Advantages of iterative reconstruction (IR) algorithms over standard filtered backprojection (FBP) algorithms include
improved resolution and better noise performance, and many IR algorithms have been described in the literature. More
recently model-based IR algorithms (MBIR) have been developed, which incorporate accurate system models into IR,
resulting in better image quality than IR algorithms without a system model. This work investigates the resolution
improvement achieved when a system optics model (SOM) has been included in a standard OS-SART algorithm.
Three OS-SART algorithms have been compared: (1) "Pencil beam"
(IR-P) with no system optics; (2) system optics
included in forward projection (IR-SOM-FP), and (3) system optics included in both forward and backprojection
(IRSOM-FPBP). Simulated reconstructions of a 0.2 mm bead show that IR-SOM-FPBP produced a FWHM resolution of
0.41 mm, considerably better than FBPJ (0.87 mm), IR-P (0.63 mm), and IR-SOM-FP (0.59 mm).
With high-speed multislice helical CT, the time needed to select the optimal cardiac phase accounts for a large percentage of the coronary CT angiography examination time because the scan time is short. To reduce the phase selection time, we have developed an automatic cardiac phase selection algorithm and implemented it in the Aquilion 64 scanner. This algorithm calculates the absolute sum of the differences between two raw data sets for subsequent cardiac phases (e.g., 4% and 0%) and generates a velocity curve representing the magnitude of cardiac motion velocity for the entire heart volume. Normally, the velocity curve has two local minimum slow-motion phases corresponding to end-systole and mid-diastole. By applying these local minimum phases in reconstruction, stationary cardiac images can be reconstructed automatically. In this report, the algorithm for generating the velocity curve and the processing time for selecting the optimal cardiac phase are discussed. The accuracy of this method is compared with that of the conventional manual method. In the manual method, a sample plane containing all four cardiac chambers was selected, reconstruction was performed for all phases at 2% intervals, and images were visually evaluated. Optimal phase selection required about 5 min/exam. With automatic phase selection, optimal phase selection required only about 1 min/exam, and the cardiac phases were close to those selected using the manual method. Automatic phase selection substantially reduces the time needed to select the optimal phase and increases patient throughput. Moreover, the influence of operator skill in selecting the optimal phase is minimized.
In many clinical applications, it is necessary to tilt the gantry of an X-ray CT system with respect to the patient. Tilting the gantry introduces no complications for single-slice fan-beam systems; however, most systems today are helical multislice systems with up to 16 slices (and this number is sure to increase in the future). The image reconstruction algorithms used in multislice helical CT systems must be modified to compensate for the tilt. If they are not, the quality of reconstructed images will be poor with the presence of significant artifacts produced by the tilt. Practical helical multislice algorithms currently incorporated in today’s systems include helical fan-beam, ASSR (Advanced single-slice rebinning), and Feldkamp algorithms. This paper presents the modifications necessary to compensate for gantry tilt for the helical cone-beam Feldkamp algorithm implemented by Toshiba (referred to as TCOT for true cone-beam tomography). Unlike some of the other algorithms, gantry tilt compensation is simple and straightforward to implement with no significant increase in computational complexity. It will be shown that the effect of the gantry tilt is to introduce a lateral shift in the isocenter of the reconstructed slice of interest, which is a function of the tilt, couch speed, and view angle. This lateral shift is easily calculated and incorporated into the backprojection algorithm. The tilt-compensated algorithm is called T-TCOT. Experimental tilted-gantry data has been obtained with 8- and 16 slice Toshiba Aquilion systems, and examples of uncompensated and tilt compensated images are presented.
Multi-slice helical CT-systems suffer from windmill artifacts: black/white patterns that spin off of features with high longitudinal gradients. The number of black/white pairs matches the number of slices (detector rows) in the multi-slive detector. The period of spin is the same as the helical pitch. We investigate the cause of the pattern by following the traces of selected voxels through the multi-slive detector array as a function of view position. This forms an "extracted sinogram" which represents the data used to reconstruct the specific voxel. Now we can determine the cause of the artifact by correlating the windmill streak in the image with the extracted data. The investigation shows that inadequate sampling along the longitudinal direction causes the artifact.
A distortion correction table compression method based on polynomial fitting has been developed for implementation in a commercial volume-CT system. To achieve the fastest processing rates, distortion correction tables must fit into the limited memory present in hardware. The number of elements in raw lookup tables is approximately 2 X N<SUB>i</SUB> X N<SUB>j</SUB> X N<SUB>(theta</SUB> ), where N<SUB>i</SUB> X N<SUB>i</SUB> is the image dimensions in pixels, and N<SUB>(theta</SUB> ) is the number of frames. Two- dimensional (2D) compression fits 4th-order polynomials to columns and rows of the raw table, reducing table size to 2 X 5 X 5 X N<SUB>f</SUB>. Three-dimensional (3D) compression further compresses 2D tables in the angle dimension; reducing table size to 2 X 5 X 5 X 5. Tradeoffs between table size, accuracy, speed, and amount of distortion were investigated with data acquired from 7', 9', 10', 12', 14', and 16' IIs. The mean error was approximately 0.11, 0.20, and 0.20 pixels for raw table, 2D and 3D corrected data; with standard deviations of 0.08, 0.12, and 0.12 pixels.