reconstruction method. The models tested included linear regression, the proportional odds model for ordinal logistic regression, the partial proportional odds model, the stereotype logistic regression model and rank-order logistic regression (for ranking data). In the first two models, random effects as well as fixed effects could be included; in the remaining three, only fixed effects.

Results: In general, the goodness of fit (AIC and McFadden's Pseudo R (2)) showed small differences between the models with fixed effects only. For the mixed-effects models, higher AIC and lower Pseudo R (2) was obtained, which may be related to the different number of parameters in these models. The estimated potential for dose reduction by new image reconstruction methods varied only slightly between models.

Conclusions: The authors suggest that the most suitable approach may be to use ordinal logistic regression, which can handle ordinal data and random effects appropriately.
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http://dx.doi.org/10.1186/s12880-015-0083-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4627379PMC
October 2015
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