oru.sePublikationer
Ändra sökning
Avgränsa sökresultatet
1 - 8 av 8
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Träffar per sida
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
Markera
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Alshamari, Muhammed
    et al.
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Geijer, Mats
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Department of Radiology, Örebro University Hospital, Örebro, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden.
    Norrman, Eva
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Department of Medical Physics, Örebro University Hospital, Örebro, Sweden.
    Lidén, Mats
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Krauss, Wolfgang
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Jendeberg, Johan
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Magnuson, Anders
    Region Örebro län.
    Geijer, Håkan
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Impact of iterative reconstruction on image quality of low-dose CT of the lumbar spine2017Ingår i: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 58, nr 6, s. 702-709Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Iterative reconstruction (IR) is a recent reconstruction algorithm for computed tomography (CT) that can be used instead of the standard algorithm, filtered back projection (FBP), to reduce radiation dose and/or improve image quality.

    Purpose: To evaluate and compare the image quality of low-dose CT of the lumbar spine reconstructed with IR to conventional FBP, without further reduction of radiation dose.

    Material and Methods: Low-dose CT on 55 patients was performed on a Siemens scanner using 120 kV tube voltage, 30 reference mAs, and automatic dose modulation. From raw CT data, lumbar spine CT images were reconstructed with a medium filter (B41f) using FBP and four levels of IR (levels 2-5). Five reviewers scored all images on seven image quality criteria according to the European guidelines on quality criteria for CT, using a five-grade scale. A side-by-side comparison was also performed.

    Results: There was significant improvement in image quality for IR (levels 2-4) compared to FBP. According to visual grading regression, odds ratios of all criteria with 95% confidence intervals for IR2, IR3, IR4, and IR5 were: 1.59 (1.39-1.83), 1.74 (1.51-1.99), 1.68 (1.46-1.93), and 1.08 (0.94-1.23), respectively. In the side-by-side comparison of all reconstructions, images with IR (levels 2-4) received the highest scores. The mean overall CTDIvol was 1.70 mGy (SD 0.46; range, 1.01-3.83 mGy). Image noise decreased in a linear fashion with increased strength of IR.

    Conclusion: Iterative reconstruction at levels 2, 3, and 4 improves image quality of low-dose CT of the lumbar spine compared to FPB.

  • 2.
    Alshamari, Muhammed
    et al.
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology.
    Geijer, Mats
    Department of Radiology, School of Medical Sciences, Örebro University, Örebro, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital, Lund; Lund University, Lund, Sweden.
    Norrman, Eva
    Department of Medical Physics, School of Medical Sciences, Örebro University, Örebro, Sweden.
    Lidén, Mats
    Örebro universitet, Institutionen för hälsovetenskaper.
    Krauss, Wolfgang
    Örebro universitet, Institutionen för hälsovetenskaper.
    Jendeberg, Johan
    Örebro universitet, Institutionen för hälsovetenskaper.
    Magnuson, Anders
    Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden.
    Geijer, Håkan
    Örebro universitet, Institutionen för hälsovetenskaper.
    Impact of iterative reconstruction on image quality of low-dose CT of the lumbar spineManuskript (preprint) (Övrigt vetenskapligt)
  • 3.
    Andersson, Karin M.
    et al.
    Department of Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Department of Medical Physics, Örebro University Hospital, Örebro, Sweden.
    Norrman, Eva
    Department of Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
    Geijer, Håkan
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Krauss, Wolfgang
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Cao, Yang
    Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden; Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.
    Jendeberg, Johan
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Geijer, Mats
    Department of Radiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Department of Clinical Sciences, Lund University, Lund, Sweden.
    Lidén, Mats
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Thunberg, Per
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Medical Physics, Örebro University Hospital, Örebro, Sweden .
    Visual grading evaluation of commercially available metal artefact reduction techniques in hip prosthesis computed tomography2016Ingår i: British Journal of Radiology, ISSN 0007-1285, E-ISSN 1748-880X, Vol. 89, nr 1063, artikel-id 20150993Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Objectives: To evaluate metal artefact reduction (MAR) techniques from four computed tomography (CT) vendors in hip prosthesis imaging.

