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Technical Note: Evaluation of pre‐reconstruction interpolation methods for iterative reconstruction of radial k ‐space data
Author(s) -
Tian Ye,
Erb Kay Condie,
Adluru Ganesh,
Likhite Devavrat,
Pedgaonkar Apoorva,
Blatt Michael,
Kamesh Iyer Srikant,
Roberts John,
DiBella Edward
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12357
Subject(s) - interpolation (computer graphics) , bilinear interpolation , iterative reconstruction , multivariate interpolation , algorithm , nearest neighbor interpolation , fast fourier transform , bicubic interpolation , computer science , imaging phantom , mathematics , iterative method , image quality , stairstep interpolation , computer vision , artificial intelligence , image (mathematics) , optics , physics
Purpose To evaluate the use of three different pre‐reconstruction interpolation methods to convert non‐Cartesian k ‐space data to Cartesian samples such that iterative reconstructions can be performed more simply and more rapidly. Methods Phantom as well as cardiac perfusion radial datasets were reconstructed by four different methods. Three of the methods used pre‐reconstruction interpolation once followed by a fast Fourier transform ( FFT ) at each iteration. The methods were: bilinear interpolation of nearest‐neighbor points ( BINN ), 3‐point interpolation, and a multi‐coil interpolator called GRAPPA Operator Gridding ( GROG ). The fourth method performed a full non‐Uniform FFT ( NUFFT ) at each iteration. An iterative reconstruction with spatiotemporal total variation constraints was used with each method. Differences in the images were quantified and compared. Results The GROG multicoil interpolation, the 3‐point interpolation, and the NUFFT ‐at‐each‐iteration approaches produced high quality images compared to BINN , with the GROG ‐derived images having the fewest streaks among the three preinterpolation approaches. However, all reconstruction methods produced approximately equal results when applied to perfusion quantitation tasks. Pre‐reconstruction interpolation gave approximately an 83% reduction in reconstruction time. Conclusion Image quality suffers little from using a pre‐reconstruction interpolation approach compared to the more accurate NUFFT ‐based approach. GROG ‐based pre‐reconstruction interpolation appears to offer the best compromise by using multicoil information to perform the interpolation to Cartesian sample points prior to image reconstruction. Speed gains depend on the implementation and relatively standard optimizations on a MATLAB platform result in preinterpolation speedups of ~ 6 compared to using NUFFT at every iteration, reducing the reconstruction time from around 42 min to 7 min.