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1.
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2.
  • Smedby, Örjan, 1956-, et al. (författare)
  • Functional imaging of the thoracic outlet syndrome in an open MR scanner
  • 2000
  • Ingår i: European Radiology. - 0938-7994 .- 1432-1084. ; 10:4, s. 597-600
  • Tidskriftsartikel (refereegranskat)abstract
    • Symptoms due to thoracic outlet syndrome may present only in abduction, a position that cannot be investigated in conventional MR scanners. Therefore, this study was initiated to test MRI in an open magnet as a method for diagnosis of thoracic outlet syndrome. Ten volunteers and 7 patients with a clinical suspicion of thoracic outlet syndrome were investigated at 0.5 T in an open MR scanner. Sagittal 3D SPGR acquisitions were made in 0 and 90░abduction. In the patients, a similar data set was also obtained in maximal abduction. To assess compression, the minimum distance between the first rib and the clavicle, measured in a sagittal plane, was determined. In the neutral position, no significant difference was found between patients and controls. In 90░abduction, the patients had significantly smaller distance between rib and clavicle than the controls (14 vs 29 mm, p <0.01). On coronal reformatted images, the compression of the brachial plexus could often be visualised in abduction. Functional MR examination seems to be a useful diagnostic tool in thoracic outlet syndrome. Examination in abduction, which is feasible in an open scanner, is essential for the diagnosis.
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3.
  • Andersson, Thord, et al. (författare)
  • Consistent intensity inhomogeneity correction in water–fat MRI
  • 2015
  • Ingår i: Journal of Magnetic Resonance Imaging. - : John Wiley & Sons. - 1053-1807 .- 1522-2586. ; 42:2, s. 468-476
  • Tidskriftsartikel (refereegranskat)abstract
    • PURPOSE:To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.RESULTS:CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).CONCLUSION:CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type. J. Magn. Reson. Imaging 2014.
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4.
  • Astaraki, Mehdi, PhD Student, 1984-, et al. (författare)
  • Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features
  • 2021
  • Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 83, s. 146-153
  • Tidskriftsartikel (refereegranskat)abstract
    • 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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5.
  • Astaraki, Mehdi, et al. (författare)
  • Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
  • 2019
  • Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 60, s. 58-65
  • Tidskriftsartikel (refereegranskat)abstract
    • PurposeTo 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.MethodsLongitudinal 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).ResultsThe proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROCSALoP = 0.90 vs. AUROCradiomic = 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.ConclusionA 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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6.
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7.
  • Blystad, Ida, 1972-, et al. (författare)
  • Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema
  • 2020
  • Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 10:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R1), transverse relaxation (R2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R1-difference-map. The quantitative R1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.
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8.
  • Brusini, Irene, et al. (författare)
  • Changes in brain architecture are consistent with altered fear processing in domestic rabbits
  • 2018
  • Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 115:28, s. 7380-7385
  • Tidskriftsartikel (refereegranskat)abstract
    • 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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9.
  • Brusini, Irene, et al. (författare)
  • Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus
  • 2020
  • Ingår i: Frontiers in Neuroscience. - : Frontiers Media S.A.. - 1662-4548 .- 1662-453X. ; 14
  • Tidskriftsartikel (refereegranskat)abstract
    • 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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10.
  • Buizza, Giulia, et al. (författare)
  • Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans
  • 2018
  • Ingår i: Physica medica (Testo stampato). - : ELSEVIER SCI LTD. - 1120-1797 .- 1724-191X. ; 54, s. 21-29
  • Tidskriftsartikel (refereegranskat)abstract
    • 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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