    Methods: Bilateral hip prosthesis phantom images, obtained by using MAR algorithms for single energy CT data or dual energy CT (DECT) data and by monoenergetic reconstructions of DECT data, were visually graded by five radiologists using ten image quality criteria. Comparisons between the MAR images and a reference image were performed for each scanner separately. Ordinal probit regression analysis was used.

    Results: The MAR algorithms in general improved the image quality based on the majority of the criteria (up to between 8/10 and 10/10) with a statistically improvement in overall image quality (P<0.001). However, degradation of image quality, such as new artefacts, was seen in some cases. A few monoenergetic reconstruction series improved the image quality (P<0.004) for one of the DECT scanners, but it was only improved for some of the criteria (up to 5/10). Monoenergetic reconstructions resulted in worse image quality for the majority of the criteria (up to 7/10) for the other DECT scanner.

    Conclusions: The MAR algorithms improved the image quality of the hip prosthesis CT images. However, since additional artefacts and degradation of image quality were seen in some cases, all algorithms should be carefully evaluated for every clinical situation. Monoenergetic reconstructions were in general concluded to be insufficient for reducing metal artifacts. Advances in knowledge: Qualitative evaluation of the usefulness of several MAR techniques from different vendors in CT imaging of hip prosthesis.

  • 4.
    Jendeberg, Johan
    et al.
    Örebro universitet, Institutionen för medicinska vetenskaper.
    Cierzniak, B.
    Alshamari, Muhammed
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län.
    Geijer, Håkan
    Örebro universitet, Institutionen för medicinska vetenskaper.
    Lidén, Mats
    Örebro universitet, Institutionen för medicinska vetenskaper.
    Prognosis of spontaneous ureteral stone passage: as revealed by CT2016Konferensbidrag (Refereegranskat)
  • 5.
    Jendeberg, Johan
    et al.
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Geijer, Håkan
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Alshamari, Muhammed
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Cierzniak, Bartosz
    Department of Surgery, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
    Lidén, Mats
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Size matters: The width and location of a ureteral stone accurately predict the chance of spontaneous passage2017Ingår i: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. 27, nr 11, s. 4775-4785Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    OBJECTIVES: To determine how to most accurately predict the chance of spontaneous passage of a ureteral stone using information in the diagnostic non-enhanced computed tomography (NECT) and to create predictive models with smaller stone size intervals than previously possible.

    METHODS: Retrospectively 392 consecutive patients with ureteric stone on NECT were included. Three radiologists independently measured the stone size. Stone location, side, hydronephrosis, CRP, medical expulsion therapy (MET) and all follow-up radiology until stone expulsion or 26 weeks were recorded. Logistic regressions were performed with spontaneous stone passage in 4 weeks and 20 weeks as the dependent variable.

    RESULTS: The spontaneous passage rate in 20 weeks was 312 out of 392 stones, 98% in 0-2 mm, 98% in 3 mm, 81% in 4 mm, 65% in 5 mm, 33% in 6 mm and 9% in ≥6.5 mm wide stones. The stone size and location predicted spontaneous ureteric stone passage. The side and the grade of hydronephrosis only predicted stone passage in specific subgroups.

    CONCLUSION: Spontaneous passage of a ureteral stone can be predicted with high accuracy with the information available in the NECT. We present a prediction method based on stone size and location.

    KEY POINTS: • Non-enhanced computed tomography can predict the outcome of ureteral stones. • Stone size and location are the most important predictors of spontaneous passage. • Prediction models based on stone width or length and stone location are introduced. • The observed passage rates for stone size in mm-intervals are reported. • Clinicians can make better decisions about treatment.

