A multiresolution image based approach for correction of partial volume effects in emission tomography

Abstract

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography. They lead to a loss of signal in tissues of size similar to the point spread function and induce activity spillover between regions. Although PVE can be corrected for by using algorithms that provide the correct radioactivity concentration in a series of regions of interest (ROIs), so far little attention has been given to the possibility of creating improved images as a result of PVE correction. Potential advantages of PVE-corrected images include the ability to accurately delineate functional volumes as well as improving tumour-to-background ratio, resulting in an associated improvement in the analysis of response to therapy studies and diagnostic examinations, respectively. The objective of our study was therefore to develop a methodology for PVE correction not only to enable the accurate recuperation of activity concentrations, but also to generate PVE-corrected images. In the multiresolution analysis that we define here, details of a high-resolution image H (MRI or CT) are extracted, transformed and integrated in a low-resolution image L (PET or SPECT). A discrete wavelet transform of both H and L images is performed by using the "à trous" algorithm, which allows the spatial frequencies (details, edges, textures) to be obtained easily at a level of resolution common to H and L. A model is then inferred to build the lacking details of L from the high-frequency details in H. The process was successfully tested on synthetic and simulated data, proving the ability to obtain accurately corrected images. Quantitative PVE correction was found to be comparable with a method considered as a reference but limited to ROI analyses. Visual improvement and quantitative correction were also obtained in two examples of clinical images, the first using a combined PET/CT scanner with a lymphoma patient and the second using a FDG brain PET and corresponding T1-weighted MRI in an epileptic patient.

Partial-Volume Effect in PET Tumor Imaging

“…The multiresolution method also assumes that the spatial resolution in reconstructed images is stationary (i.e., identical throughout the images). The details of this method were described by Boussion et al (37) but are summarized here.…”

Principles of PET/MR Imaging

“…With hybrid PET/MR, the available anatomic information can be used for partial-volume correction. Some of these methods are applied after reconstruction (69,70), whereas other methods are integrated into the reconstruction process using anatomic images as prior information (71) or use wavelets (72). Reconstruction algorithms that incorporate MR-based anatomic information promote the formation of edges in the PET image at the transition between different tissue types.…”

Image Processing Methods in CT for Radiotherapy Applications

“…This discrete wavelet transform algorithm was introduced by Dutilleux (Dutilleux, 1987), developed by Holdschneider (Holdschneider et al, 1989) and detailed by Starck (Starck et al, 1998). The process gives an image sequence of coarser and coarser spatial resolution by performing successive convolutions with a low-pass filter h (Boussion et al, 2006).…”