Bayesian inference for multidimensional NMR image reconstruction

Ji Won Yoon, Simon J. Godsill

Research output: Contribution to journalConference article

7 Citations (Scopus)

Abstract

Reconstruction of an image from a set of projections has been adapted to generate multidimensional nuclear magnetic resonance (NMR) spectra, which have discrete features that are relatively sparsely distributed in space. For this reason, a reliable reconstruction can be made from a small number of projections. This new concept is called Projection Reconstruction NMR (PR-NMR). In this paper, multidimensional NMR spectra are reconstructed by Reversible Jump Markov Chain Monte Carlo (RJMCMC). This statistical method generates samples under the assumption that each peak consists of a small number of parameters: position of peak centres, peak amplitude, and peak width. In order to find the number of peaks and shape, RJMCMC has several moves: birth, death, merge, split, and invariant updating. The reconstruction schemes are tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of a protein HasA.

Original languageEnglish
JournalEuropean Signal Processing Conference
Publication statusPublished - 2006
Event14th European Signal Processing Conference, EUSIPCO 2006 - Florence, Italy
Duration: 2006 Sep 42006 Sep 8

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Bayesian inference for multidimensional NMR image reconstruction'. Together they form a unique fingerprint.

  • Cite this