The purpose of this study was to enhance the effect in the PET image quality according to event bootstrap of small
animal PET data. In order to investigate the time difference condition, realigned sinograms were generated from
randomly sampled data set using bootstrap. List-mode data was obtained from small animal PET scanner for Ge-68 30
sec, Y-90 20 min and Y-90 60 min. PET image was reconstructed by Ordered Subset Expectation Maximization(OSEM)
2D with the list-mode format. Image analysis was investigated by Signal to Noise Ratio(SNR) of Ge-68 and Y-90 image.
Non-parametric resampled PET image SNR percent change for the Ge-68 30 sec, Y-90 60 min, and Y-90 20 min was
1.69 %, 7.03 %, and 4.78 %, respectively. SNR percent change of non-parametric resampled PET image with time
difference condition was 1.08 % for the Ge-68 30 sec, 6.74 % for the Y-90 60 min and 10.94 % for the Y-90 29 min.
The result indicated that the bootstrap with time difference condition had a potential to improve a noisy Y-90 PET image
quality. This method should be expected to reduce Y-90 PET measurement time and to enhance its accuracy.
PET image of tumor located in thoracic region was affected by various organ motions such as respiration and heartbeat.
Thoracic motion is difficult to estimate and correct accurately using external measurement or anatomical image solely.
The aim of this study was to compare the accuracy of motion correction using PET fiducial mark and 3D MRI image.
The radioactive bead for PET fiducial mark was realized from molecular sieve contained 0.37 MBq F-18 and placed in
thoracic region. PET study was performed using a small animal PET scanner after IV injection of FDG. MRI study was
performed using 3-T clinical MRI system with 3D T1-VIBE (TR/TE=5.67/1.42 ms) sequence. Motion corrected PET
image was created by mutual information registration with B-Spline interpolation to the mean image after first
realignment. FWHM of lung and liver region in static PET image was 4.77±0.87 and 4.81±0.45, respectively. Measured
FWHM of lung region in motion corrected PET image using PET fiducial mark and 3D VIBE MRI was measured
4.22±0.09 and 4.59±0.06, respectively. In case of liver region, FWHM was measured 4.47±0.16 and 4.65±0.25
respectively. The improvement of resolution was observed by proper correction method. In this study PET correction
was implemented by motion information extracted from various images. These results suggest motion correction would
be possible without external device or fiducial mark using MRI motion data. Motion correction using MRI should be
considered acquisition method and organ region in accordance with motion characteristics.
Since recent Positron Emission Tomography (PET) scanner has a high spatial resolution, head motion during brain PET study could cause motion artifact on the image, which might make serious problem in terms of image quality as well as image quantity. Several techniques have been proposed to correct head movement in PET images, for example SPM and AIR software packages. However these techniques are only applicable for correcting the motion between two scans and assume no head movement during scanning. The aim of this study is to develop a technique to correction head motion in event-by-event base during a PET scan using a list-mode data acquisition and optical motion tracking system (POLARIS). This system uses a rebinning procedure whereby the lines of response (LOR) are geometrically transformed according to six-dimensional motion data detected by the POLARIS. A motion-corrected Michelogram was directly composed using the reoriented LOR. In the motion corrected image, the blurring artifact due to the motion was reduced by the present technique. Since the list-mode acquisition stores data as event-by-event base, the present technique makes it possible to correct head movement during PET scanning and has a potential for real-time motion correction of head movement.