Multimodal Partial Volume Correction - Application to 18F-Fluoride PET/CT bone metastases studies

(18)F-Fluoride PET-CT offers the opportunity for accurate skeletal metastasis staging compared to conventional imaging methods. (18)F-Fluoride is a bone specific tracer whose uptake depends on osteoblastic activity.

The osteoblastic process can also be detected morphologically in CT images due to the resulting increase in bone mineralization and sclerosis. Whilst CT is characterized by high resolution, the potential of PET is limited by its lower spatial resolution and the resulting partial volume effect. In this context, the synergy between PET and CT presents an opportunity to resolve this limitation using a novel multimodal approach called Synergistic-Functional-Structural Resolution-Recovery (SFS-RR). Its performance is benchmarked against current resolution recovery technology employing the point-spread-function (PSF) of the scanner in the reconstruction procedure.

The SFS-RR technique takes advantage of the multiresolution property of the wavelet transform applied to both functional and structural images to create a high-resolution PET that exploits the structural information of CT. Although the method was originally conceived for PET-MRI brain data, an ad-hoc version for whole body PET-CT is here proposed. Three phantom experiments and two datasets of metastatic bone (18)F-Fluoride PET-CT images from primary prostate and breast cancer were used to test the algorithm performances. The SFS-RR images were compared with the manufacturer's PSF based reconstruction using the standardized uptake value (SUV) and the metabolic volume as metrics for quantification.

When compared to standard PET images the phantom experiments showed a bias reduction of 14% in activity and 1. 3cm(3) in volume estimates for PSF images and up to 20% and 2. 5cm(3) for the SFS-RR images. The SFS-RR images were characterized by a higher recovery coefficient (up to 60%) while noise levels remained comparable to those of standard PET. The clinical data showed an increase in the SUV estimates for SFS-RR images up to 34% for SUVpeak and 50% for SUVmax and SUVmean. Images were also characterized by sharper lesion contours and better lesion detectability.

The proposed methodology generates PET images with improved quantitative and qualitative properties. Compared to standard methods, SFS-RR provides superior lesion segmentation and quantification, which may result in more accurate tumor characterization.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2015 Jul 16 [Epub ahead of print]

Elisabetta Grecchi, Jim O' Doherty, Mattia Veronese, Charalampos Tsoumpas, Gary Cook, Federico Turkheimer

King's College London, United Kingdom;, King's College London, United Kingdom;, King's College London, United Kingdom;, University of Leeds, United Kingdom;, KCL. , King's College London, United Kingdom;



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