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SPECT Imaging in the Diagnosis of Pulmonary Embolism: Automated Detection of Match and Mismatch Defects by Means of Image-Processing Techniques

¹ Department of Nuclear Medicine, University Hospital Aachen, Aachen, Germany; and ² Department of Diagnostic Radiology, University Hospital Aachen, Aachen, Germany

View larger version (57K): [in a new window]   FIGURE 1.  Process flow of automated algorithm for detecting mismatch defects. Although ventilation (A) shows no pathologic changes, a substantial defect can be found on the perfusion scan (B). After registration, normalization, and subtraction, the image contains only mismatch defects (C = A – B). To improve topographic orientation, the subtracted image (C) is fused with the perfusion scan (D = C + B). An exemplary coronal slice is shown.

View larger version (49K): [in a new window]   FIGURE 2.  Process flow of automated algorithm for detecting match defects: patient with heterogeneous ventilation (A) and perfusion (B) due to severe chronic obstructive pulmonary disease. After normalization, registration, inversion, and subtraction, image contains only match defects (C = AInverse – B). To improve topographic orientation, the subtracted image (C) is fused with the perfusion scan (D = C + B). Multiple match defects in both lungs are displayed in yellow (D). An exemplary coronal slice is shown.

View larger version (49K): [in a new window]   FIGURE 3.  Patient with pulmonary embolism. Although ventilation (A) shows no pathologic changes, perfusion defects were found in both lungs (B). Automated algorithm detected both embolisms (C). Match defects, on the other hand, were found neither by automated approach (D) nor by conventional image interpretation. An exemplary coronal slice is shown.

View larger version (76K): [in a new window]   FIGURE 4.  Result of automated mismatch detection in a patient with a false-positive diagnosis attributed to an artifact in right costodiaphragmatic recess (arrow). In conventional reconstructed images, corresponding region showed neither perfusion defect nor hyperventilation. An exemplary coronal slice is shown.

View larger version (20K): [in a new window]   FIGURE 5.  Comparison between conventional image interpretation (Conv) and automated evaluation (Auto) with regard to number of match and mismatch defects detected on segmental and subsegmental levels. For this analysis, artifacts in pulmonary recesses were removed from dataset.

View larger version (91K): [in a new window]   FIGURE 6.  Result of automated mismatch detection in a patient with highly heterogeneous perfusion due to severe chronic obstructive pulmonary disease. An exemplary coronal slice is shown. Automated algorithm correctly confirmed absence of embolism. Affected regions would have been displayed in red. Corresponding results of computerized match detection are shown in Figure 2.