To Örebro University

oru.seÖrebro University Publications
Change search
Refine search result
1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Ahlander, Britt-Marie
    Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
    Magnetic Resonance Imaging of the Heart: Image quality, measurement accuracy and patient experience2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Background: Non-invasive diagnostic imaging of atherosclerotic coronary artery disease (CAD) is frequently carried out with cardiovascular magnetic resonance imaging (CMR) or myocardial perfusion single photon emission computed tomography (MPS). CMR is the gold standard for the evaluation of scar after myocardial infarction and MPS the clinical gold standard for ischemia. Magnetic Resonance Imaging (MRI) is at times difficult for patients and may induce anxiety while patient experience of MPS is largely unknown.

    Aims: To evaluate image quality in CMR with respect to the sequences employed, the influence of atrial fibrillation, myocardial perfusion and the impact of patient information. Further, to study patient experience in relation to MRI with the goal of improving the care of these patients.

    Method: Four study designs have been used. In paper I, experimental cross-over, paper (II) experimental controlled clinical trial, paper (III) psychometric crosssectional study and paper (IV) prospective intervention study. A total of 475 patients ≥ 18 years with primarily cardiac problems (I-IV) except for those referred for MRI of the spine (III) were included in the four studies.

    Result: In patients (n=20) with atrial fibrillation, a single shot steady state free precession (SS-SSFP) sequence showed significantly better image quality than the standard segmented inversion recovery fast gradient echo (IR-FGRE) sequence (I). In first-pass perfusion imaging the gradient echo-echo planar imaging sequence (GREEPI) (n=30) had lower signal-to-noise and contrast–to-noise ratios than the steady state free precession sequence (SSFP) (n=30) but displayed a higher correlation with the MPS results, evaluated both qualitatively and quantitatively (II). The MRIAnxiety Questionnaire (MRI-AQ) was validated on patients, referred for MRI of either the spine (n=193) or the heart (n=54). The final instrument had 15 items divided in two factors regarding Anxiety and Relaxation. The instrument was found to have satisfactory psychometric properties (III). Patients who prior CMR viewed an information video scored significantly (lower) better in the factor Relaxation, than those who received standard information. Patients who underwent MPS scored lower on both factors, Anxiety and Relaxation. The extra video information had no effect on CMR image quality (IV).

    Conclusion: Single shot imaging in atrial fibrillation produced images with less artefact than a segmented sequence. In first-pass perfusion imaging, the sequence GRE-EPI was superior to SSFP. A questionnaire depicting anxiety during MRI showed that video information prior to imaging helped patients relax but did not result in an improvement in image quality.

    Download full text (pdf)
    Magnetic Resonance Imaging of the Heart: Image quality, measurement accuracy and patient experience
    Download (pdf)
    COVER01
  • 2.
    Andersén, Christoffer
    et al.
    Örebro University, School of Medical Sciences.
    Rydén, Tobias
    Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Thunberg, Per
    Örebro University, School of Medical Sciences.
    Heydorn Lagerlöf, Jakob
    Örebro University Hospital. Örebro University, School of Medical Sciences. Department of Medical Physics, Karlstad Central Hospital, Karlstad, Sweden.
    Presults for the aI-Brachy study: Utilizing deep learning for needle reconstruction in prostate brachytherapy2019Conference paper (Refereed)
    Abstract [en]

    Purpose To develop a deep neural network for automatic reconstruction of needles in ultrasound images depicting the prostate during brachytherapy treatment of prostate cancer.

    Methods Ultrasound tomographies of the prostate from 907 treatments were used to train the artificial intelligent (AI) algorithm. The image matrices were downsampled to 128x128x128 and were used as in-data when training the AI, a 27 layer convolutional neural network. The needles were identified manually by medical physicists using conventional software. These reconstructions were used as gold standard when training the algorithm. An additional set of examinations were used for validation where the needle reconstructions by the AI were compared to the manual reconstructions. The root mean square deviation (RMSD) of needle position, including the central part (70 slices) of the needle was measured in order to avoid influence from artefacts around the needle tip. The result was also evaluated through visual inspection (see image). The times spent for manual vs. AI reconstruction were compared.

    Results RMSD for manual vs. AI reconstruction is on average (n=170) 1.18±1.0mm, whereas the difference between two manual operators is 0.02±0.06mm, which suggests that the AI is inferior to manual operators. The visual inspection, however, shows AI to be very accurate in positioning the needles. Manual reconstruction took approximately 11.0 minutes, whereas the time for the trained AI is negligible in comparison. Worth noticing regarding RMSD calculations is that, due to limited image resolution, small values may be under-estimated, hence overestimating the difference between the reconstruction methods.

