Internal respiratory surrogate in multislice 4D CT using a combination of Fourier transform and anatomical features. Issue 7 (23rd June 2015)

• Record Type: Journal Article
• Authors: Hui, Cheukkai; Suh, Yelin; Robertson, Daniel; Pan, Tinsu; Das, Prajnan; Crane, Christopher H.; Beddar, Sam
• Is Part Of: Medical physics. Volume 42:Issue 7(2015)
• Page Start: 4338
• Page End: 4348
• Publication Date: 2015-06-23
• Subjects: computerised tomography -- feature extraction -- Fourier transforms -- image classification -- medical image processing -- pneumodynamics; Computed tomography -- Pneumodyamics, respiration -- Fourier analysis -- Integral transforms; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; 4D CT -- internal surrogate -- Fourier transform -- anatomical feature -- cluster; Medical image artifacts -- Cluster analysis -- Medical image reconstruction -- Multislice computed tomography -- Data sets -- Fourier transforms -- Cone beam computed tomography -- Lungs; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4922692
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The purpose of this study was to develop a novel algorithm to create a robust internal respiratory signal (IRS) for retrospective sorting of four‐dimensional (4D) computed tomography (CT) images. Methods: The proposed algorithm combines information from the Fourier transform of the CT images and from internal anatomical features to form the IRS. The algorithm first extracts potential respiratory signals from low‐frequency components in the Fourier space and selected anatomical features in the image space. A clustering algorithm then constructs groups of potential respiratory signals with similar temporal oscillation patterns. The clustered group with the largest number of similar signals is chosen to form the final IRS. To evaluate the performance of the proposed algorithm, the IRS was computed and compared with the external respiratory signal from the real‐time position management (RPM) system on 80 patients. Results: In 72 (90%) of the 4D CT data sets tested, the IRS computed by the authors' proposed algorithm matched with the RPM signal based on their normalized cross correlation. For these data sets with matching respiratory signals, the average difference between the end inspiration times (Δ t ins ) in the IRS and RPM signal was 0.11 s, and only 2.1% of Δ t ins were more than 0.5 s apart. In the eight (10%) 4D CT data sets in which the IRS and the RPM signal did not match, the average Δ t ins was 0.73 s in the nonmatching couch positions, and 35.4% of them had a Δ t ins greater than 0.5 s. At couch positions in which IRS did not match the RPM signal, a correlation‐based metric indicated poorer matching of neighboring couch positions in the RPM‐sorted images. This implied that, when IRS did not match the RPM signal, the images sorted using the IRS showed fewer artifacts than the clinical images sorted using the RPM signal. Conclusions: The authors' proposed algorithm can generate robust IRSs that can be used for retrospective sorting of 4D CT data. The algorithm is completely automatic and requires very little processing time. The algorithm is cost efficient and can be easily adopted for everyday clinical use.