CT Images Of Phantom Research Articles

MO-D-I-611-06: Reduction of Motion Blurring Artifact Using Respiratory Gated CT: A Quantitative Evaluation

Purpose: To develop a technique for reducing respiratory motion blurring artifacts using respiratory-gated CT, and to quantitatively evaluate the artifact reduction. Method and Materials: Similar to electrocardiogram (ECG) gated imaging for the heart, a synthetic sinogram was built from multiple scans intercepting a respiration gated window. A gated CT image was then reconstructed by the filtered back-projection algorithm. CT images of wedge phantoms moving at different speeds, and 13 patients were taken with synchronized respiratory motion measurement. The scanner was operated in cine mode with 100 and 15 scans (0.5 s rotation) acquired consecutively at each couch position for phantoms and patients, respectively. Two error functions were fit to the CT profile across the air-phantom or lung-diaphragm boundaries for a quantitative evaluation of the blurring artifact. Results: The blurring artifact was reduced significantly at the air-phantom boundaries in the gated image. The gated image of phantoms with a motion of 20 mm/s showed similar blurring artifacts as the non-gated image of phantoms with a motion of 10 mm/s. The blurring artifact had a linear relationship with both the speed and the tangent of the wedge angles. The blurring artifacts were also reduced at the lung-diaphragm boundaries for patients. Centers of the two fitted error functions provided a reliable measure of large blurring, and were found equivalent to 25% and 75% locations of the CT profile. Conclusion: The respiratory gated CT imaging reduced the blurring artifacts for both moving phantoms and patients. This technique may be applied for other tomographic imaging modalities that require long imaging times with significant motion blurring artifacts, such as PET.

MR image-guided portal verification for brain treatment field

Purpose : To investigate a method for the generation of digitally reconstruction radiographs directly from MR images (DRR-MRI) to guide a computerized portal verification procedure. Methods and Materials : Several major steps were developed to perform an MR image-guided portal verification procedure. Initially, a wavelet-based multiresolution adaptive thresholding method was used to segment the skin slice-by-slice in MR brain axial images. Some selected anatomical structures, such as target volume and critical orgsns, were then manually identified and were reassigned to relatively higher intensities. Interslice information was interpolated with a directional method to achieve comparable display resolution in three dimensions. Next, a ray-tracing method was used to generate a DRR-MRI image at the planned treatment position, and the ray tracing was simply performed on summation of voxels along the ray. The skin and its relative positions were also projected to the DRR-MRI and were used to guide the search of similar features in the portal image. A Canny edge detector was used to enhanced the brain contour in both portal and simulation images. The skin in the brain portal image was then extracted using a knowledge-based searching technique. Finally, a Chamfer matching technique was used to correlate features between DRR-MRi and portal image. Results : The MIR image-guided portal verification method was evaluated using a brain phantom case and a clinical patient case. Both DRR-CT and DRR-MRI were generated using CT and MR phantom images with th same beam orientation and then compared. The matching results indicated that the maximum deviation of internal structures was less than 1 mm. The segmented results for brain MR slice images indicated that a wavelet-based image segmentation technique provided a reasonable estimation for the brain skin. For the clinical patient case with a given portal field, the MR image-guided verification method provided an excellent match between features in both DRR-MRI and portal image. Moreover, target volume could be accurately visualized in the DRR-MRI and mapped over the corresponding portal image for treatment verification. The accuracy of DRR-MRI was also examined by comparing it to the corresponding simulation image. The matching results indicated that the maximum deviation of anatomical features was less than 2.5 mm. Conclusion : A method for MR image-guided portal verification of brain treatment field was developed. Although The radiographic appearance in the DRR-MRI is different from that in the portal image, DRR-MRI provides essential anatomical features (landmarks and target volume) as well as their relative locations to be used as references for computerized portal verification.

Region-of-interest reconstruction from noisy projections using fractal models and Wiener filtering

In this paper, we present a method for region-of-interest (ROI) tomography using noisy projections. A wavelet decomposition down to the coarsest level is done on the noisy signal. The signal at various levels is estimated using a Wiener filter. By assuming that the projections are 1/f processes, the Wiener filtering reduces to a scalar multiplication. Using the Wiener filter and regularity property of the wavelets, we combine the estimation and localisation of the noisy projections for ROI imaging. Experimental results are shown on Shepp-Logan phantom and actual CT images. The validity of the 1/f model for projections of real life images is also shown.