Dennis Säring

Publications (62) View all

• Article: Improved Agreement between Experienced and Inexperienced Observers using a Standardized Evaluation Protocol for Cardiac Volumetry and Infarct Size Measurement.

  M Groth, K Muellerleile, T Klink, D Säring, S Halaj, G Folwarski, M Kaul, P Bannas, G Adam, G K Lund
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  ABSTRACT: Purpose: To study the agreement between experienced and inexperienced observers before and after training using a standardized evaluation protocol for cardiac magnetic resonance imaging (CMR) measurements of left ventricular (LV) volumes, mass and infarct size. Materials and Methods: First, 10 CMR studies from patients with myocardial infarction were analyzed by 2 experienced and 4 inexperienced observers in respect to end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), LV mass and infarct size. Subsequently, the inexperienced observers were trained using a standardized evaluation protocol. Thereafter, all observers analyzed another 10 CMR studies. Results: Before training the relative difference between experienced and inexperienced observers was -4.3 ± 8.2 % for EDV, -13.3 ± 14.2 % for ESV, 5.9 ± 8.2 % for EF, -12.2 ± 10.9 % for LV mass and -27.0 ± 29.0 % for infarct size in gram. After training, agreement significantly improved to 0.2 ± 8.8 % for EDV (p < 0.05), -2.1 ± 10.9 for ESV (p < 0.01), 1.5 ± 6.9 % for EF (p < 0.05), and -3.6 ± 17.1 % for infarct size (p < 0.0001), but no improvement was seen for LV mass (-11.2 ± 7.9, p = 0.64). A slice based analysis showed, that the variable inclusion of the most basal and apical slices were mainly responsible for the low agreement of the measurements before training.Conclusion: Training using a standardized evaluation protocol significantly improved the agreement between experienced and inexperienced observers for important CMR parameters. The proposed evaluation protocol can be used for training to improve the reproducibility of CMR measurements.
  RöFo - Fortschritte auf dem Gebiet der R 09/2012; · 2.76 Impact Factor
• Article: Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets.

  N D Forkert, A Schmidt-Richberg, J Fiehler, T Illies, D Möller, H Handels, D Säring
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  ABSTRACT: Objectives: Exact cerebrovascular segmentations are required for several applications in today's clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.
  Methods of Information in Medicine 08/2012; 51(5):415-22. · 1.53 Impact Factor
• Article: Interdisziplinäre Gesichtsrekonstruktion einer Moorleiche

  K. Püschel, D. Säring, P. Käsemann, S. Scheuermann, K. Kreutz, S. Ohlrogge, U. Wittwer-Backofen, S. Burrath, C. Wilkinson, A. Bauerochse, M. Schultz, E. Jopp
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  ABSTRACT: Im Jahr 2000 wurde im Großen Uchter Moor (Landkreis Nienburg, Niedersachsen) bei Torfabbauarbeiten eine erheblich fragmentierte Moorleiche aufgefunden. Im Rahmen der interdisziplinären Bearbeitung des Falls durch Rechtsmediziner, Anthropologen, Paläopathologen und Archäologen erfolgte die Rekonstruktion des Gesichts der als Mädchen aus dem Uchter Moor („Moora“) bekannten Moorleiche. Mit einer neu entwickelten Computeranimation wurde der Schädel neu modelliert und anschließend im „Rapid-prototyping“-Verfahren abgebildet. Auf Basis dieser Modelle haben 5Wissenschaftler unter Anwendung unterschiedlicher plastischer und bildgebender Verfahren das Gesicht der Moorleiche rekonstruiert. Die Ergebnisse werden in zweidimensionaler Form vorgestellt. In 2000 a severely fragmented bog body was uncovered in the “Großes Uchter Moor” (District of Nienburg, Lower Saxony). An interdisciplinary team of forensic scientists, anthropologists, palaeopathologists and archaeologists initiated the reconstruction of the skull and face of this female bog body, named “Moora”. With a newly developed computerized animation based on computed tomographic (CT) findings of individual bones and fragments, the skull was reconstructed digitally and subsequently by using the rapid-prototyping technique.On the basis of this skull model five scientists formed different faces of the bog body using different facial reconstruction techniques. The results are presented two dimensionally. SchlüsselwörterArchäologie–Anthropologie–Mumien–Computertomographie–Anatomische Modelle KeywordsArchaeology–Anthropology–Mummies–Computed tomography–Models, anatomic
  Rechtsmedizin 05/2012; 21(3):221-224. · 0.81 Impact Factor
• Article: Analysis of the Influence of 4D MR Angiography Temporal Resolution on Time-to-Peak Estimation Error for Different Cerebral Vessel Structures.

