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Kymogram detection and kymogram‐correlated image reconstruction from subsecond spiral computed tomography scans of the heart
Author(s) -
Kachelrieß Marc,
Sennst DirkAlexander,
Maxlmoser Wolfgang,
Kalender Willi A.
Publication year - 2002
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1487861
Subject(s) - iterative reconstruction , projection (relational algebra) , multislice , artificial intelligence , tomographic reconstruction , spiral (railway) , mutual information , image quality , tomography , reconstruction algorithm , computer vision , scanner , computer science , medical imaging , algorithm , mathematics , nuclear medicine , physics , image (mathematics) , medicine , optics , mathematical analysis
Subsecond single‐slice, multi‐slice or cone‐beam spiral computed tomography (SSCT, MSCT, CBCT) offer great potential for improving heart imaging. Together with the newly developed phase‐correlated cardiac reconstruction algorithms 180°MCD and 180°MCI [Med. Phys. 27 , 1881–1902 (2000)] or related algorithms provided by the CT manufacturers, high image quality can be achieved. These algorithms require information about the cardiac motion, i.e., typically the simultaneously recorded electrocardiogram (ECG), to synchronize the reconstruction with the cardiac motion. Neither data acquired without ECG information (standard patients) nor acquisitions with corrupted ECG information can be handled adequately. We developed a method to extract the appropriate information about cardiac motion directly from the measured raw data (projection data). The so‐called kymogram function is a measure of the cardiac motion as a function of time t or as a function of the projection angle α. In contrast to the ECG which is a global measure of the heart's electric excitation, the kymogram is a local measure of the heart motion at the z ‐position z ( α ) at projection angle α. The patient's local heart rate as well as the necessary synchronization information to be used with phase‐correlated algorithms can be extracted from the kymogram by using a series of signal processing steps. The kymogram information is shown to be adequate to substitute the ECG information. Computer simulations with simulated ECG and patient measurements with simultaneously acquired ECG were carried out for a multislice scanner providing M = 4 slices to evaluate these new approaches. Both the ECG function and the kymogram function were used for reconstruction. Both were highly correlated regarding the periodicity information used for reconstruction. In 21 out of 25 consecutive cases the kymogram approach was equivalent to the ECG‐correlated reconstruction; only minor differences in image quality between both methods were observed. For one patient the synchronization information detected by the ECG monitor turned out to be wrong; here, the kymogram constituted the only approach that provided useful reconstructions. Patient studies with 12 and 16 slices indicate the usefulness of our approach for cone‐beam CT scans. Kymogram‐correlated reconstructions also appear to have the potential to improve imaging of pericardial lung areas in general.