Current 4D-CT methods require external marker data to retrospectively sort image data and generate CT volumes. In this work we develop an automated 4D-CT sorting algorithm that performs without the aid of data collected from an external respiratory surrogate. The sorting algorithm requires an overlapping cine scan protocol. The overlapping protocol provides a spatial link between couch positions. Beginning with a starting scan position, images from the adjacent scan position (which spatial match the starting scan position) are selected by maximizing the normalized cross correlation (NCC) of the images at the overlapping slice position. The process was continued by 'daisy chaining' all couch positions using the selected images until an entire 3D volume was produced. The algorithm produced 16 phase volumes to complete a 4D-CT dataset. Additional 4D-CT datasets were also produced using external marker amplitude and phase angle sorting methods. The image quality of the volumes produced by the different methods was quantified by calculating the mean difference of the sorted overlapping slices from adjacent couch positions. The NCC sorted images showed a significant decrease in the mean difference (p < 0.01) for the five patients.

Heart disease is a leading cause of death in North America. With the increased availability of PET/CT scanners, CT is now commonly used as a transmission source for attenuation correction. Because of the differences in scan duration between PET and CT, respiration-induced motion can create inconsistencies between the PET and CT data and lead to incorrect attenuation correction and, thus, artifacts in the final reconstructed PET images. This study compared respiration-averaged CT and 4-dimensional (4D) CT for attenuation correction of cardiac PET in an in vivo canine model as a means of removing these inconsistencies. Methods: Five dogs underwent respiration-gated cardiac 18 F-FDG PET and 4D CT. The PET data were reconstructed with 3 methods of attenuation correction that differed only in the CT data used: The first method was single-phase CT at either end-expiration, end-inspiration, or the middle of a breathing cycle; the second was respiration-averaged CT, which is CT temporally averaged over the entire respiratory cycle; and the third was phase-matched CT, in which each PET phase is corrected with the matched phase from 4D CT. After reconstruction, the gated PET images were summed to produce an ungated image. Polar plots of the PET heart images were generated, and percentage differences were calculated with respect to the phase-matched correction for each dog. The difference maps were then averaged over the 5 dogs. Results: For single-phase CT correction at end-expiration, end-inspiration, and mid cycle, the maximum percentage differences were 11% 6 4%, 7% 6 3%, and 5% 6 2%, respectively. Conversely, the maximum difference for attenuation correction with respiration-averaged CT data was only 1.6% 6 0.7%. Conclusion: Respirationaveraged CT correction produced a maximum percentage difference 7 times smaller than that obtained with end-expiration single-phase correction. This finding indicates that using respiration-averaged CT may accurately correct for attenuation on respiration-ungated cardiac PET.

The purpose of this paper is to describe a non-invasive method to monitor the motion of internal organs affected by respiration without using external markers or spirometry, to test the correlation with external markers, and to calculate any time shift between the datasets. Ten lung cancer patients were CT scanned with a GE LightSpeed Plus 4-Slice CT scanner operating in a ciné mode. We retrospectively reconstructed the raw CT data to obtain consecutive 0.5 s reconstructions at 0.1 s intervals to increase image sampling. We defined regions of interest containing tissue interfaces, including tumour/lung interfaces that move due to breathing on multiple axial slices and measured the mean CT number versus respiratory phase. Tumour motion was directly correlated with external marker motion, acquired simultaneously, using the sample coefficient of determination, r(2). Only three of the ten patients showed correlation higher than r(2) = 0.80 between tumour motion and external marker position. However, after taking into account time shifts (ranging between 0 s and 0.4 s) between the two data sets, all ten patients showed correlation better than r(2) = 0.8. This non-invasive method for monitoring the motion of internal organs is an effective tool that can assess the use of external markers for 4D-CT imaging and respiratory-gated radiotherapy on a patient-specific basis.