  N D Forkert, T Illies, D Möller, H Handels, D Säring, J Fiehler
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  ABSTRACT: BACKGROUND AND PURPOSE:Time-resolved MRA imaging is a promising technique for blood flow evaluation in case of cerebrovascular malformations. Unfortunately, 4D MRA imaging is a trade-off between spatial and temporal resolution. The aim of this study was to investigate the influence of temporal resolution on the error associated with TTP estimation from indicator dilution curves derived from different vascular structures.MATERIALS AND METHODS:Monte Carlo simulation was performed to compute indicator dilution curves with known criterion standard TTP at temporal resolutions between 0.1 and 5 seconds. TTPs were estimated directly and by using 4 hemodynamic models for each curve and were compared with criterion standard TTP. Furthermore, clinical evaluation was performed by using 226 indicator dilution curves from different vessel structures obtained from clinical datasets. The temporal resolution was artificially decreased, and TTPs were estimated and compared with those obtained at the original temporal resolutions. The results of the clinical evaluations were further stratified for different vessel structures.RESULTS:The results of both evaluations show that the TTP estimation error increases exponentially when one lowers the temporal resolution. TTP estimation by using hemodynamic model curves leads to lower estimation errors compared with direct estimation. A temporal resolution of 1.5 seconds for arteries and 2.5 seconds for venous and arteriovenous malformation vessel structures appears to be reasonable to achieve TTP estimations adequate for clinical application.CONCLUSIONS:Different vessel structures require different temporal resolutions to enable comparable TTP estimation errors, which should be considered for achieving a case-optimal temporal and spatial resolution.
  American Journal of Neuroradiology 05/2012; · 2.93 Impact Factor
• Article: Fuzzy-based vascular structure enhancement in Time-of-Flight MRA images for improved segmentation.

  N D Forkert, A Schmidt-Richberg, J Fiehler, T Illies, D Möller, H Handels, D Säring
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  ABSTRACT: Cerebral vascular malformations might lead to strokes due to occurrence of ruptures. The rupture risk is highly related to the individual vascular anatomy. The 3D Time-of-Flight (TOF) MRA technique is a commonly used non-invasive imaging technique for exploration of the vascular anatomy. Several clinical applications require exact cerebrovascular segmentations from this image sequence. For this purpose, intensity-based segmentation approaches are widely used. Since small low-contrast vessels are often not detected, vesselness filter-based segmentation schemes have been proposed, which contrariwise have problems detecting malformed vessels. In this paper, a fuzzy logic-based method for fusion of intensity and vesselness information is presented, allowing an improved segmentation of malformed and small vessels at preservation of advantages of both approaches. After preprocessing of a TOF dataset, the corresponding vesselness image is computed. The role of the fuzzy logic is to voxel-wisely fuse the intensity information from the TOF dataset with the corresponding vesselness information based on an analytically designed rule base. The resulting fuzzy parameter image can then be used for improved cerebrovascular segmentation. Six datasets, manually segmented by medical experts, were used for evaluation. Based on TOF, vesselness and fused fuzzy parameter images, the vessels of each patient were segmented using optimal thresholds computed by maximizing the agreement to manual segmentations using the Tanimoto coefficient. The results showed an overall improvement of 0.054 (fuzzy vs. TOF) and 0.079 (fuzzy vs. vesselness). Furthermore, the evaluation has shown that the method proposed yields better results than statistical Bayes classification. The proposed method can automatically fuse the benefits of intensity and vesselness information and can improve the results of following cerebrovascular segmentations.
  Methods of Information in Medicine 11/2010; 50(1):74-83. · 1.53 Impact Factor