Assessment of image quality with a fast fully 3D reconstruction algorithm

True three-dimensional (3D) reconstructions from fully 3D positron emission tomography (PET) data yield high-quality images but at a high computational cost. Image representation using three-dimensional spherically-symmetric basis functions on a body-centered cubic (BCC) grid, as opposed to a simple cubic (SC) grid, can reduce the computational demands of a 3D approach without compromising image quality by reducing the number of image elements to be reconstructed. The goal of this study was to determine if the image quality improvements predicted for the 3D row action maximum likelihood algorithm (RAMLA) over 2.5D RAMLA after Fourier rebinning (FORE) would be seen with clinical PET data. Torso phantom, whole-body patient, and brain patient studies were used in this analysis. Data were corrected for detector efficiency, scatter, and randoms prior to reconstruction. Attenuation effects were either incorporated into the system model or pre-corrected prior to reconstruction. Higher contrast at comparable noise levels (or lower noise for comparable contrast) are seen with 3D RAMLA (SC or BCC grid) for both phantom and patient data. The brain patient data show improved axial resolution with 3D RAMLA, where the degradation in resolution with FORE is eliminated. Application of a fully 3D reconstruction algorithm is possible in clinically reasonable times.

[1]  J S Karp,et al.  Performance of a whole-body PET scanner using curve-plate NaI(Tl) detectors. , 2001, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[2]  Robert M. Lewitt,et al.  Practical considerations for 3-D image reconstruction using spherically symmetric volume elements , 1996, IEEE Trans. Medical Imaging.

[3]  P. Vaska,et al.  Performance of a GSO brain PET camera , 2000, 2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149).

[4]  H. Malcolm Hudson,et al.  Accelerated image reconstruction using ordered subsets of projection data , 1994, IEEE Trans. Medical Imaging.

[5]  C. Comtat,et al.  Fast reconstruction of 3-D PET data with accurate statistical modeling , 1997 .

[6]  Robert M. Lewitt,et al.  Efficient 3D grids for image reconstruction using spherically-symmetric volume elements , 1994, Proceedings of 1994 IEEE Nuclear Science Symposium - NSS'94.

[7]  R. Lewitt.  Alternatives to voxels for image representation in iterative reconstruction algorithms , 1992, Physics in medicine and biology.

[8]  Samuel Matej,et al.  Data processing and image reconstruction methods for the HEAD PENN-PET scanner , 1998 .

[9]  T. K. Narayan,et al.  Evaluation of task-oriented performance of several fully 3D PET reconstruction algorithms. , 1994, Physics in medicine and biology.

[10]  Samuel Matej,et al.  2.5-D simultaneous multislice reconstruction by series expansion methods from Fourier-rebinned PET data , 2000, IEEE Transactions on Medical Imaging.

[11]  R M Lewitt,et al.  Performance of the Fourier rebinning algorithm for PET with large acceptance angles. , 1998, Physics in medicine and biology.

[12]  Samuel Matej,et al.  Performance of a Fast Maximum Likelihood Algorithm for Fully 3D PET Reconstruction , 1996 .

[13]  Alvaro R. De Pierro,et al.  A row-action alternative to the EM algorithm for maximizing likelihood in emission tomography , 1996, IEEE Trans. Medical Imaging.

[14]  R M Lewitt,et al.  Multidimensional digital image representations using generalized Kaiser-Bessel window functions. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[15]  Robert M. Lewitt,et al.  Application of the row action maximum likelihood algorithm with spherical basis functions to clinical PET imaging , 2001 .

[16]  Michel Defrise,et al.  Exact and approximate rebinning algorithms for 3-D PET data , 1997, IEEE Transactions on Medical Imaging.