Cardiac phase-correlated image reconstruction and advanced image processing in pulmonary CT imaging.

Image quality in pulmonary CT imaging is commonly degraded by cardiac motion artifacts. Phase-correlated image reconstruction algorithms known from cardiac imaging can reduce motion artifacts but increase image noise and conventionally require a concurrently acquired ECG signal for synchronization....

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Publicado en:European Radiology Vol. 19; no. 4; pp. 1035 - 1043
Autores principales: Lapp RM, Kachelriess M, Ertel D, Kyriakou Y, Kalender WA, Lapp, Robert M, Kachelriess, Marc, Ertel, Dirk, Kyriakou, Yiannis, Kalender, Willi A
Formato: Journal Article
Publicado: Springer Nature Apr2009
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2009
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      pub: Springer Nature
      place: New York, New York
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        atl: Cardiac phase-correlated image reconstruction and advanced image processing in pulmonary CT imaging.
      aug:
        au:
          Lapp RM
          Kachelriess M
          Ertel D
          Kyriakou Y
          Kalender WA
          Lapp, Robert M
          Kachelriess, Marc
          Ertel, Dirk
          Kyriakou, Yiannis
          Kalender, Willi A
        affil: VAMP GmbH, Erlangen, Germany
      sug:
        subj:
          Heart Radiography
          Image Processing, Computer Assisted Methods
          Lung Radiography
          Algorithms
          Artifacts
          Diagnostic Imaging Methods
          Electrocardiography Methods
          Models, Statistical
          Motion
          Information Science Methods
          Phantoms, Imaging
          Time Factors
      ab: Image quality in pulmonary CT imaging is commonly degraded by cardiac motion artifacts. Phase-correlated image reconstruction algorithms known from cardiac imaging can reduce motion artifacts but increase image noise and conventionally require a concurrently acquired ECG signal for synchronization. Techniques are presented to overcome these limitations. Based on standard and phase-correlated images that are reconstructed using a raw data-derived synchronization signal, image-merging and temporal-filtering techniques are proposed that combine the input images automatically or interactively. The performance of the approaches is evaluated in patient and phantom datasets. In the automatic approach, areas of strong motion and static areas were well detected, providing an optimal combination of standard and phase-correlated images with no visible border between the merged regions. Image noise in the non-moving regions was reduced to the noise level of the standard reconstruction. The application of the interactive filtering allowed for an optimal adaptation of image noise and motion artifacts. Noise content after interactive filtering decreased with increasing temporal filter width used. We conclude that a combination of our motion-free merging approach and a dedicated interactive filtering procedure can highly improve pulmonary imaging with respect to motion artifacts and image noise.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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