    Conclusions The study implies that an AI may reconstruct needles for brachytherapy treatments of prostate cancer. The larger deviations between AI algorithm and manual operators, compared to between human operators appears to disagree with the high accuracy of the visual evaluation. However, visually, manual needle reconstructions appear to deviate more from the ultrasound image than do the AI reconstructions. This discrepancy is mainly caused by manual reconstruction software assuming straight needles, unlike the AI. We conclude that AI gives the opportunity to save a substantial amount of treatment planning time, when the patient is anesthetised. Further studies are needed to determine whether different reconstruction methods impact treatment plans.

  • 3.
    Billah, Mohammad Ehtasham
    et al.
    School of Business, Örebro University, Örebro, Sweden.
    Javed, Farrukh
    Örebro University, Örebro University School of Business.
    Bayesian Convolutional Neural Network-based Models for Diagnosis of Blood Cancer2022In: Applied Artificial Intelligence, ISSN 0883-9514, E-ISSN 1087-6545, Vol. 36, no 1Article in journal (Refereed)
    Abstract [en]

    Deep learning methods allow computational models involving multiple processing layers to discover intricate structures in data sets. Classifying an image is one such problem where these methods are found to be very useful. Although different approaches have been proposed in the literature, this paper illustrates a successful implementation of the Bayesian Convolution Neural Networks (BCNN)-based classification procedure to classify microscopic images of blood samples (lymphocyte cells) without involving manual feature extractions. The data set contains 260 microscopic images of cancerous and noncancerous lymphocyte cells. We experiment with different network structures and obtain the model that returns the lowest error rate in classifying the images. Our developed models not only produce high accuracy in classifying cancerous and noncancerous lymphocyte cells but also provide useful information regarding uncertainty in predictions.

  • 4.
    Feng, X. M.
    et al.
    Department of Microbiology, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden.
    Olsson, J.
    Centre for Human Studies of Foodstuffs, Uppsala University, Uppsala, Sweden.
    Swanberg, M.
    Department of Microbiology, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden.
    Schnürer, Johan
    Department of Microbiology, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden.
    Rönnow, D.
    Department of Electronics, University of Gävle, Gävle, Sweden.
    Image analysis for monitoring the barley tempeh fermentation process2007In: Journal of Applied Microbiology, ISSN 1364-5072, E-ISSN 1365-2672, Vol. 103, no 4, p. 1113-1121Article in journal (Refereed)
    Abstract [en]

    Aims: To develop a fast, accurate, objective and nondestructive method for monitoring barley tempeh fermentation.

    Methods and Results: Barley tempeh is a food made from pearled barley grains fermented with Rhizopus oligosporus. Rhizopus oligosporus growth is important for tempeh quality, but quantifying its growth is difficult and laborious. A system was developed for analysing digital images of fermentation stages using two image processing methods. The first employed statistical measures sensitive to image colour and surface structure, and these statistical measures were highly correlated (r = 0.92, n = 75, P < 0.001) with ergosterol content of tempeh fermented with R. oligosporus and lactic acid bacteria (LAB). In the second method, an image-processing algorithm optimized to changes in images of final tempeh products was developed to measure number of visible barley grains. A threshold of 5 visible grains per Petri dish indicated complete tempeh fermentation. When images of tempeh cakes fermented with different inoculation levels of R. oligosporus were analysed the results from the two image processing methods were in good agreement.

    Conclusion: Image processing proved suitable for monitoring barley tempeh fermentation. The method avoids sampling, is nonintrusive, and only requires a digital camera with good resolution and image analysis software.

    Significance and Impact of the Study: The system provides a rapid visualization of tempeh product maturation and qualities during fermentation. Automated online monitoring of tempeh fermentation by coupling automated image acquisition with image processing software could be further developed for process control.

  • 5.
    Jendeberg, Johan
    et al.
    Örebro University, School of Medical Sciences. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Thunberg, Per
    Örebro University, School of Medical Sciences. Örebro University Hospital. Department of Medical Physics.
    Lidén, Mats
    Örebro University, School of Medical Sciences. Örebro University Hospital. Department of Radiology.
    Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network2021In: Urolithiasis, ISSN 2194-7228, Vol. 49, p. 41-49Article in journal (Refereed)
    Abstract [en]