  • 6.
    Jendeberg, Johan
    et al.
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Geijer, Håkan
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Alshamari, Muhammed
    Örebro universitet, Institutionen för medicinska vetenskaper. Region Örebro län. Department of Radiology, Örebro University Hospital, Örebro, Sweden.
    Lidén, Mats
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Prediction of spontaneous ureteral stone passage: Automated 3D-measurements perform equal to radiologists, and linear measurements equal to volumetric2018Ingår i: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. 28, nr 6, s. 2474-2483Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    OBJECTIVES: To compare the ability of different size estimates to predict spontaneous passage of ureteral stones using a 3D-segmentation and to investigate the impact of manual measurement variability on the prediction of stone passage.

    METHODS: We retrospectively included 391 consecutive patients with ureteral stones on non-contrast-enhanced CT (NECT). Three-dimensional segmentation size estimates were compared to the mean of three radiologists' measurements. Receiver-operating characteristic (ROC) analysis was performed for the prediction of spontaneous passage for each estimate. The difference in predicted passage probability between the manual estimates in upper and lower stones was compared.

    RESULTS: The area under the ROC curve (AUC) for the measurements ranged from 0.88 to 0.90. Between the automated 3D algorithm and the manual measurements the 95% limits of agreement were 0.2 ± 1.4 mm for the width. The manual bone window measurements resulted in a > 20 percentage point (ppt) difference between the readers in the predicted passage probability in 44% of the upper and 6% of the lower ureteral stones.

    CONCLUSIONS: All automated 3D algorithm size estimates independently predicted the spontaneous stone passage with similar high accuracy as the mean of three readers' manual linear measurements. Manual size estimation of upper stones showed large inter-reader variations for spontaneous passage prediction.

    KEY POINTS:• An automated 3D technique predicts spontaneous stone passage with high accuracy.• Linear, areal and volumetric measurements performed similarly in predicting stone passage.• Reader variability has a large impact on the predicted prognosis for stone passage.

  • 7.
    Lidén, Mats
    et al.
    Örebro universitet, Institutionen för medicinska vetenskaper.
    Jendeberg, Johan
    Örebro universitet, Institutionen för medicinska vetenskaper.
    Längkvist, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Loutfi, Amy
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Thunberg, Per
    Örebro universitet, Institutionen för medicinska vetenskaper.
    Discrimination between distal ureteral stones and pelvic phleboliths in CT using a deep neural network: more than local features needed2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Purpose: To develop a deep learning method for assisting radiologists in the discrimination between distal ureteral stones and pelvic phleboliths in thin slice CT images, and to evaluate whether this differentiation is possible using only local features.

    Methods and materials: A limited field-of-view image data bank was retrospectively created, consisting of 5x5x5 cm selections from 1 mm thick unenhanced CT images centered around 218 pelvis phleboliths and 267 distal ureteral stones in 336 patients. 50 stones and 50 phleboliths formed a validation cohort and the remainder a training cohort. Ground truth was established by a radiologist using the complete CT examination during inclusion.The limited field-of-view CT stacks were independently reviewed and classified as containing a distal ureteral stone or a phlebolith by seven radiologists. Each cropped stack consisted of 50 slices (5x5 cm field-of-view) and was displayed in a standard PACS reading environment. A convolutional neural network using three perpendicular images (2.5D-CNN) from the limited field-of-view CT stacks was trained for classification.

    Results: The 2.5D-CNN obtained 89% accuracy (95% confidence interval 81%-94%) for the classification in the unseen validation cohort while the accuracy of radiologists reviewing the same cohort was 86% (range 76%-91%). There was no statistically significant difference between 2.5D-CNN and radiologists.

    Conclusion: The 2.5D-CNN achieved radiologist level classification accuracy between distal ureteral stones and pelvic phleboliths when only using the local features. The mean accuracy of 86% for radiologists using limited field-of-view indicates that distant anatomical information that helps identifying the ureter’s course is needed.

  • 8.
    Längkvist, Martin
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Jendeberg, Johan
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Thunberg, Per
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Medical Physics, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Loutfi, Amy
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lidén, Mats
    Örebro universitet, Institutionen för medicinska vetenskaper. Department of Radiology, Faculty of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks2018Ingår i: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 97, s. 153-160Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans.

1 - 8 av 8
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf