Effect of pitch in multislice spiral/helical CT

To understand the effect of pitch on raw data interpolation in multi-slice spiral/helical CT, and provide guidelines for scanner design and protocol optimization. Multi-slice spiral CT is mainly characterized by the three parameters: the number of detector arrays, the detector collimation, and the table increment per X-ray source rotation. The pitch in multi-slice spiral CT is defined as the ratio of the table increment over the detector collimation. In parallel to the current framework for studying longitudinal image resolution, the central fan- beam rays of direct and opposite directions are considered, assuming a narrow cone-beam angle. Generally speaking, sampling in the Radon domain by the direct and opposite central rays is non-uniform along the longitudinal axis. Using a recently developed methodology for quantifying the sensitivity of signal reconstruction from non-uniformly sampled finite points, the effect of pitch on raw data interpolation is analyzed in multi-slice spiral CT. Unlike single-slice spiral CT, in which image quality deceases monotonically as the pitch increases, the sensitivity of raw data interpolation in multi-slice spiral CT increases in an alternating way as the pitch increases, suggesting that image quality does not decrease monotonically in this case. The most favorable pitch can be found from the sensitivity-pitch plot for any given set of multi-slice spiral CT parameters. An example for four-slice spiral CT is provided. The study on the pitch effect using the sensitivity analysis approach reveals the fundamental characteristics of raw data interpolation in multi-slice spiral CT, and gives insights into interaction between pitch and image quality. These results may be valuable for design of multi-slice spiral CT scanners and imaging protocol optimization in clinical applications.

[1]  G. Wang,et al.  A general cone-beam reconstruction algorithm , 1993, IEEE Trans. Medical Imaging.

[2]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[3]  Ge Wang,et al.  Minimum error bound of signal reconstruction , 1999, IEEE Signal Process. Lett..

[4]  G D Rubin,et al.  Increased scan pitch for vascular and thoracic spiral CT. , 1995, Radiology.

[5]  Ping Chin Cheng,et al.  Scanning cone-beam reconstruction algorithms for x-ray microtomography , 1992, Optics & Photonics.

[6]  C J Bergin,et al.  Spiral CT of the lungs: optimal technique and resolution compared with conventional CT. , 1994, AJR. American journal of roentgenology.

[7]  L. Tanenbaum,et al.  Future directions in CT technology. , 1998, Neuroimaging clinics of North America.

[8]  H Hu,et al.  Multi-slice helical CT: scan and reconstruction. , 1999, Medical physics.

[9]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[10]  G Wang,et al.  Optimal pitch in spiral computed tomography. , 1997, Medical physics.

[11]  G Wang,et al.  Maximum volume coverage in spiral computed tomography scanning. , 1996, Academic radiology.

[12]  G Wang,et al.  Longitudinal resolution in volumetric x-ray computerized tomography--analytical comparison between conventional and helical computerized tomography. , 1994, Medical physics.

[13]  K F King,et al.  Computed tomography scanning with simultaneous patient translation. , 1990, Medical physics.

[14]  K. Taguchi,et al.  Algorithm for image reconstruction in multi-slice helical CT. , 1998, Medical physics.

[15]  W. Kalender,et al.  Evaluation of section sensitivity profiles and image noise in spiral CT. , 1992, Radiology.

[16]  A. Tarczynski Sensitivity of signal reconstruction , 1997, IEEE Signal Processing Letters.