    The objectives were to develop and validate a Convolutional Neural Network (CNN) using local features for differentiating distal ureteral stones from pelvic phleboliths, compare the CNN method with a semi-quantitative method and with radiologists' assessments and to evaluate whether the assessment of a calcification and its local surroundings is sufficient for discriminating ureteral stones from pelvic phleboliths in non-contrast-enhanced CT (NECT). We retrospectively included 341 consecutive patients with acute renal colic and a ureteral stone on NECT showing either a distal ureteral stone, a phlebolith or both. A 2.5-dimensional CNN (2.5D-CNN) model was used, where perpendicular axial, coronal and sagittal images through each calcification were used as input data for the CNN. The CNN was trained on 384 calcifications, and evaluated on an unseen dataset of 50 stones and 50 phleboliths. The CNN was compared to the assessment by seven radiologists who reviewed a local 5 × 5 × 5 cm image stack surrounding each calcification, and to a semi-quantitative method using cut-off values based on the attenuation and volume of the calcifications. The CNN differentiated stones and phleboliths with a sensitivity, specificity and accuracy of 94%, 90% and 92% and an AUC of 0.95. This was similar to a majority vote accuracy of 93% and significantly higher (p = 0.03) than the mean radiologist accuracy of 86%. The semi-quantitative method accuracy was 49%. In conclusion, the CNN differentiated ureteral stones from phleboliths with higher accuracy than the mean of seven radiologists' assessments using local features. However, more than local features are needed to reach optimal discrimination.

  • 6.
    Khodadad, Davood
    et al.
    Tehran University of Medical Science, Tehran, Islamic Republic of Iran.
    Ahmadian, Alireza
    Tehran University of Medical Science, Tehran, Islamic Republic of Iran.
    Ay, Mohammadreza
    Tehran University of Medical Science, Tehran, Islamic Republic of Iran.
    Esfahani, Armaghan Fard
    Tehran University of Medical Science, Tehran, Islamic Republic of Iran.
    Banaem, Hossein Yousefi
    Tehran University of Medical Science, Tehran, Islamic Republic of Iran.
    Zaidi, Habib
    Geneva University Hospital, Geneva, Switzerland.
    B-spline based free form deformation thoracic non-rigid registration of CT and PET images2011In: International Conference on Graphic and Image Processing (ICGIP 2011) / [ed] Yi Xie & Yanjun Zheng, SPIE - International Society for Optical Engineering, 2011, Vol. 8285, article id 82851KConference paper (Refereed)
    Abstract [en]

    Accurate attenuation correction of emission data is mandatory for quantitative analysis of PET images. One of the main concerns in CT-based attenuation correction(CTAC) of PET data in multimodality PET/CT imaging is misalignment between PET and CT images. The aim of this study, is to proposed a hybrid method which is simple, fast and accurate, for registration of PET and CT data which affected from respiratory motion in order to improve the quality of CTAC. The algorithm is composed of three methods: First, using B-spline Free Form Deformation to describe both images and deformation field. Then applying a pre-filtering on both PET and CT images before segmentation of structures in order to reduce the respiratory related attenuation correction artifacts of PET emission data. In this approach, B-spline using FFD provide more accurate adaptive transformation to align the images, and structure constraints obtained from prefiltering applied to guide the algorithm to be more fast and accurate. Also it helps to reduce the radiation dose in PET/CT by avoiding repetition of CT imaging. These advances increase the potential of the method for routine clinical application.

  • 7.
    Khodadad, Davood
    et al.
    Experimental Mechanics, Luleå University of Technology, Luleå, Sweden.
    Ahmadian, Alireza
    Institute for Advanced Medical Technologies (IAMT), Tehran University of Medical Science, Tehran, Iran.
    Banaem, Hossein Yousefi
    Department of Medical Physics and Medical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran.
    Ay, Mohammad Reza
    Research Institute for Nuclear Medicine, Tehran University of Medical Science, Tehran, Iran .
    Fard-Esfahani, Armaghan
    Research Institute for Nuclear Medicine, Tehran University of Medical Science, Tehran, Iran .
    CT and PET Image Registration: Application to Thorax Area2013In: Journal of Image and Graphics, ISSN 2301-3699, Vol. 1, no 4, p. 171-175Article in journal (Refereed)
    Abstract [en]

    Accurate attenuation correction of emission data is mandatory for quantitative analysis of PET images. One of the main concerns in CT-based attenuation correction (CTAC) of PET data in multimodality PET/CT imaging is misalignment occurred due to respiratory artifact between PET and CT images. In this paper a combined method which is simple and fast is proposed for registration of PET and CT data to correct the effect of this artifact. The algorithm is composed of two step: First step is meant to reduce the noise by applying an adaptive gradient anistropic diffusion filter then using Iterative closest point (ICP) registration method in order to obtain initial estimation to ensure fast and accurate convergence of the algorithm. At the second step, the respiratory related artifact of PET images is greatly reduced by employing Free Form Deformation algorithm based on B-spline which provides more accurate adaptive transformation to align the images.

  • 8.
    Khodadad, Davood
    et al.
    Department of Physics and Electrical Engineering, Linnaeus University, Växjö, Sweden.
    Nordebo, Sven
    Department of Physics and Electrical Engineering, Linnaeus University, Växjö, Sweden.
    Seifnaraghi, Nima
    Faculty of science and technology, Middlesex University, Hendon campus, The Burroughs, London, United Kingdom.
    Waldmann, Andreas D.
    Swisstom AG, Landquart, Switzerland.
    Müller, Beat
    Swisstom AG, Landquart, Switzerland.
    Bayford, Richard
    Faculty of science and technology, Middlesex University, Hendon campus, The Burroughs, London, United Kingdom.
    Breath detection using short-time Fourier transform analysis in electrical impedance tomography2017In: 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), IEEE, 2017, p. 1-3Conference paper (Refereed)
    Abstract [en]

    Spectral analysis based on short-time Fourier transform (STFT) using Kaiser window is proposed to examine the frequency components of neonates EIT data. In this way, a simultaneous spatial-time-frequency analysis is achieved.

  • 9.
    Lidén, Mats
    Örebro University Hospital. Örebro University, School of Medical Sciences. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    A new method for predicting uric acid composition in urinary stones using routine single-energy CT2018In: Urolithiasis, ISSN 2194-7228, Vol. 46, no 4, p. 325-332Article in journal (Refereed)
    Abstract [en]

    Urinary stones composed of uric acid can be treated medically. Prediction of uric acid stone type is, therefore, desirable when a urinary stone is diagnosed with unenhanced CT. The purpose of the present study was to describe single-energy thin slice quantitative CT parameters of urinary stones correlated to chemical stone type and to develop a method to distinguish pure uric acid stones (UA) from other stones (non-UA/Mix). Unenhanced thin slice single-energy CT images of 126 urinary stones (117 patients) with known chemical stone type were retrospectively included in the study. Among the included stones, 22 were UA and 104 were non-UA/Mix. The included CT images and Laplacian filtered images of the stones were quantitatively analyzed using operator-independent methods. A post hoc classification method for pure UA stones was created using a combination of cutoff values for the peak attenuation and peak point Laplacian. The stone types differed in most quantitative image characteristics including mean attenuation (p < 0.001), peak attenuation (p < 0.001), and peak point Laplacian (p < 0.001). The sensitivity for the post hoc-developed peak attenuation-peak point Laplacian method for classifying pure UA stones was 95% [21/22, 95% CI (77-100%)] and the specificity was 99% [103/104, 95% CI (95-100%)]. In conclusion, quantitative image analysis of thin slice routine single-energy CT images is promising for predicting pure UA content in urinary stones, with results comparable to double energy methods.

  • 10.
    Yousefi-Banaem, Hossein
    et al.
    Department of Biomedical Engineering, Faculty of Advance Medical Technology, Isfahan University of Medical Science, Isfahan Iran.
    Kermani, Saeed
    Department of Biomedical Engineering, Faculty of Advance Medical Technology, Isfahan University of Medical Science, Isfahan Iran.
    Sarrafzadeh, Omid
    Department of Biomedical Engineering, Faculty of Advance Medical Technology, Isfahan University of Medical Science, Isfahan Iran.
    Khodadad, Davood
    Experimental Mechanics, Luleå University of Technology, Luleå, Sweden.
    An improved spatial FCM algorithm for cardiac image segmentation2013In: 2013 13th Iranian Conference on Fuzzy Systems (IFSC), IEEE, 2013, p. 1-4Conference paper (Refereed)
    Abstract [en]

    Image segmentation is one of challenging field in medical image processing. Segmentation of cardiac wall is one of challenging work and it is very important step in evaluation of heart functionality by existing methods. For cardiac image analysis, Fuzzy C- Means (FCM) algorithm proved to be superior over the other clustering approaches in segmentation field. However, the nave FCM algorithm is sensitive to noise because of not considering the spatial information in the image. In this paper an improved FCM algorithm is formulated by incorporating the spatial domain neighborhood information into the membership function for clustering (ISFCM). In this paper we applied improved Fuzzy c-Means with spatial information for left ventricular wall segmentation. Obtained results showed that the proposed method can segment cardiac wall automatically with acceptable accuracy. The comparison of proposed method with nave FCM proved that ISFCM can segment with more accuracy than nave FCM.

1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf